Sepsis

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.

Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) to guide timely treatments. Traditional culture-based AST methods are slow, while existing whole-genome sequencing (WGS)-based models often suffer from overfitting, poor interpretability, and diminished performance on clinical metagenomic data. In th...

SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.

BACKGROUND: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast initialization of an appropriate therapy. Complete blood count data containing information about white blood cells, platelets, hemoglobin, red blood cells, and mean corpuscular volume could serve as early indicators. However, previous approaches are ...

Combating Antimicrobial Resistance: Role of Key Stakeholders with Focus on the Pharmaceutical Sector.

Antimicrobial resistance is a pressing global health threat fueled by a complex interplay of biological, social, and economic factors. Despite widespr...

Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.

Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the ...

Recent advancement in endoscopic diagnosis for risk stratification of gastric cancer.

Approximately 90% of cases of gastric cancer (GC) are caused by Helicobacter pylori infection, and screening esophagogastroduodenoscopy is effective f...

Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data.

BACKGROUND: Preterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of...

Identifying propionate metabolism-related genes as biomarkers of sepsis development and therapeutic targets.

The treatment of sepsis is challenging due to unclear mechanisms. Propionate is increasingly seen as critical to sepsis pathophysiology by bridging gu...

Citywide metagenomic surveillance of food centres reveals local microbial signatures and antibiotic resistance gene enrichment.

The distribution of microorganisms in built environments with high human traffic, such as food centres, can potentially have a significant impact on p...

Metabolic alterations driven by LDHA in CD8 + T cells promote immune evasion and therapy resistance in NSCLC.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Despite advancements in treatment, prognosis for patients wi...

Machine learning approaches to dissect hybrid and vaccine-induced immunity.

BACKGROUND: The spread of SARS-CoV-2 Omicron variant and its subvariants, highly transmissible but responsible of milder disease, has increased unrepo...

The application and discovery of animal models in enterovirus research.

Enterovirus infection remains a significant global public health challenge, causing severe diseases such as hand, foot, and mouth disease (HFMD) and m...

CombiANT reader: Deep learning-based automatic image processing tool to robustly quantify antibiotic interactions.

Antibiotic resistance is a severe danger to human health, and combination therapy with several antibiotics has emerged as a viable treatment option fo...

Assessing changes in whole-brain structural connectivity in the unilateral 6-hydroxydopamine rat model of Parkinson's disease using diffusion imaging and tractography.

Parkinson's disease (PD) is a multifactorial, progressive neurodegenerative disease that has a profound impact on those it afflicts. Its hallmark path...

Multi-component metabolite electrochemical detection and analysis based on machine learning.

Metabolic molecules are highly correlated with various physiological indicators and diseases, so it is particularly important to monitor the levels of...

Machine Learning and DIA Proteomics Reveal New Insights into Carbapenem Resistance Mechanisms in .

The emergence of Carbapenem-resistant (CRKP) represents a major public health concern, primarily driven by its ability to evade a wide range of antib...

Bifunctional Phyto-Synthesized Nano Silver for Mitigating Salinity-Induced Dormancy and Associated Fungal Infections During Seed Germination in Brassica juncea with Integration of Machine Learning-Based Predictive Modeling.

Salinity-induced dormancy associated with fungal infection during germination in plants is the major contributor to abiotic and biotic stress, leading...

Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders.

BACKGROUND: Artificial intelligence (AI)-based systems are receiving increasing attention in the health care sector. While the use of AI is well advan...

Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.

The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, with considerable variability in progression rates...

Optical coherence tomography for early detection of crop infection.

BACKGROUND: Fungal diseases are among the most significant threats to global crop production, often leading to substantial yield losses. Early detecti...

Combination of machine learning and protein‑protein interaction network established one ATM‑DPP4‑TXN ferroptotic diagnostic model with experimental validation.

Ferroptosis and lethal sepsis are interlinked, although this association remains largely unknown to clinical panels. Sepsis is characterized by dysfun...