Identification of PPARG as key gene to link coronary atherosclerosis disease and rheumatoid arthritis via microarray data analysis.

BACKGROUND: Inflammation is the common pathogenesis of coronary atherosclerosis disease (CAD) and rheumatoid arthritis (RA). Although it is established that RA increases the risk of CAD, the underlining mechanism remained indefinite. This study seeks to explore the molecular mechanisms of RA linked CAD and identify potential target gene for early prediction of CAD in RA patients. MATERIALS AND METHODS: The study utilized five raw datasets: GSE55235, GSE55457, GSE12021 for RA patients, and GSE42148 and GSE20680 for CAD patients. Gene Set Enrichment Analysis (GSEA) was used to investigate common signaling pathways associated with RA and CAD. Then, weighted gene co-expression network analysis (WGCNA) was performed on RA and CAD training datasets to identify gene modules related to single-sample GSEA (ssGSEA) scores. Overlapping module genes and differentially expressed genes (DEGs) were considered as co-susceptible genes for both diseases. Three hub genes were screened using a protein-protein interaction (PPI) network analysis via Cytoscape plug-ins. The signaling pathways, immune infiltration, and transcription factors associated with these hub genes were analyzed to explore the underlying mechanism connecting both diseases. Immunohistochemistry and qRT-PCR were conducted to validate the expression of the key candidate gene, PPARG, in macrophages of synovial tissue and arterial walls from RA and CAD patients. RESULTS: The study found that Fc-gamma receptor-mediated endocytosis is a common signaling pathway for both RA and CAD. A total of 25 genes were screened by WGCNA and DEGs, which are involved in inflammation-related ligand-receptor interactions, cytoskeleton, and endocytosis signaling pathways. The principal component analysis(PCA) and support vector machine (SVM) and receiver-operator characteristic (ROC) analysis demonstrate that 25 DEGs can effectively distinguish RA and CAD groups from normal groups. Three hub genes TUBB2A, FKBP5, and PPARG were further identified by the Cytoscape software. Both FKBP5 and PPARG were downregulated in synovial tissue of RA and upregulated in the peripheral blood of CAD patients and differential mRNAexpreesion between normal and disease groups in both diseases were validated by qRT-PCR.Association of PPARG with monocyte was demonstrated across both training and validation datasets in CAD. PPARG expression is observed in control synovial epithelial cells and foamy macrophages of arterial walls, but was decreased in synovial epithelium of RA patients. Its expression in foamy macrophages of atherosclerotic vascular walls exhibits a positive correlation (r = 0.6276, p = 0.0002) with CD68. CONCLUSION: Our findings suggest that PPARG may serve as a potentially predictive marker for CAD in RA patients, which provides new insights into the molecular mechanism underling RA linked CAD.

Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis.

Although several studies have reported a link between chronic atrophic gastritis (CAG) and atherosclerosis, the underlying mechanisms have not been elucidated. The present study aimed to investigate the molecular mechanisms common to both diseases from a bioinformatics perspective. Gene expression profiles were obtained from the Gene Expression Omnibus database. Data on atherosclerosis and CAG were downloaded from the GSE28829 and GSE60662 datasets, respectively. We identified the differentially expressed genes co-expressed in CAG and atherosclerosis before subsequent analyses. We constructed and identified the hub genes and performed functional annotation. Finally, the transcription factor (TF)-target genes regulatory network was constructed. In addition, we validated core genes and certain TFs. We identified 116 common differentially expressed genes after analyzing the 2 datasets (GSE60662 and GSE28829). Functional analysis highlighted the significant contribution of immune responses and the positive regulation of tumor necrosis factor production and T cells. In addition, phagosomes, leukocyte transendothelial migration, and cell adhesion molecules strongly correlated with both diseases. Furthermore, 16 essential hub genes were selected with cytoHubba, including PTPRC, TYROBP, ITGB2, LCP2, ITGAM, FCGR3A, CSF1R, IRF8, C1QB, TLR2, IL10RA, ITGAX, CYBB, LAPTM5, CD53, CCL4, and LY86. Finally, we searched for key gene-related TFs, especially SPI1. Our findings reveal a shared pathogenesis between CAG and atherosclerosis. Such joint pathways and hub genes provide new insights for further studies.

