Sepsis

Predictive modeling of ARDS mortality integrating biomarker/cytokine, clinical and metabolomic data.

Acute Respiratory Distress Syndrome (ARDS), characterized by the rapid onset of respiratory failure and mortality rates of ∼40%, remains a significant challenge in critical care medicine. Despite advances in supportive care, accurate prediction of ARDS mortality remains challenging, resulting in delayed delivery of targeted interventions and effective disease management. Traditional critical illne...

Machine Learning-Based Prediction to Support ICU Admission Decision Making among Very Old Patients with Respiratory Infections: A Proof of Concept on a Nationwide Population-Based Cohort Study.

BackgroundIntensive care unit (ICU) hospitalizations of very old patients with acute respiratory infection have risen. The decision-making process for ICU admission is multifaceted, and the prediction of long-term survival outcome is an important component. We hypothesized that data-driven algorithms could build long-term prediction by examining massive real-life data. Our objective was to assess ...

Identification and analysis of diagnostic markers related to lactate metabolism in myocardial infarction.

Lactate metabolism is implicated in myocardial infarction (MI), yet the underlying mechanisms are not fully understood. Identifying lactate metabolism...

Speech signals-based Parkinson's disease diagnosis using hybrid autoencoder-LSTM models.

Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is ...

Time-series deep learning and conformal prediction for improved sepsis diagnosis in primarily Non-ICU hospitalized patients.

PURPOSE: Sepsis, a life-threatening condition from an uncontrolled immune response to infection, is a leading cause of in-hospital mortality. Early de...

Artificial intelligence based malignant lymphoma type prediction using enhanced super resolution image and hybrid feature extraction algorithm.

In the medical field, the most common and frequent type of blood cancer is lymphoma. Accurately predicting and early response to lymphoma treatment wi...

Mesenchymal stem cell transplantation ameliorates inflammation in spinal cord injury by inhibiting lactylation-related genes.

BACKGROUND: The immune microenvironment significantly influences neural regeneration in spinal cord injury (SCI). Lactate activates central nervous sy...

Machine learning-assisted ratiometric fluorescence sensor array for recognition of multiple quinolones antibiotics.

Developing analytical methods for simultaneous detection of multiple antibiotic residues is crucial for environmental protection and human health. In ...

Harnessing advances in mechanisms, detection, and strategies to combat antimicrobial resistance.

Antimicrobial resistance (AMR) is a growing global health crisis, threatening the effectiveness of antibiotics and other antimicrobial agents, leading...

Accelerating antimicrobial stewardship: An AI-CDSS approach to combating multidrug-resistant pathogens in the era of increasing resistance.

OBJECTIVES: The World Health Organization has identified Klebsiella pneumoniae (KP) and Pseudomonas aeruginosa (PA) as significant public health threa...

Discovery of naturally inspired antimicrobial peptides using deep learning.

Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide ...

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery

Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after ...

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery

Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after ...

Development and experimental verification of a prognosis model for hypoxia- and lactate metabolism-associated genes in HNSCC.

Hypoxia and lactate metabolism are both distinctive characteristics of cancerous cells. Head and neck squamous cell carcinoma (HNSCC) is one of the mo...

Artificial intelligence approaches for anti-addiction drug discovery.

Drug addiction remains a complex global public health challenge, with traditional anti-addiction drug discovery hindered by limited efficacy and slow ...

Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models

Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retriev...

ABP-Xplorer: A Machine Learning Approach for Prediction of Antibacterial Peptides Targeting -tRNA-Methyltransferase (TrmD).

(MAB) infections pose a significant treatment challenge due to their intrinsic resistance to antibiotics, requiring prolonged multidrug regimens with...

Decoding the Structure-Activity Relationship of the Dopamine D3 Receptor-Selective Ligands Using Machine and Deep Learning Approaches.

Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia,...

SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes

Dynamic treatment regimes (DTRs) are critical to precision medicine, optimizing long-term outcomes through personalized, real-time decision-making i...

Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists

The study expands the application of scikit-learn-based machine learning (ML) to the prediction of small biomolecule functionalities based on Carbon...