Impact of severe acute respiratory syndrome-coronavirus-2 pandemic on postgraduate medical education in armed forces medical services institutions: A students' perspective

Introduction: The Severe acute respiratory syndrome coronavirus 2 pandemic situation brings us the opportunity to test the strength and limitations of our health delivery system. Residents being the backbone of quality-health-delivery of any institute have taken the brunt. Materials and Methods: A cross-sectional self-administered questionnaire-based survey was used to assess the effect on medical training and stress of postgraduate residents in clinical specialties of armed forces institutions.Results: 266 valid responses were analyzed. Eighty-seven percent of residents felt their surgical/procedure-related training was affected. Bedside/clinical training was found to be affected by 92% and theoretical learning by 78%. A significant difference was found between residents in medical and allied specialties and residents in surgery and allied specialties (81% vs. 96.3%) with regard to the negative effect of the COVID-19 pandemic on surgical/procedural skills training (P < 0.05). There was a significant difference in the likelihood of being posted for COVID duties based on gender (P = 0.01) and year of the course (P = 0.004). Posting on COVID duties did not significantly affect surgical, clinical, or theoretical training. Of the respondents, 37%, 49%, and 14% had a mild, moderate, and severe increase in stress, respectively. 18%, 52%, and 30% experienced mild, moderate, and severe increased stress among family members. Gender, age, category, year of residency, or subject of specialization did not have any significant effect on the level of personal or family stress. Conclusion: This survey attempts to bring forth the effect of the pandemic on medical training schedules and stress among residents. Such surveys would enhance understanding and bring solutions to the problem that the pandemic has brought.

Two cases of adult-onset de novo immunoglobulin A vasculitis with nephritis: Post-Covishield (ChAdOx1 nCoV-19, Oxford-Astra Zeneca) vaccination

With the ongoing worldwide COVID-19 vaccination programs, new-onset glomerular disease and relapse of the preexisting glomerular disease have been reported after COVID-19 vaccines administration. These incidences are overall very rare and had just temporal association with vaccination. It is, therefore, the causal link with the COVID 19 vaccine is not firmly established. In this case-based review, we present two cases, who presented with purpuric rashes and joint pain between 2 and 3 weeks of 2nd dose of Covishield (ChAdOx1 nCoV-19;Oxford-Astra Zeneca) vaccination. Routine evaluation in both these cases revealed significant proteinuria and microscopic hematuria. The diagnosis of immunoglobulin A (IgA) vasculitis with nephritis was established with renal biopsy suggestive of IgA nephropathy and skin biopsy findings of leukocytoclastic vasculitis. Both these cases had severe renal involvement and responded to oral glucocorticoids after 8-16 weeks of treatment. Close observation and careful monitoring of these cases are required to determine the incidence of de novo or recurrence of glomerular disease postvaccination, the need for immunosuppressive therapy, response to aggressive treatment, and long-term clinical outcomes.

Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique

To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes. This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud. K-Means is used for segmenting images into pixels and auto-encoders to remove noise (de-noising);the Random Forest regressor, tree-method based ensemble model is used for classification. The study obtained CT scan-images as datasets from ‘Kaggle’ and classifies the images into ‘Non-Covid’ and ‘Covid’ categories. The software utilized is Jupyter-Notebook, in Python. PSNR with MSE evaluation metrics is done using Python. Through testing-and-training datasets, lower MSE score (‘0’) and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images. © 2022 MECS.