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Abstract Background Pooling data from complex survey designs is increasingly used in the health and medical sciences. However, current methodological practices are not well documented in the literature while performing the pooling strategy. We aimed to review related pooling studies and evaluate the quality of pooling within the framework of specific methodological guidelines, particularly when combining complex surveys such as Demographic & Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). Methods We performed a systematic literature search focusing on studies utilizing the pooling method with DHS and MICS survey data. These studies were selected from those published between 2010 and 2021 and were retrieved from electronic databases (PubMed and Scopus) in accordance with pre-defined inclusion criteria. Then, we extracted 355 studies for the final review and evaluated the reporting quality of the pooling strategy while considering some methodological issues. Results The majority of studies (81.4%) reported using a pooled (one-stage) approach, while 11.8% used a separate (two-stage) approach, and 6.8% used both approaches. Approximately 63.3% of studies did not clearly describe their pooling strategy. Only 3.4% of the studies mentioned the variable harmonization process, while 66.9% addressed dealing with heterogeneity between surveys. All studies that used the separate (two-stage) approach conducted a meta-analytic procedure, while 38.1% of studies using the pooled approach employed a multilevel model. More than half of the studies (55.6%) mentioned the use of clustered standard errors. The Delta method, Bootstrap, and Taylor linearization were each applied in 11.1% of the studies for variance estimation. Survey weights, primary sampling unit (PSU) or cluster, and strata were used together in 30.5% of the studies. Survey weights were employed by 69.8%, PSU or cluster by 43.8%, and the strata variable by 31.7%. Sensitivity analysis was conducted in 16% of the studies. Conclusions Our study revealed that fundamental methodological issues associated with pooling complex survey databases, such as the selection of pooling procedures, data harmonization, accounting for cycle effects, quality control checks, addressing heterogeneity, selecting model effects, utilizing survey design variables, and dealing with missing values, etc., were inadequately reported in the included studies. We recommend authors, readers, reviewers, and editors examine pooling studies more attentively and utilize the customized checklist developed by our study to assess the quality of future pooling studies.

In the sentiment analysis (SA) task, we can obtain a positive or negative-typed comment or feedback from an online user or a customer about any object, such as a movie, drama, food, and others. This user’s sentiment may positively impact various decision-making processes. In this regard, a lot of studies have been done on identifying sentiments from a text in high-resource languages like English. However, a small number of studies are detected in the under-resource Bengali language because of the unavailability of the benchmark corpus, limitations of text processing application software, and so on. Furthermore, there is still enough space to enhance the classification performance of the SA task. In this research, we experiment on a recognized Bengali dataset of 11,807 comments to find positive or negative sentiments. We employ five state-of-the-art transformer-based pretrained models, such as multilingual Bidirectional Encoder Representations from Transformers (mBERT), BanglaBERT, Bangla-Bert-Base, DistilmBERT, and XLM-RoBERTa-base (XLM-R-base), with tuning of the hyperparameters. After that, we propose a combined model named Transformer-ensemble that presents outstanding detection performance with an accuracy of 95.97% and an F1-score of 95.96% compared to the existing recent methods in the Bengali SA task.

Summary: Background: Bangladesh is making progress toward achieving zero dog-mediated rabies deaths by 2030, a global goal set in 2015. Methods: Drawing from multiple datasets, including patient immunisation record books and mass dog vaccination (MDV) databases, we conducted a comprehensive analysis between 2011 and 2023 to understand the effectiveness of rabies control programmes and predict human rabies cases in Bangladesh by 2030 using time-series forecasting models. We also compared rabies virus sequences from GenBank in Bangladesh and other South Asian countries. Findings: The estimated dog population in Bangladesh was determined to be 1,668,140, with an average dog population density of 12.83 dogs/km2 (95% CI 11.14–14.53) and a human-to-dog ratio of 86.70 (95% CI 76.60–96.80). The MDV campaign has led to the vaccination of an average of 21,295 dogs (95% CI 18,654–23,935) per district annually out of an estimated 26,065 dogs (95% CI 22,898–29,230). A declining trend in predicted and observed human rabies cases has been identified, suggesting that Bangladesh is poised to make substantial progress towards achieving the ‘Zero by 30’ goal, provided the current trajectory continues. The phylogenetic analysis shows that rabies viruses in Bangladesh belong to the Arctic-like-1 group, which differs from those in Bhutan despite sharing a common ancestor. Interpretation: Bangladesh's One Health approach demonstrated that an increase in MDV and anti-rabies vaccine (ARV) resulted in a decline in the relative risk of human rabies cases, indicating that eliminating dog-mediated human rabies could be achievable. Funding: The study was supported by the Communicable Disease Control (CDC) Division of the Directorate General of Health Services (DGHS) of the People's Republic of Bangladesh.

