AIMC Topic: Salmonella Infections

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Construction of a Minimal Sensor Array Using Fingerprint Protein Corona on Nanostars for Detecting Protein Isoforms and Disease States.

ACS nano
Signature-based protein detection coupled with machine learning algorithms has revolutionized traditional sensing methods, providing rapid, inexpensive, and selectivity-driven detection without the use of specialized equipment. This strategy leverage...

Global genomic survey of Kentucky: discovery of a chromosomeborne and the emergence of ST314, an MDR clone mediated by the IncR plasmid.

Emerging microbes & infections
Antimicrobial resistance (AMR) in enterica serotype Kentucky ( Kentucky) is a global challenge, with increasing resistance to cephalosporins, ciprofloxacin, and carbapenems significantly limiting treatment strategies, yet its worldwide dissemination...

Automated identification of serotype using MALDI-TOF mass spectrometry and machine learning techniques.

Journal of clinical microbiology
UNLABELLED: serotyping is essential for epidemiological studies and clinical treatment guidance. However, traditional serological agglutination methods are time-consuming, technically complex, and difficult to adopt at scale. Matrix-assisted laser d...

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

Frontiers in cellular and infection microbiology
INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial diseases, particularly for Salmonella infections which remain a significant global health challenge...

Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study.

BMC infectious diseases
BACKGROUND: Salmonella typhimurium infection is a considerable global health concern, particularly in children, where it often leads to persistent diarrhea. This condition can result in severe health complications including malnutrition and cognitive...

Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.

mBio
UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in computation. The unprecedented resolution of whole-genome typing is achieved by genotyping the vari...

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

Nature communications
Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmon...

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Veterinary research
Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited dat...

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp.

Journal of microbiological methods
Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid d...

Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.

World journal of microbiology & biotechnology
Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM c...