Publications

Additive manufacturing technologies are revolutionizing the fabrication of ceramic catalysts through hierarchical design to enhance catalytic performance and simultaneously improving the efficiency of the manufacturing process by decreasing the initial investment and production steps. This work proposes a fabrication process of cobalt-zirconia monoliths based on Direct-Ink Writing of Co-enriched hydrogel-based ceramic inks, and the debinding and sintering at 600 degrees C in a single thermal treatment. The effect of Co precursor amount (3.0 -7.0 wt% Co) on the rheological properties of inks and the catalytic performance in ethanol steam reforming is investigated. The results reveal the successful incorporation of Co into rectilinear monoliths with 50% infill, obtaining strongly Co-rich surfaces. The remarkable catalytic performance of the 5.0 wt% Co monolith at 300-600 degrees C confirms the feasibility of this novel single-step approach, reaching an appropriate balance between catalytic activity and printability. This outcome may represent a push towards the fabrication of fully 3D-printed monolithic catalysts.

JTD Keywords: Additive manufacturing, Catalyst ethanol steam reforming, Cleanup, Co, Combustion, Direct-ink writing, Hydrogen productio, Ionically conductive supports, Nanoparticles, Oxidation, Rama, Reactors, Sulfur, Zirconia

The bone marrow is a complex tissue with distinct cellular and mechanical heterogeneity, serving as the primary site for haematopoiesis. Under certain conditions, such as on the onset of mutations to healthy cells or alterations to the environment, the bone marrow can also become the origin of haematological malignancies, often characterized by uncontrolled self-renewal of hematopoietic stem cells, overproduction of immature progeny and remodelling of the tissue microenvironment. This remodelling alters the composition and mechanics of the extracellular matrix (ECM), facilitating the proliferation and metastasis of leukaemic cells. The elastic and dissipative properties of the ECM are hallmarks of both health and disease progression in different tissues. However, studying the mechanical properties of bone marrow is difficult owing to inaccessibility in situ. Advanced three-dimensional bioengineered models offer a way to recapitulate the mechanical properties of the bone marrow, but it remains challenging to incorporate the elastic and viscous components. Understanding the physiological and disease-specific mechanical ECM signatures is crucial for advancing bone marrow research and for developing therapeutics. In this Review, we explore the structure-function relationship of the bone marrow, emphasizing its complex mechanical behaviour, and discuss the bioengineered models that recapitulate the mechanical properties in the healthy and diseased bone marrow niche, stressing the importance of replicating ECM physiological and pathological mechanical signatures in the future.

JTD Keywords: Acute myeloid-leukemia, Extracellular-matrix stiffness, Fibrosis, Hematopoietic stem-cell, Lysyl-oxidase, Mesenchymal stromal cells, Microenvironment, N-cadherin, Progenitor cells, Suppor

Curcumin, the active compound in turmeric, is renowned for its anti-inflammatory, antioxidant, and antimicrobial properties, making it beneficial for treating conditions like arthritis, neurodegenerative diseases, and various cancers. Despite its promising therapeutic potential, curcumin's poor bioavailability-due to its rapid metabolism and low solubility-limits its clinical efficacy. To address this, recent research has focused on enhancing curcumin delivery using nanoparticles, liposomes, and novel nanomaterials. Among these, laponite, a synthetic nanoclay, has shown promise in improving curcumin delivery due to its unique properties, including large surface area, dual charge, and stability in solution. This study explores the use of curcumin-laponite nanoparticles as carrier vehicles for controlled delivery to in vitro model membranes. Utilizing advanced techniques such as neutron reflectometry, atomic force microscopy, quartz crystal microbalance with dissipation, and infrared spectroscopy, the interaction between curcumin-laponite nanoparticles and solid-supported lipid bilayers is monitored, revealing enhanced stability and controlled release of curcumin across the membrane. These findings pave the way for the development of curcumin-based therapies targeting cardiovascular, neurological, and oncological diseases, leveraging the synergistic effects of curcumin's biological activity and laponite's delivery capabilities.

