Publications

JTD Keywords: 6mwt, activation, breathing pattern, burden, chronic obstructive pulmonary disease, exercise, muscles, pressure, pulmonary, signals, variability, volumes, wearables, Bioimpedance, Impedance pneumography

ppGpp is an intracellular sensor that, in response to different types of stress, coordinates the rearrangement of the gene expression pattern of bacteria to promote adaptation and survival to new environmental conditions. First described to modulate metabolic adaptive responses, ppGpp modulates the expression of genes belonging to very diverse functional categories. In Escherichia coli, ppGpp regulates the expression of cellular factors that are important during urinary tract infections. Here, we characterize the role of this alarmone in the regulation of the hlyCABD(II) operon of the UPEC isolate J96, encoding the toxin alpha-hemolysin that induces cytotoxicity during infection of bladder epithelial cells. ppGpp is required for the expression of the alpha-hemolysin encoded in hlyCABD(II) by stimulating its transcriptional expression. Prototrophy suppressor mutations in a ppGpp-deficient strain restore the alpha-hemolysin expression from this operon to wild-type levels, confirming the requirement of ppGpp for its expression. ppGpp stimulates hlyCABD(II) expression independently of RpoS, RfaH, Zur, and H-NS. The expression of hlyCABD(II) is promoted at 37 degrees C and at low osmolarity. ppGpp is required for the thermoregulation but not for the osmoregulation of the hlyCABD(II) operon. Studies in both commensal and UPEC isolates demonstrate that no UPEC specific factor is strictly required for the ppGpp-mediated regulation described. Our data further support the role of ppGpp participating in the coordinated regulation of the expression of bacterial factors required during infection.

JTD Keywords: Alpha-hemolysin, Bacterial signal molecule, Determinants, Environmental-regulation, Gene regulation, H-ns, Ppgpp, Protein, Regulator, Rfah, Secretion, Transcription, Upec, Virulence

Upon the initiation of collective cell migration, the cells at the free edge are specified as leader cells; however, the mechanism underlying the leader cell specification remains elusive. Here, we show that lamellipodial extension after the release from mechanical confinement causes sustained extracellular signal-regulated kinase (ERK) activation and underlies the leader cell specification. Live-imaging of Madin-Darby canine kidney (MDCK) cells and mouse epidermis through the use of Förster resonance energy transfer (FRET)-based biosensors showed that leader cells exhibit sustained ERK activation in a hepatocyte growth factor (HGF)-dependent manner. Meanwhile, follower cells exhibit oscillatory ERK activation waves in an epidermal growth factor (EGF) signaling-dependent manner. Lamellipodial extension at the free edge increases the cellular sensitivity to HGF. The HGF-dependent ERK activation, in turn, promotes lamellipodial extension, thereby forming a positive feedback loop between cell extension and ERK activation and specifying the cells at the free edge as the leader cells. Our findings show that the integration of physical and biochemical cues underlies the leader cell specification during collective cell migration.Copyright © 2022 Elsevier Inc. All rights reserved.

JTD Keywords: activation, c-met, contact inhibition, focal adhesions, heparan-sulfate, mechanical forces, morphogenesis, rho, stress fibers, Collective cell migration, Erk, Feedback regulation, Fret, Growth-factor receptor, Hgf, Lamellipodia, Leader cell specification, Signal transduction, Traction force, Wound healing

JTD Keywords: angiogenesis, bioactive materials, bone regeneration, bone-formation, calcium-phosphate, extracellular calcium, in-vitro, interstitial flow, ion release, microfluidic model, signalling gradient, substitutes, vascularization, vegf, Mesenchymal stem-cells, Tissue engineering

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity. © 2022 American Physical Society.

JTD Keywords: brain, entropy, epileptogenic networks, functional connectivity, hilbert transform, seizure onset, surrogate data, synchronization, time-series, Biomedical signal processing, Brain areas, Brain dynamics, Dynamics, Electroencephalographic signals, Electroencephalography, Electrophysiology, Intracranial eeg signals, Localisation, Neurological disorders, Neurology, Phase based, Phase coherence, Signal detection, Simple++, Univariate, Velocity, World population

Visual impairments are a critical medical hurdle to be addressed in modern society. Müller glia (MG) have regenerative potential in the retina in lower vertebrates, but not in mammals. However, in mice, in vivo cell fusion between MG and adult stem cells forms hybrids that can partially regenerate ablated neurons.We used organotypic cultures of human retina and preparations of dissociated cells to test the hypothesis that cell fusion between human MG and adult stem cells can induce neuronal regeneration in human systems. Moreover, we established a microinjection system for transplanting human retinal organoids to demonstrate hybrid differentiation.We first found that cell fusion occurs between MG and adult stem cells, in organotypic cultures of human retina as well as in cell cultures. Next, we showed that the resulting hybrids can differentiate and acquire a proto-neural electrophysiology profile when the Wnt/beta-catenin pathway is activated in the adult stem cells prior fusion. Finally, we demonstrated the engraftment and differentiation of these hybrids into human retinal organoids.We show fusion between human MG and adult stem cells, and demonstrate that the resulting hybrid cells can differentiate towards neural fate in human model systems. Our results suggest that cell fusion-mediated therapy is a potential regenerative approach for treating human retinal dystrophies.This work was supported by La Caixa Health (HR17-00231), Velux Stiftung (976a) and the Ministerio de Ciencia e Innovación, (BFU2017-86760-P) (AEI/FEDER, UE), AGAUR (2017 SGR 689, 2017 SGR 926).Published by Elsevier B.V.

JTD Keywords: cell fusion, expression, fusion, ganglion-cells, in-vitro, mouse, müller glia, neural differentiation, organoids, regeneration, retina regeneration, stem cells, stromal cells, transplantation, 4',6 diamidino 2 phenylindole, 5' nucleotidase, Agarose, Alcohol, Arpe-19 cell line, Article, Beta catenin, Beta tubulin, Bone-marrow-cells, Bromophenol blue, Buffer, Calcium cell level, Calcium phosphate, Calretinin, Canonical wnt signaling, Cd34 antigen, Cell culture, Cell fusion, Cell viability, Coculture, Complementary dna, Confocal microscopy, Cornea transplantation, Cryopreservation, Cryoprotection, Crystal structure, Current clamp technique, Dimethyl sulfoxide, Dodecyl sulfate sodium, Edetic acid, Electrophysiology, Endoglin, Fetal bovine serum, Fibroblast growth factor 2, Flow cytometry, Fluorescence activated cell sorting, Fluorescence intensity, Glyceraldehyde 3 phosphate dehydrogenase, Glycerol, Glycine, Hoe 33342, Immunofluorescence, Immunohistochemistry, Incubation time, Interleukin 1beta, Lentivirus vector, Matrigel, Mercaptoethanol, Microinjection, Mueller cell, Müller glia, N methyl dextro aspartic acid, Nerve cell differentiation, Neural differentiation, Nitrogen, Nonhuman, Organoids, Paraffin, Paraffin embedding, Paraformaldehyde, Patch clamp technique, Penicillin derivative, Phenolsulfonphthalein, Phenotype, Phosphate buffered saline, Phosphoprotein phosphatase inhibitor, Polyacrylamide gel electrophoresis, Potassium chloride, Povidone iodine, Promoter region, Proteinase inhibitor, Real time polymerase chain reaction, Receptor type tyrosine protein phosphatase c, Restriction endonuclease, Retina, Retina dystrophy, Retina regeneration, Retinol, Rhodopsin, Rna extraction, Stem cell, Stem cells, Subcutaneous fat, Tunel assay, Visual impairment, Western blotting

Decellularization procedures have been developed and optimized for the entire organ or tissue blocks, by either perfusion of decellularizing agents through the tissue’s vasculature or submerging large sections in decellularizing solutions. However, some research aims require the analysis of native as well as decellularized tissue slices side by side, but an optimal protocol has not yet been established to address this need. Thus, the main goal of this work was to develop a fast and efficient decellularization method for tissue slices—with an emphasis on lung—while attached to a glass slide. To this end, different decellularizing agents were compared for their effectiveness in cellular removal while preserving the extracellular matrix. The intensity of DNA staining was taken as an indicator of remaining cells and compared to untreated sections. The presence of collagen, elastin and laminin were quantified using immunostaining and signal quantification. Scaffolds resulting from the optimized protocol were mechanically characterized using atomic force microscopy. Lung scaffolds were recellularized with mesenchymal stromal cells to assess their biocompatibility. Some decellularization agents (CHAPS, triton, and ammonia hydroxide) did not achieve sufficient cell removal. Sodium dodecyl sulfate (SDS) was effective in cell removal (1% remaining DNA signal), but its sharp reduction of elastin signal (only 6% remained) plus lower attachment ratio (32%) singled out sodium deoxycholate (SD) as the optimal treatment for this application (6.5% remaining DNA signal), due to its higher elastin retention (34%) and higher attachment ratio (60%). Laminin and collagen were fully preserved in all treatments. The SD decellularization protocol was also successful for porcine and murine (mice and rat) lungs as well as for other tissues such as the heart, kidney, and bladder. No significant mechanical differences were found before and after sample decellularization. The resulting acellular lung scaffolds were shown to be biocompatible (98% cell survival after 72 h of culture). This novel method to decellularize tissue slices opens up new methodological possibilities to better understand the role of the extracellular matrix in the context of several diseases as well as tissue engineering research and can be easily adapted for scarce samples like clinical biopsies. Copyright © 2022 Narciso, Ulldemolins, Júnior, Otero, Navajas, Farré, Gavara and Almendros.

