Dermatophytosis in patients referred for evaluation at a tertiary care teaching hospital in Kashmir, India

Background: Dermatophytoses invade the stratum corneum of the skin and other keratinized tissues derived from the epidermis. They are quite common and can be fairly accurately identified by a simple laboratory test. We conducted this study to identify the common dermatophytic infections in our setting.Methods: Clinically suspected tinea infections were referred to the Department of Microbiology of SKIMS Medical College Hospital, a tertiary-care teaching hospital in Srinagar, Kashmir. The affected area was cleaned with 70% alcohol, and scrapings were obtained. 10% KOH was used for keratinolysis. Samples were thoroughly examined for the presence of filamentous, septate, branched hyphae.Results: A total of 206 samples were analyzed. The overall KOH positivity rate was 44.7%. Of the 206 patients, 119 (57.8%) were males, and 142 (68.9%) resided in rural areas. The mean age of the patients was 32 years (range 4-72). Tinea corporis was the typical clinical manifestation (58.3%).Conclusions: Young and middle-aged males and people living in rural areas are at a higher risk of dermatophyte infections

The Evolving Role of Artificial Intelligence in Radiotherapy Treatment Planning – A Literature Review The Evolving Role of AI in Radiotherapy Treatment Planning

From Elsevier via Jisc Publications RouterHistory: accepted 2024-06-11, issued 2024-06-13Article version: AMPublication status: AcceptedThis paper examines the integration of Artificial Intelligence (AI) in radiotherapy for cancer treatment. The importance of radiotherapy in cancer management and its time-intensive planning process makes AI adoption appealing especially with the escalating demand for radiotherapy. This review highlights the efficacy of AI across medical domains, where it surpasses human capabilities in areas like cardiology and dermatology. Focusing on radiotherapy, the paper details AI's applications in target segmentation, dose optimisation, and outcome prediction. It discusses adaptive radiotherapy's benefits and AI's potential to enhance patient outcomes with much improved treatment accuracy. The paper explores ethical concerns, including data privacy and bias, stressing the need for robust guidelines. Educating healthcare professionals and patients about AI's role is crucial as it acknowledges potential job role changes and concerns about patients’ trust in the use of AI. Overall, the integration of AI in radiotherapy holds transformative potential in streamlining processes, improving outcomes, and reducing costs. AI's potential to reduce healthcare costs underscores its significance with impactful change globally. However, successful implementation hinges on addressing ethical and logistical challenges and fostering collaboration among healthcare professionals and patient population data sets for its optimal utilisation. Rigorous education, collaborative efforts, and global data sharing will be the compass guiding its’ success in radiotherapy and healthcare