Comparison of susceptibility for hip dysplasia between Rottweilers and German shepherd dogs.

J Am Vet Med Assoc

Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, Philadelphia 19104-6010, USA.

Published: March 1995

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.

Download full-text PDF

Source

Publication Analysis

Top Keywords

risk developing
16
german shepherd
12
shepherd dogs
12
coxofemoral joints
12
developing djd
12
hip dysplasia
8
djd coxofemoral
8
disease susceptibility
8
djd
5
comparison susceptibility
4

Similar Publications

The Ataxia-telangiectasia mutated (ATM) is the most important gene for repairing the DNA in Myelodysplastic Neoplasm.

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 PDF

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.

View Article and Find Full Text PDF

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 PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!