JTD Keywords: biocompatibility, bioscaffold recellularization, extracellular matrix, flow, impact, lung, scaffolds, tissue slices, Ammonia, Bio-scaffolds, Biocompatibility, Biological organs, Bioscaffold recellularization, Cell removal, Cells, Collagen, Cytology, Decellularization, Dna, Dna signals, Elastin, Extracellular matrices, Extracellular matrix, Extracellular-matrix, Glycoproteins, Laminin, Lung, Mammals, Recellularization, Scaffolds (biology), Sodium deoxycholate, Sulfur compounds, Tissue, Tissue slice, Tissue slices

Background and aims: In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients. Materials and methods: A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples. Results: Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84–100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test. Conclusions: Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples. © 2021 The Author(s)

JTD Keywords: biomarkers, breath analysis, bronchiectasis, diagnosis, e-nose, fingerprints, gc-ms, identification, lung-cancer, partial least-squares, pseudomonas-aeruginosa, signal processing, validation, volatile organic-compounds, Airway bacterial-colonization, Breath analysis, Bronchiectasis, E-nose, Gc-ms, Signal processing

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance - This establishes the characterization of success and failure patients in the extubation process. © 2021 IEEE.

JTD Keywords: classification, recognition, Airway extubation, Artificial ventilation, Clinical practices, Clinical process, Diaphragm, Diaphragm muscle, Diaphragms, Electrodes, Electromyographic, Extubation, Frequency domain analysis, Human, Humans, Maximum differences, Mechanical ventilation, New protocol, Respiration, artificial, Respiratory system, Risk of failure, Spontaneous breathing, Surface electrode, Surface emg signals, Thorax, Ventilation, Ventilator weaning

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ? 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients.

JTD Keywords: apnea syndrome, biomedical signal processing, individuals, mhealth, monitoring, nasal resistance, people, position, prevalence, questionnaire, sample, sleep apnea, sleep position, sleep-disordered breathing, smartphone, time, Apnea-hypopnea indices, Biomedical signal processing, Biomedical signals processing, Cost effectiveness, Diagnosis, Mhealth, Monitoring, Noninvasive medical procedures, Oximeters, Oxygen-saturation, Patient rehabilitation, Simple++, Sleep apnea, Sleep position, Sleep research, Sleep-disordered breathing, Smart phones, Smartphone, Smartphones, Spinal cord injury, Spinal cord injury patients

Cell response to force regulates essential processes in health and disease. However, the fundamental mechanical variables that cells sense and respond to remain unclear. Here we show that the rate of force application (loading rate) drives mechanosensing, as predicted by a molecular clutch model. By applying dynamic force regimes to cells through substrate stretching, optical tweezers, and atomic force microscopy, we find that increasing loading rates trigger talin-dependent mechanosensing, leading to adhesion growth and reinforcement, and YAP nuclear localization. However, above a given threshold the actin cytoskeleton softens, decreasing loading rates and preventing reinforcement. By stretching rat lungs in vivo, we show that a similar phenomenon may occur. Our results show that cell sensing of external forces and of passive mechanical parameters (like tissue stiffness) can be understood through the same mechanisms, driven by the properties under force of the mechanosensing molecules involved. Cells sense mechanical forces from their environment, but the precise mechanical variable sensed by cells is unclear. Here, the authors show that cells can sense the rate of force application, known as the loading rate, with effects on YAP nuclear localization and cytoskeletal stiffness remodelling.

JTD Keywords: Actin cytoskeleton, Actin filament, Actin-filament, Adhesion, Animal, Animals, Atomic force microscopy, Breathing, Cell, Cell adhesion, Cell culture, Cell nucleus, Cells, cultured, Cytoplasm, Extracellular-matrix, Fibroblast, Fibroblasts, Fibronectin, Frequency, Gene knockdown, Gene knockdown techniques, Genetics, Germfree animal, Integrin, Intracellular signaling peptides and proteins, Knockout mouse, Lung, Male, Mechanotransduction, Mechanotransduction, cellular, Metabolism, Mice, Mice, knockout, Microscopy, atomic force, Mouse, Optical tweezers, Paxillin, Physiology, Primary cell culture, Pxn protein, mouse, Rat, Rats, Rats, sprague-dawley, Respiration, Signal peptide, Softening, Specific pathogen-free organisms, Sprague dawley rat, Stress, Substrate, Substrate rigidity, Talin, Talin protein, mouse, Tln2 protein, mouse, Traction, Transmission, Ultrastructure, Yap1 protein, rat

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application (‘SleepPos’ app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the ‘SleepPos’ app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The ‘SleepPos’ app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events.

JTD Keywords: accelerometry, android, apnea patients, app, association, biomedical signal processing, management, mhealth, monitoring, pathophysiology, pilot mhealth, questionnaire, sleep position, smartphone, supine position, time, Accelerometry, Android, App, Biomedical signal processing, Mhealth, Monitoring, Sleep position, Smart-phone, Smartphone, Tennis ball technique

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, left and right. An increase in sleep position resolution is necessary to better assess sleep position dynamics and to interpret more accurately intermediate sleep positions. This research aims to study the feasibility of smartphones as sleep position monitors by (1) developing algorithms to retrieve the sleep position angle from smartphone accelerometry; (2) monitoring the sleep position angle in patients with obstructive sleep apnea (OSA); (3) comparing the discretized sleep angle versus the four classic sleep positions obtained by the video-validated polysomnography (PSG); and (4) analyzing the presence of positional OSA (pOSA) related to its sleep angle of occurrence. Results from 19 OSA patients reveal that a higher resolution sleep position would help to better diagnose and treat patients with position-dependent diseases such as pOSA. They also show that smartphones are promising mHealth tools for enhanced position monitoring at hospitals and home, as they can provide sleep position with higher resolution than the gold-standard video-validated PSG.

JTD Keywords: accelerometry, actigraphy, association, biomedical signal processing, index, latency, mhealth, monitoring, pathophysiology, quality, questionnaire, score, sleep apnea, sleep position, smartphone, time, Accelerometry, Biomedical signal processing, Mhealth, Monitoring, Sleep apnea, Sleep position, Smartphone, Supine position

The extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) pathway involves a three-step cascade of kinases that transduce signals and promote processes such as cell growth, development, and apoptosis. An aberrant response of this pathway is related to the proliferation of cell diseases and tumors. By using simulation modeling, we document that the protein arginine methyltransferase 5 (PRMT5) modulates the MAPK pathway and thus avoids an aberrant behavior. PRMT5 methylates the Raf kinase, reducing its catalytic activity and thereby, reducing the activation of ERK in time and amplitude. Two minimal computational models of the epidermal growth factor (EGF)-Ras-ERK MAPK pathway influenced by PRMT5 were proposed: a first model in which PRMT5 is activated by EGF and a second one in which PRMT5 is stimulated by the cascade response. The reported results show that PRMT5 reduces the time duration and the expression of the activated ERK in both cases, but only in the first model PRMT5 limits the EGF range that generates an ERK activation. Based on our data, we propose the protein PRMT5 as a regulatory factor to develop strategies to fight against an excessive activity of the MAPK pathway, which could be of use in chronic diseases and cancer.

JTD Keywords: cancer, cell response modulation, computational model, egf-ras-erk signaling route, mapk pathway, methylation, Arginine methyltransferase 5, Cancer, Cell response modulation, Colorectal-cancer, Computational model, Egf-ras-erk signaling route, Epidermal-growth-factor, Factor receptor, Histone h3, Kinase cascade, Mapk pathway, Methylation, Negative-feedback, Pc12 cells, Prmt5, Protein, Signal-transduction

© 2020 Elsevier B.V. Hypoxia is a common medical problem, sometimes difficult to detect and caused by different situations. Control of hypoxia is of great medical importance and early detection is essential to prevent life threatening complications. However, the few current methods are invasive, expensive, and risky. Thus, the development of reliable and accurate sensors for the continuous monitoring of hypoxia is of vital importance for clinical monitoring. Herein, we report an implantable sensor to address these needs. The developed device is a low-cost, miniaturised implantable electrochemical sensor for monitoring hypoxia in tissue by means of pH detection. This technology is based on protonation/deprotonation of polypyrrole conductive polymer. The sensor was optimized in vitro and tested in vivo intramuscularly and ex vivo in blood in adult rabbits with respiration-induced hypoxia and correlated with the standard device ePOCTM. The sensor demonstrated excellent sensitivity and reproducibility; 46.4 ± 0.4 mV/pH in the pH range of 4–9 and the selectivity coefficient exhibited low interference activity in vitro. The device was linear (R2 = 0.925) with a low dispersion of the values (n = 11) with a cut-off of 7.1 for hypoxia in vivo and ex vivo. Statistics with one-way ANOVA (α = 0.05), shows statistical differences between hypoxia and normoxia states and the good performance of the pH sensor, which demonstrated good agreement with the standard device. The sensor was stable and functional after 18 months. The excellent results demonstrated the feasibility of the sensors in real-time monitoring of intramuscular tissue and blood for medical applications.

JTD Keywords: biocompatibility, blood-flow, clinical monitoring, electrochemical biosensor, electrodes, hypoxia, implantable sensor, in vivo tissue monitoring, ischemia, lactate, ph, ph sensor, rabbits, responses, vitro, Clinical monitoring, Dual signal outputs, Hypoxia, Implantable sensor, In vivo tissue monitoring, Ischemia, Ph sensor

© 2021 Elsevier B.V. Malaria is a parasitic disease caused by protists of the genus Plasmodium, which are transmitted to humans through the bite of infected female Anopheles mosquitoes. Analytical methodologies and efficient drugs exist for the early detection and treatment of malaria, and yet this disease continues infecting millions of people and claiming several hundred thousand lives each year. One of the reasons behind this failure to control the disease is that the standard method for malaria diagnosis, microscopy, is time-consuming and requires trained personnel. Alternatively, rapid diagnostic tests, which have become common for point-of-care testing thanks to their simplicity of use, tend to be insufficiently sensitive and reliable, and PCR, which is sensitive, is too complex and expensive for massive population screening. In this work, we report a sensitive simplified ELISA for the quantitation of Plasmodium falciparum lactate dehydrogenase (Pf-LDH), which is capable of detecting malaria in 45–60 min. Assay development was founded in the selection of high-performance antibodies, implementation of a poly-horseradish peroxidase (polyHRP) signal amplifier, and optimization of whole-blood sample pre-treatment. The simplified ELISA achieved limits of detection (LOD) and quantification (LOQ) of 0.11 ng mL−1 and 0.37 ng mL−1, respectively, in lysed whole blood, and an LOD comparable to that of PCR in Plasmodium in vitro cultures (0.67 and 1.33 parasites μL−1 for ELISA and PCR, respectively). Accordingly, the developed immunoassay represents a simple and effective diagnostic tool for P. falciparum malaria, with a time-to-result of <60 min and sensitivity similar to the reference PCR, but easier to implement in low-resource settings.

