Sepsis

Infusion of active compound into sliced button mushrooms through vacuum impregnation to improve functionality: Comparing response surface methodology and artificial neural network.

The present study explores the infusion of active compounds (ascorbic acid and calcium lactate) into sliced button mushrooms (Agaricus bisporus) to increase the nutritional value and reduce the browning effect of sliced mushrooms using the vacuum impregnation (VI) technique. The aim was to functionalize the vacuum-infused sliced mushrooms and evaluate the physicochemical properties of button mushr...

Assessing gait dysfunction severity in Parkinson's Disease using 2-Stream Spatial-Temporal Neural Network.

Parkinson's Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life for millions of people worldwide. PD primarily impacts dopaminergic neurons in the brain's substantia nigra, resulting in dopamine deficiency and gait impairments such as bradykinesia and rigidity. Currently, several well-established tools, such as the Movement Disorder Society-Unified Parkinson's Dis...

Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliabl...

Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as ...

Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.

Salmonella is a foodborne pathogen that causes salmonellosis, of which retail chicken meat is a major source. However, the prevalence of Salmonella in...

A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria.

Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, wit...

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition...

Sepsis mortality prediction with Machine Learning Tecniques.

OBJECTIVE: To develop a sepsis death classification model based on machine learning techniques for patients admitted to the Intensive Care Unit (ICU).

Occurrence and Distribution of Antibacterial Quaternary Ammonium Compounds in Chinese Estuaries Revealed by Machine Learning-Assisted Mass Spectrometric Analysis.

Antimicrobial resistance (AMR) undermines the United Nations Sustainable Development Goals of good health and well-being. Antibiotics are known to exa...

Deep-learning-enabled antibiotic discovery through molecular de-extinction.

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here w...

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative o...

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach.

OBJECTIVES: This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a...

TrueTH: A user-friendly deep learning approach for robust dopaminergic neuron detection.

Parkinson's disease (PD) entails the progressive loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc), leading to movement-re...

Machine Learning-Based Prediction of Helicobacter pylori Infection Study in Adults.

BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. pylori is important. Artificial intelligence has b...

Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.

Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied a...

Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients w...

Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.

Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in human. In recent years, an increasing number of st...

Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network.

In this study, we present a novel approach for predicting interventions for patients in the intensive care unit using a multivariate time series graph...

Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units.

OBJECTIVES: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its o...

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobia...