Publications

The invasion of human embryos in the uterus overcoming the maternal tissue barrier is a crucial step in embryo implantation and subsequent development. Although tissue invasion is fundamentally a mechanical process, most studies have focused on the biochemical and genetic aspects of implantation. Here, we fill the gap by using a deformable ex vivo platform to visualize traction during human embryo implantation. We demonstrate that embryos apply forces remodeling the matrix with species-specific displacement amplitudes and distinct radial patterns: principal displacement directions for mouse embryos, expanding on the surface while human embryos insert in the matrix generating multiple traction foci. Implantation-impaired human embryos showed reduced displacement, as well as mouse embryos with inhibited integrin-mediated force transmission. External mechanical cues induced a mechanosensitive response, human embryos recruited myosin, and directed cell protrusions, while mouse embryos oriented their implantation or body axis toward the external cue. These findings underscore the role of mechanical forces in driving species-specific invasion patterns during embryo implantation.

JTD Keywords: Animals, Anterior-posterior axis, Biomechanical phenomena, Cells, Collagen, Differentiatio, Embryo implantation, Embryo, mammalian, Female, Humans, Mechanotransduction, cellular, Mice, Morphogenesis, Pregnancy, Range, Self-organization, Species specificity, Trophoblast invasion, Uterine contractions

Microbial biofilms are complex three-dimensional structures where sessile microbes are embedded in a polymeric extracellular matrix. Their resistance toward the host immune system as well as to a diverse range of antimicrobial treatments poses a serious health and development threat, being in the top 10 global public health threats declared by the World Health Organization. In an effort to combat biofilm-related microbial infections, several strategies have been developed to independently eliminate biofilms or to complement conventional antibiotic therapies. However, their limitations leave room for other treatment alternatives, where the application of nanotechnology to biofilm eradication has gained significant relevance in recent years. Their small size, penetration efficiency, and the design flexibility that they present makes them a promising alternative for biofilm infection treatment, although they also present set-backs. This review aims to describe the main possibilities and limitations of nanomedicine against biofilms, while covering the main aspects of biofilm formation and study, and the current therapies for biofilm treatment. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

JTD Keywords: Anti-bacterial agents, Anti-infective agents, Antiinfective agent, Antimicrobial, Antimicrobials, Antimicrobials,bacteria,biofilm,infectious diseases,microorganism, Bacteria, Biofilm, Biofilm infections, Biofilms, Complex three dimensional structures, Diseases, Diverse range, Drug-delivery systems,in-vitro,cellular toxicity,nanoparticles,penetration,model,biocompatibility,perspectives,hyperthermia,diagnosi, Extracellular matrices, Global public health, Health risks, Infectious disease, Infectious diseases, Medical nanotechnology, Microbial biofilm, Microorganisms, Nanomedicine, Polymer, Polymers, Regulatory issues, Roadmap

Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.

JTD Keywords: Cortical networks, Direction selectivity, Excitation/inhibition balance, Fast network oscillations, Gaba concentration, Gamma oscillation frequency, Neuronal oscillations, Range temporal correlations, Self-organized criticality, Theta-oscillations

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

JTD Keywords: Bitter orange essential oil, Headspace gas chromatography–mass spectrometry, Artificial neural network, Foodomics, Chemometrics, Feature selection

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

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

In this work we report the fabrication and characterization of a label-free impedimetric immunosensor based on a silicon nitride (Si 3N 4) surface for the specific detection of human serum albumin (HSA) proteins. Silicon nitride provides several advantages compared with other materials commonly used, such as gold, and in particular in solid-state physics for electronic-based biosensors. However, few Si 3N 4-based biosensors have been developed; the lack of an efficient and direct protocol for the integration of biological elements with silicon-based substrates is still one of its the main drawbacks. Here, we use a direct functionalization method for the direct covalent binding of monoclonal anti-HSA antibodies on an aldehyde-functionalized Si-p/SiO 2/Si 3N 4 structure. This methodology, in contrast with most of the protocols reported in literature, requires less chemical reagents, it is less time-consuming and it does not need any chemical activation. The detection capability of the immunosensor was tested by performing non-faradaic electrochemical impedance spectroscopy (EIS) measurements for the specific detection of HSA proteins. Protein concentrations within the linear range of 10 -13-10 -7M were detected, showing a sensitivity of 0.128ΩμM -1 and a limit of detection of 10 -14M. The specificity of the sensor was also addressed by studying the interferences with a similar protein, bovine serum albumin. The results obtained show that the antibodies were efficiently immobilized and the proteins detected specifically, thus, establishing the basis and the potential applicability of the developed silicon nitride-based immunosensor for the detection of proteins in real and more complex samples.

