Jahangirnagar University

Objectives Problematic or addictive use of social media has been associated with psychological and health issues. The main objective of this study was to explore the relationship between Facebook addiction and eating disorders (EDs) among young adults in Bangladesh. Methods We conducted an online‐based cross‐sectional study among 550 young adults aged 18 to 27 in Bangladesh. A structured questionnaire was used to obtain the survey data. The survey tool consisted of three sections: (i) socio‐demographic, behavioral, and social media use‐related characteristics, (ii) assessment of Facebook addiction using Bergen Facebook Addiction Scale, and (iii) assessment of EDs risk using Eating Attitudes Test‐26 (EAT‐26, outcome variable). Scoring at or above 20 on the EAT‐26 scale (total score ranges from 0 to 78) indicated an ED risk. Unadjusted and adjusted binary logistic regression analyses were performed to investigate the relationship outcome and explanatory variables. Results Approximately 38% of the study participants showed addiction to Facebook, whereas 23.6% were at risk of developing an ED. Multiple adjusted logistic regression models demonstrated that Facebook addiction was significantly associated with an increased risk of EDs (OR = 1.784; 95% confidence interval [CI]: 1.154–2.760). Moreover, smoking habits, self‐rated body mass index (BMI), and physical activity level showed a significant association with the risk of EDs. Conclusions These findings may help public health professionals and policymakers to take the initiative and develop strategies to overcome these addictive behaviors and promote healthy eating habits across the country.

Background The relationship between social media use and sleep quality is complicated and may be impacted by several contextual factors, including age, socioeconomic status, living environment, and other medical issues. It is necessary to explore the relationship between social media usage and poor sleep outcomes among university students. However, little is known about the connection between sleep issues and the detrimental effects of social media use. This study aims to investigate the relationship between social media, social media addiction (SMA), social media fatigue (SMF), fear of missing out (FoMO), and sleep quality (SQ) in Bangladeshi students. Methods Primary data were collected from 611 university students using a stratified random sampling technique. The Pittsburgh Sleep Quality Index and other variables of scales such as SMA, SMF, and FoMO were used in this survey. Descriptive statistics of participants and logistic regression were used to identify significant factors, and ANOVA was used to compare the means of multiple groups to determine. Results Findings revealed that 413 (67.57%) respondents have sleep disruption, and most of the participants used social media for 0–2 h daily, however, only a small portion exceeded 8 h. This study also found that SMA, SMF, and FoMO significantly impact the SQ, where students with low SMF scores were 6.85 times more likely to report good sleep quality than those with high SMF scores. Low SMA scores are 2.04 times more likely to have good SQ compared to the high scores of SMA, and for FoMO, the low scores are 2.22 times more likely to have good SQ compared to high scorers of FoMO. Among the participating students, 47% of the students rated their health as “good”, 45% as “fair”, and 4% as “poor”. The study found that sleep SQ has a significant impact on self-reported health status, with good SQ having a 0.598 times lower risk of fair health conditions than those with bad SQ. Moreover, social media use, time spent on social media, and how many hours you usually sleep at night in the past month covariates show a significant impact on student health. Conclusion University students were more likely to have sleep issues after using social media in ways that caused negative effects like SMF, SMA, and FoMO. Social media overactivity reduces sleep quality and affects on also self-reported health, respectively.

Microplastics (MPs) are emerging pollutants that threaten the aquatic ecosystem. Aquatic insects may play a crucial role in moving MPs into different trophic levels within and across the ecosystems. However, field-level evidence is still insufficient globally despite its tremendous ecological significance. Thus, for the first time in Bangladesh, MPs were explored in six species of aquatic insects along with water and sediment of the Daleshwari River. Digestion and density separation methods were used for the extraction of MPs. Microscopic inspection and Fourier transform spectroscopy (FT-IR) were done to identify and quantify MPs. The average concentration of MPs in sediment and water is 143.1 ± 28.52 of MPs/L and 30153.8 ± 2313.62 of MPs/kg, respectively. In aquatic species, the highest MPs found in D. rusticus (57.82 ± 14.98 MPs/g), followed by B. contaminate (38.53 ± 6.87 MPs/g), Ranatra sp. (34.05 ± 5.39 MPs/g), C. servilia (26.99 ± 7.88 MPs/g), D. annulatus (16.44 ± 6.95 MPs/g), and O. sabina (14.13 ± 4.52 MPs/g). A total of eight types of polymers have been identified. It was important to notice that the studied aquatic insects bear similar MPs (size, shape, and color) found in water and sediments from the river. It reveals the potential for the insects (accumulators of MPs) to be a driving factor for the transport of the MPs across different ecosystems. It has also been found that Aquatic insect’s size, weight, feeding habitat, and host reserviour could be responsible for MPs ingestion. In addition, ecological risk assessment (Contamination Factor, Nemerrow Pollution Index, Pollution Load Index, Polymer Hazard Index) indicates different levels of risk for the pertaining river ecosystem.

