Passive laxity of the coxofemoral joints, as measured quantitatively by radiographing the joints under stress, has been shown to be an accurate measure of the risk for developing degenerative joint disease (DJD) of the coxofemoral joints. Seventy-four Rottweilers between 12 and 40 months old were evaluated subjectively for radiographic evidence of DJD, using the ventrodorsal view of the pelvis with the coxofemoral joints fully extended and the knees internally rotated (standard hip-extended view). Effect of age, sex, weight, and distraction index on the risk of developing DJD was evaluated by use of a logistic regression model. Results were compared with those from a group of German Shepherd Dogs. Results indicated that in Rottweilers the distraction index was the only statistically significant predictor of the risk of developing DJD of the coxofemoral joint. When German Shepherd Dogs were included in the model, they had a significantly greater risk of developing DJD than did Rottweilers. This finding provides further support for the theory that there are differences in disease susceptibility among breeds and emphasizes the need to develop disease susceptibility curves for all breeds affected by hip dysplasia to facilitate accurate, scientifically based recommendations for breeding or treatment.
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DNA Repair (Amst)
January 2025
Cancer Cytogenomic Laboratory, Center for Research and Drug Development (NPDM), Federal University of Ceara, Fortaleza, Ceara, Brazil; Post-Graduate Program in Medical Science, Federal University of Ceara, Fortaleza, Ceara, Brazil; Post-Graduate Program of Pathology, Federal University of Ceara, Fortaleza, Ceara, Fortaleza, Ceara, Brazil; Post-Graduate Program of Translational Medicine, Federal University of Ceara, Fortaleza, Ceara, Brazil.
Myelodysplastic Neoplasm (MDS) is a cancer associated with aging, often leading to acute myeloid leukemia (AML). One of its hallmarks is hypermethylation, particularly in genes responsible for DNA repair. This study aimed to evaluate the methylation and mutation status of DNA repair genes (single-strand - XPA, XPC, XPG, CSA, CSB and double-strand - ATM, BRCA1, BRCA2, LIG4, RAD51) in MDS across three patient cohorts (Cohort A-56, Cohort B-100, Cohort C-76), using methods like pyrosequencing, real-time PCR, immunohistochemistry, and mutation screening.
View Article and Find Full Text PDFEur J Radiol
January 2025
Department of Radiology, West China Hospital Sichuan University Chengdu Sichuan China. Electronic address:
Purpose: To develop and validate an MRI-based model for predicting postoperative early (≤2 years) recurrence-free survival (RFS) in patients receiving upfront surgical resection (SR) for beyond Milan hepatocellular carcinoma (HCC) and to assess the model's performance in separate patients receiving neoadjuvant therapy for similar-stage tumors.
Method: This single-center retrospective study included consecutive patients with resectable BCLC A/B beyond Milan HCC undergoing upfront SR or neoadjuvant therapy. All images were independently evaluated by three blinded radiologists.
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
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