Sepsis

Machine Learning Prediction of Analyte-Induced Fluorescence Perturbations in DNA-Functionalized Carbon Nanotubes.

Single-walled carbon nanotubes (SWCNTs) functionalized with single-stranded DNAs can function as near-infrared nanosensors for molecular analytes. However, predicting which analytes elicit strong optical responses for specific nanosensors remains challenging. We developed machine learning (ML) models to predict analyte-induced fluorescence changes in a DNA-SWCNT dopamine nanosensor. Using a data s...

Mechanistic insights into sulfamethoxazole removal in activated sludge systems through machine learning and microcosm experiments.

Antibiotic residues in wastewater pose serious ecological and public health risks and may even induce the more severe threat of antimicrobial resistance (AMR). The activated sludge (AS) processes, the backbone of global wastewater treatment, play a central role in mitigating these risks. However, antibiotic removal in AS systems remains highly variable and mechanistically unclear. Here, sulfametho...

Integrating Mendelian randomization and multi-omics analysis unravels gut microbiota-driven metabolic mechanisms in sepsis and identifies diagnostic biomarkers through experimental validation.

Sepsis, a life-threatening systemic inflammatory syndrome, remains a leading cause of global mortality due to its complex pathophysiology and the lack...

Using the Electronic Health Record and Artificial Intelligence to Guide Clinical Decision-Making.

Electronic medical records (EMR) have transformed how clinical information is documented, shared, and utilized over the past 60 years, and the additio...

Antimicrobial peptides-based strategies at the frontline in battling the escalating menace of methicillin-resistant Staphylococcus aureus biofilms.

Antimicrobial resistance (AMR), particularly in methicillin-resistant Staphylococcus aureus (MRSA), continues to threaten global health due to its mul...

Smart three-in-one detection platform: Chemical sensing, image recognition, and machine learning for rapid identification of tetracycline antibiotics.

The hidden allergic reactions and cumulative toxic effects caused by antibiotic residues in aquatic products have become significant public health con...

Shared inflammation in takotsubo syndrome and sepsis: Lipocalin-2 mediates neutrophil infiltration and cardiac damage.

BACKGROUND: Takotsubo syndrome (TTS) and sepsis often co-occur with poor outcomes, yet their underlying molecular mechanisms remain to be elucidated. ...

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or spec...

Development and validation of an explainable prediction model for schistosomiasis seropositivity: a population-based screening study in Hunan Province, China.

Early identification of people at risk of schistosomiasis infection is critical to interrupting disease transmission. We develop and validate an expla...

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in...

Morphological regulation of filamentous fungi improves industrial production.

Filamentous fungi have emerged as ideal chassis cells for high-value products such as industrial enzymes, therapeutic proteins, and antibiotics, due t...

A New Proposed Approach to Categorize Sepsis Sub-phenotypes Based on Combined Trajectories of Perfusion Index and Central Venous Oxygen Saturation.

BACKGROUND: Circulatory disturbances in sepsis are heterogeneous but may manifest as a mismatch between peripheral perfusion and tissue oxygenation. P...

Beyond gas bubbles: AI analysis of the "bubble bed" microenvironment improves diagnosis of infected abdominal collections.

BACKGROUND: Accurate diagnosis of infected intra-abdominal fluid collections (IAFCs) is challenging, as the conventional "gas bubble sign" on computed...

The role of digital tools and artificial intelligence in supporting antimicrobial stewardship: Study protocol for a systematic review.

INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobia...

The Future of Antibiotics and Artificial Intelligence: Some Thoughts from Discovery to Bedside.

Antibiotic discovery and antibiotic prescribing represent two domains that both stand to benefit from artificial intelligence (AI)-driven progress in ...

Acute kidney injury prediction and prognostication using machine learning.

Acute Kidney Injury (AKI) is a major health concern with high costs and poor outcomes, partly due to late diagnosis. This paper reviews the applicatio...

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study.

BACKGROUND: Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

Widely targeted metabolomics and machine learning identify succinate as a key metabolite in sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive im...

Breaking resistance with machine and deep learning: A computational intelligence hunt for AmvR (TetR) inhibitors in Acinetobacterbaumannii.

Multidrug-resistant Acinetobacter baumannii infections have driven the development of innovative therapeutic approaches to address global challenges. ...