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Abstract Background National level Sex Ratio at Birth (SRB) is normal in Bangladesh despite its patriarchal social structures, strong son preference, and low fertility level, widely recognized as preconditions for Gender-Biased Sex Selection (GBSS). To better understand this anomaly, we examine the trend in SRB in a sub-district in Bangladesh and assess the impact of the introduction of fetal sex-detection technology and the history of induced abortion on child sex using longitudinal data. Methods We have used secondary data collected routinely by icddr, b’s Matlab Health and Demographic Surveillance System (HDSS) between 1982 and 2018. All births occurring during this period (N = 206,390) were included in the analyses. We calculated the SRB and used multivariate logistic regression analyses to assess the likelihood of birth of a male child before and after the introduction of ultrasonogram in Matlab. Results Overall, SRB was within the natural limit (106) during 1982–2018 in Matlab. SRB among women with a history of induced abortion was 109.3 before the introduction of ultrasonography in 2001 and 113.5 – after 2001. Women’s history of induced abortion prior to introduction of ultrasonogram (1982–2000) increased the likelihood of birth of a male child 1.06 times (AOR 1.06; 95% CI- 1.01–1.11). In the period after, however, this likelihood was 1.08 (AOR 1.08; 95% CI- 1.02–1.15). Conclusions In a context with normal SRB, it was found to be skewed among women who had induced abortion. SRB was relatively more skewed among such women after the advent of ultrasonogram compared to a period without ultrasonogram. Moreover, induced abortion after introduction of fetal sex determination technology increased the likelihood of birth of a male child. These findings suggest the plausibility of GBSS in a sub-group. Further research is needed, particularly in regions with skewed SRB to examine whether GBSS is indeed a threat to Bangladesh.

Abstract Background Lead, a potent neurotoxin, causes irreversible damage to the nervous system, and low- and middle-income countries face huge health and economic productivity losses due to childhood lead exposure. In Bangladesh, informal Used Lead Acid Battery (ULAB) recycling sites are an important source of lead pollution. Little is known about lead awareness among communities exposed to ULAB recycling. Therefore, this study aims to assess knowledge, attitudes, and practices related to lead pollution among caregivers of young children and adolescents living adjacent to informal ULAB sites. Methods A cross-sectional study was conducted among 732 mothers of young children and adolescents in 4 districts of Bangladesh (survey and observation). Simple and multiple linear regression was conducted to describe patterns and predictors of lead-related knowledge and practices. Results 60% of respondents had heard the name ‘lead’ (“shisha”). The mean knowledge score was low (19 out of 44). Residents of high-risk districts, male respondents, and those with more than 5 years of schooling were significantly more likely to have higher knowledge scores than others. In terms of attitude, 52% of respondents perceived lead to be risky for human health but 43% thought lead pollution was controllable. Observation of households for lead exposure revealed that 63% of children and adolescents play or pass by ULAB sites, 29% ate non-food items, 41% of households had visible paint chips on the walls, 59% households used polished turmeric and 15% used lead-soldered cans to store foods. Among protective practices, 70% reported cleaning floors, 84% consumed iron-rich foods, and 48% consumed calcium-rich foods. Conclusions The population had a high potential for lead exposure. Their knowledge about lead was limited, and risk perception was moderate. To reduce lead exposure and increase knowledge and awareness among the at-risk population, it is crucial to take measures such as mass awareness campaigns through media and schools. It is important to strengthen the implementation of existing policies, such as policies on leaded gasoline, paints, and lead-acid batteries, that can address the sources of lead exposure for the community.

Handling of radioisotopes may cause external and internal contamination to occupational workers while using radiation for medical purposes. This research aims to monitor the internal hazard of occupational workers who handle 131I. Two methods are used: in vivo or direct method and in vitro or indirect method. The in vivo or direct method was performed by assessing thyroid intake with a thyroid uptake monitoring machine. The in vitro or indirect method was performed by assessing urine samples with the help of a gamma-ray spectroscopy practice using a High-Purity Germanium (HPGe) Detector. In this study, fifty-nine thyroid counts and fifty-nine urine samples were collected from seven occupational workers who were in charge of 131I at the National Institute of Nuclear Medicine and Allied Sciences (NINMAS), Dhaka. The result showed that the average annual effective dose of seven workforces from thyroid counts were 0.0208 mSv/y, 0.0180 mSv/y, 0.0135 mSv/y, 0.0169 m Sv/y, 0.0072 mSv/y, 0.0181 mSv/y, 0.0164 mSv/y and in urine samples 0.0832 mSv/y, 0.0770 mSv/y, 0.0732 mSv/y, 0.0693 mSv/y, 0.0715 mSv/y, 0.0662 mSv/y, 0.0708 mSv/y.The total annual effective dose (in vivo and in vitro method) was found among seven workers in average 0.1039 mSv/y, 0.0950 mSv/y, 0.0868 mSv/y, 0.0862 mSv/y, 0.0787 mSv/y, 0.0843 mSv/y, 0.0872 mSv/y. Following the rules of the International Commission on Radiological Protection (ICRP), the annual limit of effective dose for occupational exposure is 20 mSv per year and the finding values from this research work are lesser than this safety boundary.

Background: The purpose of a fixed prosthodontic treatment may vary from the restoration of a single tooth to the complete rehabilitation of full occlusion. Finish line design is important in any tooth preparation, as finish line ensures the success of the future prosthesis. Finish line affects not only retention but also stability of fixed prosthesis. The finish line must be distinct, uniform, and smooth. Finish line is important during tooth preparation for acceptable marginal adaptation. The finish line must be easy to prepare, it must be easy to duplicate in impression, conservative, and provide sufficient strength to restore material. Finish line design helps in measuring the surface detail recording ability of an impression material. Various tooth preparation designs have distinct finish lines. Objectives: To evaluate the finish line of the die in a fixed prosthodontic laboratory. Materials and Methods: This laboratory based descriptive, cross-sectional, observational study has been conducted in the department of prosthodontics, BSMMU, with a sample size of 125 working casts die having different finish lines. The purpose of this study was to observe the finish line of the die of the working cast by using a dental magnifying loop. Data was collected on the basis of type, location, and quality of the finish line and recorded on a predesign data collection sheet. Collected data were analyzed by using the chi-square test. A P-value

The Rema-Kalenga Wildlife Sanctuary (RKWS) is a protected forest in Bangladesh that houses a variety of rare flora and fauna and supports the sustenance of 13 ethnic communities. This forest's indigenous and other inhabitants traditionally have a strong cultural connection to the plants, particularly medicinal plants. Due to modern allopathic medicine's rapid growth and commercial tree species prioritization, many medicinal plants are now on the verge of endangerment. Under such circumstances, it is crucial to explore how the local community perceives the importance of herbal treatments in contrast to allopathy, the underlying reasons for their perceptions, and the specific ailments for which they use the plants. The main objectives are: 1) to evaluate the perceptions of the local community towards allopathy and herbal medicine using Nussbaum's central capabilities approach, 2) to identify medicinal plant diversity, therapeutic usages, and quantitative indices, 3) to determine the factors that influence the use of medicinal plants. Repeated interviews and field surveys were conducted at the RKWS, interviewing 145 people, including the indigenous community (72.42 %) and traditional healers (8.27 %) from the surrounding seven villages. The study identified 51 medicinal plant species belonging to 39 families for their potent medicinal properties in treating various ailments. The predominant parts of the plants used in the treatments were leaves and roots. The uses were classified into 12 categories according to Nussbaum's central capabilities. The findings identified Aloe vera, Phyllanthus emblica, and Azadirachta indica as highly culturally important species. In contrast, Terminalia arjuna, Swertia chirata, and Azadirachta indica were found to have the highest relative importance. The underlying determinants influencing the preferences of individual users towards herbal medicine were income from agroforestry, beliefs, knowledge, and ethnicity, as revealed by the analysis of the ordinal logit model. The respondents viewed their strong inclination toward herbal medicine with many positive attitudes. Herbal medicine users held a negative perception of allopathy except for affiliation and practical reasons being viewed as the positive outcomes. Conversely, allopathic medicine users expressed mixed perceptions towards the treatment, with both positive and negative aspects being identified. Promoting the sustainable use of medicinal plants and their conservation efforts is imperative for the benefit of present and future generations in this region.

