Veterinary Sciences最新文献

Lumpy Skin Disease (LSD) is a transboundary viral disease of wild and domesticated ruminants, and notifiable to the World Organisation for Animal Health (WOAH). LSD has spread rapidly in the cattle population across the African, European and Asian continents since 1929. Following recent outbreaks in the Southeast Asia region, Singapore observed its first occurrence of LSD in a local dairy cattle farm in March 2022. LSD was confirmed in dairy cattle exhibiting clinical signs with quantitative real-time PCR and sequencing with MinION Nanopore. Analyses of the assembled whole viral genomes also revealed high phylogenetic relatedness to LSD recombinant strains. It remains unclear how the virus was introduced into Singapore, given the absence of known vectors and strict import regulations in place. This highlights the importance of biosurveillance, and laboratory diagnostic readiness to manage outbreaks and prevent the spread of transboundary diseases.

Feline herpesvirus type-1 (FHV-1), a double-stranded DNA virus, which is a highly infectious upper respiratory tract infection of felids, particularly in kittens. Droplet digital PCR (ddPCR) provides an absolute quantification method with high sensitivity and accuracy. This study aimed to develop a highly sensitive and accurate ddPCR assay for the detection of FHV-1. We designed primers and a probe targeting the FHV-1 glycoprotein D (gD) gene and evaluated the assay's limit of detection (LOD), sensitivity, repeatability, and specificity in comparison to quantitative real-time PCR (qPCR). The developed ddPCR assay demonstrated a strong linear dynamic range (R² ≥ 0.99) and an exceptionally low LOD of 0.18 copies/μL, which was significantly more sensitive than the method qPCR (LOD ~10 copies/μL). Additionally, the assay exhibited high specificity with no cross-reactivity against other common feline pathogens (feline calicivirus, FCV; feline panleukopenia virus, FPV; feline infectious peritonitis virus, FIPV; Bordetella bronchiseptica and Chlamydia felis) and displayed outstanding repeatability (inter-run CV < 1.35). When applied to 118 clinical samples, the ddPCR assay achieved a significantly higher positive detection rate (27.4%) compared to qPCR (14.8%). In conclusion, we have successfully established a reliable ddPCR assay for the absolute quantification of FHV-1, providing a superior tool for laboratory diagnosis and research.

In 2018, the Food and Drug Administration (FDA) reported cases of dilated cardiomyopathy (DCM) in dogs, including breeds without known genetic predisposition, fed diets containing a high proportion of legumes or potatoes, many labeled grain-free. Despite concerns, grain-free diets remain increasingly popular. This narrative review focuses on the peer-reviewed literature and summarizes recent studies evaluating the effects of grain-free diets on canine cardiac health, with emphasis on taurine deficiency, metabolic alterations, and emerging hypotheses extending beyond nutritional inadequacy. These findings suggest a strong link between diet and DCM, particularly regarding legumes. Dogs of various breeds showed larger left ventricular diameters, reduced systolic function, and increased premature ventricular complexes when fed non-traditional, grain-free, legume-rich diets compared to those on traditional, low-legume diets. Many affected dogs improved clinically and functionally with dietary changes and treatment, suggesting nutritional DCM could be reversible. Though mechanisms remain unclear, the research highlights the potential roles of legumes, particularly peas, in the gut microbiota and fiber-related bile acid metabolism. This review also distinguishes taurine-deficiency-related DCM as a separate form, with certain breeds, notably Golden Retrievers, being more susceptible. Overall, further studies are required to better understand the role of nutrition in canine cardiac health.

The aim of this retrospective study was to compare lesion conspicuity-the visibility of a lesion relative to surrounding tissue-and CT characteristics of pancreatic insulinomas in dogs using multiphase dual-energy CT (DECT) and dynamic 4D perfusion CT. Seventy dogs with insulinomas, confirmed either cytologically or histologically, or with clinical and imaging findings consistent with the diagnosis, were included. Forty dogs underwent perfusion CT and 30 underwent multiphase DECT on a dual-source, dual-energy CT scanner (192 × 2 detector configuration). Imaging evaluations focused on arterial and portal phases for DECT, and early arterial, late arterial, pancreatic, and portal venous phases for perfusion CT. Tumor conspicuity was quantified using the tumor-to-pancreas ratio (TPR) and contrast-to-noise ratio (CNR), while time-to-peak (TTP) enhancement was recorded for both tumors and pancreatic parenchyma. Perfusion CT demonstrated significantly higher TPR and CNR values compared to DECT (p < 0.001), indicating improved tumor visibility. The late arterial phase of perfusion CT, although not statistically significant, showed the highest median TPR and CNR. Mean TTP for tumors was 38.8 s, slightly earlier than the pancreatic parenchyma (41.25 s). In conclusion, perfusion CT appears to enhance visualization of insulinomas in dogs, particularly between 34 and 44 s after contrast injection, aligning with the late arterial phase of perfusion CT.

