GCPBayes pipeline: a tool for exploring pleiotropy at the gene level.

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure.

Introduction: The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction. Methods: The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort. We used unsupervised and supervised machine learning to classify patients into homogenous UP clusters and to predict 3-year KF risk with UP, respectively. The predictive performance of UP was compared with the KF risk equation (KFRE), and evaluated in an external cohort of 326 patients. Results: More than 1000 peptides classified patients into 3 clusters with different CKD severities and etiologies at baseline. Peptides with the highest discriminative power for clustering were fragments of proteins involved in inflammation and fibrosis, highlighting those derived from α-1-antitrypsin, a major acute phase protein with anti-inflammatory and antiapoptotic properties, as the most significant. We then identified a set of 90 urinary peptides that predicted KF with a c-index of 0.83 (95% confidence interval [CI]: 0.81-0.85) in the case-cohort and 0.89 (0.83-0.94) in the external cohort, which were close to that estimated with the KFRE (0.85 [0.83-0.87]). Combination of UP with KFRE variables did not further improve prediction. Conclusion: This study shows the potential of UP analysis to uncover new pathophysiological CKD progression pathways and to predict KF risk with a performance equal to that of the KFRE.

Multiplexed Anodic Stripping Voltammetry Detection of Heavy Metals in Water Using Nanocomposites Modified Screen-Printed Electrodes Integrated With a 3D-Printed Flow Cell.

In this study, we present multiplexed anodic stripping voltammetry (ASV) detection of heavy metal ions (HMIs)-As(III), Cd(II), and Pb(II)-using a homemade electrochemical cell consisting of dual working, reference and counter screen-printed electrodes (SPE) on polyimide substrate integrated with a 3D-printed flow cell. Working and counter electrodes were fabricated by the screen-printing of graphite paste while the Ag/AgCl paste was screen-printed as a reference electrode (Ag/AgCl quasi-reference electrode). The working electrodes were modified with (BiO)2CO3-reduced graphene oxide (rGO)-Nafion [(BiO)2CO3-rGO-Nafion] and Fe3O4 magnetic nanoparticles (Fe3O4MNPs) decorated Au nanoparticles (AuNPs)-ionic liquid (IL) (Fe3O4-Au-IL) nanocomposites separately to enhance HMIs sensing. Electrochemical detection was achieved using square wave ASV technique. The desired structure of the flow electrochemical cell was optimized by the computational fluid dynamic (CFD). Different experimental parameters for stripping analysis of HMIs were optimized including deposition time, deposition potential and flow rate. The linear range of calibration curves with the sensing nanocomposites modified SPE for the three metal ions was from 0-50 µg/L. The limits of detection (S/N = 3) were estimated to be 2.4 µg/L for As(III), 1.2 µg/L for Pb(II) and 0.8 µg/L for Cd(II). Furthermore, the homemade flow anodic stripping sensor platform was used to detect HMIs in simulated river water with a 95-101% recovery, indicating high selectivity and accuracy and great potential for applicability even in complex matrices.

Household Cleaning and Poor Asthma Control Among Elderly Women.

BACKGROUND: Asthma control is suboptimal in nearly half of adults with asthma. Household exposure to disinfectants and cleaning products (DCP) has been associated with adverse respiratory effects, but data on their association with asthma control are scant. OBJECTIVES: To investigate the association between household use of DCP and asthma control in a large cohort of French elderly women. METHODS: We used data from a case-control study on asthma (2011-2013) nested in the E3N cohort. Among 3023 women with current asthma, asthma control was defined by the Asthma Control Test (ACT). We used a standardized questionnaire to assess the frequency of cleaning tasks and DCP use. We also identified household cleaning patterns using a clustering approach. Associations between DCP and ACT were adjusted for age, smoking status, body mass index, and education. RESULTS: Data on ACT and DCP use were available for 2223 women (70 ± 6 years old). Asthma was controlled (ACT = 25), partly controlled (ACT = 20-24), and poorly controlled (ACT ≤ 19) in 29%, 46%, and 25% of the participants, respectively. Weekly use of sprays and chemicals was associated with poorly controlled asthma (odds ratio [95% confidence interval]: 1 spray: 1.31 [0.94-1.84], ≥2 sprays: 1.65 [1.07-2.53], P trend: .01; 1 chemical: 1.24 [0.94-1.64], ≥2 chemicals: 1.47 [1.03-2.09], P trend: .02). Risk for poor asthma control increased with the patterns "very frequent use of products" (1.74 [1.13-2.70]) and "infrequent cleaning tasks and intermediate use of products" (1.62 [1.05-2.51]). CONCLUSION: Regular use of DCP may contribute to poor asthma control in elderly women. Limiting their use may help improve asthma management.