In this study, gamma activity concentrations, gross alpha and gross beta activity of natural radionuclides, and heavy metal concentrations were measured in eleven edible marine fish, four shellfish, two mollusks, and a common seaweed sample collected from a local sea fish market and sea beach area of the northern part of the Bay of Bengal, Kuakata, Bangladesh. Using HPGe gamma spectrometry, the activity concentrations of 238U, 232Th, and 40K were measured and found to be 19.7 ± 1.5 Bq/kg, 12.2 ± 0.9 Bq/kg, and 188 ± 15 Bq/kg, respectively, with the 232Th concentration surpassing that of 238U. The ZnS scintillation detector was used to assess the gross alpha and gross beta activity. The average gross alpha activity and gross beta activity were found to be 9.4 ± 1.4 Bq/kg and 26 ± 4 Bq/kg, respectively, with the latter attributed to beta emitting radionuclides's abundance. The activity concentrations of 238U, 232Th, and 40K in the analyzed samples varied in the order of Shellfish > Seaweed > Fish > Mollusks, Seaweed > Shellfish > Mollusks > Fish, and Seaweed > Shellfish > Fish > Mollusks. The annual effective dose due to consumption of analyzed seafood was found to be within the world limit of 2400 μSv/y recommended by UNSCEAR. The main contributor to the annual effective dose was 238U. The excess lifetime cancer risk (ELCR) results were below the permissible threshold of 10−3 for radiological risks. Furthermore, average concentrations of Zn and Mn were higher than WHO/FAO recommended values, and carcinogenic Pb, Cd, Cr, and Hg concentrations were below detection limits, according to heavy metal analysis performed by AAS. The average concentration of heavy metals in all of the seafood samples under investigation was in the order of Zn > Fe > Mn. The calculated hazard index (HI), target hazard quotient (THQ), and estimated daily intake (EDI) were compared to the permissible safety limits.

# Background Under-5 deaths in urban slum communities are often unnoticed in many cases. However, previous surveys reported a high burden of mortality. Therefore, we presented the distribution of the causes of death of under-5 children in selected urban slum areas in Bangladesh. # Methods Since October 2020, the Urban Health and Demographic Surveillance System (Urban HDSS) has been collecting verbal autopsy information for neonates, children, and adults to report the causes of death. We used the data collected by the trained Field Workers- from all households (30,857) in the ongoing HDSS area, comprising 118,238 people. The causes of deaths were identified by using the WHO-standardized verbal autopsy (VA) questionnaire that was reviewed and coded by a trained physician. Later, the cause-specific fractions of the deaths were evaluated by the child’s age, sex, the mother’s education, occupation, household wealth quintile, slum locations, mode and place of delivery, and antenatal and postnatal care. # Results A total of 155 deaths of children under-5 years were interviewed during October 2020–2021. Most of them were neonates (67.7%), boys (67.7%), belonged to poor households (43.9%), born at home (51%) and had vaginal delivered (77%). Most of the mothers of the deceased children took antenatal care services (80%), and a comparatively lower proportion received postnatal care (39.3% and 47.1% of mothers and children, respectively). In this slum area, birth asphyxia (25.2%) was the major leading cause of under-5 deaths occurred in the neonatal period (37.1%), followed by other infections (14.2%) and pneumonia (16.1%). Other important causes of death were cerebral ischemia (7.7%), prematurity and low birth weights (6.5%), congenital anomalies (5.2%), and other external causes (18.1%), contributed to the under-5 child deaths. The cause of mortality fraction varied by different socioeconomic and delivery-related attributes such as place, mode of delivery, and utilization of ANC and PNC visits. # Conclusions Major causes of under-5 death are birth asphyxia, pneumonia, and infectious diseases. Adequate attention and additional safety measures, education and awareness about child’s health among mothers, and proper delivery care for pregnant slum women and children could prevent under-5 deaths in the slum area.

Mushroom farming using agri-waste as substrates can offer a sustainable solution to the food security challenges of inadequate and imbalanced diets. Developing strategies to exploit the potential of the mushroom industry fully is yet to be explored in Bangladesh. We, thus, conducted this study to investigate the challenges and opportunities associated with mushroom farming, as well as the characteristics of farms and employees engaged in this industry. A directional stepwise multiple regression analysis showed self-motivation, spawn cost, farm size, and training are the key influencing factors driving profitability. Farm surveys identified SWOT factors with 24 sub-factors cross-validated with expert consultations. The sub-factors were categorized as beneficial (strength and opportunities) and cost (weakness and threats) to formulate the SWOT strategies using the Grey-TOPSIS method. Results indicate a favorable scenario exists for the industry in Bangladesh, with significant opportunities (group weight 0.53) and minimal threats (group weight 0.09). We proposed 12 strategic alternatives for the sustainable growth of this industry. This top-ranking strategy is not only to secure funding (relative closeness, C+ = 0.87) but also to provide more accessible loan options envisioning a strategic expansion of business operations in Bangladesh. The evaluation highlighted the significance of collaboration with other mushroom farmers to maximize the gain in marketing that will substantially expand the local demand (C+ = 0.697). Besides, product diversification is also underscored as an important strategy for the growth of the industry. These prioritized SWOT strategies lay the groundwork for policy development, aiding decision-makers in steering the mushroom industry towards sustainable growth for developing countries like Bangladesh. Promoting such an eco-friendly industry will generate ample opportunities for women's employment and appreciable profit while contributing to environmental improvement through recycling agri-waste.

Life Cycle Inventory (LCI) analysis is an essential and time-consuming phase of life cycle assessment (LCA). While primary data is among the most reliable and desirable data source types, it is often challenging to collect for industry-specific processes due to confidentiality concerns, in particular with respect to unique proprietary processes. In such cases, computer-based process simulation software can be used to fill gaps in inventory data based on mass and energy balances. While building process simulation models, engagement with industry is essential for verification of process models and validation of simulated data. Although process simulation-based life cycle inventory modelling is not a new research area, there has been no systematic review on this topic with respect to common methodological choices. To fill this gap, this systematic review aims to identify common practices in simulating LCI data using process simulation. Studies that used process simulation for LCI modelling were reviewed to identify the reasons for using process simulation, approaches for simulating LCI, software employed, validation processes, and processes to calculate and report uncertainty. Based on the review findings, a methodological framework was proposed to explain how process simulation-based LCI can be integrated with conventional LCA, specifically for industrial processes.