Mapping QTL controlling count traits with excess zeros and ones using a zero-and-one-inflated generalized Poisson regression model.

The research on the quantitative trait locus (QTL) mapping of count data has aroused the wide attention of researchers. There are frequent problems in applied research that limit the application of the conventional Poisson model in the analysis of count phenotypes, which include the overdispersion and excess zeros and ones. In this article, a novel model, that is, the zero-and-one-inflated generalized Poisson (ZOIGP) model, is proposed to deal with these problems. Based on the proposed model, a score test is performed for the inflation parameter, in which the ZOIGP model with a constant proportion of excess zeros and ones is compared with a standard generalized Poisson model. To illustrate the practicability of the ZOIGP model, we extend it to the QTL interval mapping application that underpins count phenotype with excess zeros and excess ones. The genetic effects are estimated utilizing the expectation-maximization algorithm embedded with the Newton-Raphson algorithm, and the genome-wide scan and likelihood ratio test is performed to map and test the potential QTLs. The statistical properties exhibited by the proposed method are investigated through simulation. Finally, a real data analysis example is used to illustrate the utility of the proposed method for QTL mapping.

Detecting epinephrine auto-injector shortages in Finland 2016-2022: Log-data analysis of online information seeking.

INTRODUCTION: Medicine shortages prevail as a worldwide problem causing life-threatening situations for adults and children. Epinephrine auto-injectors are used for serious allergic reactions called anaphylaxis, and alternative auto-injectors are not always available in pharmacies. Healthcare professionals in Finland use the dedicated internet source, Physician's Database (PD), when seeking medical information in practice, while Health Library (HL) provides health information for citizens (S1 Data). The objectives were to assess whether (1) professionals' searches for epinephrine auto-injectors and (2) citizens' anaphylaxis article openings relate to epinephrine shortages in Finland. METHODS: Monthly log data on epinephrine auto-injectors (EpiPen®, Jext®, Emerade®) from PD and on openings of anaphylaxis articles from HL were collected during 2016-2022. Professionals' searches of seven auto-injectors and citizens' openings of four anaphylaxis articles were compared to information on epinephrine shortages reported by Finnish Medicines Agency. Professionals' auto-injector prescriptions provided by Social Insurance Institution were also assessed. RESULTS: Total searches in EpiPen® (N = 111,740), Jext® (N = 25,631), and Emerade® (N = 18,329) could be analyzed during 2016-2022. EpiPen® only could visually show seasonal patterns during summertime, peaking vigorously in the summer of 2018 when the major EpiPen® shortage appeared worldwide. Anaphylaxis articles equaled 2,030,855 openings altogether. Openings of one anaphylaxis article ("Bites and Stings") peaked during summertime, while another article ("Anaphylactic Reaction") peaked only once (three-fold increase) at the end of 2020 when COVID-19 vaccinations started, and auto-injector prescriptions were lowest. Fifty EpiPen®, one Jext®, and twelve Emerade® shortages were reported. Almost a two-fold increase in peaks of auto-injector prescriptions was found during summertime. CONCLUSION: This study shows that (1) epinephrine shortages related to professionals' searching for auto-injectors, and (2) citizens' information seeking on anaphylaxis related to summertime and shortages with lesser prescriptions. Therefore, the dedicated internet databases aimed at professionals and citizens could be used as additional information sources to detect anaphylactic reactions and auto-injector shortages.

An update on antibacterial AlkylGuanidino Ureas: Design of new derivatives, synergism with colistin and data analysis of the whole library.

