Evaluation of multinomial logistic regression models for predicting causative pathogens of food poisoning cases

Evaluation of a Commercial Pregnancy Test Using Blood or Plasma Samples in High-Producing Dairy Cows

This study evaluated a commercial pregnancy-associated glycoproteins (PAGs)-based pregnancy test using whole blood or plasma samples during early pregnancy (28-55 days of gestation) in high-producing dairy cows. Transrectal ultrasonography was used as the gold standard method. The study population constituted of 284 cows. False positive diagnoses were recorded from Day 60 to 89 and from Day 60 to 99 postpartum in blood and plasma samples, respectively. In early pregnancy screening, correct positive diagnoses were recorded in 75% and 100% of blood and plasma samples, respectively. High milk production was associated with negative results in blood samples and with the lowest test line intensity in plasma samples. False positive or negative diagnoses were recorded in 0% of both types of samples in cows previously diagnosed as pregnant and showing signs of estrus. In conclusion, the use of plasma was more effective than the use of blood in early pregnancy diagnosis. In cows previously diagnosed as pregnant and showing signs of estrus, both types of samples showed the same results. Because of large individual variations, normal single pregnancies could not be differentiated from twin pregnancies, from pregnancies with a recently dead conceptus, or from pregnancies that experienced subsequent pregnancy loss.

A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media

Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak.

Quantum dot biosensor combined with antibody and aptamer for tracing food-borne pathogens

Due to the increasing number of food-borne diseases, more attention is being paid to food safety. Food-borne pathogens are the main cause of food-borne diseases, which seriously endanger human health, so it is necessary to detect and control them. Traditional detection methods cannot meet the requirements of rapid detection of food due to many shortcomings, such as being time-consuming, laborious or requiring expensive instrumentation. Quantum dots have become a promising nanotechnology in pathogens tracking and detection because of their excellent optical properties. New biosensor detection methods based on quantum dots are have been gradually developed due to their high sensitivity and high specificity. In this review, we summarize the different characteristics of quantum dots synthesized by carbon, heavy metals and composite materials firstly. Then, attention is paid to the principles, advantages and limitations of the quantum dots biosensor with antibodies and aptamers as recognition elements for recognition and capture of food-borne pathogens. Finally, the great potential of quantum dots in pathogen detection is summarized.

Electrochemical Biosensors for Detection of Foodborne Pathogens

Foodborne safety has become a global public health problem in both developed and developing countries. The rapid and precise monitoring and detection of foodborne pathogens has generated a strong interest by researchers in order to control and prevent human foodborne infections. Traditional methods for the detection of foodborne pathogens are often time-consuming, laborious, expensive, and unable to satisfy the demands of rapid food testing. Owing to the advantages of simplicity, real-time analysis, high sensitivity, miniaturization, rapid detection time, and low cost, electrochemical biosensing technology is more and more widely used in determination of foodborne pathogens. Here, we summarize recent developments in electrochemical biosensing technologies used to detect common foodborne pathogens. Additionally, we discuss research challenges and future prospects for this field of study.