Spectrum-averaged cross sections of the reactions ⁸⁵ Rb(n,α) 82xm + g Br, ⁸⁵ Rb(n,2n) 84xm + g Rb and ⁸⁵ Rb(n,p) 85m Kr were measured over the fast neutron spectrum with neutron energies from 0.5 to 20 MeV of a TRIGA reactor. In addition, data for the well-investigated reactions ¹¹³ In(n,n´) 113m In and ¹¹⁵ In(n,n´) 115m In were re-measured to confirm the validity of the neutron spectrum and the experimental techniques. The new results of the present measurements were used to perform integral tests of evaluated excitation functions in the latest nuclear data libraries, including ENDF/B-VIII.1, JEFF-3.3, JEFF-3.1/A, JENDL-5, IRDF-1.05, and TENDL-2023. The experimental data generally agree with the integrated values from the excitation functions within 8 %, with a few exceptions. For the reaction ⁸⁵ Rb(n,2n) 84xm + g Rb, the integrated value from the ENDF/B-VIII.1 library is higher by 17 % than the measured integral value. The JEFF-3.1/A and JENDL-5 libraries for the reaction ⁸⁵ Rb(n,p) 85m Kr appear to require improvement.

This study investigates the contamination status and dispersion of 11 potentially toxic elements by exploring the potential mechanistic pathways by analyzing 60 samples (coal, ash, topsoil, and subsoil) from and around the coal-based brick kilns by neutron activation analysis. The meann=10 concentrations (μg g⁻¹) of scandium (Sc: 3.85), zinc (Zn: 79.21), Antimony (Sb: 4.06), and cesium (Cs: 4.81) in coal samples and manganese (Mn: 488), antimony (Sb: 10.9), and cesium (Cs: 20.3) in ash samples were 2.8–4.3 times and 1.1–2.5 times higher than world average values, respectively. In soil samples, averagen=40 abundances (μg g⁻¹) of chromium (Cr: 109), zinc (Zn: 144), arsenic (As: 8.98), rubidium (Rb: 113), antimony (Sb: 2.29), and cesium (Cs: 14.3) are 1.1–5.7 times higher than the crustal values. Additionally, geo-environmental indices showed that cesium (Cs) and chromium (Cr) had undergone severe modification relative to the crustal value, and the corresponding soil samples were moderately contaminated. The positive matrix factorization (PMF) model reveals that aerodynamic transportation contributes 22% to the elemental transportation of manganese, titanium, and iron throughout the soil profile in distant soil. In comparison, hydrodynamic transportation contributes 25% for As, Zn, and Sc in both topsoil and subsoil in the nearby soil. However, the combined process of bio-geo-accumulation, hydrodynamic leaching, and aerodynamic convection mechanisms contributes 53% of the dispersion and distribution of cesium (Cs), cobalt (Co), rubidium (Rb), and chromium (Cr) in the ambient pedosphere around the brick kilns which local geology, soil properties, solubility, and weathering can further influence. Our research findings contribute to advancing scientific approaches for investigating soil contamination, including the mechanistic pathways of potentially toxic elements and the risks associated with brick kilns.

