Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection

Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.
Operability of atrial septal defect with borderline pulmonary vascular resistance index: A study in developing country

Background Pulmonary arterial hypertension secondary to atrial septal defect (ASD) is an important determinant of morbidity and mortality in defect closure. We aimed to compare perioperative outcome between preoperative borderline and low pulmonary vascular resistance index (≥4 WU.m2 and <4 WU.m2, respectively) in surgical closure of secundum atrial septal defect with concomitant pulmonary arterial hypertension. Methods and results This was a single-center retrospective cohort study between January 2015 and January 2020. We classified patients with low and borderline PVRI who underwent ASD closure and recorded the perioperative outcomes. Results We analyzed a total of 183 patients with atrial septal defect and pulmonary arterial hypertension; 92 patients with borderline PVRI and 91 patients with low PVRI. Borderline pulmonary vascular resistance index was not associated with increased risk of postoperative mortality (p = 0.621; OR0.48, 95% CI 0.04–5.48), but associated with higher risk of overall morbidity in bivariate analysis (p = 0.002; OR3.28, 95% CI 1.5–6.72). Multivariate analysis showed positive association of borderline pulmonary vascular resistance index (p = 0.045; OR2.63, 95% CI 1.02–6.77) and preoperative tricuspid valve gradient ≥64 mmHg (p = 0.034; OR2.77, 95% CI 1.08–7.13) with overall morbidity. Conclusion There is no difference in incidence of in-hospital mortality between preoperative borderline and low pulmonary vascular resistance index patients. However, preoperative borderline pulmonary vascular resistance index and tricuspid valve gradient ≥64 mmHg are associated with increased overall morbidity after surgical closure in secundum atrial septal defect patients with pulmonary arterial hypertension.
EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network

Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.
Case Report: High doses of Zolpidem and QT interval lengthening: Is there a relationship? A case series

Zolpidem is indicated in cases of severe insomnia in adults and, as for BDZs, its assumption should be limited to short periods under close medical supervision. Since several drugs cause corrected QT interval (QTc) elongation, the authors investigated whether high daily doses of Zolpidem could cause QTc elongation. The study was conducted in the Addiction Medicine Unit of the G.B. Rossi University Hospital in Verona. The data were collected from hospitalizations carried out between January 2015 and February 2020 and refer to a total of 74 patients, 38 males and 36 females, who were treated for detoxification from high doses of Zolpidem with the “Verona Detox Approach With Flumazenil. ” One patient out of 74 had QTc elongation (479 ms). The patient was male and took a daily dose of 50 mg of Zolpidem; he did not take concomitant therapies that could cause QTc lengthening. He had no electrolyte alterations, no contemporary or previous intake of barbiturates, heroin, cocaine, THC, alcohol, NMDA or nicotine which could cause an elongation of the QTc interval. The present study highlights the low risk of QTc elongation due to high dosages of Zolpidem; however, if, on one hand, we can affirm that Zolpidem is a safe drug, on the other, the widespread use of high dosages of this drug for prolonged periods of time is problematic and worrying.
Measure, Integration and Function Spaces

Part 1 Measure Theory: Hewitt-Yosida Decomposition Lebesgue Measure The Nikodym Convergence and Boundedness Theorems. Part 2 Integration: Measurable Functions Convergence in Mean Convergence in Measure Comparison of Modes in Convergence The Radon-Nikodym Theorem The Vitali-Hahn-Saks Theorem. Part 3 Differential and Integration: Integrating Derivatives Absolutely Continuous Functions. Part 4 Introduction to Functional Analysis: Normed Linear Spaces (NLS) Quotient Spaces The Hahn-Banach Theorem Ordered Linear Spaces. Part 5 Function Spaces: The Space of Finitely Additive Set Functions The Space of Countably Additive Set Functions The Space of Continuous Functions Hilbert Space. (Part contents).
Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle

Data integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (−0.47), ribeye area (REA) and protein 4 (prot4) (−0.33), REA and protein 2 (prot2) (−0.3), carcass and prot4 (−0.31), carcass and prot2 (−0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (−0.25). Positive correlations were observed among the four protein factors (0.45–0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central node in the network, influencing carcass, prot2, prot4, mRNA1, and meat quality, suggesting that REA is a good indicator of meat quality. The connection among miRNA latent variables, BFT, and fat content relates to the influence of miRNAs on lipid metabolism. The relationship between mirna1 and prot5 composed of isoforms of nebulin needs further investigation. The FA identified latent variables, decreasing the dimensionality and complexity of the data. The BN was capable of generating interrelationships among latent variables from different types of data, allowing the integration of omics and complex traits and identifying conditional independencies. Our framework based on FA and BN is capable of generating new hypotheses for molecular research, by integrating different types of data and exploring non-obvious relationships.
Left atrial appendage function and structure predictors of recurrent atrial fibrillation after catheter ablation: A meta-analysis of observational studies

