Malaria remains a significant public health issue, particularly in regions such as the Korean Demilitarized Zone (DMZ). Effective malaria control and prevention require precise prediction of mosquito density across both monitored and unmonitored areas. This study aimed to develop predictive models to estimate the abundance of malaria vector mosquitoes by integrating meteorological and geographical data. Data from mosquito surveillance sites and NASA MODIS land cover datasets acquired between 2009 and 2022 were utilized. Two predictive models, the Gradient Boosted Model (GBM) and Principal Component Regression (PCR), were employed and evaluated. Model performance was assessed using the coefficient of determination (R²). Results showed that PCR outperformed GBM in predictive accuracy, suggesting that PCR is more robust in handling multicollinearity among variables. However, both models did not show practically-usable level of prediction performance. This study provides a preliminary but foundational framework for extending predictive modeling to broader regions, thereby supporting malaria prevention efforts through improved risk mapping.

This study aimed to address the time, cost, and ethical issues associated with traditional animal experiment-based observational methods by utilizing in silico Physiologically Based Pharmacokinetic modeling to predict veterinary drug residues in livestock products and validate them against observational data. Using PK-sim software, we modeled the physiological conditions of pigs to predict the depletion of ceftiofur and spiramycin. We evaluated the ceftiofur (3 mg, 6 mg) and spiramycin models by comparing them with observational data using residuals, MSE, and R-squared values. Specifically, the R-squared values for the ceftiofur models were all negative, indicating poor predictive power. For Ceftiofur (3 mg), the R-squared value was <0 with MSE of 611.3764, and for Ceftiofur (6 mg), it was <0 with MSE of 2447.982, highlighting significant discrepancies. Similar shortcomings were observed in the spiramycin models, with an R-squared value of <0 . These discrepancies can be attributed to inaccuracies in literature data, limited physicochemical data, inadequate consideration of inter-individual differences, mismatches between experimental and model conditions, and limitations of benchmark observational experiments. This underscores the critical importance of enhancing data quality and refining modeling approaches. Future research should focus on validating in silico techniques across diverse animal models and drugs to broaden their applicability in safety assessments. Ultimately, leveraging in silico techniques is crucial for establishing a scientifically robust safety management system for livestock products, overcoming the constraints of current observational experimental methods.

Myxomatous mitral valve disease (MMVD) in dogs is a heart disease that is characterized by histopathologic changes in cardiomyocytes, which ultimately result in valve degeneration and blood regurgitation due to structural changes in the heart valves. A number of studies have been conducted with the objective of identifying prognostic factors that may influence the prognosis of dogs with MMVD. Nevertheless, there is a paucity of research examining the factors that predict MMVD stage progression as defined by the American College of Veterinary Internal Medicine. The objective of this study was to examine whether there are factors associated with stage progression within one year of diagnosis in dogs diagnosed with subclinical MMVD (stage B1 or B2) using physical examination findings, clinicopathologic biomarkers, and echocardiographic markers. This is a retrospective study of veterinary practice performed at Chungbuk National University Animal Hospital. The electronic medical record of the hospital was searched to obtain clinical records of canine patients diagnosed with subclinical MMVD over an 11-year period. For each patient cohort, a logistic regression analysis was conducted. The variables were initially selected using the backward elimination method, and the optimal logistic regression model was determined by removing the independent variables with the largest variance inflation factor. Among the independent variables examined in this study, heart murmur intensity was identified as a statistically significant predictor of stage progression within one year for subclinical MMVD, a finding that aligns with those of previous studies. No other independent variables were found to be significantly associated with subclinical MMVD stage progression. This is the inaugural exploratory study to concentrate on blood test results, a relatively straightforward and quantifiable test result that can be readily obtained in primary care veterinary clinics, among the factors that may be associated with the progression of subclinical MMVD stages.

Antimicrobial resistance significantly threatens human and animal health globally, with considerable mortality and economic impact. This study investigated antimicrobial usage in small animal clinics in South Korea, focusing on understanding the trends in prescriptions for therapeutic and preventive purposes. Data were collected from 12 small animal clinics that were analyzed for antimicrobial prescriptions from 2018–2020. A comprehensive dataset was used, including patient signalment, clinical notes, and prescription details, and statistically analyzed using SPSS software. The results indicated that most antimicrobials (93.1%) were prescribed for the treatment of infectious diseases, with a smaller portion (6.9%) used for preventive measures, such as surgery. High prescription rates were observed for the treatment of cutaneous and otological diseases, which may reflect common diseases in companion animals. The study highlighted a higher prescription rate for adult age groups, possibly because of the higher prevalence in those groups. Overall, this study provides valuable insights into common prescription patterns in veterinary practice and underscores the need for more stringent antimicrobial stewardship to curb the rise of antimicrobial resistance. This suggests that ongoing surveillance and education on appropriate antimicrobial use are crucial for optimizing treatment outcomes and minimizing the development of resistance.