Elevated CO2 emissions are a primary cause of the sustainability challenges, including rising sea levels and extreme weather patterns, faced by Bangladesh and the world. This study examines the intricate relationship between CO2 emissions and various economic and industrial factors in Bangladesh, using the autoregressive distributed lag (ARDL) bound test. By analyzing data from 1971 to 2020, the research identifies both short-run and long-run dynamics influencing CO2 emissions. The findings reveal that industrial production and non-renewable energy consumption have a significant positive impact on CO2 emissions, while agricultural activities and fertilizer consumption exhibit a negative effect. The study underscores the need for Bangladesh to transition towards renewable energy sources and improve agricultural practices to mitigate CO2 emissions. Advanced econometric techniques, including the ARDL Bound Test, CUSUM, and CUSUMSQ, are employed to ensure the robustness of the results. The ARDL framework yields key metrics: RMSE = 0.034, MSE = 0.001, AIC = -160.002, BIC = -139.651, R-squared = 0.801, and adjusted R-squared = 0.753, to explore the CO2 emissions nexus in Bangladesh. The study concludes that, while industrial and energy factors significantly contribute to CO2 emissions, enhancing renewable energy use and adopting climate-smart agricultural practices are essential for sustainable environmental management. Policy recommendations include promoting renewable energy adoption, implementing carbon capture technologies, and revising carbon tax policies to achieve long-term sustainability and environmental conservation.

The chaperonin containing TCP1 subunit 5 (CCT5) is believed to function as a tumor driver. However, a systematic pan-cancer analysis of CCT5 is still lacking. Therefore, this study aimed to identify the potential role of CCT5 in different types of tumors. This study comprehensively investigated the gene expression, proteomic expression, immune infiltration, DNA methylation, genetic alterations, correlation with TMB and MSI, drug sensitivity, enrichment analysis, and prognostic significance of CCT5 in 33 different tumors based on the TIMER2.0, GEPIA2, UALCAN, SMART, cBioPortal, GSCA databases, and TCGAplot R package. The results revealed significant CCT5 overexpression in most tumors and was significantly associated with poor OS and DFS in different tumor types. Reduced promoter and N-shore methylation of CCT5, indicating its potential oncogenic and epigenetic roles. Amplification was the most common type of CCT5 alterations. Immune infiltration analysis revealed a strong correlation between CCT5 and different immune cells. CCT5 exhibited a significant correlation with TMB and MSI in KIRC and STAD. Furthermore, enrichment analysis revealed associations between CCT5 and cell cycle pathway and various cellular functions. These findings suggested that CCT5 might serve as a potential prognostic biomarker and target for immunotherapy in various cancers. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-88339-z.