Accurate and non-invasive monitoring of dairy cows is a cornerstone of precision livestock farming, paving the way for proactive health management and earlier disease detection. The development of robust, AI-driven diagnostic tools, however, is hindered by a dual challenge: scarce realistic video datasets and a lack of standardized benchmarks for deep learning models. To confront these issues, this study puts forward SideCow-VSS, a video semantic segmentation dataset comprising 921 side-view clips with dense, pixel-level annotations of dairy cows under variable on-farm conditions. We systematically evaluated eight deep learning architectures, from classic convolutional neural networks to state-of-the-art Transformers. The evaluation highlighted a clear performance trade-off: the Mask2Former model with a Swin-L backbone yielded the highest mIoU at 97.32%, making it well-suited for detailed morphological analysis. In contrast, the lightweight PIDNet-s model achieved the fastest inference speed of 59.5 FPS, demonstrating its potential for real-time behavioral alerting systems. This work delivers a foundational resource and quantitative framework to inform model selection, accelerating the creation of computer vision systems for automated health monitoring and adopting preventive strategies against key metabolic and immunological disorders in dairy production.

The Iberian ribbed newt (Pleurodeles waltl) is a salamander in the Salamandridae family. Endemic to the Iberian Peninsula and North Africa, it is not commonly found in zoological institutions or wildlife rescue centers. As in other species, routine blood analysis of amphibians under human care is highly recommended, forming an essential component of preventive medicine and effective clinical management. However, despite the great utility of hematological parameters for the diagnosis and prevention of diseases in amphibians, the lack of reliable reference values for many species severely limits their clinical use. The aim of this study is to establish preliminary reference values (RV) for the main hematological parameters in the Iberian ribbed newt. Blood samples were taken from healthy adult individuals (n = 30), females (n = 9) and males (n = 21) maintained under controlled conditions in two zoological institutions. A complete hematological analysis was conducted, which included measurements of hematocrit, total erythrocyte and leukocyte counts, as well as a leukocyte differential. The reference intervals were established according to the guidelines provided by the American Society for Veterinary Clinical Pathology (ASVCP) for sample sizes between 20 and 40 individuals. No significant sex-related differences were detected in the hematological parameters analyzed. Despite the broad reference ranges obtained, these preliminary data provide an essential foundation for the clinical assessment and preventive medical management of P. waltl under human care. Expanding the dataset through collaboration with additional institutions will further refine and improve the accuracy and clinical utility of these reference values.

The objectives of this study were to estimate genetic parameters for stayability in Chinese Holstein cattle and to estimate its genetic correlations with routinely collected traits. Although knowledge and genetic breeding technologies for dairy cattle have advanced, the lifespan of dairy cows has been declining, mainly due to increased production stress and associated health problems. This makes the genetic improvement of Holstein cattle crucial for sustainable production. Stayability is an indicator trait reflecting longevity, measured as the survival probability of dairy cows at specific stages. To evaluate temporal differences in herd retention, we defined 7 stayability traits as the ability of cows to remain in the herd for 36 (S36), 42 (S42), 48 (S48), 54 (S54), 60 (S60), 72 (S72), and 84 (S84) months after first calving. Data from 56,630 cows between 2011 and 2020 across 14 dairy farms in Ningxia were considered for stayability analyses. Survival analysis using non-parametric methods showed that cattle with sterility had the longest survival time, while those with abomasal displacement had the shortest survival time. The heritability estimates (±standard error) for S36, S42, S48, S54, S60, S72 and S84 were 0.048 ± 0.006, 0.063 ± 0.006, 0.074 ± 0.007, 0.099 ± 0.007, 0.115 ± 0.007, 0.088 ± 0.007, 0.118 ± 0.008, respectively. The genetic and phenotypic correlations among stayability traits ranged from 0.382 to 0.975 and from 0.090 to 0.799, respectively. Furthermore, the approximate genetic correlations between stayability and routinely collected traits (8 production, 20 conformation, 13 fertility, 13 health traits) were assessed. Stayability showed low to moderate genetic correlations with most of these traits. In summary, developing a selection index that incorporates stayability is expected to improve the longevity of dairy cows. All unfavorable genetic relationships observed between stayability and other routinely collected traits should be accounted for in a selection index, thereby enhancing the lifespan of Holstein cattle while maintaining or improving productive performance.