The green synthesis of silver nanoparticles (SNPs) is the most economical, rapid, facile, and eco-friendly technique. In this study, pathogenic bacteria from human stool were identified, and the antibacterial potency of Nigella sativa L. seed extracts containing SNPs was assessed. The identification of the isolated pathogenic bacteria was achieved through the analysis of their 16 S rDNA sequences. The characterization of SNPs was done by ultraviolet–visible spectroscopy (UV–VIS spectroscopy), scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Six pathogenic bacteria were isolated from human stool and isolated bacteria were identified by 16 S rDNA sequencing analysis. SNPs were found in the size range of 30–60 nm and showed good antibacterial and antifungal activity. The maximum zone of inhibition was found against A. spanius at 32.0±0.8 nm. Achromobacter spanius, Pseudomonas geniculate, and Enterobacter asburiae WL115 were extremely sensitive to heated treated nanoparticles at 100 μg ml−1. In the current work, N. sativa L. was investigated because it produced SNPs effectively, may be used as a safe, affordable agent, and may have antibacterial and antifungal properties. The potential biological uses of SNPs will be encouraged by these discoveries, which will drive future research.

Abstract Background This study examined the neonatal mortality for newborn of women who delivered by caesarean section or vaginally using a prospective cohort. Methods A total of 6,989 live births registered from 2016 to 2018, were followed for neonatal survival from the selected slums of Dhaka (North and South) and Gazipur city corporations, where icddr,b maintained the Health and Demographic Surveillance System (HDSS). Neonatal mortality was compared by maternal and newborn characteristics and mode of delivery using z-test. Logistic regression model performed for neonatal mortality by mode of delivery controlling selected covariates and reported adjusted odd ratios (aOR) with 95% confidence interval (CI). Results Out of 6,989 live births registered, 27.7% were caesarean and the rest were vaginal delivery; of these births, 265 neonatal deaths occurred during the follow-up. The neonatal mortality rate was 2.7 times higher (46 vs. 17 per 1,000 births) for vaginal than caesarean delivered. Until 3rd day of life, the mortality rate was very high for both vaginal and caesarean delivered newborn; however, the rate was 24.8 for vaginal and 6.3 per 1,000 live births for caesarean delivered on the 1st day of life. After adjusting the covariates, the odds of neonatal mortality were higher for vaginal than caesarean delivered (aOR: 2.63; 95% CI: 1.82, 3.85). Additionally, the odds were higher for adolescent than elderly adult mother (aOR: 1.60; 95% CI: 1.03, 2.48), for multiple than singleton birth (aOR: 5.40; 95% CI: 2.82, 10.33), for very/moderate (aOR: 5.13; 95% CI: 3.68, 7.15), and late preterm birth (aOR: 1.48; 95% CI: 1.05, 2.08) than term birth; while the odds were lower for girl than boy (aOR: 0.74; 95% CI: 0.58, 0.96), and for 5th wealth quintile than 1st quintile (aOR: 0.59, 95% CI: 0.38, 0.91). Conclusion Our study found that caesarean delivered babies had significantly lower neonatal mortality than vaginal delivered. Therefore, a comprehensive delivery and postnatal care for vaginal births needed a special attention for the slum mothers to ensure the reduction of neonatal mortality.

Purpose – The paper aims to examine the state of electronic service delivery in Bangladesh. It reviews the structure and operation of the “e-service” centers at the district, sub-district (upazila), and union levels by taking an inventory and assessing their contributions. Design/methodology/approach – The paper is based on a review of the functions and operations of the service delivery agencies with reference to the claims made by the government. It is based on secondary materials obtained from academic studies, government documents, relevant websites, and media reports. Findings – Electronic delivery of public services in Bangladesh has not been effective as planned. There are issues regarding channels of communication, the competence of public officials, human and financial resources, and political will to support the agencies delivering public services. Originality/value – The paper examines the arrangements, practices, and problems of delivery of public services in Bangladesh through e-service centers at the local levels to determine the progress and potentials of employing digital technology for addressing problems. It proposes the strategy for public service delivery by using digital technology in the country.

ABSTRACT: Objectives: Pseudomonas aeruginosa (P. aeruginosa) stands out as a key culprit in the colonization of burn wounds, instigating grave infections of heightened severity. In this study, we have performed comparative whole genome analysis of a difficult to treat extensively drug resistant P. aeruginosa isolated from a burn patient in order to elucidate genomic diversity, molecular patterns, mechanisms and genes responsible for conferring antimicrobial resistance and virulence. Method: P. aeruginosa SHNIBPS206 was isolated from an infected burn wound of a critically injured burn patient. Whole genome sequencing was carried out and annotated with Prokka. Sequence type, serotype, antimicrobial resistance genes and mechanisms, virulence genes, metal resistance genes and CRISPR/Cas systems were investigated. Later, pangenome analysis was carried out to find out genomic diversity. Result: P. aeruginosa SHNIBPS206 (MLST 357, Serotype O11) was resistant to 14 antibiotics including carbapenems and harboured all four classes of beta lactamase producing genes: Class A (blaPME-1, blaVEB-9), Class B (blaNDM-1), Class C (blaPDC-11) and Class D (blaOXA-846). Mutational analysis of Porin D gave valuable insights. Several efflux pump, virulence and metal resistance genes were also detected. Pangenome analysis revealed high genomic diversity among different strains of P. aeruginosa. Conclusion: To our knowledge, this is the first report of an extensively drug resistant ST 357 P. aeruginosa from Bangladesh, which is an epidemic high-risk P. aeruginosa clone. Further research and in-depth comprehensive studies are required to investigate the prevalence of such high-risk clone of P. aeruginosa in Bangladesh.

The study evaluated the impacts of gold nanoparticles (AuNPs) on striped dwarf catfish’s (Mystus vittatus) growth, survival, and hematological parameters. Gold nanoparticles were synthesized using lemon (Citrus limon) extract, and four experimental diets incorporating 0, 10, 30, and 50 mg of AuNPs per Kg of fish feed were prepared as treatments T0 (control), T1, T2, and T3, respectively. Forty-five days old (0.7±0.00 g) 60 fingerlings were stocked in each glass aquarium and the diets were administered daily for 75 days. The treatment T2 resulted in a significant (P

Plants' leaf stomata are crucial for various scientific research, including identifying species, studying ecology, conserving ecosystems, improving agriculture, and advancing the field of deep learning. This dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh: nine from the Sundarbans mangrove forest and two from the Ratargul Swamp Forest. It is a valuable tool for refining machine learning algorithms that specialize in detecting stomata and categorizing species accurately. Researchers can explore a deeper understanding of plant physiology, adaptation mechanisms, and environmental interactions by employing pattern recognition, deep learning, and feature extraction techniques. Additionally, this dataset could be a potential tool for enhancing research in macroscopic metamaterials, extending its impact beyond traditional biological studies into interdisciplinary fields of technology and material science.

Fossil fuels, land use, and agriculture are the three major sources contributing to the rise in atmospheric greenhouse gases (GHGs). Agricultural activities including the production of rice, animal raising, and aquaculture directly contribute to GHG emissions. Approximately one fourth of all GHG emissions are attributable to agricultural operations primarily as a result of unsustainable farming practices. The GHG emissions from agriculture include carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4). However, measurements of GHGs are laborious, expensive, and technically challenging but are crucial to select the most appropriate and feasible system. Since it is difficult to find a comprehensive overview of the variety of techniques, including latest technologies, in one article, this review aims to provide details of available methodologies, their benefits and drawbacks, with a focus on those that have been extensively tested in various ecosystems and regions. This paper is a purely methodological one which, on purpose, tried to familiarize the reader with the vast range of different developments. Our analysis is not exhaustive and is not intended to be a systematic review. The article summarizes that micrometeorological approaches are suitable for measurement from broader footprints, whereas chamber techniques measuring gases from point sources, are more appropriate for high spatio-temporal resolutions in diverse ecosystems. Stable isotope techniques are the most accurate methods and allow a process-specific quantification of GHGs but require sophisticated equipment.