Label-free chemiresistor biosensor based on reduced graphene oxide and M13 bacteriophage for detection of coliforms.

Coliform bacteria are well known as informative indicators for bacterial contamination in water. This study presents a novel chemiresistor biosensor using M13 phage-modified reduced graphene oxide (rGO) for detection of Escherichia coli (E. coli), as coliform bacteria. M13 phage, as a biorecognition element, was immobilized on the rGO channel, so that it can bind to negatively charged E. coli bacteria, allowing the gating effect on the biosensor and the change in its resistance. The prepared materials and device were characterized using spectroscopic, microscopic, and electrical measurements. FTIR and XRD results proved the successful fabrication of GO and rGO nanosheets. AFM results showed that the prepared nanosheets were monolayer. The SEM micrographs of the M13-functionalized devices, soaked in two different concentrations of E. coli, confirmed the successful capturing of E. coli and that the signal change is concentration-dependent. As a result, a linear and specific response towards E. coli was observed and the limit of detection was determined to be 45 CFU/mL. Further, the proposed sensor system showed selectivity towards the tested coliforms. These results suggested this sensing system could be a promising tool for detecting coliforms with an economic, accurate, rapid, and directly applicable process.

Linker-Free Magnetite-Decorated Gold Nanoparticles (Fe3O4-Au): Synthesis, Characterization, and Application for Electrochemical Detection of Arsenic (III).

Linker-free magnetite nanoparticles (Fe3O4NPs)-decorated gold nanoparticles (AuNPs) were grown using a new protocol that can be used as a new platform for synthesis of other intact metal-metal oxide nanocomposites without the need for linkers. This minimizes the distance between the metal and metal oxide nanoparticles and ensures the optimum combined effects between the two material interfaces. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy confirmed the successful synthesis of the Fe3O4-Au nanocomposite, without any change in the magnetite phase. Characterization, using transmission electron microscopy (TEM), scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy, revealed the composite to consist of AuNPs of 70 ± 10 nm diameter decorated with tiny 10 ± 3 nm diameter Fe3O4NPs in Au:Fe mass ratio of 5:1. The prepared Fe3O4-Au nanocomposite was embedded in ionic liquid (IL) and applied for the modification of glassy carbon electrode (GCE) for the electrochemical detection of As(III) in water. By combining the excellent catalytic properties of the AuNPs with the high adsorption capacity of the tiny Fe3O4NPs towards As(III), as well as the good conductivity of IL, the Fe3O4-Au-IL nanocomposite showed excellent performance in the square wave anodic stripping voltammetry detection of As(III). Under the optimized conditions, a linear range of 1 to 100 µg/L was achieved with a detection limit of 0.22 µg/L (S/N = 3), and no interference from 100-fold higher concentrations of a wide variety of cations and anions found in water. A very low residual standard deviation of 1.16% confirmed the high precision/reproducibility of As(III) analysis and the reliability of the Fe3O4-Au-IL sensing interface. Finally, this proposed sensing interface was successfully applied to analyzing synthetic river and wastewater samples with a 95-101% recovery, demonstrating excellent accuracy, even in complex synthetic river and wastewater samples containing high concentrations of humic acid without any sample pretreatments.

Nano-FET-enabled biosensors: Materials perspective and recent advances in North America.

