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Summary: Background and aims: One-third of the country suffers from metabolic dysfunction-associated fatty liver disease (MAFLD) due to unhealthy, improper dietary habits and sedentary lifestyles. This study aimed to explore the specific diet and lifestyle factors responsible for MAFLD. Methods: This case-control study was conducted among the source population (n=200 diagnosed with MAFLD criteria; n=100 healthy controls). Quantitative approaches, such as interviews, were applied to measure dietary practices and physical activity levels. Results: Most (89.5%) of the MAFLD patients were obese, used to sedentary lifestyles (56.0%), and took outside foods (58.5%) than their counterparts (P300 minutes sitting times (aOR=8.6), >300 g/day carbohydrate consumption (aOR=6.9), >950 kcal/day from rice (aOR=2.2), >50 g/day fat (aOR=2.9), ≤2000 Kcal/day energy-expenditure (aOR=2.9), BMI >25 kg/m2 (Adjusted odds ratio=aOR=16.1), eating outside (aOR=7.4), being housewives (aOR=4.89), service-holders (aOR=4.12), >31 age group (aOR=2.9), are significant risk factors for MAFLD. Conclusion: Longer sitting times, outside eating habits, more energy from carbohydrates, especially rice, and a daily positive energy balance lead to obesity and, consequently, an increased risk of MAFLD.

This work incorporates the SCAPS-1D modeling program toexamine the impacts of defects in the Molybdenum Disulfide (MoS2) layer and the MoS2 interface on the electrical performance of CZTS solar cells. To get an ideal energy gap (Eg) of 1.3 eV and a carrier concentration (CC) of 1014 cm⁻³, the research attempts to optimize the CZTS absorber layer. By maintaining a consistent doping level with 1016 cm-3 ≤ CC and an Eg within 1.6 eV

Under the Belt and Road Initiative (BRI), implementing low-carbon economic cooperation is crucial to combating climate change and preserving sustainability worldwide. The paper checks the asymmetric (non-linear) effects of Chinese FDI flows on carbon dioxide (CO2) emissions to 24 selected nations from the Middle East, North Africa, and Asia regions. The study used the non-linear panel-ARDL and asymmetric causality methods to check the asymmetric relations. The result from the non-linear panel-ARDL method showed that positive (negative) shocks in Chinese FDI have positive effects on CO2 in the long run. The empirical outcomes estimated via the linear approach indicated that a rise in Chinese FDI would decrease CO2, thus supporting the pollution halo effect. Moreover, an increase (decrease) in economic growth significantly raises (decline) in CO2. From the Granger causality of Hatemi-J, a unidirectional causality is noted from the population (positive shock) and economic growth (positive shock) to CO2 (positive shock). The findings suggest that officials should entice FDI cautiously toward clean production and energy-saving industries and technology.

Phosphorene (BP), a two-dimensional (2D) material composed of monolayer or few-layers of black phosphorous (bBP), has recently emerged as a promising candidate for biomedical applications due to its unique electronic, optical, and mechanical properties. This review presents an overview of the theoretical and experimental investigations on the use of BP in various biomedical applications including drug delivery, biosensing, bioimaging, tissue engineering, and its incorporation into various treatment modalities. BP is an excellent candidate for drug delivery applications due to its tunable bandgap, large surface area, and puckered structure. In addition, due to the intrinsic photoacoustic and fluorescence properties of BP, it is regarded highly for biosensing and bioimaging applications. Researchers have taken advantage of its bioimaging capabilities to develop novel synergistic and theranostic treatment modalities, achieving excellent results. Furthermore, the mechanical and chemical properties of BP make it suitable for tissue engineering applications, particularly in the regeneration of bone tissue. The review also discusses concerns regarding the application of BP in biomedicine, including potential toxicity.

Abstract Older adults residing in refugee settlements with unhealthy living environments, inadequate access to health care services, and limited psychosocial support are vulnerable to experience mental health problems jeopardizing their mental well-being. The present study aims to explore the mental well-being status and its socio-economic determinants among the older adults living in the Rohingya refugee camp in Bangladesh. This cross-sectional study was conducted among adults aged ≥ 60 residing in five sub-camps within the Rohingya refugee camp of Cox’s Bazar, Bangladesh. Data were collected through face-to-face interviews conducted between November and December 2021. The 14-item Warwick-Edinburgh Mental Well-being Scale was used to assess mental well-being. A cumulated score was derived using the scale ranging from 14 to 70, with higher scores indicating greater levels of mental well-being. A generalized linear regression model was used to examine the socio-economic factors associated with the mental well-being of older adults. A total of 864 older adults participated in the study having a mean mental well-being score of 45.4. Regression analysis revealed that the difference in the logs of mental well-being score was expected to be significantly lower among participants aged 70–79 years (β: − 1.661; 95% CI: − 2.750 to − 0.572; p = 0.003), aged ≥ 80 years (β: − 3.198; 95% CI: − 5.114 to − 1.282; p = 0.001), and those with any non-communicable chronic conditions (β: − 2.903; 95% CI: − 3.833 to − 1.974; p

Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) to better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. This research suggests a dilated parallel deep convolutional neural network (PDCNN) architecture that preserves a wide receptive field in order to handle gridding artifacts and extract both coarse and fine features from the images. This article applies multiple preprocessing strategies to the input MRI images used to train the model. By contrasting various dilation rates, the global path uses a low dilation rate (2,1,1), while the local path uses a high dilation rate (4,2,1) for decremental even numbers to tackle gridding artifacts and to extract both coarse and fine features from the two parallel paths. Using three different types of MRI datasets, the suggested dilated PDCNN with the average ensemble method performs best. The accuracy achieved for the multiclass Kaggle dataset-III, Figshare dataset-II, and binary tumor identification dataset-I is 98.35%, 98.13%, and 98.67%, respectively. In comparison to state-of-the-art techniques, the suggested structure improves results by extracting both fine and coarse features, making it efficient.

The performance, synthesis, and characteristics of bio-based systems are the main topics of this study, which investigates the possibilities of biomaterials as energy storage devices. Biomaterials like chitin, chitosan, and other biopolymers have demonstrated promise as next-generation energy storage technologies, particularly as the world’s need for sustainable energy solutions continues to rise. In batteries and supercapacitors, they are appealing substitute for conventional materials due to their biocompatibility, biodegradability, and minimal environmental effect. The review highlights advances in biomaterial-based energy storage technologies, including improvements in conductivity, structural stability, and scalability. By integrating biomaterials into energy storage, researchers aim to create environmentally friendly systems with high performance and longevity. This review attempts to provide a thorough understanding of biomaterials’ potential in enhancing energy storage technologies toward a more sustainable future.

Carbon nanotubes (CNTs) are a significant element of nanotechnology, characterized by an exceptional length-to-diameter ratio exceeding 1,000,000. Various methodologies have been devised to manufacture CNT. These technologies show promise for use in multiple fields, including nanoelectronics, biotechnology, material science, polymers, composites, and textiles. The utilization of these new materials is expected to significantly influence future aircraft and space vehicles, especially in the aerospace sector. This is mainly owing to their exceptional thermal, mechanical, and electrical capabilities. This study comprehensively appraises the prospects of using carbon nanotubes in the aerospace sector. It draws from their current practical use and predicted future usage. Focus is directed toward the future application of carbon nanotubes, which might potentially include lightweight structural components, conductive materials for aircraft systems, composite materials and reinforcements, thermal management systems, electromagnetic interference (EMI) shielding, advanced sensors, nanoelectronics, fuel cells, energy storage systems, space elevators, tether applications, aerogels, and insulation materials, nanocomposites for aircraft coatings area. This paper provides more insights into the recent practical uses of carbon nanotubes in aircraft technology along with the existing and potential obstacles associated with their utilization in aerospace science.