JTD Keywords: malaria quantitative diagnosis, plasmodium culture, plasmodium ldh, polyhrp signal amplifier, simplified elisa, Malaria quantitative diagnosis, Plasmodium culture, Plasmodium ldh, Polyhrp signal amplifier, Simplified elisa

Osteosarcoma is the most common primary bone tumor, and its first line of treatment presents a high failure rate. The 5-year survival for children and teenagers with osteosarcoma is 70% (if diagnosed before it has metastasized) or 20% (if spread at the time of diagnosis), stressing the need for novel therapies. Recently, cold atmospheric plasmas (ionized gases consisting of UV-Vis radiation, electromagnetic fields and a great variety of reactive species) and plasma-treated liquids have been shown to have the potential to selectively eliminate cancer cells in different tumors through an oxidative stress-dependent mechanism. In this work, we review the current state of the art in cold plasma therapy for osteosarcoma. Specifically, we emphasize the mechanisms unveiled thus far regarding the action of plasmas on osteosarcoma. Finally, we review current and potential future approaches, emphasizing the most critical challenges for the development of osteosarcoma therapies based on this emerging technique.

JTD Keywords: cancer stem cells, cold atmospheric plasma, osteosarcoma, oxidative stress, plasma treated liquids, reactive oxygen and nitrogen species, Antineoplastic activity, Antineoplastic agent, Cancer chemotherapy, Cancer stem cell, Cancer stem cells, Cancer surgery, Cancer survival, Cell therapy, Cold atmospheric plasma, Cold atmospheric plasma therapy, Electromagnetism, Human, In vitro study, Intracellular signaling, Oncogene, Osteosarcoma, Oxidative stress, Plasma treated liquids, Reactive nitrogen species, Reactive oxygen and nitrogen species, Reactive oxygen metabolite, Review, Tumor microenvironment

© 1964-2012 IEEE. Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic conditions. The current assessment of COPD requires a maximal maneuver during a spirometry test to quantify airflow limitations of patients. Other less invasive measurements such as thoracic bioimpedance and myographic signals have been studied as an alternative to classical methods as they provide information about respiration. Particularly, strong correlations have been shown between thoracic bioimpedance and respiratory volume. The main objective of this study is to investigate bioimpedance and its combination with myographic parameters in COPD patients to assess the applicability in respiratory disease monitoring. We measured bioimpedance, surface electromyography and surface mechanomyography in forty-three COPD patients during an incremental inspiratory threshold loading protocol. We introduced two novel features that can be used to assess COPD condition derived from the variation of bioimpedance and the electrical and mechanical activity during each respiratory cycle. These features demonstrate significant differences between mild and severe patients, indicating a lower inspiratory contribution of the inspiratory muscles to global respiratory ventilation in the severest COPD patients. In conclusion, the combination of bioimpedance and myographic signals provides useful indices to noninvasively assess the breathing of COPD patients.

JTD Keywords: Bioimpedance, Chronic obstructive pulmonary disease, Inspiratory threshold protocol, Myographic signals, Wearables

Goel and colleagues show that CDK4/6 inhibition induces global chromatin changes mediated by AP-1 factors, which mediate key biological and clinical effects in breast cancer. Pharmacologic inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6) were designed to induce cancer cell cycle arrest. Recent studies have suggested that these agents also exert other effects, influencing cancer cell immunogenicity, apoptotic responses and differentiation. Using cell-based and mouse models of breast cancer together with clinical specimens, we show that CDK4/6 inhibitors induce remodeling of cancer cell chromatin characterized by widespread enhancer activation, and that this explains many of these effects. The newly activated enhancers include classical super-enhancers that drive luminal differentiation and apoptotic evasion, as well as a set of enhancers overlying endogenous retroviral elements that are enriched for proximity to interferon-driven genes. Mechanistically, CDK4/6 inhibition increases the level of several activator protein-1 transcription factor proteins, which are in turn implicated in the activity of many of the new enhancers. Our findings offer insights into CDK4/6 pathway biology and should inform the future development of CDK4/6 inhibitors.

JTD Keywords: Abemaciclib, Androgen receptor, Animal experiment, Animal model, Animal tissue, Apoptosis, Article, Breast cancer, C-jun, Cancer cell, Carcinoembryonic antigen related cell adhesion molecule 1, Caspase 3, Cell cycle arrest, Cells, Chromatin, Chromatin immunoprecipitation, Controlled study, Cyclin dependent kinase 4, Cyclin dependent kinase 6, Dna damage, Epidermal growth factor receptor 2, Estrogen receptor, Female, Flow cytometry, Fulvestrant, Hla drb1 antigen, Human, Human cell, Immunoblotting, Immunogenicity, Immunoprecipitation, Interferon, Luciferase assay, Mcf-7 cell line, Mda-mb-231 cell line, Microarray analysis, Morphogenesis, Mouse, Nonhuman, Palbociclib, Protein, Protein expression, Rb, Resistance, Rna polymerase ii, Rna sequence, Selective-inhibition, Senescence, Short tandem repeat, Signal transduction, Tamoxifen, Transcription elongation, Transcription factor, Transcription factor ap 1, Transcriptome, Tumor biopsy, Tumor differentiation, Tumor spheroid, Tumor xenograft, Vinculin, Whole exome sequencing

Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. OSA is also known to be position-dependent in some patients, which is referred to as positional OSA (pOSA). Screening for pOSA is necessary in order to design more personalized and effective treatment strategies. In this article, we propose analyzing accelerometry signals, recorded with a smartphone, to detect and monitor OSA at home. Our objectives were to: (1) develop an algorithm for detecting thoracic movement associated with disordered breathing events; (2) compare the performance of smartphones as OSA monitoring tools with a type 3 portable sleep monitor; and (3) explore the feasibility of using smartphone accelerometry to retrieve reliable patient sleep position data and assess pOSA. Accelerometry signals were collected through simultaneous overnight acquisition using both devices with 13 subjects. The smartphone tool showed a high degree of concordance compared to the portable device and succeeded in estimating the apnea-hypopnea index (AHI) and classifying the severity level in most subjects. To assess the agreement between the two systems, an event-by-event comparison was performed, which found a sensitivity of 90% and a positive predictive value of 80%. It was also possible to identify pOSA by determining the ratio of events occurring in a specific position versus the time spent in that position during the night. These novel results suggest that smartphones are promising mHealth tools for OSA and pOSA monitoring at home.

JTD Keywords: Accelerometry, Biomedical signal processing, mHealth, Monitoring, Sleep apnea, Sleep position, Smartphone

During collective migration of epithelial cells, the migration direction is aligned over a tissue-scale expanse. Although the collective cell migration is known to be directed by mechanical forces transmitted via cell-cell junctions, it remains elusive how the intercellular force transmission is coordinated with intracellular biochemical signaling to achieve collective movements. Here, we show that intercellular coupling of extracellular signal-regulated kinase (ERK)-mediated mechanochemical feedback yields long-distance transmission of guidance cues. Mechanical stretch activates ERK through epidermal growth factor receptor (EGFR) activation, and ERK activation triggers cell contraction. The contraction of the activated cell pulls neighboring cells, evoking another round of ERK activation and contraction in the neighbors. Furthermore, anisotropic contraction based on front-rear polarization guarantees unidirectional propagation of ERK activation, and in turn, the ERK activation waves direct multicellular alignment of the polarity, leading to long-range ordered migration. Our findings reveal that mechanical forces mediate intercellular signaling underlying sustained transmission of guidance cues for collective cell migration.

JTD Keywords: Collective cell migration, EGFR, ERK/MAPK, FRET, Front-rear polarity, Intercellular signal transfer, Mathematical model, Mechanochemical feedback, Mechanotransduction, wave propagation

This paper describes the design of a linear phase low-pass differentiator filter with a finite impulse response (FIR) for extracting transient features of gas sensor signals (the so-called “bouts”). The detection of these bouts is relevant for estimating the distance of a gas source in a turbulent plume. Our current proposal addresses the shortcomings of previous ‘bout’ estimation methods, namely: (i) they were based in non-causal digital filters precluding real time operation, (ii) they used non-linear phase filters leading to waveform distortions and (iii) the smoothing action was achieved by two filters in cascade, precluding an easy tuning of filter performance. The presented method is based on a low-pass FIR differentiator, plus proper post-processing, allowing easy algorithmic implementation for real-time robotic exploration. Linear phase filters preserve signal waveform in the bandpass region for maximum reliability concerning both ‘bout’ detection and amplitude estimation. As a case study, we apply the proposed filter to predict the source distance from recordings obtained with metal oxide (MOX) gas sensors in a wind tunnel. We first perform a joint optimization of the cut-off frequency of the filter and the bout amplitude threshold, for different wind speeds, uncovering interesting relationships between these two parameters. We demonstrate that certain combinations of parameters can reduce the prediction error to 8 cm (in a distance range of 1.45 m) improving previously reported performances in the same dataset by a factor of 2.5. These results are benchmarked against traditional source distance estimators such as the mean, variance and maximum of the response. We also study how the length of the measurement window affects the performance of different signal features, and how to select the filter parameters to make the predictive models more robust to changes in wind speed. Finally, we provide a MATLAB implementation of the bout detection algorithm and all analysis code used in this study.