JTD Keywords: Antioxidant, Apoptosis, Cell, Controlled-release, Curcumin, Drug delivery, Emulsion polymerization, Laponite, Longa, Neutron, Neutron reflectivity, Nf-kappa-b, Products, Supported lipid bilayer, Supported lipid bilayers

Nanoparticles (NPs) are commonly functionalized using targeting ligands to drive their selective uptake in cells of interest. Typical target cell types are cancer cells, which often overexpress distinct surface receptors that can be exploited for NP therapeutics. However, these targeted receptors are also moderately expressed in healthy cells, leading to unwanted off-tumor toxicities. Multivalent interactions between NP ligands and cell receptors have been investigated to increase the targeting selectivity towards cancer cells due to their non-linear response to receptor density. However, to exploit the multivalent effect, multiple variables have to be considered such as NP valency, ligand affinity, and cell receptor density. Here, we synthesize a panel of aptamer-functionalized silica-supported lipid bilayers (SSLB) to study the effect of valency, aptamer affinity, and epidermal growth factor receptor (EGFR) density on targeting specificity and selectivity. We show that there is an evident interplay among those parameters that can be tuned to increase SSLB selectivity towards high-density EGFR cells and reduce accumulation at non-tumor tissues. Specifically, the combination of high-affinity aptamers and low valency SSLBs leads to increased high-EGFR cell selectivity. These insights provide a better understanding of the multivalent interactions of NPs with cells and bring the nanomedicine field a step closer to the rational design of cancer nanotherapeutics.Copyright © 2023. Published by Elsevier B.V.

JTD Keywords: aptamer avidity and affinity, delivery, microscopy, multivalency, multivalent, nanoparticle targeting, silica -supported lipid bilayers, Aptamer avidity and affinity, Multivalency, Nanoparticle targeting, Silica-supported lipid bilayers, Supported lipid-bilayers, Tumor targeting

Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).

JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds

Quantum dots (QDs) are semiconductor nanoparticles with unique optical and electronic properties, whose interest as potential nano-theranostic platforms for imaging and sensing is increasing. The design and use of QDs requires the understanding of cell-nanoparticle interactions at a microscopic and nanoscale level. Model systems such as supported lipid bilayers (SLBs) are useful, less complex platforms mimicking physico-chemical properties of cell membranes. In this work, we investigated the effect of topographical homogeneity of SLBs bearing different surface charge in the adsorption of hydrophilic QDs. Using quartz-crystal microbalance, a label-free surface sensitive technique, we show significant differences in the interactions of QDs onto homogeneous and inhomogeneous SLBs formed following different strategies. Within short time scales, QDs adsorb onto topographically homogeneous, defect-free SLBs is driven by electrostatic interactions, leading to no layer disruption. After prolonged QD exposure, the nanomechanical stability of the SLB decreases suggesting nanoparticle insertion. In the case of inhomogeneous, defect containing layers, QDs target preferentially membrane defects, driven by a subtle interplay of electrostatic and entropic effects, inducing local vesicle rupture and QD insertion at membrane edges. © 2021

JTD Keywords: adsorption, atomic force microscopy, bilayer formation, gold nanoparticles, hydrophilic quantum dots, lipid membrane defects, model, nanomechanics, quartz crystal microbalance with dissipation, size, supported lipid bilayers, surfaces, Atomic force microscopy, Atomic-force-microscopy, Cell membrane, Cytology, Defect-free, Electronic properties, Electrostatics, Hydrophilic quantum dot, Hydrophilic quantum dots, Hydrophilicity, Hydrophilics, Hydrophobic and hydrophilic interactions, Lipid bilayers, Lipid membrane defect, Lipid membrane defects, Lipid membranes, Lipids, Nanocrystals, Nanomechanics, Optical and electronic properties, Quantum dots, Quartz, Quartz crystal microbalance techniques, Quartz crystal microbalance with dissipation, Quartz crystal microbalances, Quartz-crystal microbalance, Semiconductor nanoparticles, Semiconductor quantum dots, Supported lipid bilayers