The present study aimed to conduct phytochemical and pharmacological profiling of methanolic crude extract of leaves of Bombax ceiba Linn. via experimental and computational approaches. Six secondary metabolites were isolated chromatographically, and the structures were elucidated by extensive analyses of high-resolution 1H and 13C NMR data. The separated compounds were characterized as β-sitosterol (1), β-amyrin (2), β-amyrin acetate (3), β-amyrin palmitate (4), β-amyrone (5), and isoscopoletin (6). DPPH free radical scavenging assay, tail-tipping method, writhing assay, and castor oil-induced diarrheal mice methods, respectively, were used to assess the antioxidant, hypoglycemic, analgesic, and anti-diarrheal activities of the leaf extract of B. ceiba plant species. The study observed significant reductions (p

Recently, additive manufacturing (AM) techniques like 3D printing have emerged as a potentially game-changing example of digital manufacturing. However, high entry barriers of a tiny material library, different processing defects, and unpredictable product quality are still holding back its widespread use in the industry. Due to its remarkable success in data tasks like classification, regression, and clustering, machine learning (ML) has recently gained a great deal of interest in the subject of the material library. This paper examines the current state of ML applications in several key areas of AM, including polymer matrix composite materials and machine parameter optimization. Composite filaments have been extruded using Polylactic Acid (PLA) as it is a biodegradable material and shows how High-Density Poly Ethylene (HDPE) enhances physical strength. All the parameters for the filament extruder have been designed by machine learning. Thermal stability is a significant concern for polymers that have been overcome by introducing Titanium Dioxide nanoparticles. The microstructure, surface texture, electro-mechanical behavior, and other general features of extruded filaments made from recycled plastics have been investigated. The extrusion temperature, approximated using ML, is in excellent agreement with the surface texture and microstructure of the polymers, as confirmed by FESEM, EDX, and Particle analysis. Extruded filaments experienced 2500 Vs and confirmed their non-conductivity up to 77.7GΩ. Tensile strength and elongation at break, two measures of mechanical properties, have been examined. Incorporation of Titanium Dioxide Nanoparticles improved mechanical properties significantly. When it comes to 3D printing, the physical properties and potential uses of each composite material are different.

Natural products are being developed as possible treatment options due to the rising prevalence of cancer and the harmful side effects of synthetic medications. Arctiin is a naturally occurring lignan found in numerous plants and exhibits different pharmacological activities, along with cancer. To elucidate the anticancer properties and underlying mechanisms of action, a comprehensive search of various electronic databases was conducted using appropriate keywords to identify relevant publications. The findings suggest that arctiin exhibits anticancer properties against tumor formation and various cancers such as cervical, myeloma, prostate, endothelial, gastric, and colon cancers in several preclinical pharmacological investigations. This naturally occurring compound exerts its anticancer effect through different cellular mechanisms, including mitochondrial dysfunction, cell cycle at different phases (G2/M), inhibition of cell proliferation, apoptotic cell death, and cytotoxic effects, as well as inhibition of migration and invasion of various malignant cells. Moreover, the study also revealed that, among the various cellular pathways, arctiin was shown to be more potent in terms of the PI3K/AKT and JAK/STAT signaling pathways. However, pharmacokinetic investigation indicated the compound's poor oral bioavailability. Because of these findings, arctiin might be considered a promising chemotherapeutic drug candidate.

Biomass fuel could effectively address the existing energy crisis in developing countries, including Bangladesh, yet its potential remains largely overlooked in scholarly and policy discussions. The objective of this study was to understand the people's perception of fuelwood, LPG, and cow dung as well as to identify factors influencing the choices of solid cooking fuels and the extent of daily fuelwood consumption in Maheshkhali, a secluded island off the coast of the Bay of Bengal in Bangladesh, characterized by its diverse landscapes. Primary data was collected through a questionnaire survey and focus group discussions and were analyzed using descriptive statistics, binomial logistic regression, and ordinary least squares regression (OLSR) to identify key determinants. Our findings suggest a pronounced preference for biomass fuel, as indicated by the odds ratio and user perceptions grounded in the central capability approach. The OLSR results indicate that cooking time, quantity collection, the number of school-going children, and educational score explain 82.5% of the total variance in fuelwood consumption, making them major driving factors. The household survey revealed a stark reliance on biomass fuel, with 87% of families using it exclusively, while only 4% rely solely on LPG. Fuelwood collection, primarily a task for women and children, also involved men who spent approximately five hours to traverse 1.5 km to collect 23 kg of biomass per trip. The strong biomass preference for fuel, in terms of central capability, underscores the challenges in motivating users to cleaner alternatives like LPG.

Ransomware attacks are becoming increasingly sophisticated, thereby rendering conventional detection methods less effective. Recognizing this challenge, this study reviews advanced detection mechanisms and explores the potential of artificial intelligence (AI) techniques to improve detection capabilities. This study reviews the recent literature, including journal articles, conference proceedings, and online resources since 2017, to offer insights into the current state of AI-based ransomware detection and suggests future research directions. This study contributes significantly to the development of a systematic evaluation framework that evaluates each component of the AI-based detection model framework using specific criteria and methodologies and analyzes how various AI algorithms respond to different ransomware attacks, thereby providing insights for more effective and robust detection methods. This review begins with an overview of AI and ransomware, and discusses various types of ransomware attacks, the process of an attack chain, and emerging trends. We then review the existing literature on the core components of AI-based ransomware detection models, including the datasets and challenges arising during data collection, data pre-processing, feature engineering techniques, model training, and performance evaluation for effective model training. This study assessed the detection performance of AI models using metrics such as accuracy, precision, recall, and F1-score. By synthesizing these findings, we identify gaps in the current research and suggest future directions for enhancing AI-based ransomware detection techniques. The insights provided aim to guide researchers and practitioners in developing more robust methods for detecting and mitigating ransomware attacks by using AI.

Brain hemorrhage refers to a potentially fatal medical disorder that affects millions of individuals. The percentage of patients who survive can be significantly raised with the prompt identification of brain hemorrhages, due to image-guided radiography, which has emerged as the predominant treatment modality in clinical practice. A Computed Tomography Image has frequently been employed for the purpose of identifying and diagnosing neurological disorders. The manual identification of anomalies in the brain region from the Computed Tomography Image demands the radiologist to devote a greater amount of time and dedication. In the most recent studies, a variety of techniques rooted in Deep learning and traditional Machine Learning have been introduced with the purpose of promptly and reliably detecting and classifying brain hemorrhage. This overview provides a comprehensive analysis of the surveys that have been conducted by utilizing Machine Learning and Deep Learning. This research focuses on the main stages of brain hemorrhage, which involve preprocessing, feature extraction, and classification, as well as their findings and limitations. Moreover, this in-depth analysis provides a description of the existing benchmark datasets that are utilized for the analysis of the detection process. A detailed comparison of performances is analyzed. Moreover, this paper addresses some aspects of the above-mentioned technique and provides insights into prospective possibilities for future research.