JTD Keywords: Gas sensors, Differentiator, Low pass filter, Metal oxide semiconductor, MOX sensors, Signal processing, Feature extraction, Gas source localization, Robotics

The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

JTD Keywords: Mobile robotic olfaction, Metal oxide gas sensors, Signal processing, Sensor networks, Gas source localization, Gas distribution mapping

Peripheral nerve injuries, including motor neuron axonal injury, often lead to functional impairments. Current therapies are mostly limited to surgical intervention after lesion, yet these interventions have limited success in restoring functionality. Current activity-based therapies after axonal injuries are based on trial-error approaches in which the details of the underlying cellular and molecular processes are largely unknown. Here we show the effects of the modulation of both neuronal and muscular activity with optogenetic approaches to assess the regenerative capacity of cultured motor neuron (MN) after lesion in a compartmentalized microfluidic-assisted axotomy device. With increased neuronal activity, we observed an increase in the ratio of regrowing axons after injury in our peripheral-injury model. Moreover, increasing muscular activity induces the liberation of leukemia inhibitory factor and glial cell line-derived neurotrophic factor in a paracrine fashion that in turn triggers axonal regrowth of lesioned MN in our 3D hydrogel cultures. The relevance of our findings as well as the novel approaches used in this study could be useful not only after axotomy events but also in diseases affecting MN survival.

JTD Keywords: Neuromuscular junction, Microfluidics, Axotomy, Paracrine signaling

This book constitutes the proceedings of the )th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020, held in Freiburg, Germany, in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 32 full and 7 short papers presented in this volume were carefully reviewed and selected from 45 submissions. They deal with research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

JTD Keywords: Artificial intelligence, Soft robotics, Biomimetics, Insect navigation, Synthetic nervous system, Computer vision, Bio-inspired materials, Visual homing, Locomotion+, Image processing, Intelligent robots, Human-robot interaction, Machine learning, Snake robot, Mobile robots, Robotic systems, Drosophila, Robots, Sensors, Signal processing

In vitro surrogate models of human cardiac tissue hold great promise in disease modeling, cardiotoxicity testing, and future applications in regenerative medicine. However, the generation of engineered human cardiac constructs with tissue-like functionality is currently thwarted by difficulties in achieving efficient maturation at the cellular and/or tissular level. Here, we report on the design and implementation of a platform for the production of engineered cardiac macrotissues from human pluripotent stem cells (PSCs), which we term “CardioSlice.” PSC-derived cardiomyocytes, together with human fibroblasts, are seeded into large 3D porous scaffolds and cultured using a parallelized perfusion bioreactor with custom-made culture chambers. Continuous electrical stimulation for 2 weeks promotes cardiomyocyte alignment and synchronization, and the emergence of cardiac tissue-like properties. These include electrocardiogram-like signals that can be readily measured on the surface of CardioSlice constructs, and a response to proarrhythmic drugs that is predictive of their effect in human patients.

JTD Keywords: Cardiac tissue engineering, CardioSlice, ECG-like signals, Electrical stimulation, Heart physiology, Human induced pluripotent stem cells, Perfusion bioreactor, Tissue-like properties

Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Here we propose analyzing smartphone audio signals for screening OSA patients at home. Our objectives were to: (1) develop an algorithm for detecting silence events and classifying them into apneas or hypopneas; (2) evaluate the performance of this system; and (3) compare the information provided with a type 3 portable sleep monitor, based mainly on nasal airflow. Overnight signals were acquired simultaneously by both systems in 13 subjects (3 healthy subjects and 10 OSA patients). The sample entropy of audio signals was used to identify apnea/hypopnea events. The apnea-hypopnea indices predicted by the two systems presented a very high degree of concordance and the smartphone correctly detected and stratified all the OSA patients. An event-by-event comparison demonstrated good agreement between silence events and apnea/hypopnea events in the reference system (Sensitivity = 76%, Positive Predictive Value = 82%). Most apneas were detected (89%), but not so many hypopneas (61%). We observed that many hypopneas were accompanied by snoring, so there was no sound reduction. The apnea/hypopnea classification accuracy was 70%, but most discrepancies resulted from the inability of the nasal cannula of the reference device to record oral breathing. We provided a spectral characterization of oral and nasal breathing to correct this effect, and the classification accuracy increased to 82%. This novel knowledge from acoustic signals may be of great interest for clinical practice to develop new non-invasive techniques for screening and monitoring OSA patients at home.

JTD Keywords: Sleep apnea, Acoustics, Monitoring, Entropy, Sensors, Microphones, Acoustics, Biomedical signal processing, mHealth, Monitoring, Sleep apnea, Smartphone

Diaphragm neuromechanical coupling (NMC), which reflects the efficiency of conversion of neural activation to transdiaphragmatic pressure (Pdi), is increasingly recognized to be a useful clinical index of diaphragm function and respiratory mechanics in neuromuscular weakness and cardiorespiratory disease. However, the current gold standard assessment of diaphragm NMC requires invasive measurements of Pdi and crural diaphragm electromyography (oesEMGdi), which complicates the measurement of diaphragm NMC in clinical practice. This is the first study to compare invasive measurements of diaphragm NMC (iNMC) using the relationship between Pdi and oesEMGdi, with noninvasive assessment of NMC (nNMC) using surface mechanomyography (sMMGlic) and electromyography (sEMGlic) of lower chest wall inspiratory muscles. Both invasive and noninvasive measurements were recorded in twelve healthy adult subjects during an inspiratory threshold loading protocol. A linear relationship between noninvasive sMMGlic and sEMGlic measurements was found, resulting in little change in nNMC with increasing inspiratory load. By contrast, a curvilinear relationship between invasive Pdi and oesEMGdi measurements was observed, such that there was a progressive increase in iNMC with increasing inspiratory threshold load. Progressive recruitment of lower ribcage muscles, serving to enhance the mechanical advantage of the diaphragm, may explain the more linear relationship between sMMGlic and sEMGlic (both representing lower intercostal plus costal diaphragm activity) than between Pdi and crural oesEMGdi. Noninvasive indices of NMC derived from sEMGlic and sMMGlic may prove to be useful indices of lower chest wall inspiratory muscle NMC, particularly in settings that do not have access to invasive measures of diaphragm function.

JTD Keywords: Cardiovascular system, Diaphragms, Diseases, Electromyography, Medical signal processing, Neurophysiology, Patient monitoring, Pneumodynamics, Inspiratory muscle neuromechanical coupling, Diaphragm neuromechanical coupling, Neural activation, Transdiaphragmatic pressure, Diaphragm function, Respiratory mechanics, Diaphragm NMC, Invasive measurements, Crural diaphragm electromyography, iNMC, Noninvasive assessment, nNMC, Lower chest wall inspiratory muscles, Inspiratory threshold loading protocol, Noninvasive sMMGlic measurements, sEMGlic measurements, oesEMGdi measurements, Inspiratory threshold load, Lower ribcage muscles, Lower intercostal plus costal diaphragm activity, Crural oesEMGdi, Noninvasive indices, sEMGlic sMMGlic, Lower chest wall inspiratory muscle NMC, Surface mechanomyography, Electromyography, Inspiratory threshold loading, Mechanomyography, Neuromechanical coupling, Respiratory muscles

The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors-typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size-hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal-the so-called “bouts”-to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements-typically taken with expensive and bulky anemometers-to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.

JTD Keywords: Gas detectors, Chemical sensors, Signal processing, Machine learning, Time series analysis

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

JTD Keywords: Robotics, Signal processing, Electronics, Gas source localization, Gas distribution mapping, Gas sensors, Drone, UAV, MOX sensor, Quadcopter

This paper explores which signal features of a gas sensor are optimum for assessing the proximity to a gas source in an open environment. Specifically, we compare three statistical descriptors of the signal (mean, variance and maximum response) against the 'bout' frequency, a feature computed in the derivative of the response. The experimental setup includes a generator of turbulent plumes and a sensing board composed of three metal oxide (MOX) sensors of different types. The main conclusion is that the maximum response is the most robust feature across the three sensors. The 'bout' frequency can be very sensitive to an additional parameter (the noise threshold).

JTD Keywords: Feature extraction, Gas plume, Gas sensors, Gas source localization, MOX, Signal processing

The low bandwidth of metal oxide semiconductor (MOX) sensors (<0.1 Hz) is a major hurdle to gas source localization (GSL) in turbulent environments where detection of intermittent odor patches is key. We present a fast-response miniaturized electronic nose (Fast-eNose) composed of four naked MOX sensors and a digital band-pass filter that can boost the bandwidth of the system close to 1 Hz. The device was attached to a fast photo-ionization detector (330 Hz) to quantify the response time during exposure to turbulent gas plumes. The results indicate that the digital filter can improve the response time by at least a factor of 4, bringing new possibilities to mobile robot olfaction.

JTD Keywords: CFD, Gas plume, Gas sensors, MOX, Response time, Signal processing

Reactive oxygen species (ROS) contribute to tissue damage and remodelling mediated by the inflammatory response after injury. Here we show that ROS, which promote axonal dieback and degeneration after injury, are also required for axonal regeneration and functional recovery after spinal injury. We find that ROS production in the injured sciatic nerve and dorsal root ganglia requires CX3CR1-dependent recruitment of inflammatory cells. Next, exosomes containing functional NADPH oxidase 2 complexes are released from macrophages and incorporated into injured axons via endocytosis. Once in axonal endosomes, active NOX2 is retrogradely transported to the cell body through an importin-β1–dynein-dependent mechanism. Endosomal NOX2 oxidizes PTEN, which leads to its inactivation, thus stimulating PI3K–phosporylated (p-)Akt signalling and regenerative outgrowth. Challenging the view that ROS are exclusively involved in nerve degeneration, we propose a previously unrecognized role of ROS in mammalian axonal regeneration through a NOX2–PI3K–p-Akt signalling pathway.