Energy storage technologies, which are based on natural principles and developed via rigorous academic study, are essential for sustainable energy solutions. Mechanical systems such as flywheel, pumped hydro, and compressed air storage rely on inertia and gravitational potential to store and release energy. On the other hand, electrochemical systems, which include different types of batteries, effectively store and release energy by utilizing materials like metal hydrides and transition metal oxides. These materials are known for their high energy densities and reversible chemical properties. Although they have shown potential, issues such as high costs, limited availability of materials, and negative environmental effects continue to remain. This requires the development of sustainable and scalable production methods and presents difficulties in integrating with current infrastructure. To tackle these problems, a multidisciplinary strategy is necessary. This approach should involve the creation of effective materials, the implementation of sustainable manufacturing methods, and the establishment of comprehensive policy frameworks. Enhanced global collaboration and increased investment in research and development are crucial as well. Through promoting collaboration among scientists, engineers, policymakers, and industry stakeholders, we can address existing constraints, improve energy efficiency, and advance the ability of the energy sector to withstand challenges. This will enable the development of a strong, environmentally friendly energy future that can meet global energy needs in a sustainable and fair manner.

This paper presents the design and simulation of a surface plasmon resonance (SPR) biosensor using a Platinum diselenide (PtSe2) and Blue Phosphorus/tungsten disulfide (BlueP/WS2) heterostructure for biosensing protocols. The simulation is done by using a finite element method (FEM) based COMSOL Multiphysics software. The performance of the SPR biosensor is then optimized for obtaining maximum sensitivity, quality factor, detection accuracy, and low limit of detection (LOD). The SPR biosensor demonstrates a maximum sensitivity of 234 deg/RIU, suggesting its ability to detect minute refractive index changes with remarkable precision. Furthermore, a quality factor of 390 RIU−1 demonstrates the biosensor's capacity to detect tiny fluctuations in target analyte concentration. The achieved detection accuracy of 7.8 deg−1 presents the biosensor's ability to detect target biomolecule solutions in the desired RI range. The remarkably low LOD of 4.26 × 10−6 ensures early and accurate detection. The significance of this research lies in five layered hetero-structure based combinations of BK7 prism, gold, PtSe2, BlueP/WS2 and sensing medium respectively. The introduction of transition metal dichalcogenides (TMDC) material of PtSe2 with a hybrid 2D nanomaterials heterostructure of BlueP and TMDCs offers a rapid, sensitive, label-free and reliable platform for early detection. Additionally, the FEM method allows for the investigation of physical phenomena as part of the work. In summary, the proposed senor outcomes effectively demonstrate the speedy capability of detecting any pathogens or analytes in the RI range of 1.330–1.350 with remarkable sensitivity and accuracy. The rapid detection without giving false results is the benefit of the proposed sensor structure.

Enterococcus faecalis are often resistant to different classes of antibiotics, harbor virulence determinants, and produce biofilm. The presence of E. faecalis in raw seafood exhibits serious public health significance. This study aimed to identify antibiotic resistance patterns and virulence factors in biofilm-forming E. faecalis strains extracted from seafood in Bangladesh. A total of 150 samples of raw seafood, comprising 50 shrimps, 25 crabs, and 75 fish, were collected and subjected to culturing, biochemical, and PCR assays to detect E. faecalis. The biofilm-forming abilities of the isolates were determined by Congo Red agar (CRA) plate and Crystal Violet Micro-titer Plate (CVMP) tests. Antibiotic resistance profiles were evaluated using the disk diffusion method. Virulence genes of the isolates were detected by PCR assay. The occurrence of E. faecalis was 29.3 % (44/150), which was higher in crabs and fish (36 %) than in shrimps (16 %). In CRA and CVMP tests, biofilm-forming abilities were observed in 88.64 % of the isolates, whereas 11 (25 %) and 28 (63.6 %) were strong- and intermediate-biofilm formers, respectively. All the isolates contained at least two virulence genes, including pil and ace (97.7 %), sprE (95.5 %), gelE (90.9 %), fsrB (79.6 %), agg (70.5 %), fsrA (68.2 %), and fsrC (61.4 %). All the isolates were phenotypically resistant to penicillin, followed by ampicillin and rifampicin (86.4 %), erythromycin (13.7 %), and tetracycline, vancomycin, norfloxacin, and linezolid (2.3 %). Resistant gene blaTEM was found in 61.4 % of the isolates. Moreover, the study found that E. faecalis strains with strong biofilm-forming capabilities had significantly higher levels of virulence genes and antibiotic resistance (p

Introduction: Antimicrobial resistance (AMR), a transboundary health issue, critically impacting low- and middle-income countries (LMICs) where 80% of antibiotics are used in the community, with 20–50% being inappropriate. Southeast-Asia, including Bangladesh, faces heightened AMR risk due to suboptimal healthcare standard and unregulated antibiotic sales. This study aimed to audit antibiotic dispensing patterns from community pharmacies, identifying factors influencing purchasing behaviors. Methods: A cross-sectional survey of 385 antibiotic customers and structured observations of 1000 pharmacy dispensing events were conducted in four urban and rural areas in Bangladesh. Descriptive analysis defined antibiotic use, while Poisson regression examined how patients' demographics and health symptoms influenced prescription behaviors. Results: Among 1000 observed medicine dispensing events, 25.9% were antibiotics. Commonly purchased antibiotics included macrolides (22.8%), third-generation-cephalosporins (20.8%), and second-generation-cephalosporins (16.9%). Following WHO-AWaRe classifications, 73.5% of antibiotics were categorized as Watch, and 23.1% as Access. From the survey, 56.6% antibiotics were purchased without a prescription from drug-sellers and informal healthcare providers, primarily for “non-severe” health-symptoms such as upper-respiratory-tract infections (37.4%), fever (31.7%), uncomplicated skin infections (20%), gastrointestinal-infections (11.2%), and urinary-tract infections (7.9%). The likelihood of presenting a prescription while purchasing antibiotics was 27% lower for individuals aged 6–59 compared to those ≤5 or ≥ 60. Lower-respiratory-tract infections and enteric-fever had higher prescription rates, with adjusted prevalence ratios of 1.78 (95% CI: 1.04, 3.03) and 1.87 (95% CI: 1.07, 3.29), respectively. After adjusting for confounders, sex, urban-rural locations, income, education, and number of health-symptoms exhibited no significant influence on prescription likelihood. Conclusion: This study underscores unregulated antibiotic sales without prescriptions, urging tailored interventions considering prevailing health-seeking practices in diverse healthcare settings in LMICs. Enforcing prescription-only regulations is hindered by easy access through community pharmacies and conflicts of interest. Future strategies should consider how stewardship impacts the financial interests of pharmacy personnel in settings lacking clear authority to ensure optimal compliance.