Malnutrition remains one of the most pressing global health challenges, particularly in developing countries like Bangladesh, where it continues to significantly impact child health and contribute to chronic illness and high child mortality. Despite the potential of machine learning to improve malnutrition predictions, research in this area remains limited in the country. This study utilizes Bangladesh Demographic and Health Survey (BDHS) 2022 data to identify and quantify key determinants of under-five malnutrition (underweight, wasting, stunting) and evaluates various machine learning models for predicting malnutrition. By addressing a critical gap, this research provides deeper insights into the root causes of malnutrition in Bangladesh. Descriptive statistics were conducted to summarize the key characteristics of the dataset. Boruta algorithm was employed to identify important features related to malnutrition which were then used to evaluate several machine learning models, including K-Nearest Neighbors (KNN), Neural Networks (NN), Classification and Regression Trees (CART), XGBoost (XGBM), Support Vector Machines (SVM), and Random Forest (RF), in addition to the traditional logistic regression (LR) model. The best-performing model was selected to identify the most important factors contributing to malnutrition. The significance of these variables was further assessed using Feature Importance plot (Based on Gini Importance) and Shapley Additive Explanation (SHAP) values. Model performance was evaluated through various metrics, including accuracy, 95% Confidence Interval (CI), Cohen’s kappa, sensitivity, specificity, F1 score and precision. The study examined a cohort of 7,910 children, reporting prevalence rates of 19% for stunting, 8% for wasting, and 17% for underweight. The Boruta algorithm identified 18 confirmed features for stunting, 22 for wasting, and 19 for underweight. For stunting, the Random Forest (RF) model outperformed other methods with an accuracy of 64.19%, 95% CI of (0.623, 0.666), Cohen’s kappa of 0.158, sensitivity of 56.25%, specificity of 66.00%, F1 score of 0.750 and precision of 0.60. In wasting prediction, RF achieved the highest accuracy at 76.68%, 95% CI of (0.743, 0.787), Cohen’s kappa of 0.049, sensitivity of 27.22%, specificity of 80.98%, F1 score of 0.865 and precision of 0.810. Similarly, for underweight, RF demonstrated superior performance with an accuracy of 68.18%, 95% CI of (0.662, 0.703), Cohen’s kappa of 0.130, sensitivity of 43.02%, specificity of 73.48%, F1 score of 0.792 and precision of 0.735. Across all malnutrition types, the RF model consistently outperformed traditional logistic regression (LR) and other ML techniques in terms of accuracy, sensitivity, specificity, and other performance metrics. For stunting, key predictors identified in both the Shapley and Gini importance plots included mother’s education, father’s occupation, place of delivery, wealth index, birth order, and toilet facility; for wasting, significant predictors were antenatal care, unmet family planning, mother’s BMI, birth interval, father’s occupation, and television ownership; and for underweight, important factors included father’s occupation, mother’s education, child’s age, birth order, wealth index, and place of delivery. This study highlights the effectiveness of Random Forest (RF) in predicting malnutrition outcomes—stunting, wasting, and underweight—using key features identified by the Boruta algorithm. While RF demonstrates moderate performance in predicting stunting and underweight, it shows strong predictive ability for wasting. This underscores RF’s potential in guiding targeted interventions for wasting, though further improvements are needed for stunting and underweight predictions. Moreover, the study identifies key contributors for each malnutrition outcome. By pinpointing these determinants, the study provides actionable insights for designing targeted interventions to combat malnutrition more effectively. These findings align with the global development agenda, particularly Sustainable Development Goal (SDG) 2: Zero Hunger and SDG 3: Good Health and Well-being, reinforcing efforts to reduce malnutrition and improve child health outcomes in Bangladesh. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-99288-y.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily marked by amyloid-beta (Aβ) plaque accumulation and neurofibrillary tangles, which lead to cognitive decline. Oxidative stress and neuroinflammation are key contributors to the disease's progression, with elevated production of Reactive Oxygen Species (ROS) exacerbating neu-ronal damage. Coenzyme Q10 (CoQ10), a naturally occurring antioxidant, has been identified for its potential neuroprotective effects due to its roles in mitochondrial function, energy production , and antioxidant defense. The cytokine interleukin-17 (IL-17) is also implicated in AD, promoting neuroinflammation by disrupting the blood-brain barrier (BBB) and activating glial cells. This review explores the impact of CoQ10 on neuroinflammation and oxidative stress in AD, focusing on its role in mitigating IL-17-mediated pathways. Preclinical studies indicate that CoQ10 reduces Aβ plaques, improves cognitive functions, and restores mitochondrial stability. However, clinical trials have yielded mixed results, often limited by bioavailability challenges. This research highlights the necessity of further human trials better to understand CoQ10's therapeutic potential in AD management.

Tropical monsoon countries like Bangladesh have experienced erratic spatiotemporal rainfall distribution, heavy rainfall, and extensive erosion in recent decades. The erosive nature of the soil in the country poses a serious ecological problem. However, there is a lack of studies on the spatiotemporal distribution of rainfall erosivity and precipitation concentration trends in Bangladesh. This study intends to investigate the Rainfall erosivity over the past three decades in Bangladesh. Using the Precipitation Concentration Index (PCI) and the Modified Fournier Index (MFI), this study attempted to demonstrate precipitation concentration and erosivity distribution during 1991–2020. The PCI and MFI indices were calculated using monthly precipitation records from 30 observatories nationwide. PCI values ranged between 15.43% and 21.04%, indicating substantial irregularity in rainfall across Bangladesh, while the MFI value higher than 98 shows a very high erosion capacity of rainfall in a shorter period. The mean annual rainfall erosivity factor (R-factor) found 865 MJ mm ha− 1 hr− 1 y− 1 with a range of 711.89–1019.97 MJ mm ha− 1 hr− 1 y− 1, suggesting moderate to higher erosivity potential in annual rainfall. All the stations exhibited higher erosivity values in monsoon (597.673–902.893 MJ mm ha− 1 hr− 1 y− 1), followed by pre-monsoon (325.779–436.599 MJ mm ha− 1 hr− 1 y− 1) and post-monsoon (166.67–241.52 MJ mm ha− 1 hr− 1 y− 1). Higher rainfall erosivity is concentrated in Bangladesh’s mid-central to northeastern region, while the southwest, northeast, and southeastern areas are at higher risk of monsoon rainfall erosivity. Though decreasing trends in annual rainfall erosivity were observed in 26 stations, monsoon and post-monsoon rainfall erosivity showed an increasing trend in 19 and 8 stations, respectively. The outcome of the current study is expected to help address the challenges of climate change and sustainable development issues in Bangladesh and similar climate-vulnerable countries around the world.

