Sepsis

Machine Learning Approaches Using High-Throughput Profiling Data for Antibiotic Discovery.

Antibiotic-resistant bacterial infections continue to increase globally, creating an urgent need for new antibacterials with novel mechanisms of action. Early stages of antibiotic discovery are often limited by the difficulty of identifying compounds that act through previously unrecognized pathways and by challenges in determining their mechanisms. Machine learning (ML) integrated with high-throu...

Dynamics of botnet propagation model in complex networks considering hybrid method for botnet detection.

In this paper, a dynamic epidemic model of botnet attack propagation in scale-free networks is introduced based on the epidemic model. The proposed attack propagation model is based on the Susceptible-Exposure-Infected-Improved-Vaccinated-Recovery (SEIRVS) epidemic model. Here, an Intrusion Detection System (IDS) for botnet attack detection is also presented. This method is based on a combination ...

Development and interpretability of a machine learning-derived model for the identification of latent prostate cancer in patients initially diagnosed with benign prostatic hyperplasia: a retrospective cohort study.

BACKGROUND: Prostate cancer (PCa) is characterized by pronounced intratumoral heterogeneity and multifocality, presenting ongoing challenges for early...

A deep learning system for bacterial identification and resistance prediction from MALDI-TOF data.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) data contain underutilized information beyond species id...

Comparative metabolomics of resistant and susceptible pecan leaflets uncovers early defense responses to scab infection.

BACKGROUND: Pecan scab (Venturia effusa) is a devastating fungal disease affecting commercial pecan production. Developing resistant cultivars remains...

Machine learning methods identified cellular senescence-related hub molecules in sepsis-induced acute respiratory distress syndrome (ARDS) and their upstream regulatory network.

BACKGROUND: Sepsis-induced ARDS demonstrated greater severity and higher mortality compared to ARDS triggered by other factors. In this article, we co...

An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia.

The aim of this study is to develop and validate a machine learning-based predictive model to assess the risk of acquired bloodstream infection (BSI) ...

Association of Staphylococcus aureus bloodstream infection with 7-day incident delirium in critically Ill adults: a propensity-weighted competing risk analysis.

BACKGROUND: Delirium is a frequent manifestation of acute brain dysfunction in critically ill patients with bloodstream infections (BSI). While the as...

Mitophagy-mediated immune evasion: Shared strategies of pathogens.

Mitophagy selectively eliminates dysfunctional mitochondria, playing a pivotal role in mitochondrial quality control and cellular homeostasis. Emergin...

End to End Proximal Anastomosis is the Sole Modifiable Variable in Risk Score for Loss of Patency of Aortobifemoral Artery Bypass After Index Operative Hospitalization.

OBJECTIVE: The purpose of this study was to create a risk score for loss of aorto-bifemoral artery bypass (ABF) patency utilizing preoperative, periop...

Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.

Sepsis is a high-burden, highly heterogeneous clinical challenge that affects up to 30% of ICU patients. Reliable early prediction is essential for ti...

Prediction of delirium in trauma patients using interpretable machine learning.

This study aimed to identify key risk factors for delirium in trauma patients and to develop an interpretable machine learning model using routinely a...

Longitudinal multispectral image dataset for ToBRFV disease detection in tomato and pepper plants.

ToBRFV is a major threat to tomato and pepper crops because it spreads quickly and survives for a long time in the environment. Since there are few wa...

An AI-driven closed-loop framework: A novel enzymatic biochar enabling near-complete degradation of biodegradable plastics.

The anaerobic co-digestion (AcoD) of food waste (FW) and biodegradable plastics (BPs) is a promising waste-to-energy strategy, yet it remains severely...

Environmental conditions and animal hosts shape the spatial transmission risks of echinococcosis in China.

Echinococcosis poses a major public-health challenge and substantial socioeconomic burdens in pastoral regions. Yet the spatial transmission risks in ...

Machine Learning-Driven Parallel XGBoost Modeling of Astaxanthin Production by Haematococcus pluvialis in Wastewater Contaminated with Antibiotics and Microplastics.

Microalgae can produce high value-added products in wastewater (WW). Haematococcus pluvialis is a microalga and can synthesize high amounts of astaxan...

Survival Benefit Analysis of Beta-Blockers in Patients with Sepsis-induced TnT-positive myocardial injury Across Different Clinical Subtypes of Sepsis: A Retrospective Study Based on the MIMIC-IV Database.

OBJECTIVE: To explore the survival benefits of beta-blockers in patients with sepsis-induced TnT-positive myocardial injury across different clinical ...

Determining the relative importance of risk and protective factors for adjustment disorder symptoms during the COVID-19 pandemic by mixed-effects random forests.

BACKGROUND: The COVID-19 pandemic exposed individuals to numerous psychosocial and health-related stressors associated with adjustment disorder (AjD) ...

Tuberculosis in households with infectious cases in Kampala city: Harnessing health data science for new insights on an ancient disease with persistent, unresolved problems.

Tuberculosis (TB) is prevalent in Uganda and overlaps with a high rate of HIV/TB coinfection. While nearly all hospital-based TB cases in Kampala, the...

Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data.

BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is currently a lack of precise early prediction models. T...