AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Foodborne Diseases

Showing 21 to 30 of 34 articles

Clear Filters

A colorimetric Loop-mediated isothermal amplification (LAMP) assay based on HRP-mimicking molecular beacon for the rapid detection of Vibrio parahaemolyticus.

Biosensors & bioelectronics
In the world wide, food poisoning accidents related to Vibrio spp. are on the rise, even numbers of food poisoning by other foodborne pathogens are decreasing. Therefore, the requirement of the rapid, sensitive and convenient detection method for V. ...

[Tetrodotoxin (TTX) Monitoring of Biological Specimens and Toxin Profile in a Food Poisoning Case Caused by the Scavenging Gastropod Nassarius (Alectrion) glans "Kinshibai"].

Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan
In November 2015, a patient presented with symptoms of toxicity after eating whole boiled samples of the scavenging gastropod Nassarius (Alectrion) glans "Kinshibai" in Nagasaki. This food poisoning case was the third recorded in Japan. The case was ...

Rapid screening and multi-toxin profile confirmation of tetrodotoxins and analogues in human body fluids derived from a puffer fish poisoning incident in New Caledonia.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
In August 2014, a puffer fish poisoning incidence resulting in one fatality was reported in New Caledonia. Although tetrodotoxin (TTX) intoxication was established from the patients' signs and symptoms, the determination of TTX in the patient's urine...

Label-free screening of foodborne Salmonella using surface plasmon resonance imaging.

Analytical and bioanalytical chemistry
It is estimated that 95% of the foodborne infections are caused by 15 major pathogens. Therefore, rapid and effective multiplex screening techniques for these pathogens with improved efficiencies could benefit public health at lower costs. Surface pl...

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal .

Journal of clinical microbiology
Nontyphoidal species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for strains because of surveillance efforts from public health agencie...

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hypers...

Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks.

Applied microbiology and biotechnology
Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology co...