Trends and challenges of fruit by-products utilization: insights into safety, sensory, and benefits of the use for the development of innovative healthy food: a review.

A significant portion of the human diet is comprised of fruits, which are consumed globally either raw or after being processed. A huge amount of waste and by-products such as skins, seeds, cores, rags, rinds, pomace, etc. are being generated in our homes and agro-processing industries every day. According to previous statistics, nearly half of the fruits are lost or discarded during the entire processing chain. The concern arises when those wastes and by-products damage the environment and simultaneously cause economic losses. There is a lot of potential in these by-products for reuse in a variety of applications, including the isolation of valuable bioactive ingredients and their application in developing healthy and functional foods. The development of novel techniques for the transformation of these materials into marketable commodities may offer a workable solution to this waste issue while also promoting sustainable economic growth from the bio-economic viewpoint. This approach can manage waste as well as add value to enterprises. The goal of this study is twofold based on this scenario. The first is to present a brief overview of the most significant bioactive substances found in those by-products. The second is to review the current status of their valorization including the trends and techniques, safety assessments, sensory attributes, and challenges. Moreover, specific attention is drawn to the future perspective, and some solutions are discussed in this report.

Anthelmintic screening of Bangladeshi medicinal plants and related phytochemicals using in vitro and in silico methods: An ethnobotanical perspective.

ETHNOPHARMACOLOGICAL RELEVANCE: Infections caused by parasitic worms or helminth continue to pose a great burden on human and animal health, particularly in underdeveloped tropical and subtropical countries where they are endemic. Current anthelmintic drugs present serious limitations and the emergence of drug resistance has made it increasingly challenging to combat such infections (helminthiases). In Bangladesh, medicinal plants are often used by indigenous communities for the treatment of helminthiases. Knowledge on such plants along with screening for their anthelmintic activity has the potential to lead to the discovery of phytochemicals that could serve as novel molecular scaffolds for the development of new anthelminthic drugs. AIM OF THE STUDY: The purpose of this study was i) to conduct an ethnobotanical survey to gather data on Bangladeshi medicinal plants used in the treatment of helminthiases, ii) to test plants with the highest use values for their in vitro anthelmintic activity, and iii) to carry out in silico screening on phytochemicals present in the most active plant extract to investigate their ability to disrupt ß-tubulin function in helminths. METHODS: The ethnobotanical survey was conducted across three sub-districts of Bangladesh, namely Mathbaria, Phultala and Khan Jahan Ali. The in vitro screening for anthelmintic activity was performed in a motility test using adult Haemonchus contortus worms. Virtual screening using PyRx was performed on the phytochemicals reported from the most active plant, exploring their interactions with the colchicine binding site of the ß-tubulin protein target (PDB ID: 1SA0). RESULTS: The survey respondents reported a total of 32 plants for treating helminthiases. Based on their use values, the most popular choices were Ananas comosus (L.) Merr., Azadirachta indica A.Juss., Carica papaya L., Citrus maxima (Burm.) Merr., Curcuma longa L., Momordica charantia L., Nigella sativa L. and Syzygium cumini (L.) Skeels. In vitro anthelmintic testing revealed that A. indica leaves and bark had the highest activity with LC50 values of 16 mg/mL in both cases. Other plant extracts also exhibited good anthelmintic activity with LC50 values ranging from 16 to 52 mg/mL, while the value for albendazole (positive control) was 8.39 mg/mL. The limonoids nimbolide and 28-deoxonimbolide showed a binding affinity of -8.9 kcal/mol, and satisfied all drug-likeness parameters. The control ligand N-deacetyl-N-(2-mercaptoacetyl)colchicine had a binding affinity of -6.9 kcal/mol. CONCLUSION: Further in silico and in vitro studies are warranted on the identified limonoids to confirm the potential of these derivatives as novel drug templates for helminthiases. The current study supports the need for an ethnobotanical survey-based approach to discover novel drug templates for helminthiases.