Antimicrobial resistance (AMR) represents one of the most challenging global Public Health issues, with an alarmingly increasing rate of attributable mortality. This scenario highlights the urgent need for innovative medicinal strategies showing activity on resistant isolates (especially, carbapenem-resistant Gram-negative bacteria, methicillin-resistant S. aureus, and vancomycin-resistant enterococci) yielding new approaches for the treatment of bacterial infections. We previously reported AlkylGuanidino Ureas (AGUs) with broad-spectrum antibacterial activity and a putative membrane-based mechanism of action. Herein, new tetra- and mono-guanidino derivatives were designed and synthesized to expand the structure-activity relationships (SARs) and, thereby, tested on the same panel of Gram-positive and Gram-negative bacteria. The membrane-active mechanism of selected compounds was then investigated through molecular dynamics (MD) on simulated bacterial membranes. In the end, the newly synthesized series, along with the whole library of compounds (more than 70) developed in the last decade, was tested in combination with subinhibitory concentrations of the last resort antibiotic colistin to assess putative synergistic or additive effects. Moreover, all the AGUs were subjected to cheminformatic and machine learning analyses to gain a deeper knowledge of the key features required for bioactivity.

Coupling coordination and underlying mechanisms of urbanization development and land use efficiency in the Gansu section of the Yellow River Basin.

This swift progression of urbanization has led to increasingly prominent conflicts over the use of land, particularly around its supply and demand. Researchers, both in China and internationally, have underscored the inherent interconnection between urbanization and land utilization. This relationship has gradually become more complex with the development of urbanization. With the implementation of the Yellow River Basin's strategy to preserve the environment while ensuring high-quality development, the Yellow River Basin has become a focal point of attention for numerous scholars. This study centers on the 57 county-level administrative divisions within the Gansu segment of the Yellow River Basin. We employed an extensive array of methodologies, such as GIS technology, the entropy method, data envelopment analysis, the coupling coordination degree model, and the panel vector autoregressive model. We established an index system and a measurement model to evaluate the degree of urbanization and the efficiency of land use. We also investigated the coupling coordinated dynamics between these two variables, to further explore the dynamic interplay between urbanization and land use and reveal their underlying mechanisms. The conclusions are as follows. The urbanization level and efficiency of land use in the Gansu section of the Yellow River Basin have exhibited a consistent upward trajectory, albeit at levels that are not particularly high, indicating substantial room for improvement in the future. The level of coupling coordination between urbanization and land use efficiency in the Gansu section of the Yellow River Basin has shown a generally upward trend. However, the overall coordination level remains relatively low, characterized by an imbalance, with "high coupling but low coordination". Regarding spatial distribution patterns, considerable disparities exist in the level of coordination development, which generally decreases from the eastern toward the western regions. A strong reciprocal and interactive relationship exists between the urbanization level and land use efficiency. An elevated level of economic urbanization can initially stimulate land use efficiency. Similarly, the improvement in the level of population urbanization, social urbanization, and ecological urbanization tends to exert a restraining influence on the augmentation of land use efficiency. Conversely, the enhancement of land use efficiency makes a distinct contribution to promoting the elevation of the urbanization level.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison.

BACKGROUND: Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. RESULTS: We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. CONCLUSION: Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

Is the surge in cesarean section rates during the COVID-19 pandemic truly substantiated?

BACKGROUND: Cesarean section (C-section) rates, deemed a critical health indicator, have experienced a historical increase. The advent of the COVID-19 pandemic significantly impacted healthcare patterns including delays or lack of follow-up in treatment and an increased number of patients with acute problems in hospitals. This study aimed to explore whether the observed surge is a genuine consequence of pandemic-related factors. METHODS: This study employs an Interrupted Time Series (ITS) design to analyze monthly C-section rates from March 2018 to January 2023 in Kurdistan province, Iran. Segmented regression modeling is utilized for robust data analysis. RESULTS: The C-section rate did not show a significant change immediately after the onset of COVID-19. However, the monthly trend increased significantly during the post-pandemic period (p < 0.05). Among primigravid women, a significant monthly increase was observed before February 2020 (p < 0.05). No significant change was observed in the level or trend of C-section rates among primigravid women after the onset of COVID-19. CONCLUSION: This study underscores the significant and enduring impact of the COVID-19 pandemic in further increasing the C-section rates over the long term, the observed variations in C-section rates among primigravid women indicate that the COVID-19 pandemic had no statistically significant impact.

