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Graphene oxide (GO), the most common derivative of graphene, is an exceptional nanomaterial that possesses multiple physical properties critical for biomedical applications. GO exhibits pH-dependent fluorescence emission in the visible/near-infrared, providing a possibility of molecular imaging and pH-sensing. It is also water soluble and has a substantial platform for functionalization, allowing for the delivery of multiple therapeutics. GO physical properties are modified to enhance cellular internalization, producing fluorescent nanoflakes with low (<15%) cytotoxicity at the imaging concentrations of 15 μg/mL. As a result, at lower flake sizes GO rapidly internalizes into HeLa cells with the following 70% fluorescence based clearance at 24 h, assessed by its characteristic emission in red/near-IR. pH-dependence of GO emission is utilized to provide the sensing of acidic extracellular environments of cancer cells. The results demonstrate diminishing green/red (550/630 nm) fluorescence intensity ratios for HeLa and MCF-7 cancer cells in comparison to HEK-293 healthy cells suggesting a potential use of GO as a non-invasive optical sensor for cancer microenvironments. The results of this work demonstrate the potential of GO as a novel multifunctional platform for therapeutic delivery, biological imaging and cancer sensing.

Highlights • Tobacco use was highest, followed by alcohol and marijuana use. • The prevalence of substance use was higher in male than female adolescents. • Parental homework supervision significantly lowered the odds of alcohol and tobacco use. • Parental free-time supervision significantly reduced the odds of alcohol and tobacco use. • No significant association was found between parent-child connectedness and marijuana use. Background Adolescent substance use is recognized as a global health crisis that threatens adolescents’ physical and mental health worldwide. Alcohol is the most available one; WHO findings suggest that more than 155 million adolescents, representing over a quarter of the adolescent population aged between 11 and 15, use alcohol-based drinks worldwide. Since adolescents are the future of the world, protecting them from substance use is of paramount importance. Objectives This study aimed to explore the prevalence of adolescent substance use (alcohol, marijuana, and tobacco) among Bhutanese adolescents and examine the association with parent-child connectedness as a protective factor while controlling sociodemographic, socio-emotional distress, and other contextual factors. Methods A total of 7576 school-going adolescents’ data from the 2016 Global School-based Student Health Survey (GSHS) Bhutan dataset were used in this study. To analyze the relationship between predictor and outcome variables, both univariate and multivariate binary logistic regression models were constructed utilizing the “complex samples” tool of SPSS 25. A significance level of p  ≤ 0.05 was used for the analyses. Results An estimated 30.7% of the Bhutanese school-going adolescents used tobacco, 25.8% consumed alcohol, and 12.7% used marijuana. Parent-child connectedness: (i) child’s homework supervision and (ii) child’s free time supervision by parents significantly lower the odds of using tobacco and alcohol consumption, while parents understanding child’s problem showed no significant association with substance use among the respondents. However, no significant association was found between parent-child connectedness and marijuana use. Besides parental connectedness, anxiety, bullying, passive smoking, school truancy, being involved in fights, or being attacked were also significantly associated with adolescents’ substance use. Conclusion Parental connectedness has been found to be an important factor that can lead to a substantive reduction in substance use among the adolescents of Bhutan. However, the lesson is pertinent for any global initiatives aiming to prevent the harmful use of substances among global adolescents.

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion–exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

Background Responsiveness of Physicians (ROP) is defined as the social actions by physicians aimed at meeting the legitimate expectations of healthcare users. Even though patients’ expectations regarding ROP have increased during the COVID-19 pandemic, the psychometrically-validated ROP-Scale is difficult to apply in hospital settings. The goal of this study is to validate the existing ROP-Scale to measure the responsiveness of hospital physicians during the ongoing COVID-19 pandemic in Bangladesh. Methods We conducted a cross-sectional phone survey involving 213 COVID-19 hospital patients, randomly selected from the government database. We applied the Delphi method for content validity, exploratory and confirmatory factor analyses for construct validity, Cronbach’s alpha and corrected item-total correlation for internal consistency reliability, and Pearson’s correlation between the scale and overall patient satisfaction for concurrent validity. Results After removing survey items based on data sufficiency, collinearity, factor loading derived through exploratory factor analysis, and internal consistency, the final version of the COVID-19 ROP-Scale consisted of 7 items, grouped under Informativeness, Trustworthiness and Courteousness domains. The confirmatory factor analysis supported the three domains with acceptable model fit [Root mean squared error of approximation (RMSEA) = 0.028, Comparative fit index (CFI) = 0.997, Tucker-Lewis index (TLI) = 0.994)]. The corrected item-total correlation ranged between 0.45 and 0.71. Concurrent validity was ascertained by the high correlation (0.84) between patient satisfaction and the COVID-19 ROP-Scale. Based on the mean domain score, the highest- and the lowest-scoring responsiveness domains were ‘Trustworthiness’ (7.85) and ‘Informativeness’ (7.28), respectively, whereas the highest- and the lowest-scoring items were ‘Not being involved in illegal activities’ (7.97), and ‘Service-oriented, not business-like attitude’ (6.63), respectively. Conclusions The 7-item COVID-19 ROP-Scale was demonstrated to be feasible, valid, and internally consistent. Therefore, its application can help amend past mistakes in health service provision and improve care for the hospitalised COVID-19 patients or other patients suffering from similar conditions. This study can contribute to the national decision-making regarding hospital care, open up further avenues in the health policy and system research, and eventually improve the quality of care provided to Bangladeshi patients seeking hospital services. Moreover, findings yielded by this study can be incorporated into doctors’ medical education and in-service training.