Digital twin: Data exploration, architecture, implementation and future.

A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.

Band gap engineering in lead free halide cubic perovskites GaGeX3 (X = Cl, Br, and I) based on first-principles calculations.

Lead-free inorganic Ge-based perovskites GaGeX3 (X = Cl, Br, and I) are promising candidates for solar cell applications due to their structural, mechanical, electrical, and optical properties. In this work, we performed density functional theory (DFT) calculations using the CASTEP module to investigate these properties in detail. We found that the lattice parameters and cell volumes increase with the size of the halogen atoms, and that all the compounds are stable and ductile. GaGeBr3 has the highest ductility, machinability, and lowest hardness, while GaGeCl3 has the highest anisotropy. The band gap values, calculated using the GGA-PBE and HSE06 functionals, show a direct band gap at the R-R point, ranging from 0.779 eV and 1.632 eV for GaGeCl3 to 0.330 eV and 1.140 eV for GaGeI3. The optical properties, such as absorption coefficient, conductivity, reflectivity, refractive index, extinction coefficient, and dielectric function, are also computed and discussed. We observed that the optical properties improve with the redshift of the band gap as Cl is replaced by Br and I. GaGeI3 has the highest dielectric constant, indicating the lowest recombination rate of electron-hole pairs. Our results suggest that GaGeX3 (X = Cl, Br, and I) can be used as effective and non-toxic materials for multijunction solar cells and other semiconductor devices.

Microplastic pollution: a review of techniques to identify microplastics and their threats to the aquatic ecosystem.

Microplastics (MPs), small synthetic particles, have emerged as perilous chemical pollutants in aquatic habitats, causing grave concerns about their disruptive effects on ecosystems. The fauna and flora inhabiting these specific environments consume these MPs, unwittingly introducing them into the intricate web of the food chain. In this comprehensive evaluation, the current methods of identifying MPs are amalgamated and their profound impacts on marine and freshwater ecosystems are discussed. There are many potential risks associated with MPs, including the dangers of ingestion and entanglement, as well as internal injuries and digestive obstructions, both marine and freshwater organisms. In this review, the merits and limitations of diverse identification techniques are discussed, including spanning chemical analysis, thermal identification, and spectroscopic imaging such as Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and fluorescent microscopy. Additionally, it discusses the prevalence of MPs, the factors that affect their release into aquatic ecosystems, as well as their plausible impact on various aquatic ecosystems. Considering these disconcerting findings, it is imperative that appropriate measures should be taken to assess the potential risks of MP pollution, protect aquatic life and human health, and foster sustainable development.

Twisted van der Waals Quantum Materials: Fundamentals, Tunability, and Applications.

Twisted van der Waals (vdW) quantum materials have emerged as a rapidly developing field of two-dimensional (2D) semiconductors. These materials establish a new central research area and provide a promising platform for studying quantum phenomena and investigating the engineering of novel optoelectronic properties such as single photon emission, nonlinear optical response, magnon physics, and topological superconductivity. These captivating electronic and optical properties result from, and can be tailored by, the interlayer coupling using moiré patterns formed by vertically stacking atomic layers with controlled angle misorientation or lattice mismatch. Their outstanding properties and the high degree of tunability position them as compelling building blocks for both compact quantum-enabled devices and classical optoelectronics. This paper offers a comprehensive review of recent advancements in the understanding and manipulation of twisted van der Waals structures and presents a survey of the state-of-the-art research on moiré superlattices, encompassing interdisciplinary interests. It delves into fundamental theories, synthesis and fabrication, and visualization techniques, and the wide range of novel physical phenomena exhibited by these structures, with a focus on their potential for practical device integration in applications ranging from quantum information to biosensors, and including classical optoelectronics such as modulators, light emitting diodes, lasers, and photodetectors. It highlights the unique ability of moiré superlattices to connect multiple disciplines, covering chemistry, electronics, optics, photonics, magnetism, topological and quantum physics. This comprehensive review provides a valuable resource for researchers interested in moiré superlattices, shedding light on their fundamental characteristics and their potential for transformative applications in various fields.