Cardiomyopathies diseases affects a great number of the elderly population. An adequate identification of the etiology of a cardiomyopathy patient is still a challenge. The aim of this study was to classify patients by their etiology in function of indexes extracted from the characterization of the pulse transit time (PTT). This time series represents the time taken by the pulse pressure to propagate through the length of the arterial tree and corresponding to the time between R peak of ECG and the mid-point of the diastolic to systolic slope in the blood pressure signal. For each patient, the PTT time series was extracted. Thirty cardiomyopathy patients (CMP) classified as ischemic (ICM - 15 patients) and dilated (DCM - 15 patients) were analyzed. Forty-three healthy subjects (CON) were used as a reference. The PTT time series was characterized through statistical descriptive indices and the joint symbolic dynamics method. The best indices were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 89.6% accuracy, 78.5% sensitivity, and 100% specificity. When comparing CMP patients and CON subjects, the best model achieved 91.3% accuracy, 91.3% sensitivity, and 88.3% specificity. Our results suggests a significantly lower pulse transit time in ischemic patients.Clinical relevance - This study analyzed the suitability of the pulse transit time for the classification of ICM and DCM patients. © 2021 IEEE.

JTD Keywords: Aged, Blood pressure, Cardiomyopathies, Cardiomyopathy, Cardiomyopathy, dilated, Congestive cardiomyopathy, Human, Humans, Pulse wave, Pulse wave analysis, Support vector machine

Bioremediation with immobilized enzymes has several advantages, such as the enhancement of selectivity, activity, and stability of biocatalysts, as well as enzyme reusability. Laccase has proven to be a good candidate for the removal of a wide range of contaminants. In this study, naked or modified MnFe O magnetic nanoparticles (MNPs) were used as supports for the immobilization of laccase from Trametes versicolor. To increase enzyme loading and stability, MNPs were coated with chitosan both after the MNP synthesis (MNPs-CS) and during their formation (MNPs-CS ). SEM analysis showed different sizes for the two coated systems, 20 nm and 10 nm for MNPs-CS and MNPs-CS , respectively. After covalent immobilization of laccase by glutaraldehyde, the MNPs-CS -lac and MNPs-CS-lac systems showed a good resistance to temperature denaturation and storage stability. The most promising system for use in repeated batches was MNPs-CS -lac, which degraded about 80% of diclofenac compared to 70% of the free enzyme. The obtained results demonstrated that the MnFe O -CS system could be an excellent candidate for the removal of contaminants. 2 4 in situ in situ in situ in situ 2 4 in situ

JTD Keywords: bioremediation, chitosan, diclofenac, diclofenac removal, immobilized enzyme, laccase, magnetic nanoparticles, phase, removal, supports, Bioremediation, Chitosan, Diclofenac removal, Enzyme immobilization, Immobilized enzyme, Laccase, Magnetic nanoparticles

The transport of metabolites across robust, flexible and free-standing biomimetic membranes made of three perforated poly (lactic acid) (pPLA) layers, separated by two anodically polymerized conducting layers of poly (3,4-ethylenedioxythiophene-co-3-dodecylthiophene), and functionalized on the external pPLA layers with a voltage dependent anion channel (VDAC) protein, has been demonstrated. The three pPLA layers offer robustness and flexibility to the bioactive platform and the possibility of obtaining conducing polymer layers by in situ anodic polymerization. The incorporation of dodecylthiophene units, which bear a 12 carbon atoms long linear alkyl chain, to the conducting layers allows mimicking the amphiphilic environment offered by lipids in cells, increasing 32% the efficiency of the functionalization. Electrochemical impedance measurements in NaCl and adenosine triphosphate (ATP) solutions prove that the integration of the VDAC porin inside the PLA perforations considerably increases the membrane conductivity and is crucial for the electrolyte diffusion. Such results open the door for the development of advanced sensing devices for a broad panel of biomedical applications.