BackgroundInsufficient data on the health-related quality of life (HRQoL) of stroke survivors in less-resourced regions like Bangladesh emphasizes the need for understanding influencing factors. In this cross-sectional study, our objective was to assess potential factors associated with the HRQoL among stroke survivors in Bangladesh.MethodsThe study included 424 stroke survivors (65% male, mean age 57.25 ± 12.13 years) undergoing rehabilitation at four tertiary-level hospitals in Bangladesh. The HRQoL was assessed using the European Quality of Life Scale-5 Dimensions (EuroQol-5D), covering mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, along with a visual analog scale (VAS). Sociodemographic such as age, marital status, education, occupation, tobacco habit cohabitant situation, and health-related factors such as type and duration of stroke, co-morbidity, receipt of rehabilitation, and use of assistive devices served as independent variables. Bivariate logistic regression was utilized to ascertain the estimated risk factors for HRQOL, presenting odds ratios (OR) and a 95% confidence interval (CI) after adjusting for potential confounders.ResultsThe study primarily involved participants from rural areas (57.8%) with primary education (67.7%). Stroke survivors reported a mean EQ summary index of 0.393 ± 0.46 and a VAS score of 40.43 ± 18. A majority experienced a stroke within 1–3 months (66%), with 52.6% exhibiting left-side weakness. The results highlight significant challenges among survivors: 79.5% faced mobility issues, 81.1% were dependent in self-care, 87% had activity limitations, 70.8% suffered from bodily pain, and 84% experienced symptoms of anxiety or depression. Widowed or single survivors encountered greater difficulties in mobility (Adjusted Odds Ratio, AOR = 1.24, 95% CI = 0.35–4.45) and pain/discomfort (AOR = 2.85, 95% CI = 0.85–9.27) compared to their married counterparts. Those lacking access to rehabilitation services faced considerably higher challenges: mobility difficulties were nearly thirty times greater (AOR = 29.37, 95% CI = 8.85–97.50), difficulties in self-care were about forty-four times higher (AOR = 43.21, 95% CI = 10.02–186.41), challenges in usual activities were also forty-four times more frequent (AOR = 43.47, 95% CI = 5.88–321.65), pain/discomfort was nearly five times more prevalent (AOR = 4.35, 95% CI = 2.45–7.71), and anxiety or depression was over twenty times more common (AOR = 20.14, 95% CI = 7.21–56.35) compared to those who received rehabilitation services.ConclusionThe findings suggest that the enhancement of HRQoL in post-stroke patients in Bangladesh necessitates targeted interventions, including family support, tobacco cessation, recurrent stroke prevention, and effective rehabilitation services. Longitudinal studies are recommended for further confirmation of these findings.

Abstract Magnesium ferrite (MF0.33) impregnated flower-shaped mesoporous ordered silica foam (MOSF) was successfully synthesized in present study. MOSF was added with precursor solution of MF0.33 during MF0.33 synthesis which soaked the materials and further chemical changes occurred inside the pore. Therefore, no additional synthesis process was required for magnesium ferrite impregnated mesoporous ordered silica foam (MF0.33-MOSF) synthesis. MF0.33-MOSF showed higher morphological properties compared to other magnesium ferrite modified nanomaterials and adsorbed arsenic III [As(III)] and arsenic V [As(V)] 42.80 and 39.73 mg/g respectively. These were higher than those of other Fe-modified adsorbents at pH 7. As MOSF has no adsorption capacity, MF0.33 played key role to adsorb arsenic by MF0.33-MOSF. Data showed that MF0.33-MOSF contain about 2.5 times lower Fe and Mg than pure MF0.33 which was affected the arsenic adsorption capacity by MF0.33-MOSF. Adsorption results best fitted with Freundlich isotherm model. The possible mechanism of arsenic adsorption might be chemisorption by electrostatic attraction and inner or outer-sphere surface complex formation.