Objectives: Citrobacter freundii is a prevalent source of nosocomial infections and a well-known cause of diarrheal diseases. In recent years, it has also become increasingly resistant to various antimicrobials. In this study, we screened and characterized a multidrug-resistant (MDR) C. freundii isolate obtained from a domesticated diseased duck to better understand the genetic features, molecular epidemiology, and underlying factors linked to the antimicrobial resistance genes (ARGs) and virulence factor genes (VFGs) of the isolate. Methods: The C. freundii BAU_TM8 strain was isolated using culturing, staining, biochemical, polymerase chain reaction, and Matrix-assisted laser desorption/ionization-time of flight methods. The MDR properties of the strain were determined by a disk diffusion test. The genomic sequence of C. freundii BAU_TM8 was performed using the Illumina NextSeq2000 platform. The ARGs, VFGs, and genomic functional characteristics of the C. freundii BAU_TM8 strain were identified using several open-source databases. Results: The sequence type of this strain was ST669, and the pathogenicity index of the strain was 0.919. Moreover, the strain had an estimated genome length of 5,797,806 bp, harboring 62 contigs, a G + C content of 54.32 %, and five contig L50s with an N50 value of 443,947 bp. Using phylogenetic analysis, this strain was closely related to two strains isolated from human and environmental samples in the USA and China despite huge geographical distances. The C. freundii BAU_TM8 strain consisted of 40 AGRs encoding resistance to 19 antimicrobial categories, e.g., fluoroquinolones, macrolides, folate pathway antagonists, aminoglycosides, tetracyclines, cephalosporins, and others. According to the phenotypic assay and genome sequence, the sensitivity and specificity of resistance profiles of the strain were 100 % and 20 %, respectively. Moreover, the virulence factor database detected 66 VFGs in this strain. This strain contained 1581 subsystems, having 33 % subsystem coverage and 2275 genes encoding amino acid derivatives, carbohydrate metabolism, protein metabolism, cofactors, vitamins, prosthetic groups, pigments, respiration, motility and chemotaxis, stress response, DNA metabolism, nucleosides and nucleotides, and others. Conclusions: To the best of our knowledge, this is the first WGS report of C. freundii from a domesticated duck in Bangladesh. The ubiquitous occurrence of ARGs and VFGs in the C. freundii BAU_TM8 strain detected in this study highlights the growing concern about antimicrobial resistance in humans, animals, and environments.

The study, conducted at the Bangladesh Livestock Research Institute, aimed to assess semen quality and characteristics in three types of indigenous cocks and explore seasonal variations. Ninety bird samples from three genotypes, namely non-descript Deshi (ND), Hilly (HI), and Naked Neck (NN), were used in this research. A total of 548 semen samples were collected to evaluate the color, volume, consistency, and transparency of semen in different seasons. Semen collection was performed using an abdominal massage technique, and volume was measured with a tuberculin syringe. For determining microscopic physical and morphological characteristics, computer-assisted sperm analysis (CASA) software was used. Results showed that among three types of indigenous cocks, semen quality differed (p < 0.001) from season to season. Semen quality was comparatively good for ND in the autumn season, for HI in the summer season, and for NN in the spring season. The highest semen volume (0.318 ± 0.010 ml) was recorded in the summer season, and the lowest semen volume (0.218 ± 0.008 ml) was recorded in the spring season. Genotype had a significant (p < 0.01) effect on semen volume, and the effect of semen and genotype interaction on semen volume was non-significant (p > 0.05). The sperm concentration was higher in NN compared to ND and HI. The percentages of static, bent tail, coiled tail, DMR, distal droplet, and proximal droplet were comparatively higher in the HI and NN genotypes. The color, consistency, and volume of semen were affected by genotype and season.

The metabolic versatility of Bacillus subtilis makes it useful for a wide range of applications in biotechnology, from bioremediation to industrially important metabolite production. Understanding the molecular attributes of the biocontrol characteristics of B. subtilis is necessary for its tailored use in the environment and industry. Therefore, the present study aimed to conduct phenotypic characterization and whole genome analysis of the B. subtilis BDSA1 isolated from polluted river water from Dhaka, Bangladesh to explore its biotechnological potential. The chromium reduction capacity at 100 ppm Cr (VI) showed that B. subtilis BDSA1 reduced 40 % of Cr (VI) within 24hrs at 37 °C. Exposure of this bacterium to 200 ppm cadmium resulted in 43 % adsorption following one week of incubation at 37 °C. Molecular detection of chrA and czcC gene confirmed chromium and cadmium resistance characteristics of BDSA1. The size of the genome of the B. subtilis BDSA1 was 4.2 Mb with 43.4 % GC content. Genome annotation detected the presence of numerous genes involved in the degradation of xenobiotics, resistance to abiotic stress, production of lytic enzymes, siderophore formation, and plant growth promotion. The assembled genome also carried chromium, cadmium, copper, and arsenic resistance-related genes, notably cadA, czcD, czrA, arsB etc. Genome mining revealed six biosynthetic gene clusters for bacillaene, bacillibacin, bacilysin, subtilosin, fengycin and surfactin. Importantly, BDSA1 was predicted to be non-pathogenic to humans and had only two acquired antimicrobial resistance genes. The pan-genome analysis showed the openness of the B. subtilis pan-genome. Our findings suggested that B. subtilis BDSA1 might be a promising candidate for diverse biotechnological uses.

The Sundarbans, located in Bangladesh, is the world's largest mangrove forest with a rich and diverse microbial community. The main objective of this study was to use 16S rRNA sequencing to identify the bacterial community in the Sundarban mangrove forest, particularly pathogenic bacteria to aquatic organisms. This study conducted in Bangladesh collected 225 samples of soil, water, fish, and shellfish from five locations with a typical mangrove environment. All samples underwent genomic DNA extraction, quantification, gel documentation, and subsequent sequencing. Sanger sequencing was then carried out, followed by sequencing 11 concentrated samples using the universal bacterial primer 16S rRNA. The study found 872 bacterial species, 94 genera, 38 families, and 6 phyla. Pseudomonades or proteobacteria accounted for 92% of the phylum composition, with firmicutes, bactericides, cyanobacteria, Verrucomicrobia, and actinobacteria also present at smaller percentages. Chromatiaceae had the highest species count (20.3%), followed by Ectothiorhodospiraceae (15.5%), Methylococcaceae (12.3%), Moraxellaceae (10.7%), Thiotrichaceae (8.7%), Idiomarinaceae (5.4%), and smaller groups. All the species that were identified were gram-negative bacteria and were found in different environments, mainly halophilic ones. Based on 16 s rRNA sequencing, additional experimental and clinical data, as well as NCBI (BLAST), approximately 49.3% of the 872 bacterial species are typically harmless and not classified as pathogenic. The pathogenic characteristics of 24.9% of the species are still unidentified. 13.4% showed either pathogenic or non-pathogenic characteristics (a bacterium as having the potential to exhibit pathogenic or non-pathogenic characteristics in certain circumstances), while 8.4% were exclusively pathogenic, potentially capable of causing disease. Also, the bacterial species are categorized as 2.6% non-highly pathogenic (a bacterium can still have a significant impact on health, especially in weakened immune systems or other underlying health conditions), 1.3% sometimes pathogenic and 0.1% potentially pathogenic (a bacterium don't always cause disease). Sequence analysis, combination of phylogenetic, biochemical characterization (data were not shown), and other experimental validation conclude a genetic baseline of bacterial community, this research may serve as a valuable reference for future ecological and taxonomical studies as well as addressing further research scope on bacterial diversity found in the Sundarbans mangrove of Bangladesh.

MEMS, or microelectromechanical systems, have greatly transformed multiple industries with their small size and adaptable capabilities. MEMS technology in biosensing allows for rapid and accurate identification of biological analytes, assisting in medical diagnoses and therapies. MEMS-based biosensors provide label-free detection, improving early disease screening and customized medicine administration. MEMS devices in healthcare enable uninterrupted patient monitoring and telemedicine, leading to substantial enhancements in healthcare results. Nevertheless, obstacles remain, such as the requirement for improved biocompatibility and durability under very demanding conditions. In order to overcome these restrictions, current research is dedicated to advancing materials that are compatible with living organisms, improving processes used to create small-scale structures, and incorporating wireless monitoring systems. To fully exploit the capabilities of MEMS biosensors, it is crucial to prioritize these improvements. This will facilitate their smooth integration into different industries and contribute to the industry's significant growth.

Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.