Field-effect transistor (FET) is a very promising platform for biosensor applications due to its magnificent properties, including label-free detection, high sensitivity, fast response, real-time measurement capability, low running power, and the feasibility to miniaturize to a portable device. 1D (e.g. carbon nanotubes, Si nanowires, conductive polymer nanowires, 1D metal oxides, and others) and 2D (e.g. graphene materials, transition metal dichalcogenides, black phosphorus, and 2D metal oxides) materials, with their unique structural and electronic properties that are unavailable in bulk materials, have helped improve the sensitivity of FET biosensors and enabled detection down to single molecule. In this review, we give insights into the rapidly evolving field of 1D and 2D materials-based FET biosensors, with an emphasis on structure and electronic properties, synthesis, and biofunctionalization approaches of these nanomaterials. In addition, the progress in the 1D/2D-FET biosensors in North America, in the last decade, is summarized in tables. Moreover, challenges and future perspectives of 1D/2D-FET biosensors are covered.

Bismuth Subcarbonate Decorated Reduced Graphene Oxide Nanocomposite for the Sensitive Stripping Voltammetry Analysis of Pb(II) and Cd(II) in Water.

In this paper, bismuth subcarbonate (BiO)2CO3-reduced graphene oxide nanocomposite incorporated in Nafion matrix ((BiO)2CO3-rGO-Nafion) was synthesized and further applied, for the first time, in the sensitive detection of Pb(II) and Cd(II) by square-wave anodic stripping voltammetry (SWASV). The as-synthesized nanocomposites were characterized by energy-dispersive spectroscopy (EDS), Raman spectroscopy, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). (BiO)2CO3 composite plays a key role in the improvement of the detection sensitivity, which can form multicomponent alloy with cadmium and lead. Additionally, the unique structure of rGO can enlarge the surface area and provide abundant active sites. Moreover, Nafion incorporation in the nanocomposite can effectively increase the adhesion and mechanical strength of the film, and further improve the preconcetration ability due to the cation-exchange capacity of its abundant sulfonate groups. As expected, the (BiO)2CO3-rGO/Nafion nanocomposite-modified glassy carbon electrode ((BiO)2CO3-rGO-Nafion/GCE) achieved low detection limits of 0.24 µg/L for Pb(II) and 0.16 µg/L for Cd(II), in the linear range of 1.0-60 µg/L, and showed some excellent performance, such as high stability, good selectivity, and sensitivity. Finally, synthetic water samples were prepared and further used to verify the practicability of the (BiO)2CO3-rGO-Nafion/GCE with satisfactory results.

Non-Carbon 2D Materials-Based Field-Effect Transistor Biosensors: Recent Advances, Challenges, and Future Perspectives.

In recent years, field-effect transistors (FETs) have been very promising for biosensor applications due to their high sensitivity, real-time applicability, scalability, and prospect of integrating measurement system on a chip. Non-carbon 2D materials, such as transition metal dichalcogenides (TMDCs), hexagonal boron nitride (h-BN), black phosphorus (BP), and metal oxides, are a group of new materials that have a huge potential in FET biosensor applications. In this work, we review the recent advances and remarkable studies of non-carbon 2D materials, in terms of their structures, preparations, properties and FET biosensor applications. We will also discuss the challenges facing non-carbon 2D materials-FET biosensors and their future perspectives.

Clusters of diet, physical activity, television exposure and sleep habits and their association with adiposity in preschool children: the EDEN mother-child cohort.

BACKGROUND: Despite the growing interest in the relation between adiposity in children and different lifestyle clusters, few studies used a longitudinal design to examine a large range of behaviors in various contexts, in particular eating- and sleep-related routines, and few studies have examined these factors in young children. The objectives of this study were to identify clusters of boys and girls based on diet, sleep and activity-related behaviors and their family environment at 2 and 5 years of age, and to assess whether the clusters identified varied across maternal education levels and were associated with body fat at age 5. METHODS: At 2 and 5 years, respectively, 1436 and 1195 parents from the EDEN mother-child cohort completed a questionnaire including behavioral data. A latent class analysis aimed to uncover gender-specific behavioral clusters. Body fat percentage was estimated by anthropometric and bioelectrical impedance measurements. Association between cluster membership and body fat was assessed with mutivariable linear regression models. RESULTS: At 2 years, two clusters emerged that were essentially characterized by opposite eating habits. At 5 years, TV exposure was the most distinguishing feature, but the numbers and types of clusters differed by gender. An association between cluster membership and body fat was found only in girls at 5 years of age, with girls in the cluster defined by very high TV exposure and unfavorable mealtime habits (despite high outdoor playing and walking time) having the highest body fat. Girls whose mother had low educational attainment were more likely to be in this high-risk cluster. Girls who were on a cluster evolution path corresponding to the highest TV viewing time and the least favorable mealtime habits from 2 to 5 years of age had higher body fat at 5 years. CONCLUSIONS: Efforts to decrease TV time and improve mealtime routines may hold promise for preventing overweight in young children, especially girls growing up in disadvantaged families. These preventive efforts should start as early in life as possible, ideally before the age of two, and should be sustained over the preschool years.