JTD Keywords: Conducting polymers, Membrane proteins, Membranes, Polylactic acid, Self-supported films

Background: Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making. Objective: The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge. Methods: Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients’ functional features, and population health risk assessment, were considered. Results: We studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively. Conclusions: The results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge.

JTD Keywords: Home hospitalization, Health risk assessment, Predictive modeling, Chronic care, Integrated care, Modeling, Hospitalization, Health risk, Prediction, Mortality, Clinical decision support

Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted: beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end– systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. These results suggest a limited ability of cardiac and respiratory systems response to regulate the vascular variability in these patients.

JTD Keywords: Time series analysis, Support vector machines, Blood pressure, Sensitivity, Indexes, Electrocardiography, Kernel

Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.

JTD Keywords: Robot sensing systems, Predictive models, Logic gates, Gas detectors, Training, Temperature measurement, Support vector machines

Cardiovascular diseases are one of the most common causes of death in elderly patients. The etiology of cardiomyopathies is difficult to discern clinically. The objective of this study was to classify cardiomyopathy patients using coupling analysis, through their cardiovascular behavior and the baroreflex response. A total of thirty-eight cardiomyopathy patients (CMP) classified as ischemic (ICM, 25 patients) and dilated (DCM, 13 patients) were analyzed. Thirty elderly control subjects (CON) were used as reference. Their electrocardiographic (ECG) and blood pressure (BP) signals were studied. To characterize the cardiovascular activity, the following temporal series were extracted: beat-to-beat intervals (from the ECG signal), and end- systolic and diastolic blood pressure amplitudes (from the BP signal). Non-linear characterization techniques like high resolution joint symbolic dynamics, segmented Poincaré plot analysis, normalized shorttime partial directed coherence, and dual sequence method were used to characterize these times series. The best indices were used to build support vector machine models for classification. The optimal model for ICM versus DCM patients achieved 84.2% accuracy, 76.9% sensitivity, and 88% specificity. When CMP patients and CON subjects were compared, the best model achieved 95.5% accuracy, 97.3% sensitivity, and 93.3% specificity. These results suggest a disfunction in the baroreflex mechanism in cardiomyopathies patients.

JTD Keywords: Couplings, Time series analysis, Support vector machines, Electrocardiography, Baroreflex, Coherence, Sensitivity

Several cellular processes, including adhesion, signaling and transcription, endocytosis, and membrane resealing, among others, involve conformational changes such as bending, vesiculation, and tubulation. These mechanisms generally involve membrane separation from the cytoskeleton as well as strong bending, for which the membrane chemical composition and physicochemical properties, often highly localized and dynamic, are key players. The mechanical role of the lipid membrane in force triggered (or sensing) mechanisms in cells is important, and understanding the lipid bilayers’ physical and mechanical properties is essential to comprehend their contribution to the overall membrane. Atomic force microscopy (AFM)-based experimental approaches have been to date very valuable to deepen into these aspects. As a stand-alone, high-resolution imaging technique and force transducer with the possibility to operate in aqueous environment, it defies most other surface instrumentation in ease of use, sensitivity and versatility. In this chapter, we introduce the different AFM-based methods to assess topological and nanomechanical information on model membranes, specifically to supported lipid bilayers (SLBs), including several examples ranging from pure phospholipid homogeneous bilayers to multicomponent and phase-separated SLBs, increasing the bilayer complexity, in the direction of mimicking biological membranes.