Factors associated with child and maternal dietary diversity in the urban areas of Bangladesh.

Dietary diversity is an indicator of nutrition that has been found positively associated with diet quality, micronutrient adequacy, and improved maternal health and child growth. Due to the cultural responsibility of women in providing food at the household level, their status is very important to perform this role. Hence, this study has been conducted on the status of dietary diversity of the mother and child to understand how it relates to various factors of women in urban settings. Data were obtained from 1978 mother-child pairs living in different cities in Bangladesh. The foods taken by the women and children were categorized into 10 and 7 groups to measure women's dietary diversity (WDD) and children's dietary diversity (CDD), respectively. The study found that more than three-fourths of the mothers and half of the children had low dietary diversity. The household wealth holdings and access to resources by the women were found inadequate, while two-thirds of them had the lowest to medium level of nutritional knowledge. The binomial logistic regression model was used to measure the factors influencing WDD and CDD. The findings also indicated that children's dietary diversity was influenced by the mother's age, education, supportive attitude and behavior of husband, and access to and control over resources. While the household wealth index can enhance both child and mother's dietary variety, nutrition knowledge, dietary counseling, and access to and control over resources can improve maternal dietary diversity. This study recommends improving women's socioeconomic status by increasing their wealth and access to resources and enhancing their nutrition knowledge by providing food and nutrition counseling.

Tobacco industry interference to undermine the development and implementation of graphic health warnings in Bangladesh.

BACKGROUND: In Bangladesh, the 2013 Amendment of the Tobacco Control Act made graphic health warnings (GHWs) on the upper 50% of all tobacco packs obligatory. However, at the time of writing (May 2022), GHWs are still being printed on the lower 50% of packs. This paper seeks to explore how the tobacco industry undermined the development and implementation of GHWs in Bangladesh, a country known for a high level of tobacco industry interference (TII) that has rarely been examined in the peer-reviewed literature. METHODS: Analysis of print and electronic media articles and documents. RESULTS: Cigarette companies actively opposed GHWs, while bidi companies did not. The primary strategy used to influence the formulation and delay the implementation of GHWs was direct lobbying by the Bangladesh Cigarette Manufacturers' Association and British American Tobacco Bangladesh. Their arguments stressed the economic benefits of tobacco to Bangladesh and sought to create confusion about the impact of GHWs, for example, claiming that GHWs would obscure tax banderols, thus threatening revenue collection. They also claimed technical barriers to implementation-that new machinery would be needed-leading to delays. Tensions between government bodies were identified, one of which (National Board of Revenue)-seemingly close to cigarette companies and representing their arguments-sought to influence others to adopt industry-preferred positions. Finally, although tobacco control advocates were partially successful in counteracting TII, one self-proclaimed tobacco control group, whose nature remains unclear, threatened the otherwise united approach. CONCLUSIONS: The strategies cigarette companies used closely resemble key techniques from the well-evidenced tobacco industry playbook. The study underlines the importance of continuing monitoring and investigations into industry conduct and suspicious actors. Prioritising the implementation of WHO Framework Convention on Tobacco Control Article 5.3 is crucial for advancing tobacco control, particularly in places like Bangladesh, where close government-industry links exist.

A case report on para-kala-azar dermal leishmaniasis: an unresolved mystery.

