Structural Modification and Pharmacological Evaluation of (Thiadiazol-2-yl)pyrazines as Novel Piezo1 Agonists for the Intervention of Disuse Osteoporosis.

Piezo1 plays a pivotal role in regulating bone remodeling and homeostasis and has emerged as a promising target for chemical intervention in disuse osteoporosis. Nevertheless, the development of small-molecule Piezo1 agonists is still in its infancy, and highly efficacious Piezo1 agonists are urgently required. In this study, by shedding light on the structural novelty of the canonical Piezo1 agonist Yoda1, we initiated a structural optimization campaign based on the (thiadiazol-2-yl)pyrazine scaffold.

Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.

Background: Significant variations in immune profiles across different age groups manifest distinct clinical symptoms and prognoses in Coronavirus Disease 2019 (COVID-19) patients. Predominantly, severe COVID-19 cases that require hospitalization occur in the elderly, with the risk of severe illness escalating with age among young adults, children, and adolescents.

Objective: This study aimed to delineate the unique immune characteristics of COVID-19 across various age groups and evaluate the feasibility of detecting COVID-19-induced immune alterations through peripheral blood analysis.

Spatial transcriptomic analysis deciphers adipocyte-to-fibroblast transformation in bleomycin-induced murine skin fibrosis.

Background: Scleroderma is characterized by inflammation and fibrosis, predominantly occurring in the skin and extending to various parts of the body. The pathophysiology of scleroderma is multifaceted, with the current understanding including endothelial damage, inflammatory cell infiltration, and fibroblast activation in its progression. Nonetheless, the mechanism of cellular interactions and the precise spatial distribution of these cellular events within the fibrotic tissues remain elusive, highlighting a critical gap in our comprehensive understanding of scleroderma's pathogenesis.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells.

A brain-to-gut signal controls intestinal fat absorption.

Although fat is a crucial source of energy in diets, excessive intake leads to obesity. Fat absorption in the gut is prevailingly thought to occur organ-autonomously by diffusion. Whether the process is controlled by the brain-to-gut axis, however, remains largely unknown.

Prediction of Solubility of Proteins in Escherichia coli Based on Functional and Structural Features Using Machine Learning Methods.

Protein solubility is a critical parameter that determines the stability, activity, and functionality of proteins, with broad and far-reaching implications in biotechnology and biochemistry. Accurate prediction and control of protein solubility are essential for successful protein expression and purification in research and industrial settings. This study gathered information on soluble and insoluble proteins.

Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.

Congenital heart disease (CHD) represents a spectrum of inborn heart defects influenced by genetic and environmental factors. This study advances the field by analyzing gene expression profiles in 21,034 cardiac fibroblasts, 73,296 cardiomyocytes, and 35,673 endothelial cells, utilizing single-cell level analysis and machine learning techniques. Six CHD conditions: dilated cardiomyopathy (DCM), donor hearts (used as healthy controls), hypertrophic cardiomyopathy (HCM), heart failure with hypoplastic left heart syndrome (HF_HLHS), Neonatal Hypoplastic Left Heart Syndrome (Neo_HLHS), and Tetralogy of Fallot (TOF), were investigated for each cardiac cell type.

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory syndrome coronavirus 2 infection on the homeostatic immune status and secondary immune response in recovered patients remains unclear. To investigate these persistent alterations, we employed five feature-ranking algorithms (LASSO, MCFS, RF, CATBoost, and XGBoost), incremental feature selection, synthetic minority oversampling technique and two classification algorithms (decision tree and k-nearest neighbors) to analyze multi-omics data (surface proteins and transcriptome) from coronavirus disease 2019 (COVID-19) recovered patients and healthy controls post-influenza vaccination.

SPATS2L is a positive feedback regulator of the type I interferon signaling pathway and plays a vital role in lupus.

Through genome-wide association studies (GWAS) and integrated expression quantitative trait locus (eQTL) analyses, numerous susceptibility genes ("eGenes", whose expressions are significantly associated with common variants) associated with systemic lupus erythematosus (SLE) have been identified. Notably, a subset of these eGenes is correlated with disease activity. However, the precise mechanisms through which these genes contribute to the initiation and progression of the disease remain to be fully elucidated.

Fasting-induced miR-7a-5p in AgRP neurons regulates food intake.

Objective: The molecular control of feeding after fasting is essential for maintaining energy homeostasis, while overfeeding usually leads to obesity. Identifying non-coding microRNAs (miRNAs) that control food intake could reveal new oligonucleotide-based therapeutic targets for treating obesity and its associated diseases. This study aims to identify a miRNA modulating food intake and its mechanism in neuronal regulation of food intake and energy homeostasis.

Machine Learning Reveals Impacts of Smoking on Gene Profiles of Different Cell Types in Lung.

Smoking significantly elevates the risk of lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. This risk is attributed to the harmful chemicals in tobacco smoke that damage lung tissue and impair lung function. Current research on the impact of smoking on gene expression in specific lung cells is limited.

Novel potential lncRNA biomarker in B cells indicates essential pathogenic pathway activation in patients with SLE.

