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Flemingia macrophylla has traditionally been applied to relieve inflammation, diabetes, and circulatory complications. The leaf extract of F. macrophylla and its fractions were investigated for their in-vitro antioxidant and anti-diabetic properties. The phytochemical screening showed valuable phytochemicals, including glycosides, flavonoids, saponins, etc. GC‒MS analysis of the phytochemicals in the methanol extract detected 19 bioactive compounds. Among the diverse fractions, the ethyl acetate fraction (EFM) exhibited the highest phenol and flavonoid contents of 557 mg GAE/g and 326 mg QCE/g, respectively. The total antioxidant content of EFM was found to be 292.41±19.16 mg AAE/g, while its antidiabetic study showed the greatest level of α -glucosidase (IC50: 11.27±1.25 µg/mL) and α -amylase (IC50: 10.04±0.63 µg/mL) inhibitory effects. The docking results showed that C6 had the highest binding scores of -9.0, -7.4, and -7.6 kcal/mol against antioxidant (6NGJ), α-glucosidase (5NN5), and α-amylase (4GQR) proteins, respectively. The dynamics simulation disclosed that C6-receptor protein complexes remained stable at the binding pocket under human body conditions and retained their stiff morphology for 100 nanoseconds (ns). ADMET results demonstrated their noncarcinogenic and well-absorbed properties, where PASS prediction data confirmed their efficacy as an antioxidant, antiulcerative, thrombolytic, and antidiabetic. Therefore, F. macrophylla has potential health benefits.

The use of eco-friendly natural fiber-reinforced polymer composites is rapidly expanding across diverse sectors. The rapid spread of water hyacinth disrupts aquatic ecosystems by modifying the pH of water and salinity in Bangladesh. This work investigated on the impact of incorporating both untreated and chemically treated water hyacinth fibers into polypropylene (PP). Untreated water hyacinth (UWH) powder was treated with an alkaline solution, producing mercerized water hyacinth (MWH). MWH was further oxidized to yield oxidized water hyacinth (OWH). Analysis of attenuated total reflection-fourier transform infrared (ATR-FTIR) spectra of UWH, MWH and OWH confirmed cellulose modification. The UWH and OWH were taken in varying contents (1, 2.5, 5, 7.5, and 10 wt%) and added to PP to make UWH-PP and OWH-PP composites using compression molding technique. The 1 % fiber content OWH-PP composites exhibited enhanced tensile strength, elongation, and impact strength compared to UWH-PP composites. Enhanced mechanical properties in OWH-PP composites suggested that benzoyl peroxide treatment improves interfacial adhesion. Morphological analysis of the OWH-PP composite showed better interfacial bonding between water hyacinth and PP than that of the UWH-PP composite. Thermogravimetric analysis (TGA) depicted the thermal stability of the UWH-PP and OWH-PP composites. The chemical reaction of cellulose monomer was further studied with DFT/B3LYP level of theory using two different basis sets 6-31+G(d, p) and cc-pVTZ. The calculated vibrational spectra of the untreated, mercerized and oxidized cellulose monomer agree well with the ATR-FTIR spectra, confirming chemical modification. DFT calculations of thermodynamic properties revealed the feasibility of the reactions. Electronic property (NBO charge) indicated charge transfer and structural changes during chemical modification.

Otitis media (OM) is a common infection or inflammation of the middle ear causing conductive hearing loss that primarily affects children and may delay speech, language, and cognitive development. OM can manifest itself in different forms, and can be diagnosed using (video) otoscopy (visualizing the tympanic membrane) or (video) pneumatic otoscopy and tympanometry. Accurate diagnosis of OM is challenging due to subtle differences in otoscopic features. This research aims to develop an automated computer-aided design (CAD) system to assist clinicians in diagnosing OM using otoscopy images. The ground truths, generated manually and validated by otolaryngologists, are utilized to train the proposed nested U-Net++ model. Ten clinically relevant gray level co-occurrence matrix (GLCM) and morphological features were extracted from the segmented Region of Interest (ROI) and validated for OM classification based on a statistical significance test. These features serve as input for a Graph Neural Network (GNN) model, the base model in our research. An optimized GNN model is proposed after ablation study of the base model. Three datasets, one private dataset, and two public ones have been used, where the private dataset is utilized for both training and testing, and the public datasets are used to test the robustness of the proposed GNN model only. The proposed GNN model obtained the highest accuracy in diagnosing OM: 99.38 %, 93.51 %, and 91.38 % for the private dataset, public dataset1, and public dataset2, respectively. The proposed methodology and results of this research might enhance clinicians' effectiveness in diagnosing OM.