Stakeholder opinions on perceived sub-standard emergency obstetric and newborn care in Ghana.

BACKGROUND: Sub-Saharan Africa is unlikely to achieve sustainable development goal (SDG) 3 on maternal and neonatal health due to perceived sub-standard maternal and newborn care in the region. This paper sought to explore the opinions of stakeholders on intricacies dictating sub-standard emergency obstetric and newborn care (EmONC) in health facilities in Northern Ghana. METHODS: Drawing from a qualitative study design, data were obtained from six focus group discussions (FGDs) among 42 health care providers and 27 in-depth interviews with management members, clients and care takers duly guided by the principle of data saturation. Participants were purposively selected from basic and comprehensive level facilities. Data analysis followed Braun and Clarke's qualitative thematic analysis procedure. RESULTS: Four themes and 13 sub-themes emerged as root drivers to sub-standard care. Specfically, the findings highlight centralisation of EmONC, inadequate funding, insufficient experiential training, delay in recruitment of newly trained essential staff and provider disinterest in profession. CONCLUSION: Setbacks in the training and recruitment systems in Ghana, inadequate investment in rural health coupled with extent of health provider inherent disposition to practice may be partly responsible for sub-standard obstetric care in the study area. Interventions targeting the afore-mentioned areas may reduce events of sub-standard care.

What Is Quality Food? The Opinion of Key of the Brazilian Food System.

The aim of the study was to explore the concept of quality food in the opinion of key informants of the food system. This qualitative research included 208 key informants related to the food supply for Brazilian public food services. The participants were grouped into three groups according to their participation in the food system: 1. Food production; 2. Management and marketing; 3. Meal's production process. Key informants answered the following question: "In your opinion, what is quality food?". The answers were analysed through qualitative content analysis. The data analysis resulted in 52 codes grouped into eight categories, expressing the opinion of the study participants about what quality food is: nutritional, sustainable, sensory, hygienic-sanitary, care, regulatory, dependability and symbolic. Nutritional and sustainable dimensions were predominantly considered. The activities carried out in the food system seem to influence the opinion on food quality. The groups linked to food production put more emphasis on the sustainable dimension, followed by the nutritional dimension, while the groups linked to management and marketing and to the meals production process put more emphasis on the nutritional and sensory dimensions. These differences may indicate a difficulty in the transition towards a more sustainable and healthy food system.

Data analysis of the influence of microstructure, composition, and loading conditions on stress corrosion cracking behavior of Mg alloys.

Stress corrosion cracking (SCC) can be a crucial problem in applying rare earth (RE) Magnesium alloys in environments where mechanical loads and electrochemical driven degradation processes interact. It has been proven already that the SCC behavior is associated with microstructural features, compositions, loading conditions, and corrosive media, especially in-vivo. However, it is still unclear when and how mechanisms acting on multiple scales and respective system descriptors predictable contribute to SCC for the wide set of existing Mg alloys. In the present work, suitable literature data along SCC of Mg alloys has been analyzed to enable the development of a reliable SCC model for MgGd binary alloys. Pearson correlation coefficient and linear fitting are utilized to describe the contribution of selected parameters to corrosion and mechanical properties. Based on our data analysis, a parameter ranking is obtained, providing information on the SCC impact with regard to ultimate tensile strength (UTS) and fracture elongation of respective materials. According to the analyzed data, SCC susceptibility can be grouped and mapped onto Ashby type diagrams for UTS and elongation of respective base materials tested in air and in corrosive media. The analysis reveals the effect of secondary phase content as a crucial materials descriptor for our analyzed materials and enables better understanding towards SCC model development for Mg-5Gd alloy based implant.