The Asian Woollynecks were once thought to be a rare winter migrant to the wetlands of Bangladesh, and until very recently, only a few incidents of nesting had been observed. New information shows expanding populations across their habitat, though little is known about their movement ecology, breeding ecology and preferences for artificial nest sites. In this paper, we documented some crucial nesting and breeding information on this species. We used camera traps as a means of passive monitoring and identified Large‐billed crow Corvus macrorhynchos and House crow Corvus splendens as potential nest predators. Three nests were found in artificial structures, where anthropogenic activity, like disturbances during cell phone tower maintenance, or predators limit the breeding success of Asian Woollynecks.

This study intends to ascertain the correlation between green banking practices and the sustainable environmental performance of private banks. It further investigates the mediating role of employee green behavior and motivation. This study used a quantitative research method to test the study hypotheses. A standardized questionnaire with a 5-point Likert scale was utilized to collect data for the survey. The sample size consisted of 376 respondents who were conveniently selected. Data were analyzed using PLS software (Version 4.0). The main finding is that employees’ green motivation mediated the link between employee-related and customer-related green practices and a bank’s environmental performance. Equally, employee green behavior mediated the link between employee-related, operation-related, and customer-related green practices and a bank’s environmental performance. This study is one of few in Bangladesh’s banking sector that provide a comprehensive overview of green banking practices, employee green motivation and behavior, and their connections to banks’ sustainable environmental performance.

Background: Foreign direct investment (FDI) is a steadfast contributor to capital flows and plays an indispensable role in driving economic advancement and emerging as a pivotal avenue for financing growth in Bangladesh. Therefore, this study identifies the factors that influence FDI inflows in Bangladesh. Moreover, the authors explored the more appropriate model for predicting FDI by comparing the efficacy of other models’ predictions. Methods: This study is based on secondary data over the period 1973 to 2021 and collected from the publicly accessible website of the World Bank. A generalized additive model (GAM) was implemented for describing the proper splines. The model’s performance was assessed using the modified R-squared, the Bayesian information criterion (BIC), and the Akaike information criterion (AIC). Results: Findings depict a significant nonlinear relationship between Bangladesh’s FDI and key economic indicators, including GDP, trade openness, external debt, gross capital formation, gross national income (GNI) and government rates of exchange, total reserves, and total natural resource rent. It is also observed that the GAM R2=0.987,AIC=608.03,and BIC=658.28 outperforms multiple linear regressions and polynomial regression in predicting FDI, emphasizing the superiority of GAM in capturing complex relationships and improving predictive accuracy. Conclusion: A nonlinear relationship is observed between FDI along with the covariates considered in this study. The authors believed that this study’s findings would assist in taking efficient initiatives for FDI management and proactive economic indicator optimization to empower Bangladesh’s economic resilience and foster sustainable growth. The analysis revealed that FDI and its related risk factors follow a nonlinear pattern. The study recommends using the GAM regression as a reliable method for predicting FDI in Bangladesh. The authors suggest that the findings can guide policymakers in developing strategies to increase FDI inflows, stimulate economic growth, and ensure sustainable economic development in Bangladesh.