Background: The coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has detrimental effects on physical and mental health. Patients with severe mental illness are at higher risk of contracting the virus due to social determinants of health. Vulnerable populations include the elderly, people with pre-existing conditions, and those exposed to SARS-CoV-2. Unfortunately, only a few countries have updated vaccination strategies to prioritize patients with mental illnesses. Therefore, we aimed to explore whether individuals with mental disorders are prioritized in vaccine allocation strategies in different world regions. They are often neglected in policymaking but are highly vulnerable to the threatening complications of COVID-19. Methods: A questionnaire was developed to record details regarding COVID-19 vaccination and prioritizations for groups of persons with non-communicable diseases (NCDs), mental disorders, and substance use disorders (SUDs). NCDs were defined according to the WHO as chronic diseases that are the result of a combination of genetic, physiological, environmental, and behavioral factors such as cardiovascular diseases, cancer, respiratory diseases, and diabetes. Results: Most countries surveyed (80%) reported healthcare delivery via a nationalized health service. It was found that 82% of the countries had set up advisory groups, but only 26% included a mental health professional. Most frequently, malignancy (68%) was prioritized followed by diabetes type 2 (62%) and type 1 (59%). Only nine countries (26%) prioritized mental health conditions. Conclusion: The spread of the coronavirus has exposed both the strengths and flaws of our healthcare systems. The most vulnerable groups suffered the most and were hit first and faced most challenges. These findings raise awareness that patients with mental illnesses have been overlooked in immunization campaigns. The range of their mortality, morbidity, and quality of life could have widened due to this delay.

Objectives: Interpreting real-time reverse transcription-polymerase chain reaction (rRT-PCR) results for human avian influenza A virus (AIV) detection in contaminated settings like live bird markets (LBMs) without serology or viral culture poses a challenge. Methods: During February-March 2012 and November 2012-February 2013, we screened workers at nine LBMs in Dhaka, Bangladesh, to confirm molecular detections of AIV RNA in respiratory specimens with serology. We tested nasopharyngeal (NP) and throat swabs from workers with influenza-like illness (ILI) and NP, throat, and arm swabs from asymptomatic workers for influenza virus by rRT-PCR and sera for seroconversion and antibodies against HPAI A(H5N1) and A(H9N2) viruses. Results: Among 1273 ILI cases, 34 (2.6%) had A(H5), 56 (4%) had A(H9), and six (0.4%) had both A(H5) and A(H9) detected by rRT-PCR. Of 192 asymptomatic workers, A(H5) was detected in eight (4%) NP and 38 (20%) arm swabs. Of 28 ILI cases with A(H5) or A(H9) detected, none had evidence of seroconversion, but one (3.5%) and 12 (43%) were seropositive for A(H5) and A(H9), respectively. Conclusion: Detection of AIV RNA in respiratory specimens from symptomatic and asymptomatic LBM workers without evidence of seroconversion or virus isolation suggests environmental contamination, emphasizing caution in interpreting rRT-PCR results in high viral load settings.

Bangladesh is susceptible to climate change, thus a detailed study, including the analyses of trends, sub-trends, extreme events and indices was conducted to obtain a complete picture of the climate change pattern in Bangladesh utilizing daily rainfall, maximum, minimum and average temperature data of 26 stations from 1975 to 2019 using R 4.0.2 software. For the trend analysis Mann Kendal (MK), modified Mann Kendall (mMK), Innovative Trend Analysis (ITA) and Sen’s slope methods were used. Sub-trend analysis was conducted using ITA. Standard Anomaly Index (SAI) has been used to identify the frequency and severity of extreme events. ClimPACT2 software was used to check the homogeneity and calculate the extremes of temperature and rainfall data. Our analysis showed that during the last four decades, climate variables changed their patterns and trend heterogeneously over Bangladesh. Most stations showed decremental rainfall trend when central part of the country showed a substantial decrease. The northern and central parts of the country showed significant growth of trend for annual average temperature. The temperature in the monsoon season increased, whereas those in dry season decreased. The rainfall and maximum temperature were inversely related during monsoon whereas during dry season both of them decrease. The pre-monsoon and post-monsoonal rainfall also showed decreasing trends, indicating prevailing drought conditions especially in northern and central parts of the country. The SAI analysis showed alternating drought and wet years in almost all the stations. In the past 20 years, the country’s western region experienced more drought years than before whereas the coastal region experienced more wet years. The analysis of climate extreme indices suggests that, Consecutive Dry Days (CDD), Hot Days (TX90P) and Hot Nights (TN90P) show significant increasing trend throughout the country. The agricultural productivity, water resource management and food security are anticipated to benefit from this study.

This study evaluates the economic, technical, and environmental performance of stand-alone and grid-tied HRES in Khiriya Bharka, India; Thayet Township, Myanmar; Lower Manya Krobo, Ghana; and Mamfe, Cameroon, considering different regional solar radiation, wind speed diversity, and climate. The HRES are designed and modeled using the Hybrid Optimization of Multiple Energy Resources software (HOMER PRO) to meet consumers' daily loads in the selected places. The analysis results are compared considering the levelized cost of energy (LCOE), net present cost (NPC), greenhouse gas (GHG) emission, renewable fraction (RF), and optimum system configuration. The optimal system configurations are the combination of PV-WT-DG-BAT-CON for grid-tied and stand-alone HRES in India, Ghana, and Cameroon, respectively, and the best configuration for Thayet Township, Myanmar, is WT-DG-BAT-CON. The LCOE of the considered places without grid connection is $0.127/kWh, $0.145/kWh, $0.174/kWh, and $0.143/kWh, respectively. The LCOE of grid-tied HRES is 0.026$/kWh, 0.0286$/kWh, 0.01$/kWh, and 0.0281$/kWh for Khiriya Bharka, Thayet Township, Lower Manya Krobo, and Mamfe, respectively. This research can be useful for planning grid-tied and stand-alone HRES between Asia and African countries by comparing grid-tied and stand-alone HRES to determine the optimum system configuration and finding the best optimization results under different climate conditions. These results are further validated by finding loss of power supply probability (LPSP) and LCOE using particle swarm optimization (PSO). To better inform policymakers and stakeholders in various regions, this research aims to assess and compare renewable energy solutions in terms of their feasibility, cost-effectiveness, environmental impact, and sustainability across a range of geographical, social, and economic contexts.

Abstract We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson’s disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0–4, following the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists’ ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters’ average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.

BackgroundEscalating antibiotic resistance presents a notable worldwide dilemma, pointing a large involvement of general population. The objective of this study was to assess knowledge, attitudes, and practices regarding the utilization of antibiotics among Bangladeshi residents.MethodsA cross-sectional study, conducted from January 01 to April 25, 2022, included 1,947 Bangladeshi adults with a history of antibiotic use, via online surveys and face-to-face interviews using a pretested semi-structured questionnaire. Descriptive statistics, Chi-square tests, and multivariate linear regression models were employed.ResultsMean scores for knowledge, attitudes, and practices were 6.59±1.20, 8.34±1.19, and 12.74±2.59, with correct rates of 73.22%, 92.67%, and 57.91%. Positive predictors for knowledge included being unmarried (β = 0.10, p = 0.001), higher education (College: β = 0.09, p = 0.025; Bachelor: β = 0.22, pConclusionsParticipants exhibited adequate knowledge and positive attitudes but lagged behind in proper practice of antibiotic use. Proper initiatives should be tailored to enhance prudent antibiotic use and mitigate the risk of antimicrobial resistance.