Professional Experiences and Career Trajectories of Mid- to Senior-Career Women Clinician-Scientists: A Qualitative Study.

Importance: Despite increasing evidence and recognition of persistent gender disparities in academic medicine, qualitative data detailing the association of gender-based experiences with career progression remain sparse, particularly at the mid- to senior-career stage. Objective: To investigate the role gender has played in everyday professional experiences of mid- to senior-career women clinician-scientists and their perceptions of gender-related barriers experienced across their careers. Design, Setting, and Participants: In this qualitative study, a total of 60 of 159 invited clinician-scientists who received National Institutes of Health K08 or K23 awards between 2006 and 2009 and responded to a survey in 2021 agreed to participate. Invitees were selected using random, purposive sampling to support sample heterogeneity. Semistructured in-depth interviews were conducted January to May 2022. For this study, interviews from 31 women were analyzed using the framework approach to thematic analysis. Data analyses were performed between August and October 2023. Main Outcomes and Measures: Descriptive themes of participant experiences of gender and gender-based barriers in academic medicine. Results: A total of 31 women clinician-scientists (8 identifying as Asian [25.8%], 14 identifying as White [45.2%], and 9 identifying as members of a minority group underrepresented in medicine [29.0%]; 14 aged 40-49 years [45.2%] and 14 aged 50-59 years [45.2%]) were included. Among them, 17 participants (54.8%) had children who required adult supervision or care, 7 participants (22.6%) had children who did not require supervision or care, and 6 participants (19.4%) did not have children. There were 4 dominant themes identified within participant experiences in academic medicine: the mental burden of gendered expectations at work and home, inequitable treatment of women in bureaucratic processes, subtle and less subtle professional exclusion of women, and value of communities built on shared identities, experiences, and solidarity. Conclusions and Relevance: This study found that women perceived the institution of academic medicine as a male-centric system misaligned with the needs of women, with associated feelings of exclusion, disillusionment, and loss of trust in their institutions. Findings suggest that the confluence of domestic obligations and unaccommodating institutional environments may make it difficult for women clinician-scientists to achieve established timelines of career progression and productivity; these findings may have long-term implications for the well-being and retention of women in academic medicine.

Associations and gender differences between OHI-seeking behaviors and eHealth literacy among Chinese university students.

OBJECTIVE: to explore associations and gender differences between OHI-seeking (online health information seeking) behaviors and eHealth (electronic health) literacy among Chinese university students. METHODS: Online questionnaires of eHealth literacy scale and OHI-seeking behaviors created in software Wenjunxing were used in this survey. Chi-squared tests, t-test, and Pearson correlation analysis were performed using SPSS for data analysis. RESULTS: Among 5,383 participants, 72.4% were girls, 77.5% lived in rural areas, 51.2% majored in liberal arts, 76.6% with low education parents. The average C-eHEALS scores of boys and girls were 26.53 ± 5.861 and 26.84 ± 5.816, respectively, with no significant difference (P = 0.084). The top three OHI-seeking behaviors for boys and girls, as well as for the C-eHEALS low and high groups, were "finding information about physical exercises" "reading or sharing health information via social media" "finding information about nutrition and diet", all of which had significant gender and eHealth literacy differences. CONCLUSIONS: Gender and eHealth literacy differences should be focused for intervention when developing and implementing eHealth intervention training for parents and adolescents in schools and communities.