JTD Keywords: Atomic force microscopy, Force spectroscopy, Model membranes, Nanomechanics, Supported lipid bilayers

Casein micelles are ~200 nm electronegative particles that constitute 80 wt% of the milk proteins. During synthesis in the lactating mammary cells, caseins are thought to interact in the form of ~20 nm assemblies, directly with the biological membranes of the endoplasmic reticulum and/or the Golgi apparatus. However, conditions that drive this interaction are not yet known. Atomic force microscopy imaging and force spectroscopy were used to directly observe the adsorption of casein particles on supported phospholipid bilayers with controlled compositions to vary their phase state and surface charge density, as verified by X-ray diffraction and zetametry. At pH 6.7, the casein particles adsorbed onto bilayer phases with zwitterionic and liquid-disordered phospholipid molecules, but not on phases with anionic or ordered phospholipids. Furthermore, the presence of adsorbed caseins altered the stability of the yet exposed bilayer. Considering their respective compositions and symmetry/asymmetry, these results cast light on the possible interactions of casein assemblies with the organelles’ membranes of the lactating mammary cells.

JTD Keywords: Casein proteins, Phospholipid membrane, Supported lipid bilayer, Atomic force microscopy

Patients with Chronic Heart Failure (CHF) often develop oscillatory breathing patterns. This work proposes the characterization of respiratory pattern by Wavelet Transform (WT) technique to identify Periodic Breathing pattern (PB) and Non-Periodic Breathing pattern (nPB) through the respiratory flow signal. A total of 62 subjects were analyzed: 27 CHF patients and 35 healthy subjects. Respiratory time series were extracted, and statistical methods were applied to obtain the most relevant information to classify patients. Support Vector Machine (SVM) were applied using forward selection technique to discriminate patients, considering four kernel functions. Differences between these parameters are assessed by investigating the following four classification issues: healthy subjects versus CHF patients, PB versus nPB patients, PB patients versus healthy subjects, and nPB patients versus healthy subjects. The results are presented in terms of average accuracy for each kernel function, and comparison groups.

JTD Keywords: Chronic heart failure, Forward selection, Non-periodic breathing, Periodic breathing, Support vector machine

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

In this work, the cardiorespiratory pattern of patients undergoing extubation process is studied. First, the respiratory and cardiac signals were resampled, next the Symbolic Dynamics (SD) technique was implemented, followed of a dimensionality reduction applying Forward Selection (FS) and Moving Window with Variance Analysis (MWVA) methods. Finally, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) classifiers were used. The study analyzed 153 patients undergoing weaning process, classified into 3 groups: Successful Group (SG: 94 patients), Failed Group (FG: 39 patients), and patients who had been successful during the extubation and had to be reintubated before 48 hours, Reintubated Group (RG: 21 patients). According to the results, the best classification present an accuracy higher than 88.98 ± 0.013% in all proposed combinations.

JTD Keywords: Support vector machines, Standards, Time series analysis, Resonant frequency, Nonlinear dynamical systems, Ventilation

Biological membranes mediate several biological processes that are directly associated with their physical properties but sometimes difficult to evaluate. Supported lipid bilayers (SLBs) are model systems widely used to characterize the structure of biological membranes. Cholesterol (Chol) plays an essential role in the modulation of membrane physical properties. It directly influences the order and mechanical stability of the lipid bilayers, and it is known to laterally segregate in rafts in the outer leaflet of the membrane together with sphingolipids (SLs). Atomic force microscope (AFM) is a powerful tool as it is capable to sense and apply forces with high accuracy, with distance and force resolution at the nanoscale, and in a controlled environment. AFM-based force spectroscopy (AFM-FS) has become a crucial technique to study the nanomechanical stability of SLBs by controlling the liquid media and the temperature variations. In this contribution, we review recent AFM and AFM-FS studies on the effect of Chol on the morphology and mechanical properties of model SLBs, including complex bilayers containing SLs. We also introduce a promising combination of AFM and X-ray (XR) techniques that allows for in situ characterization of dynamic processes, providing structural, morphological, and nanomechanical information

JTD Keywords: Atomic force microscopy, Force spectroscopy, Lipid membranes, Supported lipid bilayers, Nanomechanics, Cholesterol, Sphingolipids, Membrane structure, XR-AFM combination

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.