Objectives: Systemic lupus erythematosus (SLE) is a highly heterogeneous disease, and B cell abnormalities play a central role in the pathogenesis of SLE. Long non-coding RNAs (lncRNAs) have also been implicated in the pathogenesis of SLE. The expression of lncRNAs is finely regulated and cell-type dependent, so we aimed to identify B cell-expressing lncRNAs as biomarkers for SLE, and to explore their ability to reflect the status of SLE critical pathway and disease activity.

Prevalence of Neutralizing Autoantibodies Against Type I Interferon in a Multicenter Cohort of Severe or Critical COVID-19 Cases in Shanghai.

Objective: We sought to explore the prevalence of type I interferon-neutralizing antibodies in a Chinese cohort and its clinical implications during the Omicron variant wave of SARS-CoV-2.

Methods: Type I interferon (IFN) autoantibodies possessing neutralizing capabilities were identified using luciferase assays. The capacity of the autoantibodies for in vitro interference with antiviral activity of IFN was assessed by using a SARS-CoV-2 replicon system.

Targeted knockdown of PGAM5 in synovial macrophages efficiently alleviates osteoarthritis.

Osteoarthritis (OA) is a common degenerative disease worldwide and new therapeutics that target inflammation and the crosstalk between immunocytes and chondrocytes are being developed to prevent and treat OA. These attempts involve repolarizing pro-inflammatory M1 macrophages into the anti-inflammatory M2 phenotype in synovium. In this study, we found that phosphoglycerate mutase 5 (PGAM5) significantly increased in macrophages in OA synovium compared to controls based on histology of human samples and single-cell RNA sequencing results of mice models.

Non-coding DNA variants for risk in lupus.

Systemic Lupus Erythematosus (SLE) is a multifactorial autoimmune disease that arises from a dynamic interplay between genetics and environmental triggers. The advent of sophisticated genomics technology has catalyzed a shift in our understanding of disease etiology, spotlighting the pivotal role of non-coding DNA variants in SLE pathogenesis. In this review, we present a comprehensive examination of the non-coding variants associated with SLE, shedding light on their role in influencing disease risk and progression.

Monogenic lupus: insights into disease pathogenesis and therapeutic opportunities.

Purpose Of Review: This review aims to provide an overview of the genes and molecular pathways involved in monogenic lupus, the implications for genome diagnosis, and the potential therapies targeting these molecular mechanisms.

Recent Findings: To date, more than 30 genes have been identified as contributors to monogenic lupus. These genes are primarily related to complement deficiency, activation of the type I interferon (IFN) pathway, disruption of B-cell and T-cell tolerance and metabolic pathways, which reveal the multifaceted nature of systemic lupus erythematosus (SLE) pathogenesis.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging.

Identifying Autophagy-Associated Proteins and Chemicals with a Random Walk-Based Method within Heterogeneous Interaction Network.

Background: Autophagy is instrumental in various health conditions, including cancer, aging, and infections. Therefore, examining proteins and compounds associated with autophagy is paramount to understanding cellular biology and the origins of diseases, paving the way for potential therapeutic and disease prediction strategies. However, the complexity of autophagy, its intersection with other cellular pathways, and the challenges in monitoring autophagic activity make the experimental identification of these elements arduous.

Analyzing domain features of small proteins using a machine-learning method.

Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utilized to analyze functional domains of SPs.

Biomimetic triphasic silk fibroin scaffolds seeded with tendon-derived stem cells for tendon-bone junction regeneration.

The regeneration of tendon and bone junctions (TBJs), a fibrocartilage transition zone between tendons and bones, is a challenge due to the special triphasic structure. In our study, a silk fibroin (SF)-based triphasic scaffold consisting of aligned type I collagen (Col I), transforming growth factor β (TGF-β), and hydroxyapatite (HA) was fabricated to mimic the compositional gradient feature of the native tendon-bone architecture. Rat tendon-derived stem cells (rTDSCs) were loaded on the triphasic SF scaffold, and the high cell viability suggested that the scaffold presents good biocompatibility.

PiRNA hsa_piR_019949 promotes chondrocyte anabolic metabolism by inhibiting the expression of lncRNA NEAT1.

Background: Osteoarthritis is a prevalent degenerative joint condition typically found in individuals who are aged 50 years or older. In this study, the focus is on PIWI-interacting RNA (piRNA), which belongs to a category of small non-coding RNAs. These piRNAs play a role in the regulation of gene expression and the preservation of genomic stability.

CRCS: An automatic image processing pipeline for hormone level analysis of Cushing's disease.

Due to the abnormal secretion of adreno-cortico-tropic-hormone (ACTH) by tumors, Cushing's disease leads to hypercortisonemia, a precursor to a series of metabolic disorders and serious complications. Cushing's disease has high recurrence rate, short recurrence time and undiscovered recurrence reason after surgical resection. Qualitative or quantitative automatic image analysis of histology images can potentially in providing insights into Cushing's disease, but still no software has been available to the best of our knowledge.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods.

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19.