Universal precautions

Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform.

The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. Here, we introduce a label-free plasmonic biosensing method based on the thickness-sensitive plasmonic coupling, combined with supervised deep learning ...

A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori.

Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, employing different training sets of 13,638 molecules for pre-training and fine-tuning, was aided in predicting and exploring novel molecules against ...

Artificial intelligence-assisted identification and retrieval of chronic hepatitis C patients lost to follow-up in the health area of Pontevedra and O Salnés (Spain).

OBJECTIVE: Direct-acting antivirals (DAAs) to treat hepatitis C virus (HCV) infection offer an opportunity to eliminate the disease. This study aimed ...

Machine learning-enabled colorimetric sensors for foodborne pathogen detection.

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One ...

RAIN: machine learning-based identification for HIV-1 bNAbs.

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importan...

Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation.

HIV/AIDS remains one of the world's most significant public health and economic challenges, with approximately 36 million people currently living with...

Machine Learning-Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis.

BACKGROUND AND AIMS: The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for asses...

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...

Identification of hepatic steatosis among persons with and without HIV using natural language processing.

BACKGROUND: Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identi...

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance...

A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H).

Histological assessment of autoimmune hepatitis (AIH) is challenging. As one of the possible results of these challenges, nonclassical features such a...

Development of a real-time cattle lameness detection system using a single side-view camera.

Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracki...

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium.

Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit o...

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...

Machine learning in infectious diseases: potential applications and limitations.

Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learn...

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus.

This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soi...

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...

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study.

Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims ...

The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting.

BACKGROUND: Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseas...

Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions.

Timely prediction of pathogen is important key factor to reduce the quality and yield losses. Wheat is major crop in northern part of India. In Punjab...