Anemia caused by gastrointestinal parasitism is a major constraint to small ruminant productivity, particularly in low-resource production systems where diagnostic tools and veterinary access are limited, with use of FAMACHA as a biological reference This study evaluated the potential of radio-frequency non-destructive technique (RF-NDT) wave-derived features as non-invasive biomarkers for anemia detection in goats, using FAMACHA© scores as a biological reference. Variable clustering of the top ten frequencies revealed distinct patterns across health states. Healthy (FAMACHA© 1) animals were characterized by a single frequency cluster centered at 8.43 GHz, which explained 93.7% of variation, whereas moderately affected animals (FAMACHA© 2) shifted to 9.33 GHz with reduced uniformity (88.7%). Borderline animals (FAMACHA© 3) required two clusters (9.89 and 8.23 GHz), explaining 91.0% of variation, indicating increasing tissue heterogeneity with anemia progression. Regression analysis demonstrated strong predictive power, with Linear Regression achieving R² = 1.00 and Random Forest R² = 0.79 (RMSE = 0.07), Support Vector Regression underperformed (R² = 0.31). Classification models confirmed the feasibility of categorical anemia detection. The Multilayer Perceptron achieved the highest accuracy (0.84), F1-score (0.83), and ROC-AUC (0.94), outperforming Support Vector Machine (accuracy 0.67, F1 = 0.67) and K-Nearest Neighbors (accuracy 0.60, F1 = 0.61). These findings establish proof-of-concept that RF waves capture physiologically meaningful dielectric signatures linked to anemia, reflecting hemoglobin concentration, hydration, and microcirculatory function. The integration of RF sensing with machine learning offers a rapid, and non-invasive scalable diagnostic approach. Future work should expand validation across breeds and environments, optimize sensor design, and embed neural classifiers for field-ready deployment.

Methane (CH₄) produced by methanogenic archaea during the rumen fermentation of feed carbohydrates leads to global warming and total energy loss. This study aims to compare the accuracy of multiple linear regression (MLR) models and backpropagation neural network (BPNN) in predicting ruminal CH₄ production of the carbohydrate (Carbs) components of the Cornell Net Carbohydrate and Protein System (CNCPS) in mixed rations of beef cattle with different concentrate-to-forage (C/F) ratios. Two datasets were established using the in vitro fermentation method of Menke and Steingass. One of the datasets contained 60 mixed rations with C/F ratios of 30:70, 40:60, 50:50, 60:40, 70:30, 80:20, and 90:10, respectively, which were used to develop CH₄ prediction models. Another dataset included 10 mixed rations with the same C/F ratios, which were used to validate and compare the accuracy of the prediction models. Results indicated that there was a significant multiple regression relationship between CH₄ production and the Carbs-components (CA (sugars), CB₁ (starch and pectin), CB₂ (available cell wall), CC (unavailable cell wall)) of CNCPS (r² = 0.91, p < 0.0001). An optimal BPNN model with 2 hidden-layer neuron nodes was established with the same variables (r² = 0.93, p < 0.0001). The findings demonstrated that both MLR and BPNN models (p < 0.0001) were suitable for predicting CH₄ production using the Carbs components (CA, CB₁, CB₂, CC) of CNCPS. However, compared with the MLR model, the BPNN model has a greater coefficient of determination (r²) value and concordance correlation coefficient (CCC), and a lower root mean square prediction error (RMSPE), demonstrating better prediction performance.

Toxoplasma gondii is a protozoan parasite with a complex life cycle that involves warm-blooded animals as intermediate hosts and felids as definitive hosts. Its epidemiology in alpine ecosystems remains poorly understood. This study presents the first long-term investigation of T. gondii exposure in wild ruminants in the Pyrenees (NE Spain), where definitive hosts are scarce. Pyrenean chamois (Rupicapra pyrenaica; n = 1045) and mouflon (Ovis aries musimon; n = 115) sera collected between 2001 and 2024 were tested (Modified Agglutination Test, ELISA-IDvet, ELISA-IDEXX) for the presence of T. gondii antibodies. Sera from 53 chamois and 27 mouflon foetuses and hearts and brains from 38 chamois and 35 mouflon foetuses were analysed for the presence of antibodies and parasite's DNA, respectively. Moreover, heart and brain (n = 3) and faeces (n = 91) from Pyrenean wildcats (Felis silvestris) were analysed for parasite's DNA. Seroprevalence was overall low (chamois: 5.24%; mouflon: 1.74%). In multivariate analyses performed in chamois, seroprevalence variation was mainly associated with geographic origin and the diagnostic method used, with little influence of the individual traits of sex or age. No antibodies or T. gondii DNA were detected in chamois or mouflon foetuses. However, chamois did not show a clear age-related increase in exposure to T. gondii, as would be expected if indirect horizontal transmission were the predominant route. In contrast, T. gondii DNA was detected in brain and faecal samples from wildcats, confirming their role as definitive hosts in this ecosystem. Overall, our results reinforce the hypothesis that alpine ecosystems are environments with low T. gondii oocyst contamination and that the parasite most probably relies on sylvatic cycles. These results suggest that wild ruminants, such as chamois and mouflon, could serve as effective sentinels of changes in the extent of this parasite under ongoing environmental and ecological changes.