Sepsis

HeptaTB Dx: a diagnostic model leveraging cuproptosis-ferroptosis crosstalk for distinguishing latent from active tuberculosis.

UNLABELLED: Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) remains challenging. The roles of cuproptosis-ferroptosis crosstalk in TB immunopathology and diagnostic potential are unexplored. Transcriptomic data from Gene Expression Omnibus data sets (GSE37250/GSE28623) were analyzed to identify cuproptosis-/ferroptosis-related differentially expressed genes. Bioi...

Machine learning-enhanced electrochemical sensing platform for simultaneous detection of multiple antibiotics in food samples.

The increasing prevalence of antibiotic residues in food presents a significant threat to global public health. To address this critical concern, we developed a novel artificial neural network (ANN)-enhanced electrochemical sensor based on ZIF-8/MnMoO4/MWCNTs-modified glassy carbon electrode (GCE) with high sensitivity for the simultaneous detection of chloramphenicol (CAP), nitrofurazone (NFZ), a...

Multifactor machine learning models for predicting urinary tract infections: a pilot study.

PURPOSE: Vitamin D, a fat-soluble prohormone essential for calcium-phosphate homeostasis and bone health, also regulates innate and adaptive immunity ...

Unveiling Putative Functions of Burkholderia pseudomallei K96243 Hypothetical Proteins Via High-Throughput Characterization of Structural Similarities.

Burkholderia pseudomallei (BP) infections claims tens of thousands of lives worldwide every year. The bacterium's distinctive characteristics include ...

Leveraging artificial intelligence for surgical site infection surveillance: A comparison of 5 large language models.

We conducted a retrospective study to evaluate the performance of 5 large language models in detecting surgical site infections (SSIs), compared with ...

Molecular diagnostics in clinical microbiology: Advances, applications, and future directions.

Molecular diagnostics have transformed clinical microbiology by enabling rapid, accurate, and highly sensitive detection of infectious agents, signifi...

Omics-based computational approaches for biomarker identification, prediction, and treatment of Long COVID.

Long COVID, or post-acute sequelae of COVID-19 (PASC), is a major global health problem, with cumulative estimates suggesting that around 400 million ...

Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients.

OBJECTIVE: To develop and validate a machine learning model for postoperative sepsis in critically ill traumatic spinal injury (TSI) patients, a frequ...

Targeting the DYNLL2-PAK1 axis inhibits caspase-11-dependent pyroptosis to alleviate sepsis.

Sepsis, a life-threatening syndrome caused by dysregulated host responses to infection, lacks effective therapeutic strategies due to its complex immu...

Profiling the Braak progression in Parkinson's disease: a transcriptomics and ML driven identification of progressive biomarker and prognostic ceRNA signature.

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is marked by dopaminergic neuron loss and α-synuclein aggregation. Alt...

Artificial Intelligence and Machine Learning in Health Care: Current Capabilities, Emerging Potential, and Future Directions.

This article reviews how artificial intelligence and machine learning are reshaping health care. After a historical overview and glossary, we explore ...

A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment.

As the reliability of nontreponemal tests for evaluating syphilis treatment efficacy is increasingly questioned, we propose an optimized approach usin...

Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning.

Understanding watershed emerging contaminants (ECs) transport is vital for pollution control but challenging due to complex land-climate interactions ...

Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease.

OBJECTIVES: Exclusive enteral nutrition (EEN) is a first-line treatment for induction of remission in luminal pediatric Crohn disease (pCD). However, ...

Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery.

OBJECTIVE: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. DES...

Recent advances in the management of skin and soft tissue infections.

PURPOSE OF REVIEW: Skin and soft tissue infections (SSTIs) are one of the most frequent infections that need management either in outpatient setting o...

Combatting antimicrobial-resistant Neisseria gonorrhoeae: new antibiotics and the pipeline of antigonococcal therapeutics.

PURPOSE OF REVIEW: Gonorrhea is the second most prevalent bacterial sexually transmitted infection globally. The proliferation of antimicrobial-resist...

Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.

INTRODUCTION: In the Movement Disorder Society criteria for the diagnosis of Parkinson's disease (PD), evaluation of the presynaptic dopamine system s...

Regulatory mechanism of the oprM gene in colistin resistance of acinetobacter baumannii.

BACKGROUND: Colistin resistance in Acinetobacter baumannii (AB) is a serious clinical concern. This study aimed to identify resistance-related genes a...

Attention-based deep learning for immunoglobulin typing from electrophoresis and laboratory data.

BACKGROUND: Interpretation of immunotyping results from serum protein electrophoresis (SPE) remains labor-intensive and subject to inter-observer vari...