Sepsis

Mechanistic Learning for Survival Prediction in NSCLC Using Routine Blood Biomarkers and Tumor Kinetics

Background Predicting overall survival (OS) in non-small cell lung cancer (NSCLC) is essential for clinical decision-making and drug development. While tumor and blood test markers kinetics are intrinsically linked, their joint dynamics and relationship to OS remain unknown. Methods We developed a mechanistic model capturing the interplay between tumor (T) burden and three key blood markers kineti...

Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in precision psychiatry. We integrate urinary metabolomics with an interpretable machine learning framework to identify biochemical signatures associated with ADHD. Targeted metabolomic pr...

Achieving Expert-Level Clinical Infection Detection with LLMs from Clinical Documents: Validation in Complex Patient Cases with Cirrhosis

BackgroundSystemic infections are a leading cause of hospitalization and death among patients with cirrhosis. Timely and accurate infection identifica...

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs)...

Detection of S1 spike protein in CD16+ monocytes up to 245 days in SARS-CoV-2-negative post-COVID-19 vaccine syndrome (PCVS) individuals.

Despite over 13 billion SARS-CoV-2 vaccine doses administered globally, persistent post-vaccination symptoms, termed post-COVID-19 vaccine syndrome (P...

Explainable artificial intelligence for prediction of refractory ulcerative colitis: analysis of a Japanese Nationwide Registry.

OBJECTIVE: Ulcerative colitis (UC) is a chronic inflammatory bowel disease for which remission is dependent on corticosteroid (CS) treatment. The dive...

Portable and intelligent ratio fluorometry and colorimetry for dual-mode detection of dopamine based on B, N-codoped carbon dots and machine learning.

A dual-mode approach was developed for dopamine (DA) assay based on boron (B) and nitrogen (N) co-doped carbon dots (B, N-CDs). This platform enabled ...

Supervised machine learning and molecular docking modeling to identify potential Anti-Parkinson's agents.

Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurot...

Decoding virulence and resistance in Klebsiella pneumoniae: Pharmacological insights, immunological dynamics, and in silico therapeutic strategies.

Klebsiella pneumoniae (K. pneumoniae) has become a serious global health concern due to its rising virulence and antibiotic resistance. As one of the ...

Harnessing the fish gut microbiome and immune system to enhance disease resistance in aquaculture.

The increasing global reliance on aquaculture is challenged by disease outbreaks, exacerbated by antibiotic resistance, and environmental stressors. T...

Nutritionally Responsive PMv DAT Neurons Are Dynamically Regulated During Pubertal Transition.

Pubertal development is tightly regulated by energy balance. The crosstalk between metabolism and reproduction is orchestrated by complex neural netwo...

A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS)

This study introduces a hybrid approach integrating advanced plasmonic nanomaterials and machine learning (ML) for high-precision biomolecule detect...

A Dopamine-Serotonin Theory of Consciousness

This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular dr...

GLOBAL TRENDS IN ARTIFICIAL INTELLIGENCE AND SEPSIS-RELATED RESEARCH: A BIBLIOMETRIC ANALYSIS.

Background: In the field of bibliometrics, although some studies have conducted literature reviews and analyses on sepsis, these studies mainly focus ...

Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.

Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the ...

A systematic methodological evaluation of sepsis guidelines: Protocol for quality assessment and consistency of recommendations.

BACKGROUND: Sepsis is a leading cause of mortality worldwide, characterized by a dysregulated host response to infection. Despite the development of m...

A Closed-Loop Environmental Health Management Platform: A Multifunctional MgFeO Nanoparticle Enabling AI-Enhanced Detection, Bioimaging, and Degradation of Tetracycline Antibiotics.

Emerging pollutants such as tetracycline antibiotics (TCs) have garnered attention due to their ecological impacts and the evolution of drug resistanc...

Augmentation of Infrared Microscopy of White Blood Cells and Medical Measures for Rapid and Accurate Diagnosis of Bacterial or Viral Infections in Febrile Pediatric Oncology Patients: An Expert System-Based Study.

Infectious diseases, a major contributor to high mortality rates, often exhibit similar symptoms, despite variations in immune responses to bacterial ...

Real-time FT-IR typing of Klebsiella pneumoniae: a flexible and rapid approach for outbreak detection and infection control.

BACKGROUND: Expansion of carbapenemase-producing Klebsiella pneumoniae (CP-Kp) is driven by within-hospital transmission, requiring timely typing data...