Abstract Background and Aims Two main problems the globe currently facing are migration and weather variation. Weather change has a significant impact on the agricultural industry, which affects the majority of poor people. There is a dearth of adequate methodological documentation when examining the relationship between weather variation, agricultural output, and migration. We aimed to identify methodological reporting difficulties by reviewing the quantitative literature on weather‐related migration through agricultural channels. Methods A systematic evaluation was conducted using papers published between January 2010 and June 2022, indexed in the SCOPUS, PUBMED, and Google Scholar databases. Using inclusion/exclusion criteria, we selected 22 original research articles out of 18,929 distinct articles for review, in accordance with the PRISMA guidelines. We extracted data from each study to understand how various concepts, research designs, and investigative techniques influence our understanding of migration patterns related to weather in the agricultural sector. Results The majority (64%) of the study's data consisted of time series data. In 50% of the studies, secondary data were used. Additionally, 55% of these studies did not state the sample size. In 40% of the studies, model assumptions were fully adhered to, whereas in 36% of the studies, they were not followed at all. The majority of the articles used the Ordinary Least Squares technique, while about 41% applied the Two‐Stage Least Squares technique. Various tests were conducted across these studies, such as robustness checks (59.1%), endogeneity tests (31.8%), omitted variable bias tests (22.7%), sensitivity analyses (22.7%), and weak instrument tests (13.6%), to name a few. In the research we selected, the methodology section had various shortcomings and lacked organization. Furthermore, the justifications for deviations from model assumptions were unclear, potentially affecting the study outcomes. Conclusion This study has important indications for researchers in studying climatic (weather) migration through agricultural channels besides for policymakers by giving a thorough review of the methods and techniques.

Protein source feed components are particularly important in broiler and layer feed formulation. Protein-rich insects may be the greatest substitute for protein-rich feed items such as fish and soybean meals. The black soldier fly (BSF) (Hermetia illucens) is an excellent example of an organism that can transform organic waste into protein, lipids, chitin, and other substances. Black Soldier Fly larvae (BSFL) have 42% crude protein, 29% fat, and 23% moisture content on a dry matter (DM) basis. Without specific infrastructure, commercial manufacturing of BSF is achievable. BSF could be used in poultry feed to help reduce pollution. A buildup of organic wastes may result in a possible pollution issue but BSF can transform a wide range of organic waste into a source of protein for poultry. Moreover, the most economical diets were those that had 25% and 100% of Black Soldier Fly Larvae Meal (BSFLM), respectively. The most significant benefit of BSF as a chicken feed ingredient is that it might be a sustainable protein source and a cost-effective replacement for high-cost protein feedstuffs. The importance of the BSF as a potential source of organic protein for broiler and layer chicken production is highlighted in this review of the literature.

The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). We observed that the extra tree regressor provided the best results. Significant features were also identified using statistical machine learning methods. The SHAP method was also employed to demonstrate the influence of each feature on the model’s output. This framework can be adaptable to other properties at a low computational expense.

Abstract Background Mendelian randomization (MR) studies using Genetic risk scores (GRS) as an instrumental variable (IV) have increasingly been used to control for unmeasured confounding in observational healthcare databases. However, proper reporting of methodological issues is sparse in these studies. We aimed to review published papers related to MR studies and identify reporting problems. Methods We conducted a systematic review using the clinical articles published between 2009 and 2019. We searched PubMed, Scopus, and Embase databases. We retrieved information from every MR study, including the tests performed to evaluate assumptions and the modelling approach used for estimation. Using our inclusion/exclusion criteria, finally, we identified 97 studies to conduct the review according to the PRISMA statement. Results Only 66 (68%) of the studies empirically verified the first assumption (Relevance assumption), and 40 (41.2%) studies reported the appropriate tests (e.g., R2, F-test) to investigate the association. A total of 35.1% clearly stated and discussed theoretical justifications for the second and third assumptions. 30.9% of the studies used a two-stage least square, and 11.3% used the Wald estimator method for estimating IV. Also, 44.3% of the studies conducted a sensitivity analysis to illuminate the robustness of estimates for violations of the untestable assumptions. Conclusions We found that incompleteness of the justification of the assumptions for the instrumental variable in MR studies was a common problem in our selected studies. This may misdirect the findings of the studies.

