Determinants of anxiety and depression among university teachers during third wave of COVID-19.

BACKGROUND: To estimate the determinants of anxiety and depression among university teachers in Lahore, Pakistan, during COVID-19. METHODS: A cross-sectional study was conducted by enrolling 668 teachers from the universities of Lahore, Pakistan. Data were collected using a questionnaire. Chi-square for significance and logistic regression for the association were used. RESULTS: Majorly, the university teachers, with an average age of 35.29 years, had regular jobs (72.8%), job experience of > 6 years (51.2%) and good self-reported health (55.4%). The majority of the teachers were working as lecturers (59.6%), lecturing in arts (33.5%) or general science (42.5%) departments, having MPhil (37.9%) or master (28.9%) degrees, and teaching via synchronous video (59.3%) mode. Anxiety and depression, severe and extremely severe, were higher among lecturers, MPhil or master degree holders, teachers lecturing arts and general science subjects, and in those on contract employment. Anxiety was significantly associated with academic departments; arts (OR;2.5, p = 0.001) and general science (OR;2.9, p = 0.001), poor health status (OR;4.4, p = 0.018), and contractual employment (OR;1.8, p = 0.003). Depression was associated with academic departments; arts (OR;2.7, p = 0.001) and general science (OR;2.5, p = 0.001), and health status (OR;2.3, p = 0.001). CONCLUSION: Among university teachers, anxiety and depression, severe and extremely severe, were prevalent among lecturers having MPhil or master degrees, belonging to arts and general science departments, and among contract employees. Anxiety and depression were significantly associated with academic disciplines, lower cadre, and poor health status.

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach.

Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.