JTD Keywords: Diseases, Dispersion, Empirical mode decomposition, Feature extraction, Informatics, Support vector machines

Aging population is a major concern that is reflected in the increase of chronic diseases. Heart Failure (HF) is one of the most common chronic diseases of elderly people that is punctuated with acute episodes, which result in hospitalization. The periodic modulation of the amplitude of the breathing pattern is proved to be one of the multiple symptoms of an acute episode, and thus, the features extracted from its characterization contribute in the improvement of the first diagnosis of the clinical practice. The main objective of this study is to evaluate if the features extracted from the breathing pattern along with common clinical variables are reliable enough to detect Periodic Breathing (PB). A dataset of 44 elderly patients containing clinical information and a short record of electrocardiogram and respiratory flow signal was used to train two machine learning classification methods: Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). All the available clinical parameters within the dataset along with the parameters characterizing the respiratory pattern were used to classify the observations into two groups. SVM classification was optimized and performed using a = -8 and C = 10.04 giving an accuracy of 88.2 % sensitivity of 90 % and specificity of 85.7 % Similar results were achieved with LDA classifying with an accuracy of 82.4 %, a sensitivity of 81.8% and specificity of 83.3 % PB has been accurately detected using both classifiers.

JTD Keywords: Support vector machines, Feature extraction, Training, Senior citizens, Standards, Training data, Hospitals

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyophaty patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyophaties were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and T_Tot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopaties, obtaining an accuracy of 85.7%.

JTD Keywords: Blood pressure, Electrocardiography, Joints, Kernel, Principal component analysis, Support vector machines, Time series analysis

Disconnection from mechanical ventilation, called the weaning process, is an additional difficulty in the management of patients in intensive care units (ICU). Unnecessary delays in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we propose an extubation index based on the power of the respiratory flow signal (Pi). A total of 132 patients on weaning trials were studied: 94 patients with successful trials (group S) and 38 patients who failed to maintain spontaneous breathing and were reconnected (group F). The respiratory flow signals were processed considering the following three stages: a) zero crossing detection of the inspiratory phase, b) inflection point detection of the flow curve during the inspiratory phase, and c) calculation of the signal power on the time instant indicated by the inflection point. The zero crossing detection was performed using an algorithm based on thresholds. The inflection points were marked considering the zero crossing of the second derivative. Finally, the inspiratory power was calculated from the energy contained over the finite time interval (between the instant of zero crossing and the inflection point). The performance of this parameter was evaluated using the following classifiers: logistic regression, linear discriminant analysis, the classification and regression tree, Naive Bayes, and the support vector machine. The best results were obtained using the Bayesian classifier, which had an accuracy, sensitivity and specificity of 87%, 90% and 81% respectively.

JTD Keywords: Bayes methods, Bayesian classifier, Indexes, Logistics, Niobium, Regression tree analysis, Support vector machines, Ventilation

Ubiquinone (UQ) is one of the main electron and proton shuttle molecules in biological systems, and dipalmitoylphosphatidylcholine (DPPC) is one of the most used model lipids. Supported planar bilayers (SPBs) are extensively accepted as biological model membranes. In this study, SPBs have been deposited on ITO, which is a semiconductor with good electrical and optical features. Specifically, topographic atomic force microscopy (AFM) images and force curves have been performed on SPBs with several DPPC:UQ ratios to study the location and the interaction of UQ in the SPB. Additionally, cyclic voltammetry has been used to understand the electrochemical behavior of DPPC:UQ SPBs. Obtained results show that, in our case, UQ is placed in two main different positions in SPBs. First, between the DPPC hydrophobic chains, fact that originates a decrease in the breakthrough force of the bilayer, and the second between the two leaflets that form the SPBs. This second position occurs when increasing the UQ content, fact that eventually forms UQ aggregates at high concentrations. The formation of aggregates produces an expansion of the SPB average height and a bimodal distribution of the breakthrough force. The voltammetric response of UQ depends on its position on the bilayer.