In today's society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death. This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI's GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules. Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.

Background Reliable and rigorously collected sexual, reproductive, maternal, newborn, child, and adolescent health (SRMNCAH) data in humanitarian settings is often sparse and varies in quality across different humanitarian settings. To address this gap in quality data, the World Health Organization (WHO) developed a core set of indicators for monitoring and evaluating SRMNCAH services and outcomes, and assessed their feasibility in Bangladesh, Afghanistan, Jordan, and the Democratic Republic of Congo. Methods The feasibility assessments aggregated information from global consultations and field-level assessments to reach a consensus on a set of core SRMNCAH indicators among WHO partners. The feasibility assessment in Bangladesh focused on the following constructs: relevance/usefulness of the core set of indicators, the feasibility of measurement, availability of systems and resources, and ethical issues during data collection and management. The field-level multi-methods assessment included five components; a desk review, key informant interviews, focus group discussions, and facility assessments including observations of facility-level data management. Results The findings suggest that there is widespread support among stakeholders for developing a standardized core set of SRMNCAH indicators to be collected among all humanitarian actors in Bangladesh. There are numerous resources and data collection systems that could be leveraged, built upon, and improved to ensure the feasibility of collecting this proposed set of indicators. However, the data collection load requested from donors, the national government, international and UN agencies, coordination/cluster systems must be better harmonized, standardized, and less burdensome. Conclusion This core set of indicators would only be useful if it has the buy-in from the international community that results in harmonizing and coordinating data collection efforts and relevant indicators’ reporting requirements. Plain English Summary Reliable data on sexual, reproductive, maternal, newborn, child and adolescent health (SRMNCAH) in humanitarian settings is very important for addressing the needs of the refugee population. However, the quality of data collected often varies across different humanitarian settings and organizations. To address this gap, WHO developed a core set of indicators, through global consultations and field-level assessments, for monitoring and evaluating SRMNCAH services and outcomes in humanitarian settings. In Bangladesh, the feasibility assessment was conducted in Rohingya camps in Cox’s Bazar and assessed the feasibility in terms of relevance/usefulness of the indicators, feasibility of measurement, availability of systems and resources for data collection, and data collection and management related ethical issues in the Rohingya context in Bangladesh. The field-level assessment applied a multi-method approach including a desk review, key informant interviews, focus group discussions, and facility assessments. The findings revealed existing multiple sources and vertical systems of data collection by different organizations and the indicators also varied depending on the requirements of the Bangladesh government, donor agencies, UN agencies leading different sector/sub-sectors/clusters, and organizations’ own priorities and mandates. All the stakeholders who participated in this study agreed on developing a harmonized and standardized core set of SRMNCAH indicators. However, they raised concerns regarding the adaptation of the core set of indications to the local context. They also emphasized on the importance of adequate resources for establishing strong reporting and data management systems, capacity development of human resources and the buy-in from the international community for effective implementation.

Objectives This study aimed at examining health sufferings of readymade garments (RMG) workers, the factors that affect their health sufferings, their healthcare seeking pattern, knowledge about health insurance and health related rights in Bangladesh. Methods A cross‐sectional study was conducted among 486 RMG workers recruited randomly from eight garments factories located on the periphery of Dhaka, Bangladesh. The prevalence of musculoskeletal pain, headache, fever and abdominal pain was estimated and multivariable logistic regression analysis was performed to examine association between these illnesses of workers and their socio‐demographic characteristics and other work related information. We also explored their healthcare seeking patterns, knowledge about health insurance and health related rights. Results The prevalence of musculoskeletal pain, headache, fever and abdominal pain was found to be 78.1%, 57.9%, 52.2% and 24.6%, respectively, among the RMG workers. Factors that increased the odds of: musculoskeletal pain were working for more than 10 h per day (adjusted odds ratio [AOR]: 2.3, 95% confidence interval [CI]: 1.1–4.7) and being female [AOR: 4.6, 95% CI: 2.0–10.6]; fever was living in slums [AOR: 1.9, 95% CI: 1.1–3.5]; and abdominal pain was being female [AOR: 3.6, 95% CI: 1.4–9.3]. The workers commonly reported visiting drug sellers in local pharmacies for reported illnesses. They also had better knowledge of health related rights but poor knowledge of health insurance. Conclusion In order to address the overall health and well‐being of the RMG workers, it is imperative to lay out a blueprint for a safe and healthy workplace.