BackgroundSeveral safety measures like movement restrictions, closure of educational institutions, and social distancing measures continue over the world including Bangladesh during the COVID-19 pandemic. This study aimed to examine the patterns of eating behaviors, physical activity, and lifestyle modifications among adolescents during the COVID-19 pandemic residing in Bangladesh.MethodsA cross-sectional study was performed among 490 adolescents in Bangladesh from December 2020 to May 2021. The survey was carried out through a semi-structured web-based questionnaire that asked questions about socio-demographics (i.e., age, sex, marital status, education, residence), perceived health status and quality of life, anthropometrics (i.e., height, weight), dietary habits (i.e., frequency of eating, daily intake of certain foods, number of meals eaten daily), and physical activity (i.e., modified version of the International Physical Activity Questionnaire Short Form [IPAQ-SF]), as well as, pre- and during COVID-19 information on stress, and sleep.ResultsDuring the pandemic, 43.7% participants reported weight gain; and 23.5% reported an increased number of meals per day during COVID-19. Additionally, the participants' eating habits diverged from the local balanced diet principles and were more akin to 'unhealthy' eating patterns. Though, during the COVID-19 pandemic, physical exercise slightly increased (>3 times/ week: 8.2% vs. 13.5%; p5 hours/ week: 12.2% vs. 27.3%; pConclusionsAlthough lockdowns and social distancing are important safety measures to protect people from COVID-19, findings reveal that they might cause a variety of lifestyle changes, physical inactivity, and psychological issues in Bangladeshi adolescents.

Contact tracing and quarantine are valuable public health tools to prevent the transmission of SARS-CoV-2 and control the epidemic. Many low-and middle-income countries (LMICs) adopted global contact tracing and quarantine guidelines but were unable to contextualise the guidance into policies and practices that were relevant to their setting. Therefore, we examine contact tracing policies and practices in the Indo-Pacific region and the need to design context-specific policies. We conducted a mixed-methods study, including a cross-sectional online survey followed by key-informant interviews (KIIs). Using convenience snowball sampling, we invited public health professionals primarily involved in COVID-19 pandemic response from the Indo-Pacific region. We undertook descriptive analyses using counts and percentages for survey data and framework analysis for qualitative data. Seventy-seven public health professionals participated in the survey, of whom ten also participated in the KIIs. The study identified significant gaps between policies and the local contexts. Factors that broaden the gaps were limited knowledge of the changing dynamics of COVID-19 transmission, poor leadership, and coordination, little or no formal training on contact tracing, poor understanding of the guideline recommendations, limited resources, community resistance and mistrust, social stigmatisation and fear of being ostracised, and discrimination. This study revealed substantial disparities between policies and local contexts, significantly influencing policy implementation at national, provincial, and district levels across the studied countries. To bridge these gaps, we advocate for national contact tracing and quarantine guidelines explicitly addressing the quarantine needs of specific demographics, including children, pregnant women, prisoners, and individuals affected by social exclusion issues. Furthermore, we propose strengthening contact tracing training programs, urging revised guidelines to account for social, cultural, and infrastructural nuances influencing contact tracing and quarantine implementation. We also recommend engaging local NGOs, faith-based organisations, and local administrations to reinforce community connections and strengthen contact tracing.

Innovation is key to gaining a sustainable edge in an increasingly competitive global manufacturing landscape. For Bangladesh's manufacturing sector to survive and thrive in today's cutthroat business environment, adopting transformative technologies such as the Internet of Things (IoT) is not a luxury but a necessity. This article tackles the formidable task of identifying and comprehensively evaluating the impediments to IoT adoption in the Bangladeshi manufacturing industry. We delve deeply into the complex terrain of IoT adoption challenges by synthesizing expert insights and a meticulously selected body of contemporary literature. We employ a robust methodology combining the Delphi method with the fuzzy Analytical Hierarchy Process to systematically analyze and prioritize these challenges. Using this methodology, we leveraged the combined expertise of domain specialists and subsequently employed fuzzy logic techniques to address the inherent ambiguities and uncertainties within the data. Our findings highlight this clear path. They reveal that among the myriad barriers, "Lack of top management commitment to implementing new technology" (B10), "High initial implementation investment costs" (B9), and "Risks associated with switching to a new business model" (B7) loom most extensive, demanding immediate attention. These insights are not confined to academia but serve as a pragmatic guide for industrial managers. Armed with the knowledge gleaned from this study, managers can craft tailored strategies, set well-informed priorities, and embark on a transformational journey toward harnessing the vast potential of IoT in the Bangladeshi industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and industry leaders with the tools necessary to navigate these challenges effectively. This strategic navigation, in turn, contributes significantly to enhancing the competitiveness and sustainability of Bangladeshi manufacturing in the IoT era.

BackgroundDengue fever, the most prevalent mosquito-borne viral infection, is a recurrent public health threat in Bangladesh. Despite the government's efforts, dengue outbreaks are on the upswing, and people's knowledge, belief, and preventive practices regarding the disease at the rural community level are unclear.ObjectiveThe objective of this study was to assess the level of knowledge, belief and preventive practices regarding dengue and associated factors among community people from rural Bangladesh.MethodsA cross-sectional survey was conducted involving 401 people using a convenient sampling technique from three unions of Savar from August to September 2021. Participants' responses were collected through face-to-face interviews using a pre-tested structured questionnaire that included information related to socio-demographics, dengue-related knowledge, preventive practices, and the health belief model (HBM) constructs. Multiple linear regression analysis was performed to determine the factors associated with the knowledge and preventive practices of dengue.ResultsOverall, participants (mean age = 33.47 ± 12.96 years; age range = 18-75 years) correctly answered 66.15% of the knowledge questions regarding dengue. Higher education, travel to dengue-risk regions, and self-efficacy under the HBM construct were all shown to be strongly associated with dengue knowledge. Regarding HBM constructs, about 80% of the participants perceived dengue as dangerous, but less than half (41.4%) believed themselves susceptible to dengue. Considering perceived barriers, 73.07% of the participants believed their residential area was not suitable for Aedes mosquito breeding. Nearly all (93.52%) believed they always kept their surrounding area clean as part of self-efficacy. Moreover, on average participants engaged in 53.69% of all dengue preventive practices. Being older, female, having a higher education, being a service holder, having a good quality of house structure, and perceived susceptibility as well as self-efficacy under the HBM construct were all factors in participants' dengue prevention activities. Participants' dengue preventative practices were shown to be significantly influenced by their knowledge.ConclusionsThe findings of this study found a moderate level of knowledge regarding dengue among the participants. Regarding dengue prevention, although participants scored highly in several indicators, the overall preventive practices were not satisfactory. This suggests that there is a pressing need for expanded education outreach aimed at increasing public awareness of dengue and encourage preventive practices within rural communities in Bangladesh.

Trace elements are dangerous to human health and there is a rising concern about the quality of processed foods in some parts of the world, especially in Iraq. The chemical composition (total sold, moisture, and ash) and concentrations of trace elements in canned fish (Skipjack tuna, Sardines, Tuna fish, Sardines, and Mackerel) from the Kalar market, Iraq were determined by using an inductively coupled plasma-optical emission spectrometer. The ranges obtained for the elements in mg/kg were as follows: Se (0.025–0.77), As (0.02–1.07), B (0.05–0.7), Ag (0.04–0.83), Ba (0.05–0.975), Mg (29.8–37.5), Mn (0.97–2.09), Cu (0.91–3.09), and Zn (5.12–11.7). The studied canned fishes pose no risk with respect to the estimated daily intake of Se, As, B, Ag, Ba, Mg, Mn, Cu, and Zn. The total target hazard quotients for the studied metals from individual fish species (except Fme, Fma, and Fsh) were more than one, which was responsible for noncarcinogenic risks. The target carcinogenic risk value for arsenic was also higher than the standard (10-4) set by the United States Environmental Protection Agency. It revealed that the consumption of canned fish causes a chronic cancer risk to humans.

