Bacterial Infection

Classification of bacterial images obtained optically using some pre-trained models.

Rapid and accurate detection of water-borne bacteria is critical for safeguarding public health and preventing the spread of infections. Conventional bacterial identification techniques are often time-consuming, resource-intensive, and require specialized personnel, limiting their suitability for automated water-quality monitoring. This study presents an optical detection and classification framew...

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...

Exploring the gut-lung axis in post-liver transplant acute lung injury: A multi-omics approach.

Acute lung injury (ALI) is a significant post-operative complication of liver transplant (LT), with mounting evidence suggesting a role for the gut-lu...

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...

Ceramsite-mediated enrichment of functional bacteria in biofilters: a novel strategy for simultaneous ammonia and hydrogen sulfide removal in livestock odor control.

Livestock farming odors, particularly those caused by ammonia and hydrogen sulfide, pose significant environmental and public health challenges. This ...

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) ...

High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.

The human gut microbiome plays a vital role in regulating host physiological functions and influencing the pharmacokinetics of interventions, particul...

A pH-Responsive Antimicrobial Peptide Coating with Efficient Antibacterial Activity and Preserved Oral Microbiota Homeostasis during Clear Aligner Treatment.

Bacterial adhesion and biofilm formation pose significant clinical challenges in clear aligner therapy, adversely affecting both oral health and aesth...

Computational approaches for identifying quorum sensing inhibitors.

Quorum sensing (QS) is a critical cell-to-cell communication mechanism employed by microorganisms to coordinate their collective behaviors, including ...

Quantitative RNA pseudouridine landscape reveals dynamic modification patterns and evolutionary conservation across bacterial species.

Pseudouridine (Ψ) modifications are the most abundant RNA modifications; however, their distribution and functional significance in bacteria remain la...

Intracellular acidification by microbiota-derived valeric acid facilitates trans-kingdom ecology limiting Candida parapsilosis colonization.

In hematopoietic cell transplant (HCT) patients, intestinal Candida parapsilosis expansion and translocation can cause life-threatening candidemia, ye...

Impact of nanocarrier mediated drug delivery systems: a deep dive into therapeutics and clinical management of breast cancer.

Breast cancer (BC) is the most frequently diagnosed malignancy and the leading cause of cancer-related death among women worldwide. The therapeutic li...

Genomic and socioeconomic drivers of antimicrobial resistance forecast to 2050.

Antimicrobial resistance (AMR) is rising worldwide, and a better understanding of the genetic and socioeconomic determinants tied to it may establish ...

De Novo Design of Membrane-Targeting Antimicrobial Peptides Against Gram-Negative Bacteria Using a Generative Artificial Intelligence Framework.

Antimicrobial resistance (AMR), particularly Gram-negative bacteria, poses significant challenges due to their robust outer membranes limiting antibio...

Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.

Antimicrobial resistance (AMR) is a significant global health threat. Recent studies have shown that combining MALDI-TOF mass spectrometry with machin...

Identification of Key Microbial Signatures Associated With Breast Cancer: Machine Learning-Based Approaches Using Gut Microbiome Data.

BACKGROUND: Breast cancer (BC) is one of the most common types of cancer incidence and mortality rates among women all over the world. Limitations in ...

Data-Driven Prediction of Microbiological Quality in Oregano-Active Packaged Gilthead Sea Bream Fillets using Non-Invasive Spectroscopy and Machine Learning.

This study investigated the potential of FTIR spectroscopy and multispectral imaging (MSI) combined with machine learning to monitor microbiological q...

Overcoming resolution constraints in automated colony counting via a high-performance deep learning framework using SAHI.

Lightweight models perform poorly in bacterial colony counting when high-resolution Petri dish images are downscaled to 640 × 640 pixels. This study a...

Predicting Bacterial Radiation Resistance Through Identifying Novel Genomic Features.

Radiation resistance in bacteria is a critical trait with implications for biotechnology, medicine and environmental science. Deinococcus species poss...

Machine learning-based discovery of GW3965 as a therapeutic compound against invasive emm92-type group A Streptococcus.

A multi-drug resistant emm92-type strain of group A Streptococcus (GAS) has emerged as an important causative agent of invasive infections-particularl...