BACKGROUND: Post-kala-azar dermal leishmaniasis (PKDL) is a dermatosis that occurs 2-3 years after an apparently successful treatment of visceral leishmaniasis (VL). In rare cases, PKDL occurs concurrently with VL and is characterized by fever, splenomegaly, hepatomegaly or lymphadenopathy, and poor nutritional status and is known as Para-kala-azar dermal leishmaniasis (Para-KDL). Co-association of active VL in PKDL patients is documented in Africa, but very few case reports are found in South Asia. We present a case of Para-kala-azar Dermal Leishmaniasis (Para-KDL) in a 50-year-old male patient with a history of one primary Visceral Leishmaniasis (VL) and 2 times relapse of Visceral Leishmaniasis (VL). The patient presented with fever, skin lesions, and hepatosplenomegaly. Laboratory tests revealed LD bodies in the slit skin smear and splenic biopsy. The patient was treated with two cycles of Amphotericin B with Miltefosine in between cycles for 12 weeks to obtain full recovery. CONCLUSION: This case report serves as a reminder that Para-kala-azar dermal leishmaniasis can develop as a consequence of prior visceral leishmaniasis episodes, even after apparently effective therapy. Since para-kala-azar is a source of infectious spread, endemics cannot be avoided unless it is effectively recognized and treated.

Humoral immune responses in different stages of wound healing in Black Bengal goats.

Objective: The current study was carried out to assess the humoral immune responses according to age at different stages of wound healing in Black Bengal goats (BBG). Materials and Methods: Apparently, healthy BBGs (n = 20) were collected and divided into five groups based on their age: Group A (control, 3 years), Group B (3 to 5 years), Group C (2 to <3 years), Group D (1 to <2 years), and Group E (<1 year). Except for control, all BBGs were allowed to have artificial surgical wounds, and follow-up data were collected from day 0 to 21. The humoral immune responses [immunoglobulins (Igs) and interleukin-6 (IL-6)] were determined by ELISA using commercial goat ELISA kits. Statistical Product and Service Solutions (Version 20) was used to analyze the data. Results: The normal range of immune cells in control BBGs was immunoglobulin G (IgG) (20.21 ± 0.13 mg/ml), immunoglobulin M (IgM) (2.87 ± 0.0.05 mg/ml), immunoglobulin A (IgA) (0.33 ± 0.01 mg/ml), and IL-6 (1.6 ± 0.05 pg/ml). In this experiment, higher concentrations of IgG (21.11 ± 0.20 mg/ml), IgM (2.92 ± 0.04 mg/ml), IgA (0.35 ± 0.02 mg/ml), and IL-6 (1.62 ± 0.05 pg/ml) were found in Group B BBGs, whereas the lower levels of IgG, IgM, IgA, and IL-6 were found at 17.16 ± 0.18 mg/ml, 2.12 ± 0.01 mg/ml, 0.29 ± 0.03 mg/ml, and 1.55 ± 0.05 pg/ml, respectively, in the Group E BBGs. Rapid wound healing was observed in the older groups compared to the younger groups of BBGs. The concentrations of Igs (IgG, IgM, and IgA) and IL-6 were gradually increased in all groups from day 3 (early inflammatory stage) and day 7 (late inflammatory stage), and then they decreased gradually from day 14 (proliferative stage) to reach the final stage of day 21 (remodeling stage), where the concentrations were found to be at a level comparable to their per-incisional period. No gender-related differences were detected. Conclusion: Adult BBGs (3 to 5 years old) showed faster wound repair and stronger immune responses. This finding may assist veterinarians and researchers in considering age-related immune responses for the recovery and rapid cure of surgical wounds.

A review on self-healing featured soft robotics.

Soft robots are becoming more popular because they can solve issues stiff robots cannot. Soft component and system design have seen several innovations recently. Next-generation robot-human interactions will depend on soft robotics. Soft material technologies integrate safety at the material level, speeding its integration with biological systems. Soft robotic systems must be as resilient as biological systems in unexpected, uncontrolled situations. Self-healing materials, especially polymeric and elastomeric ones, are widely studied. Since most currently under-development soft robotic systems are composed of polymeric or elastomeric materials, this finding may provide immediate assistance to the community developing soft robots. Self-healing and damage-resilient systems are making their way into actuators, structures, and sensors, even if soft robotics remains in its infancy. In the future, self-repairing soft robotic systems composed of polymers might save both money and the environment. Over the last decade, academics and businesses have grown interested in soft robotics. Despite several literature evaluations of the soft robotics subject, there seems to be a lack of systematic research on its intellectual structure and development despite the rising number of articles. This article gives an in-depth overview of the existing knowledge base on damage resistance and self-healing materials' fundamental structure and classifications. Current uses, problems with future implementation, and solutions to those problems are all included in this overview. Also discussed are potential applications and future directions for self-repairing soft robots.

Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities.

High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for those disorders more difficult. There may be a significant correlation between comorbidities and have an impact on one another's health. These associations may be due to a number of direct and indirect mechanisms. Such molecular mechanisms that underpin T2D and cancer are yet unknown. However, the large volumes of data available on these diseases allowed us to use analytical tools for uncovering their interrelated pathways. Here, we tried to present a system for investigating potential comorbidity relationships between T2D and Cancer disease by looking at the molecular processes involved, analyzing a huge number of freely accessible transcriptomic datasets of various disorders using bioinformatics. Using semantic similarity and gene set enrichment analysis, we created an informatics pipeline that evaluates and integrates Gene Ontology (GO), expression of genes, and biological process data. We discovered genes that are common in T2D and Cancer along with molecular pathways and GOs. We compared the top 200 Differentially Expressed Genes (DEGs) from each selected T2D and cancer dataset and found the most significant common genes. Among all the common genes 13 genes were found most frequent. We also found 4 common GO terms: GO:0000003, GO:0000122, GO:0000165, and GO:0000278 among all the common GO terms between T2d and different cancers. Using these genes and GO term semantic similarity, we calculated the distance between these two diseases. The semantic similarity results of our study showed a higher association of Liver Cancer (LiC), Breast Cancer (BreC), Colorectal Cancer (CC), and Bladder Cancer (BlaC) with T2D. Furthermore we found KIF4A, NUSAP1, CENPF, CCNB1, TOP2A, CCNB2, RRM2, HMMR, NDC80, NCAPG, and IGFBP5 common hub proteins among different cancers correlated to T2D. AGE-RAGE signaling pathway in diabetic complications, Osteoclast differentiation, TNF signaling pathway, IL-17 signaling pathway, p53 signaling pathway, MAPK signaling pathway, Human T-cell leukemia virus 1 infection, and Non-alcoholic fatty liver disease are the 8 most significant pathways found among 18 common pathways between T2D and selected cancers. As a result of our technique, we now know more about disease pathways that are critical between T2D and cancer.

Estimation of concrete materials uniaxial compressive strength using soft computing techniques.

This study addresses a critical gap in concrete strength prediction by conducting a comparative analysis of three deep learning algorithms: convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. Unlike previous studies that employed various machine learning algorithms on diverse concrete types, our study focuses on mixed-design concrete and fine-tuned deep learning algorithms. The objective is to identify the optimal deep learning (DL) algorithm for predicting concrete uniaxial compressive strength, a crucial parameter in construction and structural engineering. The dataset comprises experimental records for mixed-design concrete, and models were developed and optimized for predictive accuracy. The results show that the CNN model consistently outperformed GRU and LSTM. Hyperparameter tuning and regularization techniques further improved model performance. This research offers practical solutions for material property prediction in the construction industry, potentially reducing resource burdens and enhancing efficiency and construction quality.

Benthic diatom communities and a comparative seasonal-based ecological quality assessment of a transboundary river in Bangladesh.

Seasonality can play a crucial role in altering water quality in tropical rivers, and as a benthic community, diatom can show seasonal variation and changes in ecological status. During the present study, the Trophic Diatom Index (TDI) and Water Quality Index (WQI) were used to determine the ecological status of a transboundary river, the Sari-Goyain River in Bangladesh. Samplings were carried out from upstream to downstream river sites in wet and dry seasons to observe the seasonal dynamics. The benthic diatom composition and physicochemical parameters showed seasonal variation in ecological water quality assessment. In the River, 42 different diatom species from 19 genera were recorded. The mean TDI values indicated an oligotrophic condition of the river in both seasons. But, the WQI values showed excellent and good water quality in the wet and dry seasons, respectively. So, the WQI was helpful in assessing seasonal variation of ecological water quality status in the Sari-Goyain River. For the long-term monitoring of the ecological status of the river, seasonal variation and WQI-based assessment should be considered.

