Background: Depression at any age needs attention. Geriatric population being most vulnerable, are more prone to many morbidities, physically and mentally. Geriatric depression is one among them which needs prompt attention. The objectives of the present study were to estimate the prevalence of geriatric depression and association of depression with various sociodemographic factors.Methods: A cross sectional study carried out among 300 geriatric subjects. A structured interview schedule was used to collect data.Results: The prevalence of depression was found to be 44.4%. Female gender, marital status, family type was found to be positively associated with depression.Conclusions: Geriatric depression in our study is found to be on little higher side. Lifestyle modifications and support from family members are needed to improve the quality of life of these people.

Melanoma is a kind of skin cancer that develops in melanocyte cells. It is one of the most serious kind of skin cancer, yet it is not as frequent as other types of skin cancer.It is very hard to detect, even under expert supervision. A Deep Convolutional Neural Network (D-CNN) Visual Geometry Group (VGG16) model, is proposed to improve the classification performance of skin lesions.The main disadvantage of using the deep learning methods is that more time is needed for training. Thus, with the help of transfer learning technique, the training time is reduced. The datasets utilized in the proposed strategy to train the model were obtained from International Skin Imaging Collaboration (ISIC).Metrics like Accuracy (ACC), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Specificity (SPC), and Sensitivity (SE) were measured for the evaluation of the classification.The performance of the classification process done by the classifier model on a test data is represented using a confusion matrix. The proposed method of using transfer learning technique in Deep Convolutional Neural Network improved the accuracy of classification to 85% compared to 81% obtained from Convolutional Neural Network.

Chronic kidney disease (CKD) is becoming a major health concern due to its increasing incidence among adults. There are few studies that suggest the possible relation between hearing loss and chronic kidney disease. So far only a small number of large population - based studies have assessed the relation between CKD and hearing loss. The global prevalence of CKD was 9.1 % (697.5 million cases) in 2017. The age and sex wise global prevalence of CKD was higher in women (9.5 %) than in men (7.3 %). In India, prevalence of sensorineural hearing loss (SNHL) is around 28 % to 77 % among CKD patients. Studies found that the incidence was 77 % for mild to very mild hearing loss and the incidence was 46 % for moderate to severe hearing loss. Various theories behind SNHL in CKD patients are structural similarity between ear and kidney, increased blood viscosity because of hypertension and finally electrolyte imbalance which are all thought to play a role in development of SNHL in CKD patients. Major risk factors for SNHL in CKD patients are duration of CKD, hypertension, diabetes mellitus, serum urea and creatinine levels, electrolyte imbalance, packed cell volume (PCV), ototoxic drugs. CKD being a long-term illness and majority of cases of SNHL in CKD patients are permanent, it has a great negative impact on the patient’s quality of life adding to the disability burden.

Introduction: A person spends one third life in sleep, so the quality and quantity of sleep is of utmost importance. Health Care Professionals (HCPs) are more prone to inconsistency in sleep both in quality and quantity, which leads to deflection from health and well-being of themselves and care of others. This study aims to assess the various factors influencing sleep quality and daytime sleepiness among medical and nursing healthcare professionals. Methodology: A cross sectional study was conducted using a structured questionnaire to collect socio-demographic and work-related information, co-morbidity and quality of Sleep using ESS (Epworth Sleepiness Scale) and PSQI (Pittsburgh Sleep Quality Index) scale. Results: Among the 150 HCPs, 64.7% were medical and 35.3% were nursing professionals. 53.6% of medical and 66% of nursing professionals reported poor sleep quality. Increased coffee consumption influences sleep quality and it was found to be statistically significant. Nursing professionals had more excessive daytime sleepiness (58.5%) with significant p-value (p=0.01). Conclusion: According to our study results, sleep quality was poor among nursing professionals which highlights the need for measures to improve their quality of sleep.