Abstract The increasing antimicrobial resistance in Providencia stuartii (P. stuartii) worldwide, particularly concerning for immunocompromised and burn patients, has raised concern in Bangladesh, where the significance of this infectious opportunistic pathogen had been previously overlooked, prompting a need for investigation. The two strains of P. stuartii (P. stuartii SHNIBPS63 and P. stuartii SHNIBPS71) isolated from wound swab of two critically injured burn patients were found to be multidrug-resistant and P. stuartii SHNIBPS63 showed resistance to all the 22 antibiotics tested as well as revealed the co-existence of bla VEB-6 (Class A), bla NDM-1 (Class B), bla OXA-10 (Class D) beta lactamase genes. Complete resistance to carbapenems through the production of NDM-1, is indicative of an alarming situation as carbapenems are considered to be the last line antibiotic to combat this pathogen. Both isolates displayed strong biofilm-forming abilities and exhibited resistance to copper, zinc, and iron, in addition to carrying multiple genes associated with metal resistance and the formation of biofilms. The study also encompassed a pangenome analysis utilizing a dataset of eighty-six publicly available P. stuartii genomes (n = 86), revealing evidence of an open or expanding pangenome for P. stuartii. Also, an extensive genome-wide analysis of all the P. stuartii genomes revealed a concerning global prevalence of diverse antimicrobial resistance genes, with a particular alarm raised over the abundance of carbapenem resistance gene bla NDM-1. Additionally, this study highlighted the notable genetic diversity within P. stuartii, significant informations about phylogenomic relationships and ancestry, as well as potential for cross-species transmission, raising important implications for public health and microbial adaptation across different environments.

Micronutrient zinc (Zn) is crucial for both humans and plants. To improve the micronutrient concentration in the grains of food crops, biofortification has already been established as one of the best approaches across the globe. The current study was carried out to assess Zn enrichment in a few common wheat varieties using different Zn delivery techniques. The following treatments were applied: T 1 : control (without Zn), T 2 : Soil application of 4 kg Zn ha-1 at final land preparation, T 3 : Foliar application of a 0.5% Zn solution (ZnSO 4 x7H 2 O) at both tillering and booting stages, and T 4 : soil application of 2 kg Zn ha-1 at final land preparation + foliar application of a 0.2% Zn solution (ZnSO 4 x7H 2 O) at both tillering and booting stages. All treatments were arranged in a split-plot design and repeated three times The findings of the study revealed that the various Zn application techniques had an impact on the Zn concentration in grains significantly, ranging from 23.9 to 32.2 µg g-1 across the varieties, with an average of 28.2 µg g-1 in the control. Among these various application techniques, the soil + foliar application induced the highest response of the Zn concentration in the grains ranging from 33.7 to 37.60 µg g-1. The average protein content (%) in the grains of all wheat varieties varied by 1.21, 1.47, and 1.51%, respectively, as a result of the use of the different Zn application methods. Considering the Zn-use efficiency (%), the most Zn-efficient wheat variety was BARI Gom 28, followed by BARI Gom 26, Binagom-1, BARI Gom 25, and BARI Gom 29. In the case of grain yield, wheat variety BARI Gom 26 (4.15 t ha-1) performed the best in the soil + foliar Zn application. The soil + foliar application method outperformed the other application techniques regarding the Zn concentration, yield, and protein content in the grain.

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model’s performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality.

The Roma population—with a unique history marked by migration, settlement issues, and ongoing resilience—has always faced significant social marginalization and has often been subjected to forced migration. Despite being one of the largest and most diverse ethnic groups in Europe, Roma continue to face systemic discrimination and social exclusion, leading to poor outcomes in education, employment, health, and housing. This article analyses the migration pathways, settlement experiences, and persistent obstacles faced by Roma. We argue that removing historical and systemic barriers to create a more equitable and welcoming environment for the Roma community is their right. The resilience of the Roma community in the face of adversity is a testament to their cultural strength and adaptability. This research aims to document these aspects and thus provide a basis for policies that promote social inclusion, equality, and respect for cultural diversity.

Quality of life (QoL) focuses on a person’s ability to live a fulfilling life. It helps in determining successful aging in the elderly population. Because of a scarcity of information regarding predictors of QoL in the elderly population in developing countries, this study was undertaken to assess the overall QoL and its predictors in the elderly population in an urban setting of Bangladesh. In this cross-sectional study, 275 participants were enrolled by systematic sampling at the outpatient department of a tertiary care hospital in Dhaka, Bangladesh. Data were collected by using a pretested semi-structured standard questionnaire for QoL (OPQOL-35). Pearson’s correlation was used to assess the association between QoL and sociodemographic factors. Multivariate linear regression was conducted to identify predictors of QoL, after controlling for potential confounders. The median age of the participants (n = 275) was 65 years (range, 60 to 85; 25th and 75th percentile, 60 and 68, respectively). The majority (78%) of them were married, and 20% were widowed or divorced. The median score of QoL was 113 (25th and 75th percentile, 101 and 124, respectively). Most of the participants had very poor (bad as can be) or poor (bad) QoL. Only 7.3% were observed to have “good” QoL (scores 140 or more). Females had significantly poorer QoL scores compared to males (p < 0.001). Age, gender, educational status, smoking, and sleep duration significantly predicted QoL in a multiple regression analysis. In conclusion, the QoL of the elderly individuals in Bangladesh was very poor or poor. Future research should focus on service-oriented interventions, especially targeting women. Counseling elderly people to enhance their quality of life may include improving sleep patterns, healthy diets, regular exercise, and caring for their mental health.

Abstract Background Dengue is a major public health concern in Bangladesh. This study aimed to assess the perceptions and practices of community members in Dhaka regarding community engagement and social participation for dengue prevention. Methods A cross‐sectional online survey was conducted in Dhaka City from May 2022 to December 2022. The respondents were randomly selected. The association between community participation and prevention practices was tested using the χ2 test. Results The findings of this study indicate that the majority of participants (92%) believed that community effort would be relied upon in the event of a dengue outbreak. Environmental cleaning campaigns were the preferred approach, and religious leaders viewed them positively. This study also revealed significant variations in knowledge levels, with those involved in community efforts and mass gatherings demonstrating greater knowledge. This study sheds light on the demographic factors that influence dengue knowledge and provides valuable insights into the development of targeted public health interventions. Conclusion The study revealed negative perceptions and limited participation in dengue prevention among participants, with the majority demonstrating a poor understanding of preventive measures. While some showed positive attitudes towards community engagement, significant disparities existed in participation, highlighting the need for targeted educational campaigns and enhanced community mobilization efforts. Moreover, the importance of multisectoral collaboration is emphasized, underscoring the need for coordinated efforts among health departments, NGOs, religious institutions, and community leaders to effectively combat dengue transmission. Recommendations include ongoing educational initiatives, targeted interventions to promote community involvement, and fostering collaboration across sectors to strengthen dengue prevention efforts and to safeguard public health.

Abstract Background and Aims Antibiotic resistance presents a significant global public health challenge, particularly for urinary tract infections (UTIs), and is notably severe in developing countries. Surveillance of the antimicrobial susceptibility patterns of UTI‐causing bacteria is crucial for effective treatment selection. This study aimed to analyze these patterns in bacteria isolated from the urine samples of patients at Mughda Medical College Hospital, Dhaka, Bangladesh. Methods A retrospective study (January 2019 to December 2020) at Mugdha Medical College and Hospital, Dhaka, examined clinical and laboratory data from patients with positive urine cultures (≥105 CFU/mL). The study classified patients into four age groups: children (1–50 years). The standard Kirby‐Bauer method was used to assess antibiotic sensitivity to 28 common antibiotics. Results Among 243 positive urine cultures in both community‐ and hospital‐acquired UTIs, Escherichia coli was the most common uropathogen (65.84%), followed by Klebsiella spp. (12.34%), Enterococcus spp. (8.23%), and other types of bacteria. Conclusion Old adults are particularly vulnerable to UTIs, with E. coli being the predominant causative agent in the study region. The observed antimicrobial resistance patterns underscore the necessity of judicious antibiotic selection to effectively treat UTIs across different age groups.