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.

Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through XAI techniques to explicate the EEG features that contribute the most to different HAR states. The study involved an examination of 75 healthy individuals with no prior diagnosis of neurological disorders. EEG recordings were obtained during the resting state, as well as two motor control states (walking and working tasks), and a cognition state (reading task). Electrodes were placed in specific regions of the brain, including the frontal, central, temporal, and occipital lobes (Fz, C1, C2, T7, T8, Oz). Several ML models were trained using EEG data for activity recognition and LIME (Local Interpretable Model-Agnostic Explanations) was employed for interpreting clinically the most influential EEG spectral features in HAR models. The classification results of the HAR models, particularly the Random Forest and Gradient Boosting models, demonstrated outstanding performances in distinguishing the analyzed human activities. The ML models exhibited alignment with EEG spectral bands in the recognition of human activity, a finding supported by the XAI explanations. To sum up, incorporating eXplainable Artificial Intelligence (XAI) into Human Activity Recognition (HAR) studies may improve activity monitoring for patient recovery, motor imagery, the healthcare metaverse, and clinical virtual reality settings.

WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging.

Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model's hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants.

Understanding the behavioral determinants that predict barriers and enablers of screening and treatment behaviors for diabetic retinopathy among Bangladeshi women: findings from a barrier analysis.

BACKGROUND AND AIM: While early detection and timely treatments can prevent diabetic retinopathy (DR) related blindness, barriers to receiving these DR services may cause permanent sight loss. Despite having similar prevalence to diabetes and DR, women are less likely than men to perform these behaviors due to multi-faced barriers in screening and receiving follow-up treatments for DR. This study, therefore, aimed at identifying the barriers to - and enablers of - screening and follow-up treatments behaviors for DR among women aged more than 40 years with diabetes from the behavioral perspectives in Bangladesh. METHODS: This Barrier Analysis study interviewed 360 women (180 "Doers" and 180 "Non-doers") to explore twelve behavioral determinants of four DR behaviors including screening, injection of anti-vascular endothelial growth factor (anti-VEGF medication), laser therapy and vitro-retinal surgery. The data analysis was performed to calculate estimated relative risk to identify the degree of association between the determinants and behaviors, and to find statistically significant differences (at p < 0.05) in the responses between the Doers and Non-doers. RESULTS: Access to healthcare facilities was the major barrier impeding women from performing DR behaviors. Difficulty in locating DR service centers, the need to travel long distances, the inability to travel alone and during illness, challenges of paying for transportation and managing workload significantly affected women's ability to perform the behaviors. Other determinants included women's perceived self-efficacy, perceived negative consequences (e.g. fear and discomfort associated with injections or laser treatment), and cues for action. Significant perceived enablers included low cost of DR treatments, supportive attitudes by healthcare providers, government policy, and perceived social norms. CONCLUSION: The study found a host of determinants related to the barriers to and enablers of DR screening and treatment behaviors. These determinants included perceived self-efficacy (and agency), positive and negative consequences, perceived access, perceived social norms, culture, and perceived risk. Further investments are required to enhance the availability of DR services within primary and secondary health institutions along with health behavior promotion to dispel misconceptions and fears related to DR treatments.

Epidemiology and phylodynamics of multiple clades of H5N1 circulating in domestic duck farms in different production systems in Bangladesh.