Background and Aim Climate change refers to long‐term shifts in weather patterns and is one of the greatest global threats. Bangladesh is among the most vulnerable countries, facing severe climate‐induced events. Understanding climate change is crucial for identifying risks, developing adaptation strategies, and mitigating long‐term impacts. University students, as future leaders, play a vital role in addressing climate change. This study assesses their knowledge, attitudes, and perceptions of climate change in Bangladesh. Methods A cross‐sectional study was conducted among students from four universities in Bangladesh. A total of 1500 participants were selected based on inclusion criteria. Descriptive statistics summarized demographic characteristics, and perception regarding climate change, while univariate and multivariate logistic regression identified factors associated with good knowledge and positive attitudes. Results Overall, 73% of students had good knowledge of climate change, while 27% demonstrated poor knowledge. A majority (84%) expressed a positive attitude toward climate change initiatives. Participants correctly identified key climate‐related events in Bangladesh, such as increased cyclones, tidal waves, and salinity. However, awareness of rising snakebite incidents and related deaths was low, with many perceiving no change or disagreeing with their significance. Factors associated with good knowledge included gender, source of information, and mother's education. Gender, source of information, and both parents' education were associated with positive attitudes among the participants. Conclusions This study provides baseline evidence on climate change knowledge, attitudes, and perceptions among Bangladeshi university students. To our knowledge, it is the first comprehensive assessment of this issue in this population. Given their strong awareness and positive attitudes, targeted initiatives can harness students' potential in climate change mitigation and adaptation efforts, contributing to long‐term solutions for Bangladesh's climate challenges.

Purpose This study investigates Cyanodon dactylon as a sustainable resource for cellulose extraction and its modification into cellulose acetate (CA), aiming to develop eco-friendly bio-polymeric films. The research focuses on evaluating the efficiency of cellulose isolation, acetylation, and the structural and mechanical properties of CA-based composite films. Methods Cellulose was isolated from Cyanodon dactylon using sequential alkaline pulping, yielding 31.98% cellulose content, including 27.3 wt% α-cellulose. The extracted cellulose underwent rapid acetylation via transesterification with vinyl acetate in DMSO, completing within 5 min. CA was then blended with polyvinyl alcohol (PVA) to fabricate bio-polymeric films through solvent casting. Characterization techniques, including FT-IR, XRD, ¹H-NMR, TGA, and chemical saponification, were used to assess the structural, thermal, and morphological properties. Mechanical testing evaluated the films’ strength and flexibility. Results FT-IR spectra confirmed successful acetylation, while XRD analysis showed reduced crystallinity correlating with the degree of substitution. ¹H-NMR and titrimetric methods validated the degree of acetylation. The CA-PVA films demonstrated a balance between structural integrity and porosity, with slightly reduced tensile strength and elongation at break. The substantial α-cellulose content and efficient CA synthesis highlight Cyanodon dactylon’s potential as a renewable material source. Conclusion This study establishes Cyanodon dactylon as a viable, sustainable resource for eco-friendly material production. The successful synthesis of cellulose acetate and its application in bio-polymeric films promote environmental sustainability and economic viability, contributing to green material innovation. Statement of Novelty This research presents a novel approach to the sustainable synthesis of cellulose acetate (CA) from Cyanodon dactylon, a widely available and underutilized plant resource, for bio-based polymeric film applications. The study introduces a rapid transesterification process that achieves cellulose acetylation within just 5 min, significantly reducing reaction time compared to conventional methods. Comprehensive characterization using advanced analytical techniques confirms the structural and thermal modifications, emphasizing the successful integration of CA into PVA matrices for bio-composite films. This work highlights Cyanodon dactylon as a promising, renewable feedstock for the development of eco-friendly materials, offering an innovative pathway for valorizing biomass while contributing to sustainability and circular economy goals in polymer science. Graphical Abstract