The purpose of the current investigation was to conduct a detailed analysis of the chemical components and medicinal properties of the methanolic crude extract derived from the leaves of Cassia fistula. This analysis was carried out using both experimental (in vivo) and computational (in silico) methods. Eleven chemicals were chromatographically isolated using GC-MS/MS, which utilizes a library of NIST and Wiley 2020 versions. FTIR analysis of the extract was performed to identify the functional group of the compounds. The glucose-lowering capacity, analgesic, and anti-diarrheal activities of methanolic crude extract were analyzed utilizing a well-known oral glucose tolerance test, tail immersion method, writhing assay, and castor oil-induced diarrheal mice methods, respectively. After 60 min, 120 min, and 180 min of loading the drugs, a significant reduction of blood glucose levels was examined (p

Lay cycle lengths in the Canadian egg industry are currently 50 to 52 wk (68–70 wk of age). In light of increased productivity in commercial laying hens over the last few decades, the much longer lay cycle lengths already implemented in other countries, extending lay cycle lengths in Canada, should be considered with careful attention to potential environmental, economic, and animal welfare implications. However, there is a lack of information in the public domain that provides robust evidence of performance levels and potential trade-offs to support comprehensive consideration of the desirability of extending lay cycles beyond current Canadian norms. Hence, a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was undertaken. Data collection focused primarily on information related to feed-, flock management-, and hen genetics/physiology-related interventions that were studied in literature to support extension of lay cycles (review objective 1), and compiling and analyzing productivity, egg quality, and animal welfare outcomes reported at 70 wk of age or beyond (review objective 2). Several feed-related interventions such as high-protein diets, and probiotics supplements, and flock management interventions such split-feeding were found to potentially improve productivity, and especially egg quality, outcomes in the late laying phase. More studies with bigger flock sizes and in commercial lay facilities need to be undertaken before any of these interventions can be definitively recommended for commercial egg production. Under objective 2, productivity was found to be at acceptable levels well beyond 70 wk of age. Performance on most egg quality traits and animal welfare indicators were also at acceptable levels past 70 wk of age but increased variability was observed beyond ∼80 wk of age. There were also inconclusive indications on how hens in caged housing and white laying hens fare relative to hens in noncaged housing and brown-type layers during the late laying phase. Economic data were limited but suggested that lay cycle lengths beyond 90 wk might not generated net economic benefits.

ABSTRACTVaccination is important to prevent cholera. There are limited data comparing anti-O-specific polysaccharide (OSP) and anti-cholera toxin-specific immune responses following oral whole-cell with cholera toxin B-subunit (WC-rBS) vaccine (Dukoral, Valneva) administration in different age groups. An understanding of the differences is relevant because young children are less well protected by oral cholera vaccines than older children and adults. We compared responses in 50 adults and 49 children (ages 2 to

Abstract For hemodialysis patients, arteriovenous fistula (AVF) patency determines whether adequate hemofiltration can be achieved, and directly influences clinical outcomes. Here, we report the development and performance of a deep learning model for automated AVF stenosis screening based on the sound of AVF blood flow using supervised learning with data validated by ultrasound. We demonstrate the importance of contextualizing the sound with location metadata as the characteristics of the blood flow sound varies significantly along the AVF. We found the best model to be a vision transformer trained on spectrogram images. Our model can screen for stenosis at a performance level comparable to that of a nephrologist performing a physical exam, but with the advantage of being automated and scalable. In a high-volume, resource-limited clinical setting, automated AVF stenosis screening can help ensure patient safety via early detection of at-risk vascular access, streamline the dialysis workflow, and serve as a patient-facing tool to allow for at-home, self-screening.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML tasks: image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

Purpose – Inequality is increasing in Asia and the Pacific. This paper examines how inequality is affecting governments, communities and people in the Asia-Pacific region, given the 2030 Agenda's Sustainable Development Goals and the agenda's commitment to “leave no one behind.” Income inequality is just one element of larger economic and social inequalities in both developed and developing countries. Over the past decade, Bangladesh's economy has experienced one of the fastest growth rates in the world, supported by a narrowing demographic gap. The study focuses largely on the challenges of inequality and wealth distribution and uses the Singaporean experience to reduce inequality. Design/methodology/approach – The study is based on the review of secondary literature and an insightful analysis of the review. Findings – The Singapore Government has adopted four special budgets coronavirus disease 2019 (COVID-19) to help businesses cope with the economic difficulties caused by the epidemic, protect lives and create an economically and socially resilient Singapore. To sustain this increase in real gross domestic product (GDP) per capita, the Singapore Government continues to pursue growth-oriented policies. Importing technology and skilled labor, investing heavily in research and development, importing technology and developing export markets are some examples of these growth-oriented policies. The Singapore Government is committed to improving human capital through retraining and lifelong learning, which can be seen in all these growth-oriented policies. Bangladesh can learn more about reducing inequality and put these policies into practice. Originality/value – This study has frankly revealed the inequality issues in Bangladesh. This study has spotted the scarcities of development and the accurate picture of achievement from the perspective of inequality and prosperity dissemination.

ABSTRACTIn recent years, Bangladesh has seen significant development in the digitalization of various healthcare services. Although many mobile applications and social platforms have been developed to automate the services of the healthcare sector, there is still scope to make the process smooth and easily accessible for general people. This paper describes a system where the users can give their health-related problems or symptoms in the native Bengali language, and the system would recommend the medical specialist the user should visit based on their stated symptoms. The data is processed using various Natural Language Processing techniques. In this study, we have applied both Machine Learning and Deep Learning-based approaches. Three different models of Machine learning and four models of deep learning have been applied, analyzed and the accuracy of various models is evaluated to determine the best one that could provide superior performance on the given dataset. From the pool of traditional machine learning algorithms, the Random Forest (RF) classifier gives the highest accuracy of about 94.60% and Convolutional Neural Network performs the best among the deep-learning models, with an accuracy of 94.17%.

Question generation (QG), the task of generating questions from text or other forms of data, a significant and challenging subject, has recently attracted more attention in natural language processing (NLP) due to its vast range of business, healthcare, and education applications through creating quizzes, Frequently Asked Questions (FAQs) and documentation. Most QG research has been conducted in languages with abundant resources, such as English. However, due to the dearth of training data in low-resource languages, such as Bengali, thorough research on Bengali question generation has yet to be conducted. In this article, we propose a system for producing varied and pertinent Bengali questions from context passages in natural language in an answer-aware input format using a series of fine-tuned text-to-text transformer (T5) based models. During our studies with various transformer-based encoder-decoder models and various decoding processes, along with delivering 98% grammatically accurate questions, our fine-tuned BanglaT5 model had the highest 35.77 F-score in RougeL and 38.57 BLEU-1 score with beam search. Our automated and human evaluation results show that our answer-aware QG models can create realistic, human-like questions relevant to the context passage and answer. We also release our code, generated questions, dataset, and models to enable broader question generation research for the Bengali-speaking community.