JTD Keywords: Adult neurogenesis, Endocytosis, Exocytosis, Monocytes and macrophages, Stress signalling

Skin wound healing aims to repair and restore tissue through a multistage process that involves different cells and signalling molecules that regulate the cellular response and the dynamic remodelling of the extracellular matrix. Nowadays, several therapies that combine biomolecule signals (growth factors and cytokines) and cells are being proposed. However, a lack of reliable evidence of their efficacy, together with associated issues such as high costs, a lack of standardization, no scalable processes, and storage and regulatory issues, are hampering their application. In situ tissue regeneration appears to be a feasible strategy that uses the body's own capacity for regeneration by mobilizing host endogenous stem cells or tissue-specific progenitor cells to the wound site to promote repair and regeneration. The aim is to engineer instructive systems to regulate the spatio-temporal delivery of proper signalling based on the biological mechanisms of the different events that occur in the host microenvironment. This review describes the current state of the different signal cues used in wound healing and skin regeneration, and their combination with biomaterial supports to create instructive microenvironments for wound healing.

JTD Keywords: Instructive biomaterials, Skin regeneration, Wound healing, Signalling release, In situ tissue engineering

The actin cytoskeleton is central to many cellular processes involving changes in cell shape, migration, and adhesiveness. Therefore, there is a great interest in the identification of the signaling pathways leading to the regulation of actin polymerization and assembly into stress fibers (SFs). However, to date it is not well understood how the mechanical interactions between cells and their environment activate the assembly of SFs. Here, we demonstrate that the mechanosensitive Piezo2 channel is required to sense physical cues from the environment to generate a calcium signal that maintains RhoA active and the formation and orientation of SFs and focal adhesions. Besides, this Piezo2-initiated signaling pathway has implications for different hallmarks of cancer invasion and metastasis.

JTD Keywords: Mechanotransduction, Calcium signaling, RhoA, Actin stress fibers, Cancer

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (Ti/Ttot) decrements, from 0.54 to 0.18. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD). The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdifse). The neural onset was detected through a dynamic threshold over the EMGdifse using the kernel density estimation method, while neural offset was detected by finding when the EMGdifse had decreased to 70 % of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the Ti/Ttot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the Ti/Ttot protocol. The relationship between pairs of RR values (Pearson’s correlation coefficient of 0.99, Bland- Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of Ti/Ttot values (Pearson’s correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on non-invasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

JTD Keywords: Kernel density estimation (KDE),, Surface diaphragm electromyographic,, (EMGdi) signal,, Inspiratory time,, Neural respiratory drive (NRD),, Neural inspiratory time,, Fixed sample entropy (fSampEn)

N-methyl-D-aspartate receptors (NMDARs) respond to glutamate to allow the influx of calcium ions and the signaling to the mitogen-activated protein kinase (MAPK) cascade. Both MAPK- and Ca2+-mediated events are important for both neurotransmission and neural cell function and fate. Using a heterologous expression system, we demonstrate that NMDAR may interact with the EF-hand calcium-binding proteins calmodulin, calneuron-1, and NCS1 but not with caldendrin. NMDARs were present in primary cultures of both neurons and microglia from cortex and hippocampus. Calmodulin in microglia, and calmodulin and NCS1 in neurons, are necessary for NMDA-induced MAP kinase pathway activation. Remarkably, signaling to the MAP kinase pathway was blunted in primary cultures of cortical and hippocampal neurons and microglia from wild-type animals by proteins involved in neurodegenerative diseases: α-synuclein, Tau, and p-Tau. A similar blockade by pathogenic proteins was found using samples from the APPSw,Ind transgenic Alzheimer’s disease model. Interestingly, a very marked increase in NMDAR–NCS1 complexes was identified in neurons and a marked increase of both NMDAR–NCS1 and NMDAR–CaM complexes was identified in microglia from the transgenic mice. The results show that α-synuclein, Tau, and p-Tau disrupt the signaling of NMDAR to the MAPK pathway and that calcium sensors are important for NMDAR function both in neurons and microglia. Finally, it should be noted that the expression of receptor–calcium sensor complexes, specially those involving NCS1, is altered in neural cells from APPSw,Ind mouse embryos/pups.

JTD Keywords: Alzheimer’s disease, Calmodulin, Calneuron-1, Caldendrin, NCS1, Extracellular signal-regulated kinase, Glutamate receptor, Proximity ligation assay

Extracellular calcium has been proved to influence the healing process of injuries and could be used as a novel therapy for skin wound healing. However, a better understanding of its effect, together with a system to obtain a controlled release is needed. In this study, we examined whether the ionic dissolution of the calcium–phosphate-based ormoglass nanoparticles coded SG5 may produce a similar stimulating effect as extracellular calcium (from CaCl2) on rat dermal fibroblast in vitro. Cells were cultured in the presence of medium containing different calcium concentrations, normally ranging from 0.1 to 3.5 mM Ca2+. A concentration of 3.5 mM of CaCl2 increased metabolic activity, in vitro wound closure, matrix metalloproteinases (MMP) activity, collagen synthesis and cytokine expression, and reduced cell contraction capacity. Interestingly, the levels of migration and contraction capacity measured followed a dose-dependent behavior. In addition, media conditioned with SG5 stimulated the same activities as media conditioned with CaCl2, but undesired effects in chronic wound healing such as inflammatory factor expression and MMP activity were reduced compared to the equivalent CaCl2 concentration. In summary, calcium-releasing particles such as SG5 are potential biological-free biostimulators to be applied in dressings for chronic wound healing.

JTD Keywords: Nanomaterials, Cell signaling, Skin wound healing

The time delay estimation problem associated with an ensemble of misaligned, repetitive signals is revisited. Each observed signal is assumed to be composed of an unknown, deterministic signal corrupted by Gaussian, white noise. This paper shows that maximum likelihood (ML) time delay estimation can be viewed as the maximization of an eigenvalue ratio, where the eigenvalues are obtained from the ensemble correlation matrix. A suboptimal, one-step time delay estimate is proposed for initialization of the ML estimator, based on one of the eigenvectors of the inter-signal correlation matrix. With this approach, the ML estimates can be determined without the need for an intermediate estimate of the underlying, unknown signal. Based on respiratory flow signals, simulations show that the variance of the time delay estimation error for the eigenvalue-based method is almost the same as that of the ML estimator. Initializing the maximization with the one-step estimates, rather than using the ML estimator alone, the computation time is reduced by a factor of 5M when using brute force maximization (M denoting the number of signals in the ensemble), and a factor of about 1.5 when using particle swarm maximization. It is concluded that eigenanalysis of the ensemble correlation matrix not only provides valuable insight on how signal energy, jitter, and noise influence the estimation process, but it also leads to a one-step estimator which can make the way for a substantial reduction in computation time.

JTD Keywords: Biomedical signals, Time delay estimation, Eigenanalysis, Ensemble analysis

Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

JTD Keywords: Time series analysis, Electrocardiography, Support vector machines, Kernel, Standards, Correlation, RF signals

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese populations. Despite constituting a serious health, social and economic problem, most patients remain undiagnosed and untreated due to limitations in current equipment. In this work, we propose a novel method to diagnose OSA and monitor therapy adherence and effectiveness at home in a non-invasive and inexpensive way: combining acoustic analysis of breathing and snoring sounds with oral appliance therapy (OA). Audiodontics has introduced a new sensor, a tooth microphone coupled to an OA device, which is the main pillar of this system. The objective of this work is to characterize the response of this sensor, comparing it with a commercial tracheal microphone (Biopac transducer). Signals containing OSA-related sounds were acquired simultaneously with the two microphones for that purpose. They were processed and analyzed in time, frequency and time-frequency domains, in a custom MATLAB interface. We carried out a single-event approach focused on breaths, snores and apnea episodes. We found that the quality of the signals obtained by both microphones was quite similar, although the tooth microphone spectrum concentrated more energy at the high-frequency band. This opens a new field of study about high-frequency components of snores and breathing sounds. These characteristics, together with its intraoral position, wireless option and combination with customizable OAs, give the tooth microphone a great potential to reduce the impact of sleep disorders, by enabling prompt detection and continuous monitoring of patients at home.

JTD Keywords: Microphones, Teeth, Sleep apnea, Time-frequency analysis, Signal to noise ratio, Monitoring, Acoustics

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

JTD Keywords: Chemical sensor arrays, Pattern recognition, Chemometrics, Electronic noses, Robustness, Signal and data processing

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas.

JTD Keywords: Gas chromatography, Instruments, Radioactivity measurement, Signal processing, Analytical instrument, Analytical Instrumentation, Asymmetric least squares, Baseline estimation, Baseline removal, Gas chromatography-mass spectrometries (GC-MS), Instrumental techniques, Noise levels, Iterative methods

Electronic nose is an instrument which is composed of gas sensor array and pattern recognition unit. It is generally used for classifying, identifying or quantifying the odors or volatile organic components for these commonly used devices, calibration transfer is an important issue because of differences in each instrument, sensor drift, changes in environmental conditions or background changes. Calibration transfer is a transfer of model between different instruments which have different conditions. In this study, calibration transfer is applied to the e-noses which have different temperature conditions. Also the results of the direct standardization, piecewise direct standardization and orthogonal signal correction which are different calibration methods were compared. The results of the piecewise direct standardization method are more successful than the other methods for the dataset which is used in this study.