Non-lytic M13 phage-based highly sensitive impedimetric cytosensor for detection of coliforms.

A highly sensitive and selective non-lytic M13 phage-based electrochemical impedance spectroscopy (EIS) cytosensor for early detection of coliforms is introduced for the first time. Gold nanoparticles were electrochemically deposited on the surface of glassy carbon electrode, and the M13 phage particles were immobilized on them using 3-mercaptopropionic acid linker and zero-length crosslinking chemistry (EDC/NHS). Next, the sensor surface was blocked to avoid non-specific binding. The M13-EIS cytosensor was tested for detection of F+ pili Escherichia coli species, using XL1-Blue and K12 strains, as examples of coliforms. The selectivity against non-host strains was demonstrated using Pseudomonas Chlororaphis. The binding of E. coli to the M13 phage on the cytosensor surface increased the charge transfer resistance, enabling detection of coliforms. The biosensor achieved a limit of detection (LOD) of 14 CFU/mL, the lowest reported to-date using EIS-phage sensors, and exhibited a high selectivity towards the tested coliforms. The SEM micrographs confirmed the successful capturing of E. coli on the M13-based EIS cytosensor. Moreover, the sensor showed almost the same sensitivity in the simulated river water samples as in phosphate buffer, reflecting its applicability to real samples. On the other hand, this sensor system exhibited high stability under harsh environmental conditions of pH (3.0-10.0) and temperature as high as 45 °C for up to two weeks. Overall, this sensor system has excellent potential for real field detection of fecal coliforms.

Nanoformulated ellagic acid ameliorates pentylenetetrazol-induced experimental epileptic seizures by modulating oxidative stress, inflammatory cytokines and apoptosis in the brains of male mice.

The present study evaluated the neuroprotective and antiepileptic efficacy of ellagic acid (EA) encapsulated in calcium-alginate nanoparticles (Ca2+-ALG NPs) in pentylenetetrazol (PTZ)-induced seizures in male mice. EA was encapsulated in ALG NPs using a nanospray drying method followed by ionotropic crosslinking with Ca2+. Characterization of the developed Ca2+-crosslinked EA-ALG NPs showed spherical, high stability NPs; successful loading of EA within crosslinked ALG NPs; and sustained release of EA. Male Swiss albino mice were divided into ten groups as follows; Group I- (control), Group II (50 mg EA /kg) - (EA), Group III polyethylene glycol (PEG), Group IV EA NPs (50 mg/kg) - (EA NP), Group (50 mg/kg alginate) V void V NPs - (void NPs), Group VI: (37.5 PTZ mg/kg) -(PTZ), Group VII: PTZ and EA - (PTZ-EA). Group VIII: animals received PTZ and PEG concurrently (PTZ-PEG). Group IX; animals received PTZ and void NPs concurrently - (PTZ-void). Group X: animals received PTZ and EA NPs concurrently (PTZ-EA NPs). PTZ was used to induce experimental epilepsy. Ca2+-ALG NPs prevented seizures throughout the experimental period and had a more prominent effect than free EA did. Ca2+-ALG NPs prevented increased glutamate, decreased GABA concentrations and ameliorated increased amyloid-ß and homocysteine levels in the serum and brain. Ca2+-EA-ALG NPs were superior to free EA in improving increased IL-6 and TNF-α. Ca2+-ALG NPs ameliorated PTZ-induced oxidative stress, as evidenced by decreased 4HNE levels and enhanced GSH, GR and GPx levels in the brain. These changes were accompanied by amelioration of apoptosis and its regulating proteins, including Cytochrome C, P53, Bax, Bcl2 and caspase-3 and caspase-9, and protected against DNA damage. Histological examination of the hippocampus confirmed that the neuroprotective effect of Ca2+-EA-ALG NPs was superior and more effective than that of free EA.

Green Synthesis of Chitosan-Silver/Gold Hybrid Nanoparticles for Biomedical Applications.

Silver and gold nanoparticles (NPs) attract great attention nowadays because of their unique characteristics that entitle them for various biomedical applications. However, there is still a need for successful green synthesis methods for these two metal NPs, especially in a hybrid form, as well-established protocols. On the other hand, chitosan (Cs) is a polysaccharide of great promise for green synthesis of metal NPs, especially in the presence of some plant/fruit extracts. Together, Cs and the appropriate natural products in the extracts play the roles of both capping and reducing agents toward the green synthesis and stabilization of the silver/gold hybrid NPs. In this chapter, we introduce a well-established protocol for the green synthesis of Cs-Ag/Au hybrid NPs which could incorporate a therapeutic agent. In this approach, Cs plays the role of a carrier for the therapeutic agent, in addition to its capping/reducing contributions.

Online-monitoring of biofilm formation using nanostructured electrode surfaces.

The direct monitoring of biofilm formation enables valuable insights into the industrial processes, microbiology, and biomedical applications. Therefore, in the present study, nano-structured bioelectrochemical platforms were designed for sensing the formation of biofilm of P. aeruginosa along with monitoring its electrochemical/morphological changes under different stresses. Through the assay optimizations, the performances of different electrode modifiers such as reduced graphene oxide (rGO) nanosheets, hyperbranched chitosan nanoparticles (HBCs NPs), and rGO-HBCs nano-composite were tested to assess the influence of the electrode materials on biofilm progression. As a need for the anodic respiration, the bioelectrochemical responses of the adhered bacterial cells changed from a non-electrochemically active (planktonic state) to an electrochemically active (biofilm matrix) state. Our results demonstrated that electrode modifications with conductive nanostructured elements is highly sensitive and enable direct assay for the biofilm formation without any preachments. Consequently, the morphological changes in bacterial cell wall, upon switching from the planktonic state to the biofilm matrix were imaged using scanning electron microscopy (SEM), and the changes in cell wall chemical composition were monitored by the Energy Dispersive X-ray analysis (EDX). Thus, the designed microbial electrochemical system (MES) was successfully used to monitor changes in the biofilm matrix under different stresses through direct measurements of electron exchanges.

VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values.

SUMMARY: VarSelLCM allows a full model selection (detection of the relevant features for clustering and selection of the number of clusters) in model-based clustering, according to classical information criteria. Data to be analyzed can be composed of continuous, integer and/or categorical features. Moreover, missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random. Thus, VarSelLCM also allows data imputation by using mixture models. A Shiny application is implemented to easily interpret the clustering results. AVAILABILITY AND IMPLEMENTATION: VarSelLCM is available to download at https://CRAN.R-project.org/package=VarSelLCM/. TUTORIAL: vignette is available online at http://varsellcm.r-forge.r-project.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hybrid nanocarrier system for guiding and augmenting simvastatin cytotoxic activity against prostate cancer.

Prostate cancer is the most common non-skin cancer among men. Though statins are mainly used as antihyperlipidemic drugs, many studies have reported their proapoptotic and antimetastatic activities on prostate cancer. However, the poor solubility and insufficient delivery of statins in tumor site limit their anticancer activity. The present study introduces an efficient hybrid drug delivery system for the treatment of prostate cancer. The system involves the chemical conjugation of Simvastatin (SMV), a statin compound, to acid-terminated poly(D, L-lactic-co-glycolic acid), PLGA chains followed by its conversion into nanoparticles (NPs), with in situ physical incorporation of more SMV and superparamagnetic iron oxide nanoparticles (SPIONS) into the PLGA NPs. The PLGA-based hybrid nanocarrier system has been designed in such a way to evade the low bioavailability of SMV, confer sustained release of both encapsulated and chemically conjugated SMV, as well as enhancing the anti-cancer effect of the formula via the magnetic targeting with the aid of the encapsulated SPIONS. Magnetism, morphological and physicochemical characterizations, as well as in-vitro release studies were performed. Besides, cytotoxicity on human prostate cancer cell line (PC-3) was evaluated using MTT assay, cell cycle arrest analysis, annexin V/propidium iodide apoptosis assay and ELISA immunoassay for apoptotic enzyme. Optimum PLGA-based hybrid nanocarrier significantly improved the SMV anticancer activity against human prostate cancer cell line through both apoptosis mechanism and retardation of G2-M phase of cell cycle. Also, the up-regulation of the Caspase 3 was aligned with cytotoxicity study's findings.

