Sepsis

Advancements in Tuberculosis Diagnostics: An Update.

Tuberculosis (TB) is one of the major life-threatening diseases caused by a single pathogen which has become a social menace owing to its high resistance. TB has even surpassed AIDS prior the COVID 19 pandemic. Every year the number of affected persons is increasing exponentially. In 2023 8.2 million new cases of TB were reported. There are various factors responsible for such infectivity rate of ...

5-Hydroxymethylcytosine signatures as diagnostic biomarkers for septic cardiomyopathy.

At present, there are currently no molecular biomarkers for the early diagnosis of sepsis cardiomyopathy (SCM) in clinical practice. This study focuses on an in-depth examination of the DNA hydroxymethylation profiles within plasma extracellular vesicles and explores potential molecular biomarkers during the process of SCM. The 5hmC-Seal sequencing technology was utilized to examine the hydroxymet...

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk ...

Machine learning for the prediction of urosepsis using electronic health record data.

Urosepsis, a medical condition resulting from the progression of urinary tract infection (UTI), is a leading cause of death in the US. Urosepsis occur...

Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.

This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency de...

Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.

Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leadi...

Development and validation of a modified SOFA score for mortality prediction in candidemia patients.

Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk st...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 a...

Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rat...

Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry.

Antibiotic resistance in Burkholderia pseudomallei (Bp) is a growing public health concern requiring urgent attention. Matrix-assisted laser desorptio...

A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.

Segmental/lobar pneumonia in children following Mycoplasma pneumoniae (MP) infection has a significant threat to the children's health, so early recog...

Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis.

For modeling dairy cattle data, fuzzy logic offers the capability to manage uncertainty, enhance accuracy, facilitate informed decision-making, and op...

Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning.

OBJECTIVE: To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chr...

Multiomics and Machine Learning Identify Immunometabolic Biomarkers for Active Tuberculosis Diagnosis Against Nontuberculous Mycobacteria and Latent Tuberculosis Infection.

This study utilized multiomics combined with a comprehensive machine learning-based predictive modeling approach to identify, validate, and prioritize...

Identification of key genes and development of an identifying machine learning model for sepsis.

OBJECTIVE AND DESIGN: This study aims to identify key genes of sepsis and construct a model for sepsis identification through integrated multi-organ s...

[Early warning scores: a rapid umbrella review].

BACKGROUND: Early warning scores (EWS) are used for monitoring and evaluating vital signs in hospitalized patients. With EWS, escalating measures for ...

Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study.

BACKGROUND: Substance use disorder (SUD) involves excessive substance consumption and persistent reward-seeking behaviors, leading to serious physical...

Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection.

Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a p...

Exploring the complex interplay between oral infection, periodontitis, and robust microRNA induction, including multiple known oncogenic miRNAs.

() is an oral commensal bacterium that can become pathogenic and is associated with periodontitis, adverse pregnancy outcomes, and colorectal cancer ...

Renal Dysfunction Across the Spectrum of Cardiogenic Shock: Mechanisms, Clinical Implications, and Therapeutic Strategies.

PURPOSE OF REVIEW: This review aims to elucidate the complex interplay between cardiogenic shock (CS) and renal function, detailing the mechanisms of ...