JTD Keywords: Calibration, Conferences, Electronic noses, Ethanol, Instruments, Signal processing, Standardization

Cerebral Palsy (CP) is the most frequent disability affecting children. Although the effects of CP are diverse this chapter focuses on the impaired motor control of children suffering from spastic diplegia, particularly in the lower limb. The chapter collects the most relevant techniques that are used or might be useful to overcome the current limitations existing in the diagnosis and rehabilitation of CP. Special emphasis is placed on the role that emerging technologies can play in this field. Knowing in advance the type and site of brain injury could assist the clinician in selecting the appropriate therapy. In this context, neuroimaging techniques are being recommended as an evaluation tool in children with CP; we describe a variety of imaging technologies such as Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), etc. But creating new knowledge in itself is not enough; there must be a transfer from progress through research to advances in the clinical field. The classic therapeutic approach of CP thus hampers the optimal rehabilitation of the targeted component. Traditional therapies may be optimized if complemented with treatments. We try to collect a wide range of emerging technologies and provide some criteria to select the adequate technology based on the characteristics of the neurological injury. For example, exoskeleton based over-ground gait training is suggested to be more effective than treadmill-based gait training. So, we suggest a new point of view combining different technologies in order to provide the foundations of a rational design of the individual rehabilitation strategy.

JTD Keywords: Cerebral palsy, Robotics, Neurostimulation, Neuroimaging, Myoelectric signals

Alzheimer's disease and prion diseases are neuropathological disorders that are caused by abnormal processing and aggregation of amyloid and prion proteins. Interactions between amyloid precursor protein (APP) and PrPc proteins have been described at the neuron level. Accordingly to this putative interaction, we investigated whether β-amyloid accumulation may affect prion infectivity and, conversely, whether different amounts of PrP may affect β-amyloid accumulation. For this purpose, we used the APPswe/PS1dE9 mouse line, a common model of Alzheimer's disease, crossed with mice that either overexpress (Tga20) or that lack prion protein (knock-out) to generate mice that express varying amounts of prion protein and deposit β-amyloid. On these mouse lines, we investigated the influence of each protein on the evolution of both diseases. Our results indicated that although the presence of APP/PS1 and β-amyloid accumulation had no effect on prion infectivity, the accumulation of β-amyloid deposits was dependent on PrPc, whereby increasing levels of prion protein were accompanied by a significant increase in β-amyloid aggregation associated with aging.

JTD Keywords: Aging, Amyloid, Neurodegeneration, Prion, Signaling

The performance of three different types of ion mobility spectrometer (IMS) devices: GDA2 with a radioactive ion source (Airsense, Germany), UV-IMS with a photo-ionization source (G.A.S. Germany) and VG-Test with a corona discharge source (3QBD, Israel) was studied. The gas-phase ion chemistry in the IMS devices affected the species formed and their measured reduced mobility values. The sensitivity and limit of detection for trimethylamine (TMA), putrescine and cadaverine were compared by continuous monitoring of a stream of air with a given concentration of the analyte and by measurement of headspace vapors of TMA in a sealed vial. Preprocessing of the mobility spectra and the effectiveness of multivariate curve resolution techniques (MCR-LASSO) improved the accuracy of the measurements by correcting baseline effects and adjusting for variations in drift time as well as enhancing the signal to noise ratio and deconvolution of the complex data matrix to their pure components. The limit of detection for measurement of the biogenic amines by the three IMS devices was between 0.1 and 1.2 ppm (for TMA with the VG-Test and GDA, respectively) and between 0.2 and 0.7 ppm for putrescine and cadaverine with all three devices. Considering the uncertainty in the LOD determination there is almost no statistically significant difference between the three devices although they differ in their operating temperature, ionization method, drift tube design and dopant chemistry. This finding may have general implications on the achievable performance of classic IMS devices.

JTD Keywords: Biogenic amines, Comparison of performance, Ion mobility spectrometry, Sensitivity, Signal processing, Vapor concentration

The prion protein (PrP) plays a key role in prion disease pathogenesis. Although the misfolded and pathologic variant of this protein (PrPSC) has been studied in depth, the physiological role of PrPC remains elusive and controversial. PrPC is a cell-surface glycoprotein involved in multiple cellular functions at the plasma membrane, where it interacts with a myriad of partners and regulates several intracellular signal transduction cascades. However, little is known about the gene expression changes modulated by PrPC in animals and in cellular models. In this article, we present PrPC-dependent gene expression signature in N2a cells and its implication in the most overrepresented functions: cell cycle, cell growth and proliferation, and maintenance of cell shape. PrPC over-expression enhances cell proliferation and cell cycle re-entrance after serum stimulation, while PrPC silencing slows down cell cycle progression. In addition, MAP kinase and protein kinase B (AKT) pathway activation are under the regulation of PrPC in asynchronous cells and following mitogenic stimulation. These effects are due in part to the modulation of epidermal growth factor receptor (EGFR) by PrPC in the plasma membrane, where the two proteins interact in a multimeric complex. We also describe how PrPC over-expression modulates filopodia formation by Rho GTPase regulation mainly in an AKT-Cdc42-N-WASP-dependent pathway.

JTD Keywords: Cell signaling, Cellular prion protein, Filopodia, Gene expression, Microarray, Proliferation

Assessment of the dynamic interactions between cardiovascular signals can provide valuable information that improves the understanding of cardiovascular control. Heart rate variability (HRV) analysis is known to provide information about the autonomic heart rate modulation mechanism. Using the HRV signal, we aimed to obtain parameters for classifying patients with and without chronic heart failure (CHF), and with periodic breathing (PB), non-periodic breathing (nPB), and Cheyne-Stokes respiration (CSR) patterns. An electrocardiogram (ECG) and a respiratory flow signal were recorded in 36 elderly patients: 18 patients with CHF and 18 patients without CHF. According to the clinical criteria, the patients were classified into the follow groups: 19 patients with nPB pattern, 7 with PB pattern, 4 with Cheyne-Stokes respiration (CSR), and 6 non-classified patients (problems with respiratory signal). From the HRV signal, parameters in the time and frequency domain were calculated. Frequency domain parameters were the most discriminant in comparisons of patients with and without CHF: P_Tot (p = 0.02), P_LF (p = 0.022) and fp_HF (p = 0.021). For the comparison of the nPB vs. CSR patients groups, the best parameters were RMSSD (p = 0.028) and SDSD (p = 0.028). Therefore, the parameters appear to be suitable for enhanced diagnosis of decompensated CHF patients and the possibility of developed periodic breathing and a CSR pattern.

JTD Keywords: cardiovascular system, diseases, electrocardiography, frequency-domain analysis, geriatrics, medical signal processing, patient diagnosis, pneumodynamics, signal classification, Cheyne-Stokes respiration patterns, ECG, autonomic heart rate modulation mechanism, cardiovascular control, cardiovascular signals, chronic heart failure, decompensated CHF patients, dynamic interaction assessment, elderly patients, electrocardiogram, enhanced diagnosis, frequency domain parameters, heart rate variability analysis, patient classification, periodic breathing, respiratory flow signal recording, Electrocardiography, Frequency modulation, Frequency-domain analysis, Heart rate variability, Senior citizens, Standards

Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BP_sl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BP_sl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.

JTD Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation

This work investigates the very low frequency (VLF) modulation of QRS slopes and heart rate variability (HRV). Electrocardiogram (ECG) and respiratory flow signal were acquired from patients with dilated cardiomyopathy and ischemic cardiomyopathy. HRV as well as the upward QRS slope (I_US) and downward QRS slope (I_DS) were extracted from the ECG. The relation between HRV and QRS slopes in the VLF band was measured using ordinary coherence in 5-minute segments. Partial coherence was then used to remove the influence that respiration simultaneously exerts on HRV and QRS slopes. A statistical threshold was determined, below which coherence values were considered not to represent a linear relation. 7 out of 276 segments belonging to 5 out of 29 patients for I_US and 10 segments belonging to 5 patients for I_DS presented a VLF modulation in QRS slopes, HRV and respiration. In these segments spectral coherence was statistically significant, while partial coherence decreased, indicating that the coupling HRV and QRS slopes was related to respiration. 4 segments had a partial coherence value below the threshold for I_US, 3 segments for I_DS. The rest of the segments also presented a notable decrease in partial coherence, but still above the threshold, which means that other non-linearly effects may also affect this modulation.

JTD Keywords: diseases, electrocardiography, feature extraction, medical signal processing, pneumodynamics, statistical analysis, ECG, QRS slopes, cardiomyopathy patients, dilated cardiomyopathy, electrocardiogram, feature extraction, heart rate variability, ischemic cardiomyopathy, ordinary coherence, partial coherence value, respiration, respiratory flow signal acquisition, spectral coherence, statistical threshold, time 5 min, very low frequency modulation, Coherence, Educational institutions, Electrocardiography, Frequency modulation, Heart rate variability

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

JTD Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform

Rat model of Obstructive Sleep Apnea (OSA) is a realistic approach for studying physiological mechanisms involved in sleep. Rats are usually anesthetized and autonomic nervous system (ANS) could be blocked. This study aimed to assess the effect of anesthesia on ANS activity during OSA episodes. Seven male Sprague-Dawley rats were anesthetized intraperitoneally with urethane (1g/kg). The experiments were conducted applying airway obstructions, simulating 15s-apnea episodes for 15 minutes. Five signals were acquired: respiratory pressure and flow, SaO2, ECG and photoplethysmography (PPG). In total, 210 apnea episodes were studied. Normalized power spectrum of Pulse Rate Variability (PRV) was analyzed in the Low Frequency (LF) and High Frequency (HF) bands, for each episode in consecutive 15s intervals (before, during and after the apnea). All episodes showed changes in respiratory flow and SaO₂ signal. Conversely, decreases in the amplitude fluctuations of PPG (DAP) were not observed. Normalized LF presented extremely low values during breathing (median=7,67%), suggesting inhibition of sympathetic system due to anesthetic effect. Subtle increases of LF were observed during apnea. HRV and PPG analysis during apnea could be an indirect tool to assess the effect and deep of anesthesia.