The instrumental variable (IV) method with a Cox proportional hazard (PH) model has been used to evaluate treatment effects in epidemiological studies involving survival data. The effectiveness of the IV methods in these circumstances has yet to be fully understood, though. The study aimed to evaluate the performance of IV methods using a Cox model. We evaluated the validity of treatment effects estimated by two-stage IV models using simulated scenarios with varying confounder strengths and baseline hazards. Our simulation demonstrated that when observed confounders were not taken into account in the IV models, and the confounder strength was moderate, the treatment effects based on the two-stage IV models were similar to the true value. However, the effect estimates diverged from the true value when observed confounders were taken into account in the IV models. In the case of a null treatment effect (i.e., hazard ratio=1), the estimates from the unadjusted and adjusted IV models (only two-stage) were close to the true value. The implication of our study findings is that the treatment effects obtained through IV analyses using the Cox PH model remain valid if the estimates are reported from unadjusted IV models with moderate confounding effects or if the treatment does not impact the outcome. • For every simulation, we utilized a sample size of 10,000 and performed 1,000 replications. • The true treatment effects (HR) of 3, 2, and 1 (null effect) were evaluated. • The 95% confidence intervals (CI) were calculated as the range between the 2.5 and 97.5 percentiles of the 1000 estimates.

Abstract Background Functional difficulties in children can be transmitted from mother to child, which is a major concern. We sought to determine whether there was a correlation between a mother's functional difficulty and functional difficulty in kids between the ages of 2–4 and 5–17. We also want to evaluate other fundamental aspects that influence on child's functionality. Methods We used Multiple Indicator Cluster Survey (MICS) data sets. For two different age groups, the children's difficulty status was evaluated. The sociodemographic factors served as explanatory variables in this study. We used χ2 tests and survey logistic regression models to analyze the data. Results Functional difficulties were less common in children aged 2–4 years (2.78%) but 8.27% in those aged 5–17 years. The study specifies that the mother's functional difficulty (odds ratio [OR]: 2.66, confidence interval [CI]: 1.35–5.24 for children aged 2–4 years and OR: 3.36, CI: 2.80–4.03 for children aged 5–17 years) were significantly associated with the functional difficulty of both age groups' children. Not attending early childhood education programs (OR: 1.89, CI: 1.16–3.10 for children aged 2–4 years and OR: 2.66, CI: 2.19–3.22 for children aged 5–17 years) and divisions were also significantly affecting the functional difficulty of both age groups' children. Moreover, area of residence and gender were significant factors for the older age group. Conclusions The prevalence of difficulty among children in Bangladesh is high. Children's functional difficulty, regardless of age, is greatly influenced by the functional difficulty of their mothers, their absence from early childhood education programs, and divisions. Reducing the prevalence of child functioning difficulties will be more successful if the government and NGOs consider these factors while developing appropriate intervention programs.

Bangladesh is situated at the confluence of GBM basins, with 90% of the basin area locating outside the country. Future climate change will lead to intense, prolonged, and frequent floods in Bangladesh. An integrated flood risk assessment that transforms flood risks from transboundary river basins to the local administrative level is necessary. A 1D/2D hydrodynamic model is developed for flood vulnerable Arial Khan River feed by basin-scale hydrologic model for low (RCP2.6) and high (RCP8.5) climate scenarios. An increasing trend in flood depth, duration, and area is observed from the early (2020s) to the end (2080s) of the century for both scenarios. The difference between both RCPs is minimal from the 2020s to 2050s but becomes very pronounced in the 2080s. The depth-duration-area with equal weightage provides better hazard results for the area. Flood risk is assessed using the IPCC AR5 framework incorporating vulnerability and exposure. Some medium-hazard zones fall into high-risk zones due to high exposure and vulnerability to flooding. The areas along the left reach are found more hazard-prone, while the areas on the right side are more risk-prone in the 2080s of RCP8.5. The hazard/risk maps will help policymakers identify priority areas for planning a sustainable flood management strategy. HIGHLIGHTS Flood risk is transferred from transboundary river basins to local administrative levels using a multi-model hydrological-hydrodynamic-statistical modelling system.; High-resolution bias-corrected GCM climate data is used.; IPCC AR5 climate scenarios and risk framework are considered.; Future flood hazard and risks are assessed for Arial Khan River Floodplain for the 2020s, 2050s, and 2080s.; Medium hazard zones might have high flood risk in the future.;

