Bacterial Infection

Pre-trained molecular representations enable antimicrobial discovery.

The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of new chemical compounds through experimental methods remains time-consuming and costly. While compound-centric deep learning models promise to accelerate this search and prioritization process, current strategies require large amounts of custom traini...

Synergistic effect of horizontal transfer of antibiotic resistance genes between bacteria exposed to microplastics and per/polyfluoroalkyl substances: An explanation from theoretical methods.

Microplastics (MPs) and per/polyfluoroalkyl substances (PFASs), as emerging pollutants widely present in aquatic environments, pose a significant threat to human health through the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Molecular dynamics simulations and machine learning can accurately capture the complex interactions between molecules. This study utilized them to id...

An integrated AI knowledge graph framework of bacterial enzymology and metabolism.

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applicat...

A framework predicting removal efficacy of antibiotic resistance genes during disinfection processes with machine learning.

Disinfection has been applied widely for the removal of antibiotic resistance genes (ARGs) to curb the spread of antibiotic resistance. Quantitative p...

4-Hydroxyboesenbergin B of Alpinia japonica protected gastrointestinal tract by inhibiting vancomycin-resistant enterococcus and balancing intestinal microbiota.

ETHNOPHARMACOLOGICAL RELEVANCE: Alpinia japonica, a traditional herb utilized in Miao medicine in southwestern China, has been employed to alleviate s...

Colonial bacterial memetic algorithm and its application on a darts playing robot.

In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CB...

Machine learning assisted paper-based fluorescent sensor array with metal-doped multicolor carbon quantum dots for identification and inactivation of bacteria.

Bacterial infection is a thorny threat in a variety of fields, including medicine, environment, food, and agriculture. A multifunctional platform that...

Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.

BACKGROUND: Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain ...

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.

Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public ...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infect...

Machine learning analysis of magnetic covalent organic framework based heterostructures extracted intracellular metabolic fingerprint for direct hypervirulent Klebsiella pneumoniae prediction.

Hypervirulent Klebsiella pneumoniae (hvKP), known for its high virulence and epidemic potential, has emerged as a significant global public health thr...

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections.

A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune syste...

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome seq...

Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images.

PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of bacterial and fungal keratitis using different types...

Ratiometric, 3D Fluorescence Spectrum with Abundant Information for Tetracyclines Discrimination via Dual Biomolecules Recognition and Deep Learning.

Tetracyclines are widely used in bacteria infection treatment, while the subtle chemical differences between tetracyclines make it a challenge to accu...

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, in...

A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.

BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to ov...

Aggregation induced emission luminogen bacteria hybrid bionic robot for multimodal phototheranostics and immunotherapy.

Multimodal phototheranostics utilizing single molecules offer a "one-and-done" approach, presenting a convenient and effective strategy for cancer the...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threa...

BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and .

Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at b...