JTD Keywords: electrocardiography, fluctuations, medical disorders, medical signal detection, medical signal processing, neurophysiology, photoplethysmography, pneumodynamics, sleep, ECG, SaO₂ flow, SaO₂ signal, airway obstructions, amplitude fluctuations, anesthesia effects, anesthetized nervous system, autonomic evaluation, autonomic nervous system, breathing, heart rate variability, high-frequency bands, low-frequency bands, male Sprague-Dawley rats, normalized power spectrum, obstructive sleep apnea, photoplethysmography, physiological mechanisms, pulse rate variability, rat model, respiratory flow, respiratory pressure, signal acquisition, sympathetic system inhibition, time 15 min, time 15 s, Abstracts, Atmospheric modeling, Computational modeling, Electrocardiography, Rats, Resonant frequency

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

JTD Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

JTD Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials

Robot-assisted rehabilitation therapies usually focus on physical aspects rather than on cognitive factors. However, cognitive aspects such as attention, motivation, and engagement play a critical role in motor learning and thus influence the long-term success of rehabilitation programs. This paper studies motor-related EEG activity during the execution of robot-assisted passive movements of the upper limb, while participants either: i) focused attention exclusively on the task; or ii) simultaneously performed another task. Six healthy subjects participated in the study and results showed lower desynchronization during passive movements with another task simultaneously being carried out (compared to passive movements with exclusive attention on the task). In addition, it was proved the feasibility to distinguish between the two conditions.

JTD Keywords: Electrodes, Electroencephalography, Induction motors, Medical treatment, Robot sensing systems, Time frequency analysis, Biomechanics, Cognition, Electroencephalography, Medical robotics, Medical signal detection, Medical signal processing, Patient rehabilitation, Attention, Cognitive aspects, Desynchronization, Engagement, Motivation, Motor learning, Motor task, Motor-related EEG activity, Physical aspects, Robot-assisted passive movement detection, Robot-assisted rehabilitation therapies, Upper limb

In this paper, the problem of time alignment is revisited by adopting an ensemble-based approach with all signals jointly aligned. It is shown that the maximization of an eigenvalue ratio is synonymous to maximizing the signal-to-jitter-and-noise ratio. Since optimization of this criterion is extremely time consuming, a relaxed optimization procedure is introduced which converges much more quickly. Using simulations based on respiratory flow signals, the results suggest that the time delay error variance of the new method is much lower than that obtained with the well-known Woody’s method.

JTD Keywords: Time alignment, Signal ensemble, Subsample precision, Eigenvalue decomposition

The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson's correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

JTD Keywords: Adaptive filters, Frequency modulation, Interference, Muscles, Noise cancellation, Vibrations, Cardiology, Medical signal processing, Muscle, Signal denoising, ANC algorithm, MCG interference, Pearson correlation coefficient, Adaptive noise cancellation, Cardiac vibration interference, Cardiac vibration noise, Diaphragm muscle, Mechanocardiographic signal, Mechanomyographic signals, Respiratory muscles effort

Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (T_I), expiratory time (T_E), breathing cycle duration (T_Tot), tidal volume (V_T), inspiratory fraction (T_I/T_Tot), half inspired flow (V_T/T_I), and rapid shallow index (f/V_T), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

JTD Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1_st and 2_nd ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO₂ and periodic breathing cycles significantly increased with acclimatization (p-value <; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO₂, through a significant negative correlation (p-value <; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with >; 5 periodic breathing cycles through a significant positive correlation (p-value <; 0.01). Our data demonstrate that quantification of the respiratory volum- signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

JTD Keywords: Frequency domain analysis, Frequency modulation, Heart, Sleep apnea, Ventilation, Visualization, Cardiology, Medical disorders, Medical signal processing, Plethysmography, Pneumodynamics, Sensitivity analysis, Sleep, Spectral analysis, Cheyne-Stokes respiration, Climbing periods, Dataset, Heart failure patients, High altitude PB, High altitude periodic breathing, Hypobaric hypoxia, Linear discriminant analysis, Pathophysiologic aspects, Physical activity, Physiologic mechanisms, Power spectral density, Receiver operating characteristic curve, Respiratory control, Respiratory frequency, Respiratory inductive plethysmography, Respiratory pattern, Respiratory volume signal, Sleep apnea, Spectral analysis, Spectral parameters

Sleep in patients with Sleep Apnea-Hypopnea Syndrome (SAHS) is frequently interrupted with arousals. Increased amounts of arousals result in shortening total sleep time and repeated sleep-arousal change can result in sleep fragmentation. According to the American Sleep Disorders Association (ASDA) an arousal is a marker of sleep disruption representing a detrimental and harmful feature for sleep. The nature of arousals and its role on the regulation of the sleep process raises controversy and has sparked the debate in the last years. In this work, we analyzed and compared the EEG spectral content of respiratory and spontaneous arousals on a database of 45 SAHS subjects. A total of 3980 arousals (1996 respiratory and 1984 spontaneous) were analyzed. The results showed no differences between the spectral content of the two kinds of arousals. Our findings raise doubt as to whether these two kinds of arousals are truly triggered by different organic mechanisms. Furthermore, they may also challenge the current beliefs regarding the underestimation of the importance of spontaneous arousals and their contribution to sleep fragmentation in patients suffering from SAHS.

JTD Keywords: Adaptive filters, Correlation, Databases, Electroencephalography, Hospitals, Sleep apnea, Electroencephalography, Medical signal processing, Pneumodynamics, Sleep, EEG spectral content, Organic mechanism, Respiratory, Sleep apnea hypopnea syndrome, Sleep fragmentation, Spectral content, Spontaneous arousal

Cellular prion protein (PrP(C)) is a glycosyl-phosphatidylinositol-anchored glycoprotein. When mutated or misfolded, the pathogenic form (PrP(SC)) induces transmissible spongiform encephalopathies. In contrast, PrP(C) has a number of physiological functions in several neural processes. Several lines of evidence implicate PrP(C) in synaptic transmission and neuroprotection since its absence results in an increase in neuronal excitability and enhanced excitotoxicity in vitro and in vivo. Furthermore, PrP(C) has been implicated in the inhibition of N-methyl-D-aspartic acid (NMDA)-mediated neurotransmission, and prion protein gene (Prnp) knockout mice show enhanced neuronal death in response to NMDA and kainate (KA). In this study, we demonstrate that neurotoxicity induced by KA in Prnp knockout mice depends on the c-Jun N-terminal kinase 3 (JNK3) pathway since Prnp(%) Jnk3(%) mice were not affected by KA. Pharmacological blockage of JNK3 activity impaired PrP(C)-dependent neurotoxicity. Furthermore, our results indicate that JNK3 activation depends on the interaction of PrP(C) with postsynaptic density 95 protein (PSD-95) and glutamate receptor 6/7 (GluR6/7). Indeed, GluR6-PSD-95 interaction after KA injections was favored by the absence of PrP(C). Finally, neurotoxicity in Prnp knockout mice was reversed by an AMPA/KA inhibitor (6,7-dinitroquinoxaline-2,3-dione) and the GluR6 antagonist NS-102. We conclude that the protection afforded by PrP(C) against KA is due to its ability to modulate GluR6/7-mediated neurotransmission and hence JNK3 activation.

JTD Keywords: Ischemic brain-injury, Prion protein PrP(C), Stress-inducible protein-1, Synaptic plasticity, Neurite outgrowth, Signaling module, Caspase-3 activation, Organotypic cultures, Cerebral-ischemia

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

JTD Keywords: Gene-expression measurements, Quality-control maqc, Cancer-cell-lines, Real-time pcr, Oligonucleotide microarrays, Phosphorylation dynamics, In-vivo, Networks, Signal, Technologies

This paper depicts a virtual sensor array that allows the user to generate gas sensor synthetic data while controlling a wide variety of the characteristics of the sensor array response: arbitrary number of sensors, support for multi-component gas mixtures and full control of the noise in the system such as sensor drift or sensor aging. The artificial sensor array response is inspired on the response of 17 polymeric sensors for three analytes during 7 month. The main trends in the synthetic gas sensor array, such as sensitivity, diversity, drift and sensor noise, are user controlled. Sensor sensitivity is modeled by an optionally linear or nonlinear method (spline based). The toolbox on data generation is implemented in open source R language for statistical computing and can be freely accessed as an educational resource or benchmarking reference. The software package permits the design of scenarios with a very large number of sensors (over 10000 sensels), which are employed in the test and benchmarking of neuromorphic models in the Bio-ICT European project NEUROCHEM.

JTD Keywords: Data analysis, Circuit noise, Data acquisition, Signal processing

Current analytical instrumentation and continuous sensing can provide huge amounts of data. Automatic signal processing and information evaluation is needed to overcome drowning in data. Today, statistical techniques are typically used to analyse and extract information from continuous signals. However, it is very interesting to note that biology (insects and vertebrates) has found alternative solutions for chemical sensing and information processing. This is a brief introduction to the developments in the European Project: Bio-ICT NEUROCHEM: Biologically Inspired Computation for Chemical Sensing (grant no. 216916) Fp7 project devoted to biomimetic olfactory systems.

JTD Keywords: Signal processing, Chemioception, Neural nets, Computational complexity

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.