CCNs are a specific type of matricellular protein, which are essential signaling molecules, and play multiple roles in multicellular eukaryotes. This family of proteins consists of six separate members, which exist only in vertebrates. The architecture of CCN proteins is multi-modular comprising four distinct modules. CCN Proteins achieve their primary functional activities by binding with several integrin7 receptors. The CCN family has been linked to cell adhesion, chemotaxis and migration, mitogenesis, cell survival, angiogenesis, differentiation, tumorigenesis, chondrogenesis, and wound healing, among other biological interactions. Breast cancer is the most commonly diagnosed cancer worldwide and CCN regulated breast cancer stands at the top. A favorable or unfavorable association between various CCNs has been reported in patients with breast carcinomas. The pro-tumorigenic CCN1, CCN2, CCN3, and CCN4 may lead to human breast cancer, although the anti-tumorigenic actions of CCN5 and CCN6 are also present. Several studies have been conducted on CCN proteins and cancer in recent years. CCN1 and CCN3 have been shown to exhibit a dual nature of tumor inhibition and tumor suppression to some extent in quiet recent time. Pharmacological advances in treating breast cancer by targeting CCN proteins are also reported. In our study, we intend to provide an overview of these research works while keeping breast cancer in focus. This information may facilitate early diagnosis, early prognosis and the development of new therapeutic strategies.

Cucumber plants commonly suffer from Fusarium wilt disease, which is caused by Fusarium oxysporum f. sp. cucumerinum (Foc). Although resistant cultivars assist with Fusarium wilt disease control, enhancement of the virulence of Foc has been identified after monoculture of wilt-resistant cultivars. To investigate the biological characteristics that contribute to the virulence evolution of Foc, a wildtype strain foc-3b (WT) and its virulence-enhanced variant Ra-4 (InVir) were compared in terms of their growth, reproduction, stress tolerance, and colonization in cucumber plants. The InVir strain showed similar culture characteristics on PDA media to the WT strain but produced significantly more conidia (>two fold), with a distinctly higher germination rate (>four fold) than the WT strain. The colony diameter of the InVir strain increased faster than the WT strain on PDA plates; however, the mycelia dry weight of the InVir was significantly lower (Fusarium oxysporum f. sp. cucumerinum. This study will provide an insight into its virulence evolution and help us understand the mechanisms underlying the evolutionary biology of F. oxysporum.

Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

Abstract Bangladesh is one of the largest flood-prone deltas of the GBM (Ganges–Brahmaputra–Meghna) basins, and recently, it is categorized as the 7th worst climate-affected country in the world. Future climate change along with economic development, urbanization, and increase in population may worsen this situation manifolds. To cope with future flood situations and lessen probable flood losses, it is essential to develop flood maps of the major flood-prone rivers of Bangladesh considering climate change scenarios. In this study, the flood inundation of the Arial Khan River and its floodplain has been assessed for the predicted climate change scenario of RCP 8.5 (Representative Concentration Pathway 8.5) using open-source mathematical models. A calibrated and validated hydrologic model of GBM basins in SWAT (Soil and Water Assessment Tool) model has been used to estimate the future flow magnitudes at Bahadurabad Transit (Brahmaputra River) and Hardinge Bridge (Ganges River) using extreme emission scenario RCP 8.5. Using the flow magnitude of these two stations as the upstream boundaries, an HEC-RAS 1D model has been set up for the Brahmaputra, Ganges, and Padma rivers for generating future flow magnitude at the offtake of the Arial Khan River. Later, an HEC-RAS 1D-2D coupled model is set up for the Arial Khan River floodplain and flood maps are prepared considering flood depth, duration, and inundation extent. The flood assessment for different projections of RCP 8.5 shows that there is an increasing trend of flood in terms of depth, duration, and inundation from the 2020s to the 2080s. Hence, the floodplain becomes more hazardous by the end of this century. The climate change impact on the projected population for the RCP 8.5 scenario is assessed under SSP5 (Shared Socioeconomic Pathways 5) which indicates that the total flood-affected population will be nearly twice in the 2080s compared to the 2020s. So, future climate change is going to have a dreadful effect on the flood situation of the Arial Khan River floodplain.

Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children’s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians.

Long-term, often lifelong care for cardiovascular disease (CVD) patients requires consistent use of medicine; hence, the availability of essential medicine for CVD (EM-CVD) is vital for treatment, quality of life, and survival. We aimed to assess the availability of EM-CVD and explore healthcare facility (HCF) characteristics associated with the availability of those medicines in Bangladesh. This study utilized publicly available cross-sectional data from the 2014 and 2017 waves of the Bangladesh Health Facilities Survey (BHFS). The analysis included 204 facilities (84 from the 2014 BHFS and 120 from the 2017 BHFS) that provide CVD diagnosis and treatment services. The outcome variable "EM-CVD availability" was calculated as a counting score of the following tracer medicines: angiotensin-converting enzyme (ACE) inhibitors (enalapril), thiazide, beta-blockers (atenolol), calcium channel blockers (amlodipine and nifedipine), aspirin, and simvastatin/atorvastatin. A multivariable Poisson regression model was used to identify the HCF characteristics associated with EM-CVD availability. The number of Bangladeshi HCFs that provide CVD screening and treatment services increased just a little between 2014 and 2017 (from 5.4% to 7.9%). Since 2014, there has been an increase in the availability of calcium channel blockers (from 37.5% to 38.5%), aspirin (from 25.3% to 27.9%), and simvastatin/atorvastatin (from 8.0% to 30.7%), whereas there has been a decrease in the availability of ACE inhibitors (enalapril) (from 12.5% to 6.5%), thiazide (from 15.7% to 11.1%), and beta-blockers (from 42.5% to 32.5%). The likelihood of EM-CVD being available was higher among private and urban facilities than among public and rural facilities. Furthermore, facilities that had 24-hour staff coverage and performed quality assurance activities had a higher chance of having EM-CVD available than those that did not have 24-hour staff coverage and did not undertake quality assurance activities. Government authorities should think about a wide range of policy implications, such as putting more emphasis on public and rural facilities, making sure staff is available 24 hours a day, and performing quality assurance activities at facilities to make EM-CVD more available.

The internet has become an essential part of our daily life. But excessive usage can have a negative impact on the physical health of its users. Over the last decade, the use of Social Media (Facebook) has been increasing rapidly and the younger generations getting addicted to it. But all possible health impacts of excessive use of internet are yet to be thoroughly evaluated, especially in such a developing country as Bangladesh. The present study aims to understand possible health deteriorations from excessive use of Facebook in a cohort of university students of Bangladesh. A cross-sectional study was conducted on 1186 students from two public universities and 1472 from several private universities of Bangladesh using a comprehensive questionnaire. The data were analyzed using the chi-square test to understand the association between Facebook usage behaviors and physical health status. We found that ~70% of the students used the internet for at least 4–6 hours/day, and ~27% of them used Facebook for >3 hrs. Students frequently use social media (mostly Facebook) for news and social communication. About 50% of the students reported wasting time on Facebook and going to sleep late because of it. Importantly, 47.3% students reported that excessive use of Facebook results sleeping disturbance and has a negative impact on the concentration of daily works/studies (p < 0.001). In addition, they experienced several other health problems, including worsening eyesight (71.2%), headaches (15.4%), back and neck pain (28%). Although not statistically important, a fair number of students sought medical attention due to the daily excessive use of internet (p-value = 0.112). These findings demands better understanding of the all possible impacts of using excessive internet among the University students, which can help take the necessary initiatives to encourage good use of the internet. Further extension of this study is suggested at all education levels to reveal the full scenario of degree of excessive internet use and its impact on the healths of Bangladeshi students.

