Allergy & Immunology

Role of WNK4 and FKBP5 in asthma: Insights from bioinformatics analysis, machine learning, and preliminary validation.

BACKGROUND: The complex and heterogeneous molecular mechanisms of asthma are currently unclear, and treatment outcomes are dismal in some patients. This study aimed to identify biomarkers with diagnostic and therapeutic potential. METHODS: Gene Expression Omnibus was used to obtain asthma-related gene expression data for identifying differentially expressed genes. They were subjected to functional...

A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.

Cell detection is ubiquitous in the analysis of microfluidic cell assays. In cell biology, immunology, oncology, and toxicology research, studying cellular response starts with identifying the cells on chip. The large amount of data generated in such assays requires automating image analysis. While multitudes of image processing tools exist, the microfluidic channel network and crowded cell enviro...

Allosteric Insights into TCR-pMHC Dynamics: Understanding the Effects of Melanoma-Associated Epitopes.

Allostery, a crucial phenomenon for comprehending protein function, interactions, and regulation, involves the transmission of perturbations induced b...

Immune system up-regulation in securely attached infants during early childhood.

Early caregiving relationships shape the coordination of stress and immune systems, yet their biological correlates in early infancy remain insufficie...

Microbiome-Based Modeling of CAR-T Therapy Response in Lymphoma: Insights From Shotgun Metagenomics Sequencing.

The interplay between the commensal microbiota and the mammalian immune system may influence the outcomes of T cell-driven cancer immunotherapies. How...

[Sphincter-preserving surgery for rectal cancer--From limits to perfection].

Sphincter-preserving therapy for low rectal cancer has always been a difficult and hot topic in colorectal cancer. Advanced understanding of the biolo...

Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies.

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and c...

Predicting kidney graft survival with a machine learning model based on for-cause biopsy transcriptomics.

Kidney transplantation significantly improves outcomes in end-stage renal disease, however, long-term graft survival remains suboptimal. Traditional p...

Utilization of machine learning algorithms for the identification of the RLN associated prognostic model and feature biomarkers of RLN-related subtypes in breast cancer.

BACKGROUND: Breast cancer (BC) is the most common malignancy afflicting women worldwide, yet the role of relaxin-related genes (RLN) in BC progression...

Prediction of renal cell carcinoma: Development and validation of machine learning model.

Renal cell carcinoma (RCC) is the leading cause of urinary system morbidity and mortality. Early identification is crucial for improving RCC patient o...

Reproducing real-world clinical prediction models using the DIVE platform: A comparative validation study across three chronic diseases.

OBJECTIVES: The aim of this analysis is to evaluate the performance and reproducibility of the Python-based Data Insight Validation Engine (DIVE), a m...

Efferocytosis-associated transcriptomic patterns characterize prognosis and immune landscape in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents, characterized by high heterogeneity and poor prognosis. ...

Characterizing prognostic and immunological traits of kidney renal clear cell carcinoma via mitochondria-associated membranes and identifying DNM1L as a potential therapeutic target using machine learning.

Immune Checkpoint Activity and Prognostic Roles of TNFRSF8, CD160, and TNFSF9 in Primary and Brain Metastatic NSCLC.

Here, we utilized advanced bioinformatics approaches alongside experimental validation to identify key prognostic biomarkers and potential immune chec...

A machine learning-based prediction model for poor prognosis in sepsis using lymphocyte count: a national, multicenter prospective cohort.

Sepsis-induced immunosuppression leads to poor prognosis. Circulating lymphocyte count (LC), as an easily accessible clinical marker, closely reflects...

Multi-omics integrative analysis of stemness-associated pathological signatures to guide prognosis and therapeutic strategies in uveal melanoma.

BACKGROUND: Uveal melanoma (UVM) is the most common intraocular malignancy in adults and exhibits poor prognosis upon metastasis. Stemness, a hallmark...

Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.

Recent advances in digital pathology have enabled comprehensive analyses of Whole-Slide Images (WSIs) from tissue samples, leveraging high-resolution ...

Identification of hypoxia- and mitophagy-related diagnostic biomarkers for ulcerative colitis based on bioinformatic analysis and machine learning.

BACKGROUND: Ulcerative colitis (UC) is a chronic nonspecific inflammatory bowel disease of unknown etiology that is associated with a significant risk...

Stratification of immunotherapy responses by adaptive immune receptor repertoires.

A growing number of diseases are now treated with immunotherapies, which consist of interventions that suppress or stimulate the patient's immune syst...

Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.

Non-small cell lung cancer (NSCLC) patient management relies on molecular analysis to determine eligibility for targeted therapy. Furthermore, neoadju...