Perceived Physical Health and Cognitive Behavioral Therapy vs Supportive Psychotherapy Outcomes in Adults With Late-Life Depression: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Physical diseases co-occur with late-life depression (LLD). The influence of physical diseases and the subjective perception of physical health (PPH) on treatment outcome in LLD, however, is not well understood. Objective: To assess the association of physical diseases and PPH with the outcomes of 2 different types of psychotherapy in LLD. Design, Setting, and Participants: This post hoc secondary analysis of a multicenter, observer-blinded, controlled, parallel-group randomized clinical trial assessed participants 60 years or older with moderate to severe depression recruited at 7 psychiatric-psychotherapeutic outpatient trial sites in Germany from October 1, 2018, to November 11, 2020. Data analysis was performed from April 1 to October 31, 2023. Interventions: Patients received LLD-specific cognitive behavioral therapy (LLD-CBT) or supportive unspecific intervention (SUI). Main Outcomes and Measures: Depression severity, response, and remission were measured during treatment and at 6-month follow-up by the change in the 30-item Geriatric Depression Scale (GDS) score. Physical health and PPH were assessed by the number of physical diseases, Charlson Comorbidity Index (CCI), and the World Health Organization Quality of Life Brief Version physical health subscale. Results: A total of 251 patients were randomized to LLD-CBT (n = 126) or SUI (n = 125), of whom 229 (mean [SD] age, 70.2 [7.1] years; 151 [66%] female) were included in the intention-to-treat analysis. Patients with low and moderate PPH at baseline had significantly less reduction in the GDS score across both treatment groups than patients with high PPH (estimated marginal mean difference [EMMD], 2.67; 95% CI, 0.37-4.97; P = .02 for low PPH and EMMD, 1.82; 95% CI, 0.22-3.42; P = .03 for moderate vs high PPH). Higher PPH at baseline was associated with higher likelihood of response (odds ratio [OR], 1.04; 95% CI, 1.00-1.06; P = .009) and remission at the end of treatment (OR, 1.04; 95% CI, 1.02-1.08; P = .002) and response (OR, 1.05; 95% CI, 1.02-1.08; P < .001) and remission at follow-up (OR, 1.06; 95% CI, 1.03-1.10; P < .001) across both treatment groups. However, a significant interaction of PPH with treatment group was observed with low PPH at baseline being associated with significantly larger reduction in GDS scores in SUI compared with LLD-CBT at the end of treatment (EMMD, -6.48; 95% CI, -11.31 to -1.64; P = .009) and follow-up (EMMD, -6.49; 95% CI, -11.51 to -1.47; P = .01). In contrast, patients with high PPH at baseline had a significantly greater reduction in GDS scores in LLD-CBT compared with SUI at all time points (week 5: EMMD, -4.08; 95% CI, -6.49 to -1.67; P = .001; end-of-treatment: EMMD, -3.67; 95% CI, -6.72 to -0.61; P = .02; and follow-up: EMMD, -3.57; 95% CI, -6.63 to -0.51; P = .02). The number of physical diseases or CCI at baseline did not have an effect on the change in GDS score, response, or remission, neither across both groups nor within either group. Conclusions and Relevance: In this secondary analysis of a randomized clinical trial, subjective PPH was associated with treatment outcome, response, and remission in psychotherapy of LLD. Patients with LLD responded differently to LLD-CBT and SUI, depending on their baseline PPH score. Treatment approaches for patients with LLD should address PPH in personalized interventions. Trial Registration: ClinicalTrials.gov Identifier: NCT03735576; Deutsches Register Klinischer Studien Identifier: DRKS00013769.

CT Scan Data Analysis in Malformations of Cortical Development.

Malformations of cortical development (MCDs) are a diverse group of disorders that result from abnormal neuronal migration, proliferation, and differentiation during brain development. Head computed tomography (CT) has limited use in the diagnosis of MCDs and should be reserved for selected cases with specific indications or when magnetic resonance imaging is not available or contraindicated. CT can detect brain calcifications associated with MCDs, thus helping in the differential diagnosis between acquired and genetic MCDs or in the identification of different genetic patterns. Moreover, CT can provide high-resolution images of the skull and bones, thus identifying associated malformations, such as craniosynostosis, inner and middle ear malformations, and vertebral anomalies. In this chapter, we review the CT scan technique, data analysis, and indications in the investigation of MCDs.

