Infectious Disease

Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.

This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Ente...

A preliminary study on the effects of long-term robot suit exercise training on gait function and quality of life in patients with spinal and bulbar muscular atrophy.

Spinal and bulbar muscular atrophy (SBMA) progressively impairs gait function, resulting in the need for patients to use a wheelchair approximately 20 years after onset. No reports have investigated the effects of long-term exercise training using the Hybrid Assisted Limb (HAL) in patients with multiple SBMA. This study investigated the effects of long-term exercise training using HAL in patients ...

Image cropping for malaria parasite detection on heterogeneous data.

Malaria is a deadly disease of significant concern for the international community. It is an infectious disease caused by a Plasmodium spp. parasite a...

Delineating yeast cleavage and polyadenylation signals using deep learning.

3'-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit...

Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.

BACKGROUND: Sexual and gender minority (SGM) young people are disproportionately affected by HIV in the United States, and substance use is a major dr...

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational mo...

Artificial intelligence and prescription of antibiotic therapy: present and future.

INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exem...

Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.

INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioide...

COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset.

The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. ...

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of myc...

Surface enhanced Raman spectroscopy and machine learning for identification of beta-lactam antibiotics resistance gene fragment in bacterial plasmid.

BACKGROUND: Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative...

MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission...

Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for b...

A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection.

TB/HIV coinfection poses a complex public health challenge. Accurate forecasting of future trends is essential for efficient resource allocation and i...

Prediction of sepsis mortality in ICU patients using machine learning methods.

PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is cruc...

Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies.

Thymidylate kinase (TMK) is a pivotal enzyme in Mycobacterium tuberculosis (Mtb), crucial for phosphorylating thymidine monophosphate (dTMP) to thymid...

Distinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.

Novel artificial intelligence methods can aide in identification of cases of conditions using only unstructured electronic health record data. This gr...

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and...

Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.

Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learnin...

C-reactive protein (CRP) evaluation in human urine using optical sensor supported by machine learning.

The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical di...