Patterns of cleaning product exposures using a novel clustering approach for data with correlated variables.

PURPOSE: Clustering methods may be useful in epidemiology to better characterize exposures and account for their multidimensional aspects. In this context, application of clustering models allowing for highly dependent variables is of particular interest. We aimed to characterize patterns of domestic exposure to cleaning products using a novel clustering model allowing for highly dependent variables. METHODS: To identify domestic cleaning patterns in a large population of French women, we used a mixture model of dependency blocks. This novel approach specifically models within-class dependencies, and is an alternative to the latent class model, which assumes conditional independence. Analyses were conducted in 19,398 participants of the E3N study (women aged 61-88 years) who completed a questionnaire regarding household cleaning habits. RESULTS: Seven classes were identified, which differed with the frequency of cleaning tasks (e.g., dusting/sweeping/hoovering) and use of specific products (e.g., bleach, sprays). The model also grouped the variables into conditionally independent blocks, providing a summary of the main dependencies among the variables. CONCLUSIONS: The mixture model of dependency blocks, a useful alternative to the latent class model, may have broader application in epidemiology, in particular, in the context of exposome research and growing need for data-reduction methods.

Magnetic nanoparticles-based drug and gene delivery systems for the treatment of pulmonary diseases.

Magnetic nanoparticles (MNPs) have gained much attention due to their unique properties such as biocompatibility and biodegradability as well as magnetic and heat-medicated characteristics. Due to these inherent properties, MNPs have been widely used in various biomedical applications including targeted drug delivery and hyperthermia-based therapy. Hyperthermia is a promising approach for the thermal activation therapy of several diseases, including pulmonary diseases. Additionally, due to their large loading capacity and controlled release ability, several MNP-based drug delivery systems have been emerged for treatment of cystic fibrosis and lung cancer. This review provides an overview on the unique properties of MNPs and magnetic-mediated hyperthermia with emphasis on the recent biomedical applications of MNPs in treatment of both lung cancer and cystic fibrosis.

Core-shell hyperbranched chitosan nanostructure as a novel electrode modifier.

The present study reports, for the first time, the development and use of core-shell amino-terminated chitosan (Cs) hyperbranched nanoparticles (HBCs-NH2 NPs) as a novel natural polymer-based electrode modifier for efficient electrochemical systems. The electrochemical activity of the developed HBCs-NH2 NPs as compared to Cs NPs was identified by standard oxidation-reduction reactions of ferricyanide. The oxidation-reduction peaks height was about twofold higher than the response of Cs-modified electrode. On the other hand, NADH oxidation at the nanostructured surfaces confirmed the electrocatalytic activity where the oxidation of NADH appeared at a lower overpotential (from 805mV to 635mV vs Ag/AgCl). Eventually, a diffusion-controlled process was confirmed from the scan rate effect.

Bayesian model selection in logistic regression for the detection of adverse drug reactions.

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. In this context, disproportionality measures can be used. Their main idea is to project the data onto contingency tables in order to measure the strength of associations between drugs and adverse events. However, due to the data projection, these methods are sensitive to the problem of coprescriptions and masking effects. Recently, logistic regressions have been used with a Lasso type penalty to perform the detection of associations between drugs and adverse events. On different examples, this approach limits the drawbacks of the disproportionality methods, but the choice of the penalty value is open to criticism while it strongly influences the results. In this paper, we propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. Thus, we avoid the calibration of penalty or threshold. During our application on the French pharmacovigilance database, the proposed method is compared to well-established approaches on a reference dataset, and obtains better rates of positive and negative controls. However, many signals (i.e., specific drug-event associations) are not detected by the proposed method. So, we conclude that this method should be used in parallel to existing measures in pharmacovigilance. Code implementing the proposed method is available at the following url: https://github.com/masedki/MHTrajectoryR.