Abstract Background Breast cancer, one of the most prevalent cancer types among women worldwide as well as in Bangladesh, is the leading cause of cancer death in women throughout the globe. The risk of breast cancer development was found to be associated with genetic polymorphism according to several studies. As a convenient prognostic marker, a biomarker helps to identify disease progression, can lead to an effective therapeutic strategy, development of prognostic marker is very important for any cancer to initiate treatment strategy early to increase the possibility of the success rate of the treatment along with reduction of the treatment cost. This study aims to establish the correlation between polymorphism of SMAD4 rs10502913 and risk of breast cancer development in Bangladeshi women. This study was conducted on 70 breast cancer patients and 60 healthy volunteers through blood sample collection followed by DNA separation between the intervals of August 2019–October 2019. The collected DNA sample was arranged for the RFLP analysis of a PCR amplified fragments followed by gel electrophoresis. The obtained data was analyzed by structured multinomial logistic regression model. Results Obtained different fragment size after gel electrophoresis indicated different genotypes in this experiment. Our findings demonstrated that mutant homozygous A/A genotype, plays a significant role in breast cancer development among Bangladeshi women (P = 0.006, OR = 4.9626, 95% CI = 1.9980–12.3261) compared to the reference homozygous G/G genotype. Moreover, heterozygous G/A genotype was also found to be significantly associated with the risk of breast cancer development (P = 0.0252, OR = 2.6574, CI = 1.1295–6.2525). Considering the A/A genotype and G/A genotype combined, it also indicates a strong association of breast cancer development in Bangladeshi women (P = 0.008, OR = 3.5630, CI = 1.6907–7.5068). Conclusion Our study indicated a novel association between SMAD4 (rs10502913) polymorphism and increased risk of breast cancer development in Bangladeshi women.

Abstract Background Food adulteration is an increasingly recognized global public health problem. In low- and middle-income countries like Bangladesh, adulteration is difficult to detect and respond to. We explored customers’ perceptions on food adulteration, perception of risk and connections between information, participant characteristics and patterns of adulterated food concerns that impact risk perception in urban Bangladesh. Methods A formative study was conducted in Dhaka, between June and August 2015 at a supermarket and a wet market. We explored community awareness and response to chemical contaminants (adulterants) among participants from a range of socio-economic backgrounds. The team conducted 38 in-depth interviews with 12 customers and 4 staff from a supermarket, and 12 customers and 10 vendors from a wet market. Participants were selected purposively. Audio recorded data were coded based on thematic content and analyzed manually. Results We asked participants how common foods were likely adulterated, and most gave figures of 70% or more. They reported that foods were adulterated with chemicals or artificial colors, especially fish, milk, and vegetables. The supermarket more commonly sold packaged foods with nutritional and expiry information on the label; and offered convenience in terms of building size, layout, and cleanliness. All customers from the wet market thought that foods were cheaper and fresher than from supermarkets. Supermarket customers expressed greater concern about adulterated foods than wet market customers. Most participants from both markets reported that food adulteration is invisible, adulterated foods cannot be avoided, and have long-term negative health impacts including cancer, diabetes, paralysis, heart attack, and others. Nearly half of customers from both markets were concerned about the poor nutritional value of adulterated food. Participants from both settings expressed the need for access to credible information about adulteration to help choose safe foods. The majority expressed the need for government action against those who are responsible for adulteration. Conclusions Food adulteration was considered a major health threat. The government could act on food adulteration prevention if provided credible population-based data on disease burden, a model food sampling and testing protocol, a model for inspections, organizational strengthening and training, example social and behavioral change communications with estimated costs.

This work assesses the contamination of trace elements (Cr, Cu, Ni, As, Zn Cd, Mn, Fe, and Pb) in soil and different tissues of the banana plant (Musa spp.), the ecological risks of trace elements using various indices, and the probable health risks using a chemometric approach. Soil and different banana plant tissues were collected from banana fields around the industrial area of the capital of old Pundranagar (the earliest urban archaeological location), Bangladesh. Samples were digested by acid digestion, and trace elements were measured by inductively coupled plasma spectrophotometer (ICP-MS). The concentrations of Cr, Ni, Cu, As, Cd, Pb, Fe, Mn, and Zn in soil ranged from 1.50–61.7, 2.42–87.4, 2.00–100.8, 0.25–31.2, 0.10–12.7, 0.60–91.8, 11330–23782, 8.69–105.9, and 7.50–125.9 mg/kg, respectively. The mean concentrations of trace elements in four tissues descend in order of roots > leaves > stems > fruits. The abundances of trace elements varied in both soil and plant samples, which apparently occurred due to the variations of soil parent materials and the excessive use of agrochemicals for long-term banana cultivation. The soil exhibited a moderate to high degree of contamination with trace elements, and Cr, Pb, Zn, Cd, and As mainly originated from anthropogenic sources. Both non-cancer and cancer risks were perceived due to Cr and As exposure from the fruit tissue of banana plants in the study area.

In the modern world, plastic trash has been recognized as a global issue, and studies on microplastics (MPs) in the marine and inland environments have previously been conducted. Marine ecosystems act as a bio-diverse ecosystem where coral reefs contribute to make a sound living of the coastal people by gathering natural resources. The current study indicates that MPs and heavy metals (HMs) accumulation to biofilm and organic matter through sedimentation, precipitation, adsorption, and desorption that may have potential effect on growth and development of coral reefs in the marine ecosystems. However, the knowledge of distribution, impact, mechanism, degradation, and association mechanisms between MPs and HMs in the natural environment may open a new window for conducting analytical research from an ecological viewpoint. The current study thus summarizes the types of marine samples with the analytical techniques, polymers of MPs, and their impact on corals and other marine biota. This study also identifies existing knowledge gaps and recommends fresh lines of inquiry in light of recent developments in MPs and HMs research on the marine ecosystems. Overall, the present study suggests a sustainable intervention for reducing MPs and HMs from the marine ecosystems by demonstrating their existence in water, sediment, fish, corals, and other biota, and their impending ecotoxicological impacts on the environment and human health. The impacts of MPs and HMs on coral reefs are critically assessed in this study in light of the most recent scientific knowledge, existing laws, and new suggestions to minimize their contamination in the marine ecosystems.

N and P are essential elements for crop-based food production. With increasing ammonia and phosphate fertilizers used in agricultural land, water pollution in nearby waterbodies is also increasing from respective runoffs. In this study, ZSM-5 (Zeolite Socony Mobil-5), zeolite having Si/Al ratio of 22, with and without iron-incorporated was hydrothermally synthesized using extracted silica by alkali-acid treatment from rice husk ash. The synthesized zeolites were characterized using XRD, SEM, EDS, N2-sorption, XPS and STEM. Batchwise sorption studies of NH4+-N and PO43--P from synthetic solutions were carried out. Enhanced N and P adsorption capacities were found for Fe-ZSM-5 zeolites. Notably, a 3-fold better P-sorption ability was found for Fe-ZSM-5 compared to Na-ZSM-5. A regeneration study using 0.5 M NaCl solution was carried out and Fe-ZSM-5 zeolite retains 97% and 88% of N and P-removal ability. The simple synthesis step, utilization of waste rice husk, and excellent N and P removal capabilities suggest that the synthesized Fe-ZSM-5 zeolite is an excellent choice for sustainable adsorbent for water pollution control.

Pristine and nitrogen (N) doped zinc oxide (ZnNxO1-x, x = 0, 0.005, 0.01, and 0.02) nanoparticles (NPs) were successfully synthesized using chemical co-precipitation approach. The formation of pure crystalline wurtzite ZnO phase without any second phase during N-doping was confirmed by X-ray diffraction (XRD) analysis of N-doped ZnO samples. X-ray photoelectron spectroscopic (XPS) analysis ensured the effective inclusion of nitrogen into ZnO matrix. The morphological analysis revealed the formation of nanorods as a result of N-doping. The optical band gap calculated from UV–vis spectroscopy was observed to decrease up to 1 mol.% N doping followed by a subtle increase. Photoluminescence (PL) spectra revealed that electron-hole recombination was the least for 1 mol.% N doped ZnO NPs. ZnN0.01O0.99 NPs showed superior photocatalytic activity among all samples due to rod-shaped NPs and reduced electron-hole recombination, which was accessed by the photodegradation of Rhodamine B (RhB).