Objective: The study is aimed to understand the antibacterial sensitivity of native and Indian varieties of garlic (Allium sativum) and ginger (Zingiber officinale) crude extracts against multi¬drug-resistant (MDR) poultry pathogen (Escherichia coli and Salmonella sp.). Materials and Methods: Thin layer chromatography (TLC) is used to identify the target spices' bioactive antibacterial compounds. MDR E. coli and Salmonella sp. were isolated from poultry. The TLC-Bioautography technique was applied to explore the antibacterial potentiality of garlic and ginger. Results: Inhibitory activities of garlic were Zone of inhibition (ZI) = 14.03 ± 0.15 mm and 19.70 ± 0.36 mm, Minimum inhibitory concentration (MIC): 0.625 and 0.325 mg/ml, and ginger were ZI = 14.63 ± 0.30 mm and 11.56 ± 0.51mm, MIC: 9.0 mg/ml against E. coli and Salmonella sp., respec¬tively. Two bands of garlic (Rf value = 0.31 and 0.50) and one band of ginger (Rf value = 0.71) showed inhibitory potential in TLC-Bioautography against both MDR isolates. Conclusion: Garlic and ginger were effective against MDR E. coli and Salmonella sp. These spices could be a suitable alternative during the antibiotic void. [J Adv Vet Anim Res 2023; 10(2.000): 151-156]

Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced from the Kaggle repository to generate practical recommendations for crop selection or determination of required nutrient(s) in a given site. The datasets contain information on NPK, soil pH, and three climatic variables: temperature, rainfall, and humidity. The models namely Support vector machine, XGBoost, Random forest, KNN, and Decision Tree were trained using yields of individual data sets of 11 agricultural and 10 horticultural crops, as well as combined yield of both agri-horticultural crops. The results strongly suggest to evaluate individual data sets separately for each crop category rather than using combined the data sets of both categories for better predictions. Comparing the five ML models, the XGBoost demonstrated the highest level of accuracy. The precision rates of XGBoost for recommending agricultural crops, horticultural crops, and a combination of both were 99.09 % (AUC 1.0), 99.3 % (AUC 1.0), and 98.51 % (AUC 0.99), respectively. This non-intrusive method for generating crop recommendations in diverse environmental conditions holds the potential to provide valuable insights for the development of a user-friendly AI cloud-based interface. Such an interface would enable rapid decision-making for optimal fertilizer applications and the selection of suitable crops for cultivation at specific sites.

IntroductionSexual and reproductive health and rights (SRHR) are vital for both individual well-being and development. Bangladesh has made long strides in improving SRHR over the last few decades. However, the progress has been uneven across various groups of reproductive-aged females, with the married adolescent girls (MAGs) often being more vulnerable to denial of SRHR than other women. This study intends to develop Balika Bodhu, a combined empowerment and social norm intervention for promoting SRHR among the MAGs and assess its impact.MethodsThe evaluation will employ a mixed-method two-arm Cluster Randomized Controlled Trial (CRCT) design, where Arm 1 receives the intervention, and Arm 2 serves as the control. The trial will cover 32 clusters (villages) in Rajbari Sadar sub-district, randomized into two equally distributed study arms. A total of 1,120 MAGs aged 15-19 years will be randomly selected from the clusters (35 per cluster) to form a cohort. The MAGs, their husbands, selected elderly women (26 per village) and influential community members (26 per village) will receive group sessions in the intervention clusters. The MAGs and their husbands will be interviewed at baseline and endline. A randomly selected cross-sectional sample of community members aged 35-59 years at baseline and endline will also be surveyed to measure attitudes and social norm regarding SRHR of MAGs. Qualitative data will be collected using 32 In-depth Interviews, six Key Informant Interviews, and eight Focus Group Discussions from two intervention villages. Intention-to-treat (ITT) analysis will be performed to assess the impact of the intervention. Narrative analysis and the Grounded Theory approach will be used to analyze the qualitative data.ConclusionRigorous evaluation of Balika Bodhu should contribute to the literature on what works and what does not in addressing denial of SRHR to MAGs using empowerment and social norm intervention and inform policies and programs.Trial registrationClinicaltrials.gov: identifier: NCT06126770; Date: Oct 7, 2023. Version 1.

This review delves into the historical context, current epidemiological landscape, genomics, and pathobiology of monkeypox virus (MPXV). Furthermore, it elucidates the present vaccination status and strategies to curb the spread of monkeypox. Monkeypox, caused by the Orthopoxvirus known as MPXV, is a zoonotic ailment. MPXV can be transmitted from person to person through respiratory droplets during prolonged face-to-face interactions. While many cases of monkeypox are self-limiting, vulnerable groups such as young children, pregnant women, and immunocompromised individuals may experience severe manifestations. Diagnosis predominantly relies on clinical presentations, complemented by laboratory techniques like RT-PCR. Although treatment is often not required, severe cases necessitate antiviral medications like tecovirimat, cidofovir, and brincidofovir. Vaccination, particularly using the smallpox vaccine, has proven instrumental in outbreak control, exhibiting an efficacy of at least 85% against mpox as evidenced by data from Africa. Mitigating transmission requires measures like wearing surgical masks, adequately covering skin lesions, and avoiding handling wild animals.

BackgroundAlthough under-five mortality has declined appreciably in Bangladesh over the last few decades, neonatal mortality still remains high. The objective of the study is to assess the level and determinants of preterm birth and the contribution of preterm birth to neonatal mortality.MethodsData for this study came from selected slums in and around Dhaka city, where; since 2015, icddr,b has been maintaining the Health and Demographic Surveillance System (HDSS). The HDSS data were collected by female Field Workers by visiting each household every three months; however, during the visit, data on the Last Menstrual Period (LMP) were also collected by asking each eligible woman to ascertain the date of conception. Gestational age was estimated in complete weeks by subtracting LMP from the date of the pregnancy outcome. In this study, 6,989 livebirths were recorded by HDSS during 2016-2018, and these births were followed for neonatal survival; both bivariate and multivariate analyses were performed.ResultsOut of total births, 21.7% were born preterm (before 37 weeks of gestation), and sub-categories were: 2.19% for very preterm (28 to 31 weeks), 3.81% for moderate preterm (32 to 33 weeks), and 15.71% for late preterm (34 to 36 weeks). The study revealed that preterm babies contributed to 39.6% of neonatal deaths; however, the probability of death was very high on the 1st day of birth (0.124 for very preterm, 0.048 for moderate preterm, 0.024 for late preterm, and 0.013 for term birth), and continued until the 3rd day. In the regression analysis, compared to the term neonates, the odds of neonatal mortality were 8.66 (CI: 5.63, 13.32, pConclusionsAlthough urban slums are in proximity to many health facilities, a substantial proportion of preterm births contribute to neonatal deaths. So, pregnant women should be targeted, to ensure timely care during pregnancy, delivery, and post-partum periods to improve the survival of new-borns in general and preterm birth in particular.

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models. We meticulously trained VGG16, VGG19, DenseNet201, ResNet50V2, Xception, InceptionResNetV2, and InceptionV3 deep neural network models. This training utilized a dataset comprising 5878 images encompassing 30 medicinal species distributed among 20 families. Our approach involved two avenues: the utilization of public data (PI) and a blend of public and field data (PFI), the latter featuring intricate backgrounds. Our study elucidates the robustness of these models in accurately identifying and classifying both interfamily and interspecies variations. Despite variations in accuracy across diverse families and species, the models demonstrated adeptness in these classifications. Comparing the models, we unearthed a crucial insight: the Normalized leverage factor (γω) for DenseNet201 stands at 0.19, elevating it to the pinnacle position for PI with a remarkable 99.64 % accuracy and 98.31 % precision. In the PFI scenario, the same model achieves a γω of 0.15 with a commendable 97 % accuracy. These findings serve as a guiding beacon for shaping future application tools designed to automate medicinal plant identification at the user level.

