Bacterial Infection

Antimycobacterial and Antifungal Activities of Leaf Extracts From .

The global problem of infectious and deadly diseases caused by microbes such as candida and mycobacteria presents major scientific and medical challenges. Antimicrobial drug resistance is a rapidly growing problem with potentially devastating consequences. Various pathogens can cause skin infections, such as bacteria, fungi, and parasites. Antimicrobial resistance has caused the urgency to seek al...

Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools.

BACKGROUND: Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble. Machine learning tools allow the straightforward in silico identification of non-hemolytic antimicrobial peptides.

Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP.

Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the ...

A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface.

The emergence and rapid spread of multidrug-resistant bacterial strains is a growing concern of public health. Inspired by the natural bactericidal su...

Intelligent identification of foodborne pathogenic bacteria by self-transfer deep learning and ensemble prediction based on single-cell Raman spectrum.

Foodborne pathogenic infections pose a significant threat to human health. Accurate detection of foodborne diseases is essential in preventing disease...

High-content imaging and deep learning-driven detection of infectious bacteria in wounds.

Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h ...

A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy.

Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal stra...

Extending visual range of bacteria with upconversion nanoparticles and constructing NIR-responsive bio-microrobots.

The motility of bacteria is crucial for navigating competitive environments and is closely linked to physiological activities essential for their surv...

Graphene FET biochip on PCB reinforced by machine learning for ultrasensitive parallel detection of multiple antibiotics in water.

Antibiotics like Ciprofloxacin (Cfx), tetracycline (Tet) and Tobramycin (Tob) are commonly used against a broad-spectrum of bacterial infection. Recen...

Neural network procedures for the cholera disease system with public health mediations.

Severe gastrointestinal infections and watery diseases like cholera are still a major worldwide medical concern in the developing nations. A mathemati...

Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic loss...

Lightweight convolutional neural network for chest X-ray images classification.

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a...

Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning.

BACKGROUND: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based me...

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bact...

Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.

Antimicrobial peptides (AMPs) are a promising class of antimicrobial drugs due to their broad-spectrum activity against microorganisms. However, their...

Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review.

Antimicrobial resistance (AMR) is a critical global health challenge, undermining the efficacy of antimicrobial drugs against microorganisms like bact...

Feasibility study of machine learning to explore relationships between antimicrobial resistance and microbial community structure in global wastewater treatment plant sludges.

Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistan...

Bacteriophage-Activated DNAzyme Hydrogels Combined with Machine Learning Enable Point-of-Use Colorimetric Detection of Escherichia coli.

Developing cost-effective, consumer-accessible platforms for point-of-use environmental and clinical pathogen testing is a priority, to reduce relianc...

Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.

BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting dr...

Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria.

Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rap...