The study of optical resonators is of significant importance in terms of their ability to confine light in optical devices. A major drawback of optical resonators is the phenomenon of light emission due to their limited capacity for light confinement. Bound states in the continuum are gaining significant attention in the realization of optical devices due to their unique ability for reducing light scattering via interference mechanisms. This process can potentially suppress scattering, leading to improved optical performance. Using this concept, a metasurface having two elliptical silicon (Si) resonators nonidentically angled to create an out‐of‐plane asymmetry is studied. Various parameters are optimized by employing a genetic algorithm (GA) to subsequently achieve a high‐Q factor at terahertz frequencies. Herein, the device is fabricated using a novel method, and a thick high‐index resonator is achieved. Terahertz measurements are carried out to validate the results. It is indicated in the experimental results that plasmons appear at the top surface of the metasurface and create strong sharp resonances that are sensitive to the external environment. Owing to strong field confinement ability, and high‐Q factor, the metasurface is sensitive to its surrounding environment and can be essentially employed in terahertz sensing applications.

This study looks into ICT's asymmetrical impact on emissions of carbon dioxide (CO2) controlling energy use and financial development (FD) in GCC countries. It uses panel data covering 1995–2019, employs 2nd generation unit root and Westerlund cointegration tests, and applies panel-pooled “generalized least square (GLS)” techniques. The “pooled mean group (PMG)” method and the “Dumitrescu-Hurlin (D-H)” causality test are employed to verify the robustness of GLS estimation. The bootstrap cointegration test corroborates a long-run cointegration among variables. GLS findings exhibit an asymmetric relation between ICT and CO2 emissions; both the affirmative and negative shocks of ICT and FD are associated with decreased CO2 emissions, while energy use enhances the latter. Hence, promoting ICT is essential to reduce CO2 discharges in the GCC region due to its large negative stimulus on CO2 emissions. The PMG estimation also produces findings similar to those of the GLS outcomes and verifies the GLS outcomes' robustness. The D-H causality check also validates the GLS and PMG outcomes.

In the realm of artificial intelligence, knowledge graphs have become an effective area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1500 generic medicines. It presents an innovative method of storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, and queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as a diverse range of healthcare professionals, and AI applications.

Abstract Introduction Infection prevention and control (IPC) in healthcare settings is imperative for the safety of patients as well as healthcare providers. To measure current IPC activities, resources, and gaps at the facility level, WHO has developed the Infection Prevention and Control Assessment Framework (IPCAF). This study aimed to assess the existing IPC level of selected tertiary care hospitals in Bangladesh during the COVID-19 pandemic using IPCAF to explore their strengths and deficits. Methods Between September and December 2020, we assessed 11 tertiary-care hospitals across Bangladesh. We collected the information from IPC focal person and/or hospital administrator from each hospital using the IPCAF assessment tool.. The score was calculated based on eight core components and was used to categorize the hospitals into four distinct IPC levels– Inadequate, Basic, Intermediate, and Advanced. Key performance metrics were summarized within and between hospitals. Results The overall median IPCAF score was 355.0 (IQR: 252.5–397.5) out of 800. The majority (73%) of hospitals scored as ‘Basic’ IPC level, while only 18% of hospitals were categorized as ‘Intermediate’. Most hospitals had IPC guidelines as well as environments, materials and equipments. Although 64% of hospitals had IPC orientation and training program for new employees, only 30% of hospitals had regular IPC training program for the staff. None of the hospitals had an IPC surveillance system with standard surveillance case definitions to track HAIs. Around 90% of hospitals did not have an active IPC monitoring and audit system. Half of the hospitals had inadequate staffing considering the workload. Bed occupancy of one patient per bed in all units was found in 55% of hospitals. About 73% of hospitals had functional hand hygiene stations, but sufficient toilets were available in only 37% of hospitals. Conclusion The majority of sampled tertiary care hospitals demonstrate inadequate IPC level to ensure the safety of healthcare workers, patients, and visitors. Quality improvement programs and feedback mechanisms should be implemented to strengthen all IPC core components, particularly IPC surveillance, monitoring, education, and training, to improve healthcare safety and resilience.

Abstract Background Hospital-acquired infections (HAIs) and antimicrobial resistance (AMR) are major global health challenges. Drug-resistant infectious diseases continue to rise in developing countries, driven by shortfalls in infection control measures, antibiotic misuse, and scarcity of reliable diagnostics. These escalating global challenges have highlighted the importance of strengthening fundamental infection prevention and control (IPC) measures and implementing effective antimicrobial stewardship programs (ASP). This study aims to present a framework for enhancing IPC measures and ASP efforts to reduce the HAI and AMR burden in Bangladesh. Methods This implementation approach will employ a mixed-methods strategy, combining both quantitative and qualitative data from 12 tertiary hospitals in Bangladesh. A baseline assessment will be conducted using the Infection Prevention and Control Assessment Framework (IPCAF) developed by the WHO. We will record IPC practices through direct observations of hand hygiene, personal protective equipment (PPE) utilization, and hospital ward IPC infrastructure. Additionally, data on healthcare providers’ knowledge, attitudes, and practices regarding IPC and antibiotic prescribing will be collected using both structured questionnaires and qualitative interviews. We will also assist the hospital leadership with establishing and/or strengthening IPC and ASP committees. Based on baseline assessments of each healthcare facility, tailored interventions and quality improvement projects will be designed and implemented. An end-line assessment will also be conducted after 12 months of intervention using the same assessment tools. The findings will be compared with the baseline to determine changes in IPC and antibiotic stewardship practices. Discussion Comprehensive assessments of healthcare facilities in low-resource settings are crucial for strengthening IPC measures and ASP activities,. This approach to assessing existing IPC and ASP activities will provide policy-relevant data for addressing current shortfalls. Moreover, this framework proposes identifying institutionally-tailored solutions, which will ensure that response activities are appropriately contextualized, aligned with stakeholder priorities, and offer sustainable solutions. Conclusion Findings from this study can guide the design and implementation of feasible and sustainable interventions in resource-constrained healthcare settings to address gaps in existing IPC and ASP activities. Therefore, this protocol will be applicable across a broad range of settings to improve IPC and ASP and reduce the burden of hospital-acquired infections and AMR.

Background: Brain tumour categorisation can be assisted with computer-aided diagnostic (CAD) for medical applications. Biopsies to classify brain tumours can be costly and time-consuming. Radiologists may also misclassify brain tumour types when handling large amounts of data with multiple classes. In this case, technological advancements and machine learning can help. Objective: This study proposes hybrid deep learning approaches for classifying brain tumours using convolutional neural networks (CNN) and machine learning (ML) classifiers. Methods: A new 23-layer CNN architecture is developed for brain deep feature extraction from magnetic resonance imaging (MRI). Random forest (RF) and support vector machine (SVM) classifiers are then used to evaluate the extracted in-depth features from the flattened layer of the CNN model. This study is unique because it employs CNN, CNN-RF, CNN-SVM, and tuned Inception V3 deep learning models on multi-class brain MRI datasets. The proposed hybrid method is run on two publicly available datasets. Results: Among the four models, the CNN-RF model achieves 96.52% accuracy on the Fig share 3c dataset, while the CNN-SVM model achieves 95.41% accuracy on the large Kaggle 4c dataset with four classes (glioma, meningioma, normal, pituitary). Conclusion: Experimental outcomes show that the hybrid techniques can significantly enhance the classification performance, especially on multi-class datasets (glioma, meningioma, normal, pituitary). This study also examines the various weight strategies for dealing with overfitting analytics. Keywords: Brain Tumour, Convolutional Neural Network, Feature Extraction, Multi-Classification, Machine Learning Classifiers

Vulnerability to presentation attacks (PAs) remains one of the main security concerns of the widely used fingerprint-based authentication systems, especially for unattended and remote applications. PAs can be carried out by presenting artifact, corpse, or conformant samples to a biometric sensor with the intention of circumventing the system policy. In this study, we develop a multilayer biometric authentication system robust against PAs by the fusion of fingerprint and heart-signal. In the first layer, artifact attacks are prevented by using a fine-tuned convolutional neural network (CNN). In the second layer, a lightweight CNN is used for the prevention of corpse attacks by using heart-signal (also known as ECG signal) with duration of 0.5 s. In the subsequent layers, robust fingerprint matcher at a specific threshold are utilized for the prevention of conformant attacks. In the final layer, a score-level fusion of the fingerprint and heart-signal is used for biometric authentication. The proposed system was evaluated by different authentication and attack scenarios using a multimodal dataset comprising two public databases of fingerprints and heart-signals available online. The experimental results yielded a false match rate (FMR) of approximately zero (0.1%) with an acceptable false non-match rate (FNMR). The obtained results are encouraging for the incorporation of the system into applications requiring high-security authentication.