MRI Data Analysis in Malformations of Cortical Development.

Brain magnetic resonance imaging (MRI) is a noninvasive imaging modality that utilizes powerful magnets and radio waves to generate detailed images of the brain, making it a valuable tool for investigating malformations of cortical development (MCD). Various MRI techniques, including 3D T1-weighted, multiplanar thin-sliced T2-weighted, and 3D fluid-attenuated inversion recovery (FLAIR) sequences, can provide high-resolution images with excellent spatial and contrast resolution, allowing for a detailed visualization of cortical anatomy and abnormalities. Almost all MCD can be detected and characterized using MRI. Advanced techniques, such as arterial spin labeling MR perfusion, diffusion tensor imaging (DTI), and functional MRI (fMRI), may be used to improve the detection rate of these malformations and to plan surgery in case of drug-resistant epilepsy. However, there are also limitations related to high cost, relatively low availability, need for sedation or anesthesia, and limited sensitivity for detecting subtle focal cortical malformations. Despite these limitations, brain MRI plays a crucial role in the investigation of MCD, providing valuable information for diagnosis, treatment planning, and patient management.

Phenotypic analysis of ataxia in spinocerebellar ataxia type 6 mice using DeepLabCut.

This study emphasizes the benefits of open-source software such as DeepLabCut (DLC) and R to automate, customize and enhance data analysis of motor behavior. We recorded 2 different spinocerebellar ataxia type 6 mouse models while performing the classic beamwalk test, tracked multiple body parts using the markerless pose-estimation software DLC and analyzed the tracked data using self-written scripts in the programming language R. The beamwalk analysis script (BAS) counts and classifies minor and major hindpaw slips with an 83% accuracy compared to manual scoring. Nose, belly and tail positions relative to the beam, as well as the angle at the tail base relative to the nose and tail tip were determined to characterize motor deficits in greater detail. Our results found distinct ataxic abnormalities such as an increase in major left hindpaw slips and a lower belly and tail position in both SCA6 ataxic mouse models compared to control mice at 18 months of age. Furthermore, a more detailed analysis of various body parts relative to the beam revealed an overall lower body position in the SCA684Q compared to the CT-longQ27PC mouse line at 18 months of age, indicating a more severe ataxic deficit in the SCA684Q group.

Comprehensive evaluation and performance analysis of machine learning in heart disease prediction.

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model's robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model's predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system's sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes.

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.

RNA sequencing (RNA-Seq) is widely used to capture transcriptome dynamics across tissues, biological entities, and conditions. Currently, few or no methods can handle multiple biological variables (e.g., tissues/ phenotypes) and their interactions simultaneously, while also achieving dimension reduction (DR). We propose INSIDER, a general and flexible statistical framework based on matrix factorization, which is freely available at https://github.com/kai0511/insider. INSIDER decomposes variation from different biological variables and their interactions into a shared low-rank latent space. Particularly, it introduces the elastic net penalty to induce sparsity while considering the grouping effects of genes. It can achieve DR of high-dimensional data (of > = 3 dimensions), as opposed to conventional methods (e.g., PCA/NMF) which generally only handle 2D data (e.g., sample × expression). Besides, it enables computing 'adjusted' expression profiles for specific biological variables while controlling variation from other variables. INSIDER is computationally efficient and accommodates missing data. INSIDER also performed similarly or outperformed a close competing method, SDA, as shown in simulations and can handle complex missing data in RNA-Seq data. Moreover, unlike SDA, it can be used when the data cannot be structured into a tensor. Lastly, we demonstrate its usefulness via real data analysis, including clustering donors for disease subtyping, revealing neuro-development trajectory using the BrainSpan data, and uncovering biological processes contributing to variables of interest (e.g., disease status and tissue) and their interactions.