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Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey
The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media-Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961-8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736-1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students.
Journal Article
Nanoparticles and its biomedical applications in health and diseases: special focus on drug delivery
Nanotechnology is an emerging technology that deals with nanosized particles possessing crucial research roles and application. Disciplines like chemistry, biology, physics, engineering, materials science, and health sciences provide an accumulated knowledge of nanotechnology. Nonetheless, it has vast submissions precisely in biology, electronics, and medicine. Aimed at drug delivery system, nanoparticles are based on the mechanism of entrapment of the drugs or biomolecules into the interior structure of the particles; another mechanism could be that the drugs or the biomolecules can be absorbed onto the exterior surfaces of the particles. Currently, nanoparticles (NPs) are used in the delivery of drugs, proteins, genes, vaccines, polypeptides, nucleic acids, etc. In recent years, various applications of the drug delivery system via NPs have encountered an enormous position sector like pharmaceutical, medical, biological, and others. Considering the impact of NPs in drug delivery systems, this review focuses on the detailed profile of NPs, its impact on biology and medicine, and their commercialization prospects.
Journal Article
Heat treatment effects on Inconel 625 components fabricated by wire + arc additive manufacturing (WAAM)—part 1: microstructural characterization
Wire + arc additive manufacturing (WAAM) is a versatile, low-cost, energy-efficient technology used in metal additive manufacturing. This WAAM process uses arc welding to melt a wire and form a three-dimensional (3D) object using a layer-by-layer stacking mechanism. In the present study, a Ni-based superalloy wire, i.e., Inconel 625, is melted and deposited additively through a cold metal transfer (CMT)-based WAAM process. The deposited specimens were heat-treated at 980 °C (the recommended temperature for stress-relief annealing) for 30, 60, and 120 min and then water quenched to investigate the effect of heat treatment on microstructure and phase transformation and to identify the optimum heat treatment condition. Microstructural results show that the heat treatment, in general, eliminates the brittle Laves phases regardless of the time without changing the grain morphology. However, an increment in the amount of the delta phase is observed with the longer heat treatment periods. Additionally, the size of MC (metal carbide) of Nb is also observed to increase with heat treatment time. This study provides an in-depth understanding of the correlation between heat treatment time and microstructure in additively manufactured Inconel 625, which can facilitate determining the optimum heat treatment condition in a later study.
Journal Article
Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis
We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.
We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist.
Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models.
We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.
Journal Article
Factors associating different antenatal care contacts of women: A cross-sectional analysis of Bangladesh demographic and health survey 2014 data
Antenatal care (ANC) contacts have long been considered a critical component of the continuum of care for a pregnant mother along with the newborn baby. The latest maternal mortality survey in Bangladesh suggests that progress in reducing maternal mortality has stalled as only 37% of pregnant women have attended at least four ANC contacts. This paper aims to determine what factors are associated with ANC contacts for women in Bangladesh. We analysed the data, provided by Bangladesh demographic and health survey 2014, covering a nationally representative sample of 17,863 ever married women aged 15-49 years. A two-stage stratified cluster sampling was used to collect the data. Data derived from 4,475 mothers who gave birth in the three years preceding the survey. Descriptive, inferential, and multivariate statistical techniques were used to analyse the data. An overall 78.4% of women had ANC contacts, but the WHO recommended ≥8 ANC contacts and ANC contacts by qualified doctors were only 8% for each. The logistic regression analysis revealed that division, maternal age, women's education, husband's education, wealth index and media exposure were associated with the ANC contacts. Likewise, place of residence, women's education, religion, and wealth index were also found to be associated with the WHO recommended ANC contacts. Furthermore, the husband's education, division, religion and husband's employment showed significant associations with ANC contacts by qualified doctors. However, Bangladeshi women in general revealed an unsatisfactory level of ANC contacts, the WHO recommended as well as ANC contacts by qualified doctors. In order to improve the situation, it is necessary to follow the most recent ANC contacts recommended by the WHO and to contact the qualified doctors. Moreover, an improvement in education as well as access to information along with an increase of transports, care centres and reduction of service costs would see an improvement of ANC contacts in Bangladesh.
Journal Article
Variable selection strategies and its importance in clinical prediction modelling
Clinical prediction models are used frequently in clinical practice to identify patients who are at risk of developing an adverse outcome so that preventive measures can be initiated. A prediction model can be developed in a number of ways; however, an appropriate variable selection strategy needs to be followed in all cases. Our purpose is to introduce readers to the concept of variable selection in prediction modelling, including the importance of variable selection and variable reduction strategies. We will discuss the various variable selection techniques that can be applied during prediction model building (backward elimination, forward selection, stepwise selection and all possible subset selection), and the stopping rule/selection criteria in variable selection (p values, Akaike information criterion, Bayesian information criterion and Mallows’ Cp statistic). This paper focuses on the importance of including appropriate variables, following the proper steps, and adopting the proper methods when selecting variables for prediction models.
Journal Article