Irish journal of medical science
-
Asprosin is an emerging biomarker that plays a role in metabolic diseases. This study investigates asprosin as a predictive marker for coronary artery disease (CAD) severity in diabetic patients. ⋯ This is the first study in the literature to demonstrate a positive correlation between asprosin levels and SYNTAX scores in diabetic patients with CAD. More comprehensive studies with larger groups are needed.
-
There have been limited reports on the duration of labor progression in pregnant women undergoing vaginal birth after cesarean (VBAC). This study aimed to investigate the duration of labor progression during VBAC in Hubei, China. ⋯ The duration of labor progression of the first, second, and total stages of VBAC is shorter than that in primiparous women in our observation in China.
-
To assess the most common lower limb acute muscle injuries on MRI imaging in a national specialist centre for orthopaedics and sports medicine and to explore potential gender differences. ⋯ Grade 1a is the most common lower limb AMI grade in our institution, accounting for 25%. Biceps femoris is the most commonly injured muscle (45%) with grade 1b and grade 2b being the most frequently encountered grades of biceps femoris injuries. Lower-grade injuries are more common in females compared to males, although not significantly so. Further studies are required to explore possible reasons for this gender gap.
-
Sclerosing cholangitis recurs in some patients following liver transplantation. These high-risk patients may provide clues to the pathogenesis of this disease. ⋯ Recurrent PSC following liver transplantation is common, particularly in younger patients. It occurs earlier and is more frequent following a second transplant.
-
The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and incidence of skin diseases. Medical technology based on lasers and photonics has made it possible to identify skin illnesses considerably more rapidly and correctly. ⋯ The algorithm developed was free from any inherent bias and treated all classes equally. The present model, which was trained using the Xception algorithm, is highly efficient and accurate for 20 different skin conditions, with a dataset of over 10,000 photos. The developed system was able to classify 20 different dermatological diseases with high accuracy and precision.