Background The results of studies evaluating the left atrial appendage (LAA) function and structure as predictors of atrial fibrillation (AF) recurrence after catheter ablation (CA) are contradictory. Therefore, we performed a meta-analysis to assess whether the LAA function and structure can predict the recurrence of AF after CA. Methods The PubMed, EMBASE, Web of Science, and Cochrane library databases were used to conduct a comprehensive literature search. Finally, 37 studies encompassing 11 LAA parameters were included in this meta-analysis. Results Compared with those in the non-recurrence group, the recurrence group had increased LAA volume (SMD 0.53, 95% CI [0.36, 0.71] p < 0.00001), LAA volume index, LAA orifice area, and LAA orifice short/long axis and decreased LAA emptying flow velocity (SMD -0.54, 95% CI [-0.68, -0.40], P < 0.00001), LAA filling flow velocity, and LAA ejection fraction, while there was no significant difference in LAA morphology or LAA depth. Conclusion Large LAA structure of pre-ablation (LAA volume, orifice area, orifice long/short axis, and volume index) and decreased LAA function of pre-ablation (LAA emptying flow velocity, filling flow velocity, ejection fraction, and LASEC) increase the odds of AF recurrence after CA. Systematic review registration [https://www.crd. york.ac. uk/prospero/], identifier [CRD42022324533].
Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.
Mathematical Modeling and Performance Evaluation of 3D Ferroelectric Negative Capacitance FinFET

Ferroelectric negative capacitance materials have now been proposed for lowering electronics energy dissipation beyond basic limitations. In this paper, we presented the analysis on the performance of negative capacitance (NC) FinFET in comparison with conventional gate dielectrics by using a separation of variables approach, which is an optimal quasi-3D mathematical model. The result has been signified steeper surface potential (ψ), lower threshold voltage (Vth), 1.2 mA of on-state current (Ion), and enhanced immunity of negative capacitance FinFET against short channel effects (SCE’s) like 35.3 mV/V of drain-induced barrier lowering (DIBL), 60 mV/dec of subthreshold swing (SS) along with smallest off state current (Ioff) among another conventional gate dielectric. Hence, NC FinFET can be a potential candidate for low power and high-performance device.
Mental and physical health correlates of the psychological impact of the first wave of COVID-19 among general population of Pakistan

The primary aim was to assess the role of mental and physical health of COVID-19 and its psychological impact in the general population of Pakistan during the first wave of COVID-19. It was hypothesized that there would be a significant predictive association among socio-demographic variables, psychological impact and mental health status resulting from COVID-19, and poor self-reported physical health would be significantly associated with adverse psychological impact and poor mental health status because of COVID-19. A cross-sectional survey research design was used in which 1,361 respondents were sampled online during lockdown imposed in the country. The Impact of Events Scale-Revised (IES-R) was used to assess the psychological impact of COVID-19, and the Depression Anxiety Stress Scales (DASS-21) was used to assess participants’ mental health status. 18% of the respondents reported moderate to severe event-specific distress, 22.6% reported moderate to severely extreme depression, 29% reported moderate to extreme anxiety, and 12.1% reported moderate to extreme stress. Female gender, having graduate-level education, currently studying, and self-reported physical symptoms (persistent fever, chills, headache, cough, breathing difficulty, dizziness, and sore throat) were significantly associated with higher levels of psychological impact exhibited through higher scores on the IES-R and poorer mental health status exhibited through higher scores on the DASS-21 (Depression, Anxiety, and Stress Subscales).
Circulating microRNA203 and its target genes' role in psoriasis pathogenesis

Numerous microRNAs (miRNAs) have been found to have an aberrant expression in the peripheral blood or psoriasis patients' lesions. Psoriasis was shown to have the abnormal expression of microRNA-203 (miR-203). It is a skin-specific signal that governs cellular proliferation in a protein kinase C-dependent manner and is mostly generated by keratinocytes. This work evaluated the expression levels of the circulating miR-203 target genes SOCS3, SOCS6, TP63, TNF-, IL8, and IL24 in psoriasis patients. Using a relative quantitation PCR technique, we determined the expression levels of miR-203 and its target genes (SOCS3, SOCS6, TP63, TNF-, IL8, and IL24) in the plasma of 120 psoriatic patients and matched healthy controls. The disease characteristics of the patients were then correlated with the expression results. We also conducted numerous enrichment analyses for the diseases, functions, and pathways connected to the under-researched biomarkers. Compared to healthy controls, psoriatic patients had significantly increased levels of miR-203 expression; 7.1 (4.4–9.9). In contrast, psoriatic patients had significantly lower expression of all the examined genes compared to healthy controls. Regarding all the study biomarkers, the receiver operating characteristic (ROC) curve analysis demonstrated significant sensitivity and specificity for differentiating between psoriatic patients and healthy controls. According to the results of the disease matching score generated by miR-203 and its target genes, psoriasis was ranked first with a score of 4.45. The third-place finisher with a value of 3.98, it also demonstrated that miR-203 and its target genes are connected to various skin disorders. Our results show that miR-203 contributes to psoriasis pathogenesis not only locally in skin lesions but also in circulation, indicating that it may contribute to the systemic symptoms of the illness. MiR-203 overexpression in psoriasis suggests that miR-203 may be involved in an anti-inflammatory response because it targets both SOCS gene family members and pro-inflammatory cytokines.
Importance of exponentially falling variability in heat generation on chemically reactive von kármán nanofluid flows subjected to a radial magnetic field and controlled locally by zero mass flux and convective heating conditions: A differential quadrature analysis

