Background: Covid 19 lockdown measures were taken all of a sudden during the devastating second wave in India, when there was a considerable loss and suffering in the country. The coronavirus disease 2019 (COVID 19) pandemic has led to unprecedented hazards to mental well-being globally. Purpose: To assess the prevalence and evaluate risk factors of depression, insomnia or sleep disturbances and suicidal ideation among covid 19 positive patients admitted in covid wards with mild-to-moderate disease. Materials and Methods: A total of 635 hospitalised patients who were covid-19 positive were requested to fill an online quality of life pre-validated questionnaire comprising of 4 sectionsthe sociodemographic information section, health care assessment related to depression symptoms, insomnia assessment and assessment of suicidal ideation. The survey comprised of prevalidated questions on sociodemographics, knowledge of covid 19, fear of covid 19, insomnia, feeling of sadness, depression, feeling of rejection and suicidal ideation among the covid 19 positive inpatients in quarantine due to mild or moderate covid 19 disease. Results: The prevalence of depression and insomnia or sleep disturbances after being diagnosed as covid 19 positive and hospitalized was nearly 40% and 28.8%, respectively, among the inpatients. Depression was significantly observed in female group (p < 0.001), unmarried or separated individuals (p < 0.001), housewives (p < 0.001) and patients with comorbidities (p < 0.001). Insomnia was more likely to be present in elderly covid positive patients (p < 0.001) and separated or divorced group of participants (p < 0.001). The prevalence of suicidal ideation was 5% of the total covid 19 positive patients participated in this study, and it was significantly observed among separated or divorced patients, cancer patients, patients from suburban residence and among graduates (p < 0.001). Conclusion: Covid 19 is associated with major psychological impact among the patients suffering from thus warrants counselling.
The human ovary is a complex structure that is controlled by endocrine, paracrine, and autocrine mechanisms. The number of eggs retrieved after controlled ovarian stimulation in in vitro fertilization depends on the physiological follicular reserve pool of ovaries. Ovarian reserve is decided genetically and decreases with advancing age and gets affected by ovarian surgery, chemotherapy, radiotherapy, and autoimmune disorders. Environmental influences like chronic smoking, hyperglycemia, and conditions interfering ovarian vascularity also reduce the ovarian reserve. This chapter summarizes the methods to assess the ovarian reserve. This helps in deciding the initiating dose of gonadotropins for controlled ovarian hyper stimulation for optimal follicular response.
Over the last few decades, there has been a gradual deterioration in higher education in all three areas: the academic setting (both staff and students), as well as research and development output (including graduates). All colleges and universities are essentially focused on improving management decision-making and educating pupils. High-quality higher education can be obtained through a variety of methods. One method is to accurately forecast pupils’ achievement in their chosen educational context. There are numerous prediction models from which to pick. While it is unclear whether there are any markers that can predict whether a kid will be an academic genius, a dropout, or an average performer, the researcher reports student achievement. This article presents a metaheuristics and machine learning-based method for the classification and prediction of student performance. Firstly, features are selected using a relief algorithm. Machine learning classifiers such as BPNN, RF, and NB are used to classify student academic performance data. BPNN is having better accuracy for classification and prediction of student academic performance.
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