The dengue virus (DENV) causes dengue infection and has become a significant global public health concern. The successful application of immunotherapies in treating dengue infection has paved the way for developing various treatment strategies. Among these strategies, the multiepitope vaccine has emerged as a promising approach. We applied an in silico immune-informatics approach to develop a multiepitope vaccine including MHC-I, MHC-II, and B cell epitopes of the NS1, E, and M proteins. The vaccine was found to be a structurally stable protein (Z score = −4.71), indicating its effectiveness. High affinity was shown by molecular docking studies between the vaccine and human receptors (toll-like receptor 2 and toll-like receptor 4). Following codon optimization and in silico cloning, the vaccine was successfully expressed (CAI value of 0.9588) after being inserted into the pET-30a (+) plasmid of the E. coli K12 strain. However, the vaccine's potential to elicit immunological responses (such as B cell, T cell, antibody, and cytokine responses) against the dengue virus serotype 3 (DENV-3) has been established through immune simulation. The majority of existing dengue vaccines have shown little efficacy in the management of dengue infection. Furthermore, there is currently no licenced multiepitope vaccine for dengue virus serotype 3 (DENV-3) that encompasses all three significant proteins. Consequently, our proposed vaccine has the potential to be an effective weapon in combating dengue infection. Further studies should be conducted to evaluate the safety, effectiveness, and potential adverse effects of this multiepitope vaccine in human subjects to facilitate its further development.

ObjectivesDespite high levels of physical violence against children (VAC) globally (40–50%), the literature on the determinants of VAC remains inconclusive. Most of the literature on this topic is based on cross-sectional data, and the multi-level nature of the drivers of VAC is widely ignored. This leads to model specification problems and an inability to draw causal inferences. Moreover, despite the higher prevalence of VAC in low-and middle-income countries, studies from high income countries dominate the field. We examined the determinants of physical domestic VAC to address these gaps in the literature.MethodsData were collected between 2001 and 2020 from 762 mother–child dyads recruited in the Maternal and Infant Nutrition Interventions in Matlab (MINIMat) study in Bangladesh. We conducted multi-level logistic regression analyses to identify the determinants of physical domestic VAC.ResultsPrevalence of physical domestic violence against girls (69%) and boys (62%) was extremely high. Community-level prevalence of physical domestic VAC increased the likelihood of physical domestic VAC at the individual level across gender (girls - OR-5.66; 95% CI- 3.11-10.32; boys - OR-7.67; CI- 3.95-14.91). While physical domestic violence against mothers was not associated with physical domestic violence against girls, it reduced the likelihood of such violence against boys by 47%. Having 3 or more siblings predicted physical domestic violence against girls (OR-1.97; 95% CI- 1.01-3.81 for 3 siblings; OR-4.58; 95% CI- 2.12-9.90 for 4 or more siblings), but not against boys. While girls in Hindu families were more likely to experience this violence, the boys were not. Mother’s education, employment non-governmental organization (NGO) participation and, household wealth did not predict this violence against any gender.ConclusionWe contend that physical domestic violence against mothers reflects an emphasized patriarchal culture in a family where a boy is less likely to experience physical domestic violence. Social norms and social learning theories explain the greater likelihood of a child experiencing physical domestic violence in a village with a higher level of such violence. We conclude that social norms around physical domestic VAC and patriarchal culture need to be changed to effectively address this violence.

Abstract Thalassemia is one of the most common autosomal recessive hereditary blood disorders worldwide, especially in developing countries, including Bangladesh. Thus, this study aimed to determine HRQoL and its determinants of thalassemia patients (TP) in Bangladesh. A cross-sectional survey was performed on 356 randomly selected thalassemia patients. Participants were invited to face-to-face interviews. Descriptive statistics (frequencies and percentages), independent t-test, ANOVA, and multivariate (linear and logistic regression) analysis was performed to analyze the data. Our demographic data showed that among 356 patients, 54% and 46% were male and female, respectively, with an average age of 19.75 (SD = 8.02) years. Most were transfusion-dependent (91%), 26% had comorbidities, and 52% were from low-income families. In the case of HRQoL, male patients showed significantly higher scores of bodily pains and physical health summaries than female patients. Lower income, high blood transfusion status, disease severity, comorbidities, and medical expenses (p

Anthracnose is one of the most devastating fungi causing twig dieback and postharvest fruit decay. Present study was aimed to identification of anthracnose fungus. Infected leaves of Citrus limon were collected and cultured on potato dextrose agar (PDA) media for pathogenic fungus isolation. The isolated fungal pure culture was characterized by physiological and morphological characterization methods. Biological control measures of the fungus were evaluated by disc diffusion methods. The highest growth and development of isolated fungus was detected in PDA media pH 7 in fructose as the best carbon source and 0.05gm NaCl concentrations at 37°C. Pathogenicity potency of isolate was performed on lemon, orange and malta, belonging to the Citrus sp. For antifungal activity, 200µgm/disc methanolic extract of Psidium guajava showed 14.33±0.66 mm inhibition zone against the isolated fungus. From the present investigations, identified anthracnose causing fungus and it's controlling techniques may help for further research for the isolation of drugs related compound for controlling this disease. [ J Adv Biotechnol Exp Ther 2023; 6(1.000): 58-66]

Abstract Enteric bacteria such as Escherichia coli and Salmonella spp. are significant fish pathogens and related to thousands of cases of food-borne diseases every year in human. Since aquatic environments are reservoirs of these pathogens, they may contaminate the food fish and result in outbreaks. Therefore, it is crucial to reduce or eliminate these pathogens from aquaculture facilities. We tested effectiveness of three commonly used aquaculture chemicals, viz., lime, hydrogen peroxide and zeolite on bacterial load, Escherichia coli and Salmonella spp. under laboratory and earthen pond conditions where they were applied at a dose recommended for freshwater aquaculture. Results of the bacteriological study showed that lime had a significant role in reducing bacterial abundance from an initial value of 1.5 × 103 to 1.9 × 103 cfu/ml and 3.9 × 103 to 6.3 × 103 to a final value of 1.2 × 103 to 1.5 × 103 cfu/ml and 1.9 × 103 to 6.3 × 103 cfu/ml within 24 h under aquaria and pond condition, respectively. A complete inactivation of E. coli was observed in lime treated aquaria and ponds 24 h post-treatment, whereas Salmonella spp. remained unaffected in pond water but inactivated under aquarium condition after same period. However, neither hydrogen peroxide nor zeolite treatment reduced total bacterial count as well as E. coli and Salmonella even after 1-week post-treatment. It is, therefore, recommended that the water treatment chemicals should be applied in aquaculture ponds at appropriate dose, and farmers need to adopt biosecurity measures to ensure food safety.

Objectives This study was designed to identify the patterns, prevalence and risk factors of intimate partner violence (IPV) against female adolescents and its association with mental health problems.Design Cross-sectional survey.Settings Dumuria Upazila (subdistrict) under the Khulna district of Bangladesh.Participants A total of 304 participants were selected purposively based on some specifications: they must be female adolescents, residents of Dumuria Upazila and married during the COVID-19 pandemic when under 18 years of age.Outcome measures By administering a semi-structured interview schedule, data were collected regarding IPV using 12 five-point Likert scale items; a higher score from the summation reflects frequent violence.Results The findings suggest that the prevalence of physical, sexual and emotional IPV among the 304 participants, who had an average age of 17.1 years (SD=1.42), was 89.5%, 87.8% and 93.7%, respectively, whereas 12.2% of the participants experienced severe physical IPV, 9.9% experienced severe sexual IPV and 10.5% experienced severe emotional IPV. Stepwise regression models identified age at marriage (p=0.001), number of miscarriages (p=0.005), education of spouse (p=0.001), income of spouse (p=0.016), age gap between spouses (p=0.008), marital adjustment (p