JTD Keywords: Bimodal distribution, Biological models, Dipalmitoyl phosphatidylcholine, Electrochemical behaviors, Hydrophobic chains, Supported lipid bilayers, Supported planar bilayers, Voltammetric response

Classification algorithms with unbalanced datasets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine (SVM)-based optimized feature selection method, to select the most relevant features and maintain an accurate and well-balanced sensitivity–specificity result between unbalanced groups. A new metric called the balance index (B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients' weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30 min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analysis to cardiac interbeat and breath durations. First, the most suitable parameters (C+,C−,σ) are selected to define the appropriate SVM. Then, the feature selection process is carried out with this SVM, to maintain B lower than 40%. The best result is obtained using 6 features with an accuracy of 80%, a B of 18.64%, a sensitivity of 74.36% and a specificity of 82.42%.

JTD Keywords: Support vector machines, Balance index, Sensitivity-specificity balance, Cardiorespiratory interaction, Joint symbolic dynamics, Feature selection, Weaning procedure

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

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

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JTD Keywords: AFM, Force spectroscopy, Breakthrough force, Supported lipid bilayers, Lipid membranes, (nano)mechanics

Understanding the effect of mechanical stress on membranes is of primary importance in biophysics. Here we use force spectroscopy AFM to quantitatively characterize the nanomechanical stability of supported lipid bilayers as a function of their chemical composition. The onset of plastic deformation reveals itself as a repetitive jump in the approaching force curve, which represents a molecular fingerprint for the bilayer mechanical stability. By systematically probing a set of chemically distinct supported lipid bilayers (SLBs), we first show that both the headgroup and tail have a decisive effect on their mechanical properties. While the mechanical stability of the probed SLBs linearly increases by 3.3 nN upon the introduction of each additional -CH2- in the chain, it exhibits a significant dependence on the phospholipid headgroup, ranging from 3 nN for DPPA to 66 nN for DPPG. Furthermore, we also quantify the reduction of the membrane mechanical stability as a function of the number of unsaturations and molecular branching in the chemical structure of the apolar tails. Finally, we demonstrate that, upon introduction of cholesterol and ergosterol, contrary to previous belief the mechanical stability of membranes not only increases linearly in the liquid phase (DLPC) but also for phospholipids present in the gel phase (DPPC). Our results are discussed in the framework of the continuum nucleation model. This work highlights the compelling effect of subtle variations in the chemical structure of phospholipid molecules on the membrane response when exposed to mechanical forces, a mechanism of common occurrence in nature.

JTD Keywords: Atomic-force microscopy, Molecular-dynamics simulation, Aqueous-electrolyte solutions, Supported planar membranes, Phospholipid-bilayers, Biological-membranes, Physical-properties, Fluid membranes, Model membranes, Chain-length

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h ( GSucc ); 39 patients who failed the weaning trial ( GFail ) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h ( GRein ). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients ( GSucc , GFail and GRein ) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications.

JTD Keywords: Mechanical ventilation, Weaning, Support vector machines, Joint symbolic dynamics

In this paper, we propose a biologically inspired architecture for a Hopfield-like associative memory applied to artificial olfaction. The proposed algorithm captures the projection between two neural layers of the insect olfactory system (Antennal Lobe and Mushroom Body) with a kernel based projection. We have tested its classification performance as a function of the size of the training set and the time elapsed since training and compared it with that obtained with a Support Vector Machine.