Stroke is one of the most common causes of disability and death worldwide. With the rapidly growing stroke survivor population, it is crucial to identify an effective method for their healthcare. Recovery from stroke is followed by physiotherapy to promote rehabilitation. Task-oriented circuit training is designed to improve stroke patients’ overall functioning during rehabilitation. This research aims to assess the effectiveness of task-oriented circuit training compared with conventional physiotherapy. The investigators have planned an 8-week parallel, two-arm, prospective, randomised clinical study. Participants will be enrolled from eight branches of the centre for the rehabilitation of the paralysed (CRP). We have planned to recruit 506 stroke survivors via a 1:1 random assignment procedure for 24 months. As a main objective, the Action Arm Research Test and the Timed Up and Go will be used to test upper and lower limb motor function. The secondary objectives will include daily living and balance activities, which will be evaluated using the Barthel Index and the Berg Balance Scale. The post-test and follow-up data will be collected after 8 and 12 weeks. The final analysis will include dropouts and treatment side effects. This study has been granted ethical approval by the Ethics Review Committee of the CRP (CRP-R&E-0401-357)). All activities and interventions will be carried out following the Helsinki Declaration of 2020. The findings will be published in peer-reviewed journals and disseminated at international conferences. Trial registration number: CTRI/2023/09/057907 (21 September 2023) (Prospectively registered).

The purpose of this study was to determine the technical efficiency of the handloom sector of Bangladesh, along with the factors affecting the technical efficiency by employing the data envelopment technique. This study used a random sample of 314 handloom entrepreneurs from the district of Sirajganj, Bangladesh. The results revealed that only 45% of handloom firms were technically efficient, implying growth potential. Moreover, bootstrap truncated regression reveals that entrepreneur-specific factors (such as education, training, gender, and working experience), as well as firm-specific factors including (advanced technology, ICT, location, labor productivity, female labor ratio, and capital-labor ratio, government support), has a significant influence on technical efficiency of the handloom microenterprises of Sirajganj. The study recommends enhancing the personal development of handloom entrepreneurs through training, education, and ICT integration. Additionally, it suggests that development organizations provide supportive measures such as education access and low-cost loans to facilitate technology adoption and modernization.

Background In Bangladesh, pre-eclampsia poses a significant concern, evident in the low attendance (37%) for antenatal care (ANC). Despite efforts to reduce maternal and neonatal mortality, the latest Bangladesh Maternal Mortality Survey (BMMS-2016) indicates limited progress. Access to essential maternal and newborn health services, including ANC, remains constrained, highlighting the challenge of translating service coverage into improved outcomes. A research gap on pre-eclampsia symptoms such as severe headache, blurred vision, high blood pressure, and oedema emphasizes the need for targeted interventions for these symptoms early so that we can reduce the prevalence of pre-eclampsia and therefore maternal mortality in Bangladesh. The aim of this study is to investigate and compare the risk factors for three stages of pre-eclampsia among Bangladeshi women living in urban and rural areas. Methods The study utilized BMMS-2016 data, employing statistical analyses, including binary logistic regression, to identify associations. It assessed four pre-eclampsia symptoms prevalence across pregnancy stages, considering factors like maternal age, stillborn births, residency, ANC, healthcare facility delivery, education, and children. Results Logistic regression highlights key associations with pre-eclampsia symptoms. Urban mothers aged 36 + face the highest risk during delivery (AOR = 2) and the lowest in rural areas after delivery (AOR = 1.43). Two or more stillborn births increase the risk in urban delivery by 97%. Complete ANC raises odds, notably in urban pregnancy (AOR = 1.5) and rural post-delivery (AOR = 1.16). Skilled ANC providers elevate risks during all stages, with the highest in urban pregnancy (AOR = 1.54) and lowest after rural delivery (AOR = 1.28). Unskilled ANC associates with symptoms only during pregnancy. Healthcare facility delivery increases odds at all stages, particularly in rural delivery (AOR = 1.74) and urban pregnancy (AOR = 1.26). Multifetal gestation raises urban delivery risk (AOR = 2.11). Rural areas show higher chances during both pregnancy and delivery. Higher education in rural pregnancy and 2 to 3 birth order in urban delivery reduce odds of pre-eclampsia symptoms. Conclusions Addressing pre-eclampsia symptoms in Bangladesh, especially among urban women, is urgent. Identified risk factors necessitate targeted interventions to enhance ANC and overall maternal health. Advocating findings to policymakers is crucial for effective policies, reducing pre-eclampsia and eclampsia, contributing to lower maternal mortality.