Waterfowl are considered to be natural reservoirs of the avian influenza virus (AIV). However, the dynamics of transmission and evolutionary patterns of AIV and its subtypes within duck farms in Bangladesh remain poorly documented. Hence, a cross-sectional study was conducted in nine districts of Bangladesh between 2019 and 2021, to determine the prevalence of AIV and its subtypes H5 and H9, as well as to identify risk factors and the phylodynamics of H5N1 clades circulating in domestic duck farms. The oropharyngeal and cloacal swab samples were tested for the AIV Matrix gene (M-gene) followed by H5, H7, and H9 subtypes using rRT-PCR. The exploratory analysis was performed to estimate AIV and its subtype prevalence in different production systems, and multivariable logistic regression model was used to identify the risk factors that influence AIV infection in ducks. Bayesian phylogenetic analysis was conducted to generate a maximum clade credibility (MCC) tree and the maximum likelihood method to determine the phylogenetic relationships of the H5N1 viruses isolated from ducks. AIV was detected in 40% (95% CI: 33.0-48.1) of the duck farms. The prevalence of AIV was highest in nomadic ducks (39.8%; 95% CI: 32.9-47.1), followed by commercial ducks (24.6%; 95% CI: 14.5-37.3) and backyard ducks (14.4%; 95% CI: 10.5-19.2). The H5 prevalence was also highest in nomadic ducks (19.4%; 95% CI: 14.0-25.7). The multivariable logistic regression model revealed that ducks from nomadic farms (AOR: 2.4; 95% CI: 1.45-3.93), juvenile (AOR: 2.2; 95% CI: 1.37-3.61), and sick ducks (AOR: 11.59; 95% CI: 4.82-32.44) had a higher risk of AIV. Similarly, the likelihood of H5 detection was higher in sick ducks (AOR: 40.8; 95% CI: 16.3-115.3). Bayesian phylogenetic analysis revealed that H5N1 viruses in ducks belong to two distinct clades, 2.3.2.1a, and 2.3.4.4b. The clade 2.3.2.1a (reassorted) has been evolving silently since 2015 and forming at least nine subgroups based on >90% posterior probability. Notably, clade 2.3.4.4b was introduced in ducks in Bangladesh by the end of the year 2020, which was genetically similar to viruses detected in wild birds in Japan, China, and Africa, indicating migration-associated transmission of an emerging panzootic clade. We recommend continuing AIV surveillance in the duck production system and preventing the intermingling of domestic ducks with migratory waterfowl in wetlands.

Analysis of E-cadherin (CDH1) Gene Polymorphism and Its Association with Cervical Cancer Risk in Bangladeshi Women.

BACKGROUND: E-cadherin (CDH1), a tumor suppressor gene, encodes a transmembrane glycoprotein that helps in maintaining squamous epithelium integrity of the cervix. We aimed to investigate the association between -160C/A genetic polymorphism in CDH1 and the risk of cervical cancer in Bangladeshi females. METHOD: The present case-control study included 117 cervical cancer cases and 147 age-matched controls. The genomic DNA was extracted from peripheral blood and genotyped by using PCR-RFLP analysis. RESULTS: Genotyping results demonstrated that the occurrences of normal homozygous (-160C/C), heterozygous (-160C/A) and variant homozygous (-160A/A) genotypes were 64.10, 27.35 and 8.55% in cases, and 77.55, 19.73 and 2.72% in controls, respectively. Compared to normal C/C genotype, variant A/A and combined (C/A+A/A) or 'any A' genotypes exhibited 3.80-fold (95% CI=1.150-12.561, P=0.029) and 1.93-fold (95% CI=1.126-3.323, P=0.017) increased risk of cervical cancer development. The -160C allele was found to be positively linked to cervical cancer incidence and raised the risk by 1.81-fold (OR= 1.814, 95% CI=1.152-2.857, p=0.01). Moreover, women carrying -160A/A variant homozygosity along with an early marital history (<18 years) were more susceptible to cervical cancer development (χ2 =6.605, p=0.037). CONCLUSION: The study suggests that the (A/A) and combined (C/A +A/A) genotypes are associated with greater risk of cervical cancer in Bangladeshi women.