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Academic-related demands that exceed students' adaptive capabilities are collectively known as academic stress. High levels of academic stress are associated with an increased likelihood of depression, insomnia, substance addiction, self-harm, suicidal ideation, and subsequently, quitting education. Globally, academic stress is now a common phenomenon due to COVID-19-induced changes in the education system. Knowledge of the magnitude of academic stress and its factors can enable early recognition, intervention, and alleviation of the problem. The objective of this study was to assess the magnitude of perceived academic stress and identify the main stressors through a cross-sectional survey using the Manipal Inventory of Academic Stress scale. The study participants involved 2152 Grade 11 and 12 Commerce students enrolled in 34 pre-university colleges in coastal Karnataka. A stratified cluster sampling method was used in the study. Statistical methods, namely descriptive statistics, multiple linear regression analysis, two-sample independent t-test, and one-way ANOVA tests, were used in the study. The study observed that one in every four pre-university students experienced high levels of perceived academic stress. Parent expectations, academic queries from neighbours and relatives, and lack of time for revision were identified as the top three stressors. Gender, grade, and mother's education were associated with academic stress. Interventions at the individual, family, institutional, and community levels are the need of the hour to safeguard adolescents from negative experiences that might deprive them of wellness in their future. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) has emerged as a major player in this era of cutting-edge technological advancement. A key role that HAR plays is its ability to remotely monitor people. The objective of this paper is to classify human activities based on the data retrieved from smartphone sensors (accelerometer and gyroscope). The human activities that will be classified are namely; sitting, standing, climbing up and down the staircase, walking and laying down. To perform HAR from the data obtained, machine learning models are formed and finetuned in order to achieve the best results. The classic Machine Learning algorithms that have been put to use are Logistic regression, Linear and Kernel SVM, Decision Tree and Random Forest. Furthermore, a feed-forward Deep Neural Network and a 1D Convolutional Neural Network are proposed and compared with the aforementioned machine learning algorithms. Evaluation of a particular model has been carried out based on its Recall score, Precision and F1 score. Based on the results obtained from the evaluation process, it was found that the SVM and the proposed 1D convolution neural network were the best-performing models. [ABSTRACT FROM AUTHOR]

Abstract Background The differentiation of CD8+ T lymphocytes following priming of naïve cells is central in the establishment of the adaptive immune response. Yet, the molecular events underlying this process are not fully understood. MicroRNAs have been recently shown to play a key role in the regulation of haematopoiesis in mouse, but their implication in peripheral lymphocyte differentiation in humans remains largely unknown. Methods In order to explore the potential implication of microRNAs in CD8+ T cell differentiation in humans, microRNA expression profiles were analysed using microarrays and quantitative PCR in several human CD8+ T cell subsets defining the major steps of the T cell differentiation pathway. Results We found expression of a limited set of microRNAs, including the miR-17~92 cluster. Moreover, we reveal the existence of differentiation-associated regulation of specific microRNAs. When compared to naive cells, miR-21 and miR-155 were indeed found upregulated upon differentiation to effector cells, while expression of the miR-17~92 cluster tended to concomitantly decrease. Conclusions This study establishes for the first time in a large panel of individuals the existence of differentiation associated regulation of microRNA expression in human CD8+ T lymphocytes in vivo, which is likely to impact on specific cellular functions.

Background: The differentiation of CD8+T lymphocytes following priming of naïve cells is central in the establishment of the adaptive immune response. Yet, the molecular events underlying this process are not fully understood. MicroRNAs have been recently shown to play a key role in the regulation of haematopoiesis in mouse, but their implication in peripheral lymphocyte differentiation in humans remains largely unknown. Methods: In order to explore the potential implication of microRNAs in CD8+T cell differentiation in humans, microRNA expression profiles were analysed using microarrays and quantitative PCR in several human CD8+T cell subsets defining the major steps of the T cell differentiation pathway. Results: We found expression of a limited set of microRNAs, including the miR-17∼92 cluster. Moreover, we reveal the existence of differentiation-associated regulation of specific microRNAs. When compared to naive cells, miR-21 and miR-155 were indeed found upregulated upon differentiation to effector cells, while expression of the miR- 17∼92 cluster tended to concomitantly decrease. Conclusions: This study establishes for the first time in a large panel of individuals the existence of differentiation associated regulation of microRNA expression in human CD8+T lymphocytes in vivo, which is likely to impact on specific cellular functions. [ABSTRACT FROM AUTHOR]

The cancer stem cell concept assumes that cancers are mainly sustained by a small pool of neoplastic cells, known as cancer stem cells or tumor initiating cells, which are able to reproduce themselves and produce phenotypically heterogeneous cells with lesser tumorigenic potential. Cancer stem cells represent an appealing target for development of more selective and efficient therapies. However, direct testing of the cancer stem cell concept and assessment of its therapeutic implications in human cancers have been complicated by the use of immunocompromised mice. Genetically defined immunocompetent autochthonous mouse models of human cancer provide a valuable tool to address this problem. Furthermore, they allow for a better understanding of the relevance of mechanisms controlling normal stem cell compartment to carcinogenesis. Advantages and disadvantages of some of the existing mouse models are reviewed, and future challenges in cancer stem cell research are outlined. [ABSTRACT FROM PUBLISHER]

A personal narrative is presented which explores the author's experience of organizing informal reflection seminars in his college for helping other medical students.