Owing to the various physical aspects of nanofluids as thermally enhanced working fluids and the significance of swirling flows in rheological devices as well as in the spin coating and lubrication applications, the current comprehensive examination aimed to explore the important features of spinning flows of chemically reactive Newtonian nanofluids over a uniformly revolving disk in the existence of a radially applied magnetic field along with an exponentially decaying space-dependent heat source, in the case where the disk surface is heated convectively and unaffected by the vertical nanoparticles’ mass flux. Based on feasible boundary layer approximations and Buongiorno’s nanofluid formulation, the leading coupled differential equations are stated properly in the sense of Arrhenius’s and Von Kármán’s approaches. By employing an advanced generalized differential quadrature algorithm, the obtained boundary layer equations are handled numerically with a higher order of accuracy to generate adequate graphical and tabular illustrations for the different values of the influencing flow parameters. As findings, the graphical results confirm that the nanofluid motion decelerates meaningfully thanks to the resistive magnetic influence. A significant thermal amelioration can be achieved by strengthening the magnetic impact, the generation of heat, the thermal convective process, and the thermophoresis mechanism. Moreover, it is found that the thermo-migration of nanoparticles can be reinforced more via the intensification in the convective process, the thermo-migration of nanoparticles, and the activation energy.
The use of SCL-k-9 to measure general psychopathology in women and men with skin conditions

Objectives To measure general psychopathology in dermatologic outpatients using the Symptom-Checklist-K-9 (SCL-K-9); to investigate whether the SCL-K-9 is able to categorize patients with and without significant non-psychotic disorders; and to perform a single-item analysis of the SCL-K-9, with a focus on gender differences. Methods Cross-sectional study on consecutive dermatological patients. We used two self-administered questionnaires to assess general psychopathology symptoms: General Health Questionnaire-12 (GHQ-12) and SCL-K-9. Sociodemographic information was collected with standardized forms. The performance of the SCL-K-9 in classifying patients according to their current emotional distress severity was assessed using a ROC procedure. Finally, we measured differences in scores obtained among women and men in SCL-K-9 single items. Results A total of 292 patients were studied (71.2% women). We observed statistically significant differences in SCL-K-9 total mean scores and in most single items among genders. We found that it would be more appropriate to use gender-specific cut-offs when using SCL-K-9 to screen dermatological patients for general psychopathology. Conclusion The SCL-K-9, with its compact format could provide, in a short time, a wide range of information related to critical areas that challenge the mental health of patients with skin diseases.
IL6 gene polymorphism association with calcific aortic valve stenosis and influence on serum levels of interleukin-6

Aortic valve stenosis is the most frequent valve disease in developed countries and its prevalence will increase with population aging. There is still no pharmaceutical treatment nor biomarker to determine the susceptibility to develop aortic stenosis. Therefore, we analyzed the association of polymorphisms in risk loci with calcific aortic stenosis. Patients with aortic valve disease were genotyped for PALMD rs6702619, LPA rs10455872, and IL6 rs1800795 polymorphisms and circulating levels of interleukin-6 (IL-6) were measured. Calcium content of leaflets obtained in valve replacement surgeries was determined by micro-computed tomography. In the genotyping of 578 individuals, we found significant association between PALMD and IL6 polymorphisms and aortic stenosis in patients with tricuspid aortic valve, independently of other potentially confounding variables such as age and dyslipidemia. There was no association of these polymorphisms with valve calcium content, but this value correlated with the mean aortic pressure gradient (r = 0.44; P < 0.001). The CC genotype of IL6 polymorphism was associated with higher levels of serum IL-6 compared to other genotypes (23.5 vs. 10.5 pg/ml, respectively; P = 0.029). Therefore, patients carrying the CC genotype of IL6 rs1800795 polymorphism present higher levels of circulating IL-6 and this could contribute to the severity of the aortic valve stenosis. Our results agree with the identification of IL6 as a locus risk for stenosis and also with the intervention of this cytokine in aortic valve calcification. A more exhaustive follow-up of those patients carrying risk genotypes is therefore recommended.