JTD Keywords: Aldehyde, Electrochemical impedance spectroscopy, Human serum albumin, Immunosensor, Silicon nitride, Bovine serum albumins, Chemical reagents, Complex samples, Covalent binding, Detection capability, Electrochemical impedance, Electrochemical impedance spectroscopy measurements, Functionalizations, Human serum albumins, Impedimetric immunosensors, Label free, Limit of detection, Linear range, Protein concentrations, Silicon-based, Specific detection, Aldehydes

The relationship between molecular properties of odorants and neural activities is arguably one of the most important issues in olfaction and the rules governing this relationship are still not clear. In the olfactory bulb (OB), glomeruli relay olfactory information to second-order neurons which in turn project to cortical areas. We investigate relevance of odorant properties, spatial localization of glomerular coding sites, and size of coding zones in a dataset of 2-deoxyglucose images of glomeruli over the entire OB of the rat. We relate molecular properties to activation of glomeruli in the OB using a nonparametric statistical test and a support-vector machine classification study. Our method permits to systematically map the topographic representation of various classes of odorants in the OB. Our results suggest many localized coding sites for particular molecular properties and some molecular properties that could form the basis for a spatial map of olfactory information. We found that alkynes, alkanes, alkenes, and amines affect activation maps very strongly as compared to other properties and that amines, sulfur-containing compounds, and alkynes have small zones and high relevance to activation changes, while aromatics, alkanes, and carboxylics acid recruit very big zones in the dataset. Results suggest a local spatial encoding for molecular properties.

JTD Keywords: Molecular-receptive range, Odor, Olfactory bulb, Olfactory coding, Property-activity relationship, Structure-odor relationship

We present a method to measure directly and at the single-molecule level the distance decay constant that characterizes the rate of electron transfer (ET) in redox proteins. Using an electrochemical tunneling microscope under bipotentiostatic control, we obtained current-distance spectroscopic recordings of individual redox proteins confined within a nanometric tunneling gap at a well-defined molecular orientation. The tunneling current decays exponentially, and the corresponding decay constant (β) strongly supports a two-step tunneling ET mechanism. Statistical analysis of decay constant measurements reveals differences between the reduced and oxidized states that may be relevant to the control of ET rates in enzymes and biological electron transport chains.

JTD Keywords: Long-range electron transfer (LRET), Distance decay constant, Single-molecule electrochemistry, Redox enzyme, Metalloprotein, Blue copper protein, Azurin, Electrochemical scanning tunneling microscopy and spectroscopy, Nanoelectrodes, Debye length, Electrochemical charge screening

The roles of different ribonucleotide reductases (RNRs) in bacterial pathogenesis have not been studied systematically. In this work we analyzed the importance of the different Pseudomonas aeruginosa RNRs in pathogenesis using the Drosophila melanogaster host-pathogen interaction model. P. aeruginosa codes for three different RNRs with different environmental requirements. Class II and III RNR chromosomal mutants exhibited reduced virulence in this model. Translational reporter fusions of RNR gene nrdA, nrdJ, or nrdD to the green fluorescent protein were constructed to measure the expression of each class during the infection process. Analysis of the P. aeruginosa infection by flow cytometry revealed increased expression of nrdJ and nrdD and decreased nrdA expression during the infection process. Expression of each RNR class fits with the pathogenicities of the chromosomal deletion mutants. An extended understanding of the pathogenicity and physiology of P. aeruginosa will be important for the development of novel drugs against infections in cystic fibrosis patients.

JTD Keywords: Broad-host-range, Anaerobic growth, Drosophila-melanogaster, Bacterial biofilms, Escherichia-coli, Cystic-fibrosis, Model host, Virulence, Promoter, Vectors