Swine influenza is a respiratory infectious disease in pigs caused by Orthomyxoviridae influenza virus A. As a multihost pathogen, the virus can infect humans, birds, and pigs and has pandemic potential due to rapid mutation rate. This study investigated the seroprevalence of influenza A antibodies in pigs in Chungbuk Province to overview its temporal and spatial distribution. From March to November 2021, blood samples collected for swine fever and foot-and-mouth disease antibody tests from swine farms located in Cheongju, Jincheon, Jeungpyeong, and Goesan within the jurisdiction of the Chungbuk Animal Health Laboratory were used. Blood samples from both sows and growing pigs were collected. Additionally, three farms participating in the Expendable Disease Guidance Support Project were chosen to investigate the seroprevalence status by parity of sows and age of piglets. A total of 468 sows and 1,519 growing-finishing pigs were employed in this study. The results showed that Jincheon had the highest seropositivity rate, suggesting that more effort should be made in biosecurity to prevent mechanical transmission, given the close proximity of farms. The analysis of antibody levels in farms targeted by the Expendable Disease Guidance Support Project could suggest that once the virus enters a farm, it spreads throughout the entire pig population regardless of age. Farms that were positive in the first half of the year remained positive in 86% of cases in the second half, suggesting continuous infection within the farm unless depopulation or all-in-all-out practices are implemented. Moreover, 67% of farms that were negative in the first half remained negative in the second half, and farms managed by the same person showed identical antibody change patterns, indicating that the swine influenza virus can be transmitted by humans or vehicles. The results highlight the need for further analysis of biosecurity systems and geographical risk factors.

Global concerns have grown regarding emerging infectious diseases (EIDs) caused by previously unknown pathogens. Considering that strengthening surveillance capacity for unknown diseases is one of the core capacities for preparedness and early response to EIDs, identifying areas with poor capacity could be beneficial to prioritize regions for the improvement of surveillance. In this regard, we aimed to develop prediction models to identify high risk areas for low surveillance capacity for unknown diseases in a global scale. Unexplained death events reported between 2015 and 2019 were collected from two internet-based surveillance systems, ProMED-mail and Global Public Health Intelligence Network. From the reports, the number of reported unexplained deaths at the first report and the time gap between death and report were extracted as measures for sensitivity and timeliness of surveillance capacity, respectively. Using geographical locations of the reports and published global scale spatial data, including demographic, socioeconomic, public health and geographical variables, we fitted two boosted regression tree models to predict regions with the low sensitivity and timeliness. The performance of prediction model for the low sensitivity showed moderate validity, but in terms of the model for timeliness, the performance was unreliable. Therefore, we provided predicted risk only for low sensitivity. The mean predicted risks of low sensitivity were, respectively, 45.2%, 37.4%, 12.5%, and 3.0% in low-income, lower middle-income, upper middle-income, and high-income countries. Enhancing surveillance capacity in low-income countries is highly required, given the predicted low level of sensitivity despite the importance of early response.

Swine atrophic rhinitis is a respiratory disease that causes nasal turbinate loss and septal deformation due to Bordetella bronchiseptica and Pasteurella multocida. Turbinate loss facilitates pathogens to infect lungs, which leads to various respiratory diseases and productivity reduction. In this study, descriptive analysis was implemented for atrophic rhinitis and pneumonia. From 6 pig farms shipped to slaughterhouses in Chungbuk province, 20 heads and 20 lungs were collected by each farm from March 2020 to September 2020. Their atrophic rhinitis lesions and lung lesions were scored and blood samples were also collected to test the seroprevalence of several respiratory diseases. Pasteurella multocida from nasal swab was cultured and antibiotic resistance tests were performed. Correlation between atrophic rhinitis scores and lung lesion scores was not found. Abdominal nasal lesions were more severe than dorsal lesions. Differences in lung lesion scores were relatively small between lobes. The score of pneumonia was higher in castrated pigs than in female pigs. There was no relationship between lesion score and seroprevalence of respiratory diseases. Antibiotic resistance levels for Pasteurella multocida differed by farm, and several antibiotics were not effective. The results of this study imply that antimicrobial susceptibility tests are highly recommended before administration.