The application of AI (Artificial Intelligence) in emergency medicine helps significantly improve the quality of diagnostics under limitations of resources and time constraints in emergency cases. We have designed a comprehensive AI-based diagnostic and treatment plan decision-support system for emergency medicine by integrating the available LLMs (Large Language Models), like ChatGPT, Gemini, Claude, and others, and tuning them up with additional training on actual emergency cases. There is a special focus on early detection of life-threatening and time-sensitive diseases like sepsis, stroke, and heart attack, which are the major causes of death in emergency medicine. Additional training was conducted on a total of 600 cases (300 sepsis; 300 non-sepsis). The collective capability of the integrated LLMs is much stronger than each individual engine. Emergency cases can be predicted based on information from multiple sensors and streaming sources combining traditional IT (Information Technology) infrastructure with Internet of Things (IoT) schemes. Medical personnel compare and validate the AI models used in this work.

Salmonellosis is one of the most common foodborne diseases worldwide, with the ability to infect humans and animals. Antimicrobial resistance (AMR) and, particularly, multidrug resistance (MDR) among Salmonella enterica poses a risk to human health. Antimicrobial use (AMU) regulations in livestock have been implemented to reduce AMR and MDR in foodborne pathogens. In this study, we used an integrated machine learning approach to investigate Salmonella AMR and MDR patterns before and after the implementation of AMU restrictions in agriculture in the United States. For this purpose, Salmonella isolates from cattle in the National Antimicrobial Resistance Monitoring System (NARMS) dataset were analysed using three descriptive models consisting of hierarchical clustering, network analysis, and association rule mining. The analysis showed the impact of the United States' 2012 extra-label cephalosporin regulations on AMR trends and revealed a distinctive MDR pattern in the Dublin serotype. The results also indicated that each descriptive model provides insights on a specific aspect of resistance patterns and, therefore, combining these approaches make it possible to gain a deeper understanding of AMR.

Background: Rett Syndrome (RTT) is a rare, neurodevelopmental disorder characterised by a range of problematic symptoms. There is yet to be a robust instrument to adequately capture the range of disease severity across the lifespan. In this study, we aimed to develop and assess the validity of an RTT-specific electronic Observer Reported Outcome (eObsRO), the Multi-System Profile of Symptoms Scale (MPSS). Methods: The study was conducted in two phases. Phase 1 consisted of a systematic literature review, focus groups, expert feedback, and a pilot test of the new scale. Modifications were made based on preliminary analysis and feedback collected in the pilot phase. Phase 2 consisted of the validation of the questionnaire based on two samples (Sample 1, n = 18; Sample 2, n = 106). Participants were all parents or caregivers of individuals with RTT. Results: The MPSS consists of 12 validated sub-scales (mental health problems, autonomic problems, cardiac problems, communication problems, problems in social behaviour, problems in engagement, gastrointestinal problems, problems in motor skills, neurological problems, orofacial problems, respiratory problems, and sleep problems), which explore symptom frequency in the past month and a supplement to the scale consisting of five sub-scales (sensory problems, immune dysfunction and infection, endocrine problems, skeletal problems, and dermatological problems), which is designed to capture symptom changes over a longer time period. The frequency of symptoms was rated on a 10-point slider scale, which then was automatically transformed into a 0 to 5 Likert score. All 12 sub-scales showed strong internal consistency (α ≥ 0.700) and good stability, ranging from 0.707 to 0.913. Pearson’s correlation showed a statistically significant (r = 0.649) correlation between the MPSS and the Rett Syndrome Behaviour Questionnaire (RSBQ) total score and significant correlations between sub-scales with items that were presented in both the MPSS and RSBQ. Conclusions: The MPSS is a psychometrically validated eObsRO using the HealthTracker^TM platform and has the potential to be used in clinical trials.