River water is very much important for domestic, agriculture and industrial use in Bangladesh which is in critical condition from long time based on research data. During last 40 years, extreme pollution events occurred in peripheral rivers surrounding Dhaka city and Karnaphuli River in Chittagong city. Present data showed that other urban rivers are also in critical condition especially Korotoa, Teesta, Rupsha, Pashur and Padma. The pollutants flowing with water made a severe pollution in downstream areas of rivers. Metals concentrations in river water was found to be higher in dry season. Dissolve oxygen (DO) was nearly zero in Buriganga River and several points in Turag, Balu, Sitalakhya and Karnaphuli River. NO3-, NO2- and PO43- pollution occurred in different rivers. Zn, Cu, Fe, Pb, Cd, Ni, Mn, As and Cr concentration was above drinking water standard in most of the river and some metals was even above irrigation standard in water from several rivers. Sediment data showed very much higher metal concentrations in most of the rivers especially peripheral rivers in Dhaka and Karnaphuli, Korotoa, Teesta, Rupsha and Meghna River. Metal concentrations in sediment was above US EPA threshold value in most of the rivers. Metal concentrations in fish and agricultural crops showed that bioaccumulations of metals had occurred. The concentration of metals showed the trend like: water

Clonostachys rosea is an important mycoparasite, with great potential for controlling numerous plant fungal diseases. Understanding the mechanisms and modes of action will assist the development and application of this biocontrol fungus. In this study, the highly efficient C. rosea 67-1 strain was marked with the green fluorescent protein (GFP), and the transformant possessed the same biological characteristics as the wild-type strain. Fungal interactions with Botrytis cinerea during co-culture and encounter on tomato leaves were assessed by fluorescence confocal and electron microscopy. The results indicated that once the two fungi met, the hyphae of C. rosea grew alongside those of B. cinerea, then attached tightly to the host and developed special structures, via which the biocontrol fungus penetrated the host and absorbed nutrients, eventually disintegrating the cells of the pathogen. Mycoparasitism to B. cinerea was also observed on tomato leaves, suggesting that C. rosea can colonize on plants and act following the invasion of the pathogenic fungus.

Fucoxanthin, belonging to the xanthophyll class of carotenoids, is a natural antioxidant pigment of marine algae, including brown macroalgae and diatoms. It represents 10% of the total carotenoids in nature. The plethora of scientific evidence supports the potential benefits of nutraceutical and pharmaceutical uses of fucoxanthin for boosting human health and disease management. Due to its unique chemical structure and action as a single compound with multi-targets of health effects, it has attracted mounting attention from the scientific community, resulting in an escalated number of scientific publications from January 2017 to February 2022. Fucoxanthin has remained the most popular option for anti-cancer and anti-tumor activity, followed by protection against inflammatory, oxidative stress-related, nervous system, obesity, hepatic, diabetic, kidney, cardiac, skin, respiratory and microbial diseases, in a variety of model systems. Despite much pharmacological evidence from in vitro and in vivo findings, fucoxanthin in clinical research is still not satisfactory, because only one clinical study on obesity management was reported in the last five years. Additionally, pharmacokinetics, safety, toxicity, functional stability, and clinical perspective of fucoxanthin are substantially addressed. Nevertheless, fucoxanthin and its derivatives are shown to be safe, non-toxic, and readily available upon administration. This review will provide pharmacological insights into fucoxanthin, underlying the diverse molecular mechanisms of health benefits. However, it requires more activity-oriented translational research in humans before it can be used as a multi-target drug.