Happiness is a state of contentment, joy, and fulfillment, arising from relationships, accomplishments, and inner peace, leading to well-being and positivity. The greatest happiness principle posits that morality is determined by pleasure, aiming for a society where individuals are content and free from suffering. While happiness factors vary, some are universally recognized. The World Happiness Report (WHR), published annually, includes data on ‘GDP per capita’, ‘social support’, ‘life expectancy’, ‘freedom to make life choices’, ‘generosity’, and ‘perceptions of corruption’. This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. We also show the impact of COVID-19 pandemic on the happiness features. We design two ensemble ML and DL models using blending and stacking ensemble techniques, namely, Blending RGMLL, which combines Ridge Regression (RR), Gradient Boosting (GB), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Linear Regression (LR), and Stacking LRGR, which combines LR, Random Forest (RF), GB, and RR. Among the trained models, Blending RGMLL demonstrates the highest predictive accuracy with R 2 of 85%, MSE of 0.15, and RMSE of 0.38. We employ Explainable Artificial Intelligence (XAI) techniques to uncover changes in happiness indices, variable importance, and the impact of the COVID-19 pandemic on happiness. The study utilizes an open dataset from the WHR, covering 156 countries from 2018 to 2023. Our findings indicate that ‘GDP per capita’ is the most critical indicator of happiness score (HS), while ‘social support’ and ‘healthy life expectancy’ are also important features before and after the pandemic. However, during the pandemic, ‘social support’ emerged as the most important indicator, followed by ‘healthy life expectancy’ and ‘GDP per capita’, because social support is the prime necessity in the pandemic situation. The outcome of this research helps people understand the impact of these features on increasing the HS and provides guidelines on how happiness can be maintain during unwanted situations. Future research will explore advanced methods and include other related features with real-time monitoring for more comprehensive insights.

Happiness is a state of contentment, joy, and fulfillment, arising from relationships, accomplishments, and inner peace, leading to well-being and positivity. The greatest happiness principle posits that morality is determined by pleasure, aiming for a society where individuals are content and free from suffering. While happiness factors vary, some are universally recognized. The World Happiness Report (WHR), published annually, includes data on 'GDP per capita', 'social support', 'life expectancy', 'freedom to make life choices', 'generosity', and 'perceptions of corruption'. This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. We also show the impact of COVID-19 pandemic on the happiness features. We design two ensemble ML and DL models using blending and stacking ensemble techniques, namely, Blending RGMLL, which combines Ridge Regression (RR), Gradient Boosting (GB), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Linear Regression (LR), and Stacking LRGR, which combines LR, Random Forest (RF), GB, and RR. Among the trained models, Blending RGMLL demonstrates the highest predictive accuracy with R2 of 85%, MSE of 0.15, and RMSE of 0.38. We employ Explainable Artificial Intelligence (XAI) techniques to uncover changes in happiness indices, variable importance, and the impact of the COVID-19 pandemic on happiness. The study utilizes an open dataset from the WHR, covering 156 countries from 2018 to 2023. Our findings indicate that 'GDP per capita' is the most critical indicator of happiness score (HS), while 'social support' and 'healthy life expectancy' are also important features before and after the pandemic. However, during the pandemic, 'social support' emerged as the most important indicator, followed by 'healthy life expectancy' and 'GDP per capita', because social support is the prime necessity in the pandemic situation. The outcome of this research helps people understand the impact of these features on increasing the HS and provides guidelines on how happiness can be maintain during unwanted situations. Future research will explore advanced methods and include other related features with real-time monitoring for more comprehensive insights.

In this work, we utilize a bottom-up approach to synthesize nitrogen self-doped graphene quantum dots (NGQDs) from a single glucosamine precursor via an eco-friendly microwave-assisted hydrothermal method. Structural and optical properties of as-produced NGQDs are further modified using controlled ozone treatment. Ozone-treated NGQDs (Oz-NGQDs) are reduced in size to 5.5 nm with clear changes in the lattice structure and I D / I G Raman ratios due to the introduction/alteration of oxygen-containing functional groups detected by Fourier-transform infrared (FTIR) spectrometer and further verified by energy dispersive X-ray spectroscopy (EDX) showing increased atomic/weight percentage of oxygen atoms. Along with structural modifications, GQDs experience decrease in ultraviolet–visible (UV–vis) absorption coupled with progressive enhancement of visible (up to 16 min treatment) and near-infrared (NIR) (up to 45 min treatment) fluorescence. This allows fine-tuning optical properties of NGQDs for solar cell applications yielding controlled emission increase, while controlled emission quenching was achieved by either blue laser or thermal treatment. Optimized Oz-NGQDs were further used to form a photoactive layer of solar cells with a maximum efficiency of 2.64% providing a 6-fold enhancement over untreated NGQD devices and a 3-fold increase in fill factor/current density. This study suggests simple routes to alter and optimize optical properties of scalably produced NGQDs to boost the photovoltaic performance of solar cells.