Electrocardiogram (ECG) signal produced by the human heart has been investigated as a potential entropy source for cryptographic random bit generation for a long time. The throughput of existing methods remains as the bottleneck for its deployment in practical applications. To overcome this problem, we develop a Bernoulli entropy source by processing a single heartbeat ECG signal to obtain a long random bit sequence (RBS). The proposed method converts the signal into an IID (independent and identically distributed) source of entropy using efficient interpolation and optimization techniques. Several heartbeat signals, obtained from two different databases, were used to test the entropy source generating RBSs with one million bits. The entropy source was evaluated with the latest NIST recommendation for IID source validation and it passed all recommended tests and the average min-entropy obtained from several heartbeat signals of different individuals was close to the perfect entropy value of 1.0. It was also observed that the entropy increases monotonically with the increase of the length of keys. The proposed method can efficiently produce a long RBS for cryptographic applications, such as key generation for one-time pad and image encryption. The method could be further explored to generate a true random number with a personalized signature, which is crucial for information security in the future generation of computing.

Novel, non-thermal and economically benign pre-treatment process was developed for enhancing valorisation potential of brown sludge generated by dairy industry wastewater treatment plant (WWTP). Vortex-based hydrodynamic cavitation (HC) device was used to quantify influence of pretreatment by measuring biomethane potential (BMP) of untreated and treated brown sludge. Pre-treatment parameters, primarily, pressure drop and number of passes through the cavitation device were varied to quantify influence on BMP. BMP tests were performed at 39 °C containing 5% of total solids in each reactors using an automatic BMP measurement system containing 15 reactors with each volume of 500 mL fitted with overhead stirrer. HC treatment increased the soluble chemical oxygen demand (sCOD) by more than 25% which increased the BMP. HC treatment was able to push the BMP of treated sludge to more than 80% of the theoretical BMP. Volatile solids (VS) removal was more than 65%. Highest methane yield was 376 mL/g-VS of sludge. The methodology and results presented here show significant potential to valorise brown dairy sludge via vortex based hydrodynamic cavitation.

Background: Bipolar disorder is one of the major neuropsychiatric illnesses. It is responsible for 6.8% of disability-adjusted life years among all mental disorders. Few studies have evaluated the biochemical basis of bipolar disorder. Prolidase is an enzyme that degrade extracellular matrix into proline. In stress condition, prolidase by releasing proline maintain ATP generation, redox state, apoptosis and cell proliferation. The relationship between serum prolidase with bipolar disorder was investigated in this study. Method: A case - control study was conducted at the Department of Laboratory Medicine in collaboration with the Department of Psychiatry of Bangabandhu Sheikh Mujib Medical University, Dhaka from March 2021 to February 2022. Thirty-five patients with bipolar disorder type I consisting of 15 euthymic and 20 manic patients were enrolled in the study according to the availability of patients as cases. Thirty-five age - and sex-matched healthy individuals were recruited from the Department of Laboratory Medicine as controls. Serum prolidase level was measured in cases and controls using venous blood. Result: Serum prolidase level was significantly higher in cases than controls (P=0.021). There was no significant differences between euthymic and manic patients (P=0.629). Significant positive correlation was found between Young Mania Rating Scale severity score and serum prolidase level (P=0.001). Conclusion: Serum prolidase activity may be associated with bipolar disorder. This needs further corroboration because our sample size was small. Bangabandhu Sheikh Mujib Medical University Journal 2023;16(2): 96-100

The effect of proton implantation as isolation implant and subsequent annealing on the optical absorption and electrical resistivity of low-bandgap p -GaSb is reported. The measured transmittance spectra indicates that implantation creates a distribution of energy levels extending into the bandgap. Electrical measurements show that the average sheet resistance of the implanted layer increases only by an order of magnitude from its pre-implantation value at a proton dose of ∼10 ^13 cm ^−2 followed by 200 °C annealing. It is also shown that annealing reduces the implantation-induced optical absorption while still retaining a high electrical resistivity.

Convolutional neural network (CNN) is widely used to classify brain tumors with high accuracy. Since CNN collects features randomly without knowing the local and global features and causes overfitting problems, this research proposes a novel parallel deep convolutional neural network (PDCNN) topology to extract both global and local features from the two parallel stages and deal with the over-fitting problem by utilizing dropout regularizer alongside batch normalization. To begin, input images are resized and grayscale transformation is conducted, which helps to reduce complexity. After that, data augmentation has been used to maximize the number of datasets. The benefits of parallel pathways are provided by combining two simultaneous deep convolutional neural networks with two different window sizes, allowing this model to learn local and global information. Three forms of MRI datasets are used to determine the effectiveness of the proposed method. The binary tumor identification dataset-I, Figshare dataset-II, and Multiclass Kaggle dataset-III provide accuracy of 97.33%, 97.60%, and 98.12%, respectively. The proposed structure is not only accurate but also efficient, as the proposed method extracts both low-level and high-level features, improving results compared to state-of-the-art techniques.

With online shopping gaining massive popularity over the past few years, this study identifies a promising opportunity for E-commerce platforms to tackle climate change and other environmental problems. This study investigates a redesign of E-commerce platforms to bridge the existing “attitude-behavior gap” regarding environmental sustainability in online shopping. It introduces a concept design named Sustainable E-commerce with Environmental-impact Rating (SEER) – a way of communicating products’ environmental impact when displaying them on E-commerce platforms. A quasi-randomized case-control experiment with 98 subjects demonstrates the efficacy and user-friendliness of the proposed concept design. The case group using SEER showed significantly more eco-friendly behavior than the control group (p

Monitoring of heavy metal concentrations in soil and their accumulation in vegetables grown in the newly shifted tannery area of Savar, Bangladesh, is crucial for human health. Heavy metals (i.e., Cr, Pb, Cu, Zn, Ni, and Cd) concentrations in soil and vegetable samples were determined by atomic absorption spectrophotometer (AAS). In soil, 3220 mg/kg Cr was observed, which was 32-fold greater than the WHO/FAO recommended limit. Ecological risk indices such as the contamination factor, enrichment factor, pollution load index, and geoaccumulation index showed metal levels as moderately to very highly contaminated. The non-carcinogenic risk (NCR) was found to be higher, and the carcinogenic risk (CR) exceeded the acceptable value 1 × 10−6 and posed greater risks to children than adults, especially for Cr in soil. The main exposure pathway for soil metals was 97.8–99.9% due to oral ingestion. The concentration of heavy metals especially Cr, Pb, Zn, and Cd, in vegetables was alarming as they crossed the safety limit. The calculated mean hazard index (8.71) for vegetable samples showed elevated levels of potential NCR, while CR for Cr and Cd, exceeded the acceptable limit of 1 × 10−6, indicating the probability of cancer risk to humans through the consumption of vegetables. This study revealed a to-and-fro analysis of the present scenario of the tannery area, giving importance to human health and the environment.