Women in Bangladesh share a greater risk of Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS) infection compared to men. Levels of knowledge and awareness largely contribute to the prevalence of the HIV epidemic and its consequences. So, it is required to conduct studies based on most recent data to explore the determinants of HIV awareness. Therefore, we aimed to find the awareness level and factors influencing HIV related awareness among the married women in Bangladesh. We used data from 2014 Bangladesh Demographic and Health Survey (BDHS). About two-third of total respondents who heard about the HIV/AIDS were selected and interviewed successfully (n = 12,593) about 11 basic questions related to individual’s awareness. A score of the respondent’s knowledge and awareness was determined based on these questions. We used logistic regression models for analysing the data. We found about 62% of the respondents had an adequate knowledge and consciousness about the HIV/AIDS. Respondents’ education status, mass-media access, place of living, and working status played significant role on the awareness. As expected, respondents with higher education were more aware than those with no education (odds ratio (OR) = 3.56, 95% confidence interval (CI): 2.99–4.23). Moreover, respondents who had access to the mass media were more likely to be aware compared to those who did not have the access (OR = 1.14, 95% CI: 1.04–1.26). Although a sizeable proportion of women had an adequate knowledge and awareness regarding the HIV/AIDS, we recommend implementing educational programmes related to HIV/AIDS in the curriculum to ensure a standard level of awareness throughout the nation. Since the respondents from rural areas scored significantly lower than the urban areas, awareness through mass media, particularly in rural areas, is of prime concern for raising awareness.

Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.

Tenascin-C (TN-C), an extracellular matrix (ECM) glycoprotein, is specifically induced upon tissue injury and infection and during septic conditions. Carbon monoxide (CO) gas is known to exert various anti-inflammatory effects in various inflammatory diseases. However, the mechanisms underlying the effect of CO on TN-C-mediated inflammation are unknown. In the present study, we found that treatment with LPS significantly enhanced TN-C expression in macrophages. CO gas, or treatment with the CO-donor compound, CORM-2, dramatically reduced LPS-induced expression of TN-C and proinflammatory cytokines while significantly increased the expression of IL-10. Treatment with TN-C siRNA significantly suppressed the effects of LPS on proinflammatory cytokines production. TN-C siRNA did not affect the CORM-2-dependent increase of IL-10 expression. In cells transfected with IL-10 siRNA, CORM-2 had no effect on the LPS-induced expression of TN-C and its downstream cytokines. These data suggest that IL-10 mediates the inhibitory effect of CO on TN-C and the downstream production of proinflammatory cytokines. Additionally, administration of CORM-2 dramatically reduced LPS-induced TN-C and proinflammatory cytokines production while expression of IL-10 was significantly increased. In conclusion, CO regulated IL-10 expression and thus inhibited TN-C-mediated inflammation in vitro and in vivo.

The title compound, [Cu2Br2(C7H8NO)2], was synthesized by reaction of CuBr2 with 2-(pyridin-2-yl)ethanol (hep-H) in methanol. The asymmetric unit consists of one hep ligand and a CuBr unit. The Cu2+ ion is thereby coordinated by the N atom and the deprotonated hydroxy O atom in a distorted square-planar geometry that is completed by another O atom. The latter acts as bridging ligand towards the second, symmetry-equivalent, Cu atom, thus generating a centrosymmetric dimeric unit, with the inversion centre halfway between the Cu atoms. These units are linked via C—H...Br and C—H...O hydrogen bonds, leading to the formation of a hydrogen-bonded one-dimensional-polymeric chain along a..

Protein source feed components are particularly important in broiler and layer feed formulation. Protein-rich insects may be the greatest substitute for protein-rich feed items such as fish and soybean meals. The black soldier fly (BSF) (Hermetia illucens) is an excellent example of an organism that can transform organic waste into protein, lipids, chitin, and other substances. Black Soldier Fly larvae (BSFL) have 42% crude protein, 29% fat, and 23% moisture content on a dry matter (DM) basis. Without specific infrastructure, commercial manufacturing of BSF is achievable. BSF could be used in poultry feed to help reduce pollution. A buildup of organic wastes may result in a possible pollution issue but BSF can transform a wide range of organic waste into a source of protein for poultry. Moreover, the most economical diets were those that had 25% and 100% of Black Soldier Fly Larvae Meal (BSFLM), respectively. The most significant benefit of BSF as a chicken feed ingredient is that it might be a sustainable protein source and a cost-effective replacement for high-cost protein feedstuffs. The importance of the BSF as a potential source of organic protein for broiler and layer chicken production is highlighted in this review of the literature. [ABSTRACT FROM AUTHOR]