JTD Keywords: Gas sensor array, Drift, Orthogonal signal correction, Component correction, Cross-validation, Electronic nose, Data shift

Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI Study Design: Individual Case-Control Study. Methods: Thirty-seven snorers (12 females and 25 males, ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >= 5, <15) and G3 (AHI >= 15) Snore and breathing sounds were : recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. Results: There were significant differences in supine position between G1 and G3 in sound intensity, number of snores; standard deviation of the spectrum, power ratio in bands 0-500, 100-500, and 0-800 Hz, and the symmetry coefficient (P < .03); Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

JTD Keywords: Breathing sounds, Signal interpretation, Sleep apnea syndromes, Snoring

In the present work, we have studied the QRS loop in the Vectorcardiogram (VCG) of 95 chronic chagasic patients classified in different groups (I, II and III) according to their degree of myocardial damage. For comparison, the VCGs of 11 healthy subjects used as control group (Group O) were also examined. The QRS loop was obtained for each patient from the XYZ orthogonal leads of their High-Resolution Electrocardiogram (HRECG) records. In order to analyze the variations of QRS loop in each detected beat, it has been proposed in this study the following vectorcardiographic parameters a) Maximum magnitude of the cardiac depolarization vector, b) Volume, c) Area of QRS loop, d) Ratio between the Area and Perimeter, e) Ratio between the major and minor axes of the QRS loop and f) QRS loop Energy. It has been found that one or more indexes exhibited statistical differences (p<0.05) between groups 0-II, O-III, I-II, I-III and II-III. We concluded that the proposed method could be use as complementary diagnosis technique to evaluate the degree of myocardial damage in chronic chagasic patients.

JTD Keywords: Practical, Experimental/ bioelectric phenomena, Diseases, Electrocardiography, Medical signal, Processing/ QRS loop, Vectorcardiogram, Cardiac depolarization vector, Myocardial damage, Chagas disease, Complementary diagnosis technique, High-resolution electrocardiogram

The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the non-invasive differentiation of obstructive and central hypopneas.

JTD Keywords: Practical, Experimental/ biomedical measurement, Feature extraction, Flow measurement, Medical disorders, Medical signal processing, Patient diagnosis, Pneumodynamics, Pressure measurement, Signal classification, Sleep, Spectral analysis/ automatic noninvasive differentiation, Obstructive hypopnea, Central hypopnea, Inspiratory flow limitation, Nasal airflow, Esophageal pressure, Polysomnography, Feature extraction, Discriminant analysis, Spectral analysis

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

JTD Keywords: Practical, Theoretical or Mathematical, Experimental/cardiology diseases, Fourier transforms, Medical signal processing, Pattern classification, Pneumodynamics, Spectral analysis, Statistical analysis, Stochastic processes/ correntropy based nonlinearity test, Chronic heart failure, Correntropy function, Respiratory pattern nonlinearities, CHF patients, Nonperiodic breathing pattern, Dataset statistical distribution, Dataset temporal structure, Nonlinear information, Null hypothesis, Gaussian linear stochastic process, Statistical test, Correntropy spectral density, Iteratively refined amplitude adjusted Fourier transform, Surrogate data, Periodic breathing pattern

A new method for the quantification of amplitude variations in biomedical signals through moving approximate entropy is presented. Unlike the usual method to calculate the approximate entropy (ApEn), in which the tolerance value (r) varies based on the standard deviation of each moving window, in this work ApEn has been computed using a fixed value of r. We called this method, moving approximate entropy with fixed tolerance values: ApEn/sub f/. The obtained results indicate that ApEn/sub f/ allows determining amplitude variations in biomedical data series. These amplitude variations are better determined when intermediate values of tolerance are used. The study performed in diaphragmatic mechanomyographic signals shows that the ApEn/sub f/ curve is more correlated with the respiratory effort than the standard RMS amplitude parameter. Furthermore, it has been observed that the ApEn/sub f/ parameter is less affected by the existence of impulsive, sinusoidal, constant and Gaussian noises in comparison with the RMS amplitude parameter.

JTD Keywords: Practical, Theoretical or Mathematical/ biomechanics, Entropy, Gaussian noise, Medical signal processing, Muscle, Random processes/ approximate entropy interpretation, Fixed tolerance values, Diaphragmatic mechanomyographic signals, ApEnf curve, Respiratory effort, Gaussian noises

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).

JTD Keywords: Electrocardiography, Medical signal processing, Neural nets, Pattern recognition, Pneumodynamics, Signal classification, Statistical analysis, ECG, Kurtosis coefficient, Lempel Ziv complexity, Breath durations, Cardiac interbeat durations, Electrocardiography, Multiparameter analysis, Pattern recognition neural network, Power spectral density, Respiratory flow signals, Signal classification, Spontaneous breathing, Statistical analysis, Weaning trials

The study of mechanomyographic (MMG) signals of respiratory muscles is a promising noninvasive technique in order to evaluate the respiratory muscular effort and efficiency. In this work, the MMG signal of the diaphragm muscle it is evaluated in order to assess the respiratory muscular function in Chronic Obstructive Pulmonary Disease (COPD) patients. The MMG signals from left and right hemidiaphragm were acquired using two capacitive accelerometers placed on both left and right sides of the costal wall surface. The MMG signals and the inspiratory pressure signal were acquired while the COPD patients carried out an inspiratory load respiratory test. The population of study is composed of a group of 6 patients with severe COPD (FEV1>50% ref and DLCO<50% ref). We have found high positive correlation coefficients between the maximum inspiratory pressure (IPmax) developed in a respiratory cycle and different amplitude parameters of both left and right MMG signals (RMS, left: 0.68+/-0.11 - right: 0.69+/-0.12; Re nyi entropy, left: - 0.73+/-0.10 - right: 0.77+/-0.08; Multistate Lempel-Ziv, left: 0.73+/-0.17 - right: 0.74+/-0.08), and negative correlation between the Pmax and the maximum frequency of the MMG signal spectrum (left: -0.39+/-0.19 - right: -0.65+/-0.09). Furthermore, we found that the slope of the evolution of the MMG amplitude parameters, as the load increases during the respiratory test, has positive correlation with the %FEV1/FVC pulmonary function test parameter of the six COPD patients analyzed (RMS, left: 0.38 - right: 0.41; Re nyi entropy, left: 0.45 - right: 0.63; Multistate Lempel-Ziv, left: 0.39 - right: 0.64). These results suggest that the information provided by MMG signals could be used in order to evaluate the respiratory effort and the muscular efficiency in COPD patients.

JTD Keywords: Accelerometers, Biomechanics, Biomedical measurement, Diseases, Medical signal processing, Muscle

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH/sub adaptive/ and TH/sub median/) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index)<5h/sup -1/, AHI<10 h/sup -1/, AHI<15h/sup -1/, AHI<30h/sup -1/). Results showed that TH/sub adaptive/ outperformed TH/sub median/ on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

JTD Keywords: Practical, Experimental/ acoustic signal processing, Bioacoustics, Biomedical measurement, Diseases, Feature extraction, Medical signal processing, Patient diagnosis, Pneumodynamics, Sleep/ nonregular snore features, SAHS markers, Sleep apnea hypopnea syndrome, Overnight multichannel polysomnography, Snore mechanism

A considerable number of patients in weaning process have problems to keep spontaneous breathing during the trial and after it. This study proposes to extract characteristic parameters of the RR series and respiratory flow signal according to the patients' condition in weaning test. Three groups of patients have been considered: 93 patients with successful trials (group S), 40 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but that had to be reintubated before 48 hours (group R). The characterization was performed using spectral analysis of the signals, through the power spectral density, cross power spectral density and Coherence method. The parameters were extracted on the three frequency bands (VLF, LF and HF), and the principal statistical differences between groups were obtained in bands of VLF and HF. The results show an accuracy of 76.9% in the classification of the groups S and F.

JTD Keywords: Biomedical measurement, Electrocardiography, Medical signal processing, Pneumodynamics, Spectral analysis, RR series, Coherence method, Cross power spectral density, Electrocardiography, Principal statistical differences, Respiratory flow signal, Spectral analysis, Spontaneous breathing, Weaning test

It is well known that gas chemical sensors are strongly affected by drift. Drift consist on changes in sensors responses along the time, which make that initial statistical models for gas or odor recognition become useless after a period of time of about weeks. Gas sensor arrays based instruments periodically need calibrations that are expensive and laborious. Many different statistical methods have been proposed to extend time between recalibrations. In this work, a simple preprocessing technique based on a double projection is proposed as a prior step to a posterior drift correction algorithm (in this particular case, Direct Orthogonal Signal Correction). This method highly improves the time stability of data in relation with the one obtained by using only such drift correction method. The performance of this technique will be evaluated on a dataset composed by measurements of three analytes by a polymer sensor array along ten months.

JTD Keywords: Drift, Direct orthogonal signal correction

In this work we successfully prepared p-type semiconducting Cu2-xTe layers on Cu substrates by applying a potential multistep signal. Spontaneously deposited tellurium layers were reduced in a single cathodic sweep. The X-ray diffraction characterization showed the presence of single-phased, crystalline Cu2-xTe in the weissite form. A further anodization step allows crystallization of several phases such as CU1.75Te, Cu0.664Te0.336 and CU7Te4. This type of sample was found to be photoactive. The prepared films are p-type and have carrier concentrations in the order of 10(21) CM-3, suitable for CdTe-CU2-xTe contacts.

JTD Keywords: Copper telluride, Electrochemical signal, XRD, Morphology, EIS, Photocurrent, Telluride thin-films, Solar cells, Deposition, Cu

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.

JTD Keywords: Automatic Data Processing, Databases, Factual, Electrocardiography, Humans, Models, Statistical, Respiration, Respiration, Artificial, Respiratory Insufficiency, Respiratory Mechanics, Respiratory Muscles, Signal Processing, Computer-Assisted, Time Factors, Ventilator Weaning, Ventilators, Mechanical, Work of Breathing