JTD Keywords: Biocomputing, Chemioception, Content-addressable storage, Hopfield neural nets, Support vector machines

In this study, we compare for the first time the nanomechanical properties of lipid bilayer islands on flat and porous surfaces. 1,2-Dimyzistoyl-sn-glycero-3-phosphatidylcholine (DMPC) and 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) bilayers were deposited on flat (silicon and mica) and porous silicon (pSi) substrate surfaces and examined using atomic force spectroscopy and force volume imaging. Force spectroscopy measurements revealed the effects of the underlying substrate and of the lipid phase on the nanomechanical properties of bilayers islands. For mica and silicon, significant differences in breakthrough force between the center and the edges of bilayer islands were observed for both phospolipids. These differences were more pronounced for DMPC than for DPPC, presumably due to melting effects at the edges of DMPC bilayers. In contrast, bilayer islands deposited on pSi yielded similar breakthrough forces in the central region and along the perimeter of the islands, and those values in turn were similar to those measured along the perimeter of bilayer islands deposited on the flat substrates. The study also demonstrates that pSi is suitable solid support for the formation of pore-spanning phospholipid bilayers with potential applications in transmembrane protein studies, drug delivery, and biosensing.

JTD Keywords: Black lipid-membranes, Gold surfaces, Supported bilayers, Channel activity, Micro-BLMS, Silicon, Proteins, Vesicles, AFM, Temperature measurement

The Ischemic Heart Disease (IHD) is characterized by an insufficient supply with blood of the myocardium usually caused by an artherosclerotic disease of the coronary arteries (coronary artery disease CAD). The IHD and its consequences have become a leading problem in the industrialized nations. The aim of this study was to evaluate a new diagnosing method, the cardiogoniometry, using two different classifying techniques: the method of linear discriminant function analysis (LDA) and the method of Support Vector Machines (SVM). Data of a group of 109 female subjects (62 healthy, 47 with IHD) were analyzed on the basis of extracted parameters from the three-dimensional vector loops of the heart. The LDA achieved an accuracy of 83,5% (Sensitivity 78,7%, Specificity 87,1%), whereas the SVM achieved an accuracy of 86% (Sensitivity 80,5%, Specificity 89,8%). It could be shown that cardiogoniometry, an electrophysiological diagnostic method performed at rest, detects variables that are helpful in identifying ischemic heart disease. As it is easy to apply, non-invasive, and provides an automated interpretation it may become an inexpensive addition to the cardiologic diagnostic armamentarium, possibly useful for early diagnosis of IHD or CAD, as well as in patients who do not tolerate exercise testing. It was also proven that by applying Support Vector Machines an increased diagnostic precision in comparison to the conventional discriminant function analysis can be achieved.

JTD Keywords: Cardiogoniometry, Support Vector Machines, Nonlinear classifier, Linear discriminant analysis, Vector loop

In this work the presence of inverted hexagonal phases HII of 1-palmitoy-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) and cardiolipin (CL) (0.8:0.2, mol/mol) in the presence of Ca2+ were observed via 31P-NMR spectroscopy. When suspensions of the same composition were extended onto mica, HII phases transformed into structures which features are those of supported planar bilayers (SPBs). When characterized by atomic force microscopy (AFM), the SPBs revealed the existence of two laterally segregated domains (the interdomain height being ∼ 1 nm). Cytochrome c (cyt c), which binds preferentially to acidic phospholipids like CL, was used to demonstrate the nature of the domains. We used 1-anilinonaphtalen-8-sulfonate (ANS) to demonstrate that in the presence of cyt c, the fluorescence of ANS decreased significantly in lamellar phases. Conversely, the ANS binding to HII phases was negligible. When cyt c was injected into AFM fluid imaging cells, where SPBs of POPE:CL had previously formed poorly defined structures, protein aggregates (∼ 100 nm diameter) were ostensibly observed only on the upper domains, which suggests not only that they are mainly formed by CL, but also provides evidence of bilayer formation from HII phases. Furthermore, a model for the nanostructure of the SPBs is herein proposed.

JTD Keywords: 31P-NMR, AFM, ANS fluorescence, Cytochrome c (cyt c), Hexagonal phase (HII), Liposome, Supported planar bilayers (SPBs)