This study aims to assess the antimicrobial resistance (AMR) trends among Escherichia coli isolated from cats between 2008 and 2022, utilizing MIC data, within a one-health framework. The study analyzed MIC results from 1,477 feline E coli isolates that were obtained from samples submitted to the Cornell University Animal Health Diagnostic Center, primarily from the northeastern US. MIC values were categorized as susceptible or not susceptible using the Clinical and Laboratory Standards Institute breakpoints. Multidrug resistance (MDR) was analyzed using a Poisson regression model. Additionally, accelerated failure time models were employed to analyze MIC values. Out of the 1,477 E coli isolates examined, 739 (50%) showed susceptibility to all tested antimicrobials. Among the tested antimicrobials, cefazolin (69%) and ampicillin (74% for urinary tract isolates) exhibited the lowest susceptibility. Overall, 15% of isolates were not susceptible to cefovecin. E coli isolates were highly susceptible (> 95%) to antibiotics typically reserved for human use. Almost one-third of the isolates were classified as MDR, with nonurinary isolates more likely to exhibit an MDR pattern. A decrease in MICs for fluoroquinolones and gentamicin in recent years was identified. However, MICs for cephalexin increased from 2016 to 2022 and cefovecin from 2012 to 2019. This study highlights the challenge of AMR in feline medicine, emphasizing the importance of responsible antimicrobial use and surveillance to address E coli AMR. The related Currents in One Health by Cazer et al, JAVMA, December 2023, addresses additional feline antimicrobial stewardship topics. This study aims to assess the antimicrobial resistance (AMR) trends among Escherichia coli isolated from cats between 2008 and 2022, utilizing MIC data, within a one-health framework. The study analyzed MIC results from 1,477 feline E coli isolates that were obtained from samples submitted to the Cornell University Animal Health Diagnostic Center, primarily from the northeastern US. MIC values were categorized as susceptible or not susceptible using the Clinical and Laboratory Standards Institute breakpoints. Multidrug resistance (MDR) was analyzed using a Poisson regression model. Additionally, accelerated failure time models were employed to analyze MIC values. Out of the 1,477 E coli isolates examined, 739 (50%) showed susceptibility to all tested antimicrobials. Among the tested antimicrobials, cefazolin (69%) and ampicillin (74% for urinary tract isolates) exhibited the lowest susceptibility. Overall, 15% of isolates were not susceptible to cefovecin. E coli isolates were highly susceptible (> 95%) to antibiotics typically reserved for human use. Almost one-third of the isolates were classified as MDR, with nonurinary isolates more likely to exhibit an MDR pattern. A decrease in MICs for fluoroquinolones and gentamicin in recent years was identified. However, MICs for cephalexin increased from 2016 to 2022 and cefovecin from 2012 to 2019. This study highlights the challenge of AMR in feline medicine, emphasizing the importance of responsible antimicrobial use and surveillance to address E coli AMR. The related Currents in One Health by Cazer et al, JAVMA, December 2023, addresses additional feline antimicrobial stewardship topics.

OBJECTIVE: To investigate (1) variables associated with the likelihood of obtaining a positive culture, (2) commonly isolated microorganisms, and (3) antimicrobial resistance patterns of isolates from horses with presumptive synovial sepsis. SAMPLES: Synovial fluid, synovium, and bone samples from equine cases with presumptive synovial sepsis submitted to the Cornell University Animal Health Diagnostic Center from 2000 to 2020 for microbial culture and antimicrobial sensitivity testing. PROCEDURES: Univariable and multivariable analyses were performed to determine the effect of variables on the likelihood of positive culture. Frequency distributions for isolated organisms and antimicrobial resistance were generated. Multidrug resistance patterns and associations were assessed with association rule mining. RESULTS: The positive culture rate for all samples was 37.4%, while the positive culture rate among samples confirmed to be septic by a combination of clinical pathological variables and case details was 43%. Blood culture vial submissions were 1.7 times more likely to yield a positive culture compared to samples submitted in a serum tube. Structure sampled, tissue submitted, and horse age were associated with a positive culture. Staphylococcus spp (23.7%), Streptococcus spp (22.4%), and Enterococcus spp (9.67%) were commonly isolated. Multidrug resistance prevalence decreased from 92% (2000 to 2009) to 76% (2010 to 2020) of gram-negative isolates and 60% (2000 to 2009) to 52% (2010 to 2020) of gram-positive isolates. CLINICAL RELEVANCE: The positive culture rate from synovial fluid submissions with traditional sampling and culture methods remains low and may be optimized by submitting samples in blood culture vials. Overall, antimicrobial resistance was frequently observed but did not increase from the first to second decade for most genera.