Neoplasms are an important cause of morbidity and mortality in horses, with approximately 50% of cases related to the integumentary system. Equine sarcoid is the most common cutaneous neoplasm of this species worldwide and it is characterized by its locally aggressive behavior. Cross-species infection by the Bovine Papillomavirus (BPV) genus Deltapapillomavirus (types BPV-1, BPV-2, and BPV-13) is a key factor for sarcoid development. However, no studies have been conducted in Chile on the prevalence and distribution of the disease or the identification of circulating BPV types. Therefore, this study focused on identifying the BPV types present in equine skin biopsies from different regions of the country. For this study, 23 paraffin blocks of equine biopsies with a histopathological diagnosis of sarcoid were collected from five localities in Chile, including cases from 2004 to 2025. Histopathological diagnoses were verified before DNA extraction, and PCRs were performed to detect BPV-1, BPV-2, and BPV-13. Fisher’s exact test was applied to evaluate the associations between viral type and sex, breed, and age of the sampled individuals. Of the 23 samples analyzed, 100% met at least one histopathological criterion for inclusion in the study. PCR analysis revealed that 35% (8/23) of the samples tested positive for BPV-1, 91% (21/23) tested positive for BPV-2, 26% (6/23) exhibited co-detection of BPV-1 and BPV-2, and no sample (0/23) tested positive for BPV-13. Regarding geographical distribution, BPV-1 was identified in three regions, whereas BPV-2 was identified in all five regions (5/5). There were no statistically significant associations between viral type and sex, breed, or age (P > 0.05). This study provides a baseline for the viral characterization of this equine disease across the country, allowing for the development of prevention and treatment strategies that align with the context of the affected local population.
A FeLV-negative Persian cat with severe non-regenerative anemia had a splenic round-cell neoplasm initially interpreted as large-cell lymphoma. CD3/PAX5 immunohistochemistry did not support conventional lymphoma, and bone marrow findings favored acute erythroid leukemia (AML-M6) with probable splenic involvement. The cat died 53 days after presentation, despite receiving cytarabine treatment.
Accurate measurement of body temperature is essential for the clinical evaluation of rabbits. Although rectal temperature (RT) measurement is the standard method, obtaining RT measurements can be stressful for patients. Axillary temperature (AT) measurement is proposed as a less invasive alternative; however, the reliability of AT measurements in rabbits has not been sufficiently evaluated. This study aimed to determine the association between AT and RT measurements in clinically healthy rabbits and to evaluate whether AT measurement can be a reliable alternative to RT measurement. Thirty-nine clinically healthy rabbits treated at the UNAM Exotic Pet and Wildlife Teaching Hospital were evaluated between December 2024 and February 2025. Three measurements of AT and RT were recorded for each individual using a flexible digital thermometer. Averages were calculated, and statistical tests were performed to evaluate differences and correlations between AT and RT. No significant differences were observed in either axillary temperature (P = 0.061) or rectal temperature (P = 0.072) between rabbits. AT was significantly lower than RT, with a mean difference of 2.32 °C (95 % CI: 2.05 – 2.58, P < 0.001). The average AT was 36.37 °C and the average RT was 38.68 °C. RT demonstrated greater repeatability, and the agreement between the two methods was insufficient to consider them interchangeable in clinical practice. Furthermore, the difference between AT and RT increased slightly with increasing body temperature (P = 0.028) and was influenced by body weight (P = 0.003) and body condition score (P = 0.033). AT was not correlated with RT in clinically healthy rabbits, indicating that AT measurement is not a reliable alternative for assessing body temperature in this species. The use of RT measurement as a standard method in clinical practice is recommended.
Sperm cryopreservation is an essential tool for animal breeding and genetic conservation programs. However, the freeze–thaw process induces structural and functional alterations that compromise sperm survival and fertilizing ability. Although inter–individual variability in sperm cryotolerance has been widely reported in livestock species, integrative approaches for evaluating semen freezability in goats remain limited. Therefore, the present study evaluated sperm cryotolerance in Saanen bucks using composite Sperm Quality Indexes (SQI) derived from multiple post-thaw sperm functional parameters. A total of 124 ejaculates from seven adult Saanen bucks were cryopreserved and evaluated after thawing for progressive motility, viability, acrosome integrity, capacitation status, and plasma membrane fluidity. Two indices were calculated: SQI1, which includes parameters associated with sperm functionality preservation, and SQI2, which includes indicators of cryo-induced cellular damage. Significant inter–male differences were observed for both indices (P < 0.05). Male 1 exhibited lower SQI1 values and higher SQI2 values than males 6 and 7, indicating reduced sperm cryotolerance and increased susceptibility to cryo–induced membrane destabilization and capacitation–like changes. The remaining males showed intermediate responses without significant differences among them. The proposed SQIs successfully detected biologically meaningful inter-male differences in sperm cryotolerance and constitute a useful approach for identifying bucks with different freezing responses.
Respiratory syndromes are a major cause of morbidity and mortality in the feedlot industry, making the accurate identification of lung lesions essential for effective health management. This study proposes an automated approach for identifying and segmenting pulmonary structures and lesions using a visual machine learning model trained on a limited dataset of post-mortem images captured at a slaughterhouse. The implementation was performed using Python 3.12 with computer vision libraries and the Ultralytics framework employing YOLO11n-seg. Training incorporated both dorsal and ventral views to identify lungs, trachea and extra tissues, and lesions (designated as “findings”). Data augmentation and k-fold cross-validation were applied to address the data limitations. In addition, we developed an automated algorithm that quantifies lung health by calculating the proportion of affected versus healthy tissue through a pixel overlap comparison between segmented lung regions and detected lesions. The proposed method enables objective and reproducible assessments of pulmonary conditions from different anatomical perspectives. The results demonstrate strong performance in both anatomical segmentation and lesion detection, while also highlighting the challenges inherent in data-constrained conditions. These findings contribute to the development of computer-aided tools for veterinary diagnostics.
