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ChatGPT was launched by OpenAI in November 2022 and within 2 months it became popular across a wide range of industrial, social, and intellectual contexts including healthcare education. This article reviews the impact of ChatGPT on research and health professions education by identifying the challenges and opportunities in these fields. Additionally, it aims to provide future directions to mitigate the challenges and maximize the benefits of this technology in health professions education. ChatGPT has the potential to revolutionize the field of research and health professions education. However, there is a need to address ethical concerns and limitations such as lack of real-time data, data inaccuracies, biases, plagiarism, and copyright infringement before its implementation. Future research can highlight the ways to mitigate these challenges; establish guidelines and policies; and explore how effectively ChatGPT and other AI tools can be used in the field of research and healthcare professions education., (© The Author(s) 2023. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: To explore veterinary students' perceptions of online anatomy teaching during the COVID-19 pandemic., Materials and Methods: A cross-sectional study was conducted at the School of Veterinary Medicine, where we collected data from year 1 and 2 undergraduate students. A Google Form link to the questionnaire and an introductory message were sent via email to all 68 students who had participated in an online anatomy class between March 2020 and May 2021., Results: The response rate was 82.2% (56/68). Most students were female (71.43%) and between the ages of 18 and 22 years old (83.93%). Overall, 58.1% of the students found it difficult to learn online anatomy, and most (82%) agreed that it was difficult to learn practical topics online. Nevertheless, 58.9% of the responding students recommended the continued use of online strategies in teaching this subject. Students reported that they could communicate with the lecturer and receive feedback. Perceived benefits of online learning included the convenience of attending class at home and having more available time, as well as the availability of prerecorded videos of course topics., Conclusions: During the pandemic, students got the opportunity to attend online classes from home. Students thought that online practical anatomy was less useful, and this is an area where new ideas can be suggested to help students see how anatomical structures work., Competing Interests: There are no conflicts of interest., (Copyright: © Journal of Advanced Veterinary and Animal Research.)

Irrigation with water of poor microbiological quality can elevate levels of bacteria on produce. This study aimed to identify climate and management variables associated with generic Escherichia coli in irrigation water on leafy green produce farms and to measure the prevalence of E. coli O157 and Salmonella spp. in irrigation and non-irrigation water sources on these farms. Water and sediment samples collected from various points along irrigation systems, as well as from streams and ponds on farms on the Central California coast between May 27th, 2008 and October 26th, 2010 were cultured for generic E. coli (MPN/100 mL or cfu 100 g) (n=436), E. coli O157 (n=437), and (n=163) Salmonella. Variables were based on grower's management practices, landscape features in proximity to samples (e.g., distance to roads and ranches/livestock), and climate data accessed from an online database. Negative binomial regression models were constructed to test associations between generic E. coli (MPN/100 mL) in water from farms and variables. Arithmetic mean concentration of E. coli for water, not including those from Moore swabs, and sediment samples, was 7.1×10(2) MPN/100 mL and 1.0×10(4) cfu/100 g, respectively. Matched by collection day, E. coli concentration in sediment (cfu/100 g) was typically 10- to 1000-fold higher than the overlying water (MPN/100 mL) for these irrigation systems. Generic E. coli concentration (MPN/100 mL) increased by 60.1% for each 1m/s increase in wind speed and decreased by 3% for each 10 m increase in the distance between the sample location and rangeland. Moore swabs detected a higher proportion of E. coli O157 (13.8%) positive water samples compared to grab samples (1.8%); 1.7% of sediment samples had detectable levels of this pathogen. Interestingly, season was not significantly associated with E. coli O157 presence in water or sediments from produce farms or water sources with public access. Salmonella was detected in 6% (6/96) water and 4.3% (3/67) sediment samples. Generic E. coli concentration was not significantly associated with the presence of either E. coli O157 or Salmonella in water or sediment samples, suggesting that, for this 2.5-year period and geographical location, generic E. coli would likely be an unreliable indicator bacteria for predicting the presence of these food- and waterborne pathogens in a key produce production environment., (Copyright © 2013 Elsevier B.V. All rights reserved.)

One thousand three hundred and twenty-four adult beef cattle were tested for paratuberculosis using 2 antibody enzyme-linked immunosorbent assays (ELISA), an interferon-gamma (INF-gamma) ELISA, and radiometric bacterial culture of feces from 5 populations. Two populations of cattle (n=226) had data available to calculate a ratio of humoral to cell-mediated immunity based on results from one antibody test and the INF-gamma ELISA. Latent class analysis was used to estimate accuracy of the 4 paratuberculosis assays within a Bayesian framework. Determination of test accuracy and paratuberculosis prevalence in the latent class analysis allowed for estimation of predictive value positive (PVP) functions. The estimated PVP functions were used to iteratively assign paratuberculosis status to sampled cattle. Accuracy of the immunity ratio, an antibody ELISA, and the INF-gamma ELISA were determined for multiple cutoffs based on probabilistically assigned paratuberculosis status. Area under the receiver-operating characteristic (ROC) curves (95% probability interval) were estimated as 0.78 (0.66, 0.89), 0.81 (0.68, 0.92), and 0.59 (0.47, 0.71) for the immunity ratio, antibody ELISA, and INF-gamma ELISA, respectively. The Youden index (sensitivity+specificity-1) peaked at immunity ratios of 0.5 (J=0.48) and 1.0 (J=0.46). Sensitivity and specificity (95% probability interval) at an immunity ratio cutoff of 0.5 were 0.65 (0.44, 0.85) and 0.83 (0.78, 0.88), respectively. Sensitivity and specificity (95% probability interval) at the 1.0 cutoff were 0.55 (0.33, 0.77) and 0.91 (0.87, 0.95), respectively. An immunity ratio could be used to diagnosis paratuberculosis in beef cattle but requires further investigation.