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Background: Inflammatory myopathy and perivasculitis have been recently described in horses with chronic equine piroplasmosis (EP). These alterations may be linked to poor performances. The aims of this study were to evaluate the prevalence for EP in clinically healthy Italian Standardbred (IS) racehorses and to compare laboratory parameters and performance metrics between positive and negative horses. Real-time PCR was applied for the detection of T. equi and B. caballi positivity. Haematology parameters, blood chemistry results, subjective muscle mass scores, and performance metrics were compared between PCR-positive and -negative horses.
Results: This cross-sectional study included 120 well-trained IS racehorses and was performed over a two-years period. The prevalence of T. equi was 36.3%, whereas all samples were negative for B. caballi. Red blood cells count, haemoglobin concentration, aspartate aminotransferase, alkaline phosphatase, and gamma-glutamyl transferase activities were significantly higher in PCR-positive horses, whereas blood urea nitrogen, globulin concentration and globulin-to-albumin ratio were significantly lower in PCR-positive horses compared to PCR-negative ones. Nonetheless, all values fell within the physiological range. The best racing time, which was selected as the most representative of the performance metrics at the principal component analysis, was not affected by PCR positivity, the muscle mass score or the training yard. The best racing time was significantly better in horses with a mild or no signs of muscular atrophy, within the PCR-positive group. The muscle mass score was associated with the training yard in PCR-negative horses.
Conclusions: Prevalence of T. equi was high in IS racehorses in southern Italy. The absence of obvious changes in haematological and biochemical parameters, as well as performance metrics in positive horses, highlights the need for specific diagnostic tests to identify chronically infected horses.
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http://dx.doi.org/10.1186/s12917-024-03908-0 | DOI Listing |
Comput Biol Med
September 2025
INSIGNEO Institute for in silico medicine, University of Sheffield, UK; School of Mechanical, Aerospace and Civil Engineering, University of Sheffield, UK. Electronic address:
Modelling cardiovascular disease is at the forefront of efforts to use computational tools to assist in the analysis and forecasting of an individual's state of health. To build trust in such tools, it is crucial to understand how different approaches perform when applied to a nominally identical scenario, both singularly and across a population. To examine such differences, we have studied the flow in aneurysms located on the internal carotid artery and middle cerebral artery using the commercial solver Ansys CFX and the open-source code HemeLB.
View Article and Find Full Text PDFBiomol Biomed
September 2025
Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China.
Coronary heart disease (CHD) is a leading cause of morbidity and mortality; patients with type 2 diabetes mellitus (T2DM) are at particularly high risk, highlighting the need for reliable biomarkers for early detection and risk stratification. We investigated whether combining the stress hyperglycemia ratio (SHR) and systemic inflammation response index (SIRI) improves CHD detection in T2DM. In this retrospective cohort of 943 T2DM patients undergoing coronary angiography, associations of SHR and SIRI with CHD were evaluated using multivariable logistic regression and restricted cubic splines; robustness was examined with subgroup and sensitivity analyses.
View Article and Find Full Text PDFJ Craniofac Surg
September 2025
Department of Oral and Maxillofacial Surgery, University of Ulsan Hospital, University of Ulsan College of Medicine.
This study aimed to develop a deep-learning model for the automatic classification of mandibular fractures using panoramic radiographs. A pretrained convolutional neural network (CNN) was used to classify fractures based on a novel, clinically relevant classification system. The dataset comprised 800 panoramic radiographs obtained from patients with facial trauma.
View Article and Find Full Text PDFJ Craniofac Surg
September 2025
Department of Breast Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shijingshan, Beijing, China.
Background: With the development of artificial intelligence, obtaining patient-centered medical information through large language models (LLMs) is crucial for patient education. However, existing digital resources in online health care have heterogeneous quality, and the reliability and readability of content generated by various AI models need to be evaluated to meet the needs of patients with different levels of cultural literacy.
Objective: This study aims to compare the accuracy and readability of different LLMs in providing medical information related to gynecomastia, and explore the most promising science education tools in practical clinical applications.
J Cataract Refract Surg
July 2025
Department of Ophthalmology, West China Hospital of Sichuan University, Chengdu City, Sichuan Province, China.
Purpose: To develop and validate a multimodal deep-learning model for predicting postoperative vault height and selecting implantable collamer lens (ICL) sizes using Anterior Segment Optical Coherence Tomography (AS-OCT) and Ultrasound Biomicroscope (UBM) images combined with clinical features.
Setting: West China Hospital of Sichuan University, China.
Design: Deep-learning study.