Carrot is an important root vegetable and salinity has strong effect on its seed germination and seedling characters. An experiment was conducted at the Postgraduate Laboratory of the Department of Horticulture, Bangladesh Agricultural University, Mymensingh during the period from July to August, 2023 to study the effect of salinity stress on the seed germination and seedling characters of four carrot varieties. The two-factor experiment consisted of four carrot varieties viz. Orange king, Orange star, Sangal and Red lady, and five salinity levels viz. control (tap water), 25 mM, 50 mM, 75 mM and 100 mM NaCl. The experiment was laid out in completely randomized design with three replications. Results revealed that salinity and carrot varieties significantly influenced all the parameters studied. The highest seed germination (44.44%) was obtained from Orange king while the lowest (32.89%) was obtained from Red lady. The highest seed germination (41.11%) was obtained from 100 mM NaCl solution and the lowest (34.44%) was found from 75 mM NaCl. Among the treatment combinations, the highest seed germination (60.00%) was obtained from Orange king along with 50 mM NaCl. The maximum radicle (15.46 mm) and plumule length (12.36 mm), leaf size (5.50 mm), number of leaves (2.73) and seedling height (27.82 mm) was obtained from Orange king. Maximum plumule length (13.27 mm), leaf size (5.98 mm) and seedling height (23.27mm) was obtained from control treatment and maximum number of leaves (2.08) from 25 mM NaCl solution. Among the treatment combinations, highest radicle length (18.27 mm), plumule length (17.40 mm), leaf size (7.60 mm), seedling height (35.67 mm) was obtained from Orange king along with 25 mM NaCl solution. Therefore, Orange king along with 50 mM NaCl solution and 25 mM NaCl solution was found to be better in respect of seed germination and seedling characters of carrots compared to other treatments respectively. [Fundam Appl Agric 2024; 9(2.000): 113-121]

Personalized cancer care requires molecular characterization of neoplasms. While the research community accepts frozen tissues as the gold standard analyte for molecular assays, the source of tissue for testing in clinical cancer care comes almost universally from formalin-fixed, paraffin-embedded tissue (FFPE). As newer technologies emerge for DNA characterization that requires higher molecular weight DNA, it was necessary to compare the quality of DNA in terms of DNA length between FFPE and cryopreserved samples. We hypothesized that cryopreserved samples would yield higher quantity and superior quality DNA compared to FFPE samples. We analyzed DNA metrics by performing a head-to-head comparison between FFPE and cryopreserved samples from 38 human tumors representing various cancer types. DNA quantity and purity were measured by UV spectrophotometry, and DNA from cryopreserved tissue demonstrated a 4.2-fold increase in DNA yield per mg of tissue (p-value < 0.001). DNA quality was measured on a fragment microelectrophoresis analyzer, and again, DNA from cryopreserved tissue demonstrated a 223% increase in the DNA quality number and a 9-fold increase in DNA fragments > 40,000 bp (p-value < 0.0001). DNA from the cryopreserved tissues was superior to the DNA from FFPE samples in terms of DNA yield and quality.

Knee osteoarthritis (KOA) is a prevalent condition characterized by gradual progression, resulting in observable bone alterations in X-ray images. X-rays are the preferred diagnostic tool for their ease of use and cost-effectiveness. Physicians use the Kellgren and Lawrence (KL) grading system to understand the severity of an individual condition of KOA. This system categorizes the disease from normal to a severe stage. Early detection of the condition with this approach enables knee deterioration to be slowed down with therapy. In this study, we aggregated four datasets to generate an extensive dataset comprising 110,232 raw images by applying an augmentation technique called deep convolutional generative adversarial network (DCGAN). We employed advanced image pre-processing methods (adaptive histogram equalization (AHE), fast non-local means), including image resizing, to generate a substantial dataset and enhance image quality. Our proposed approach involved developing a modified compact convolutional transformer (CCT) model known as KOA-CCTNet as the foundational model. We further investigated optimal configurations by adjusting various parameters and hyperparameters in the final model to handle large datasets and address training time concerns efficiently. We investigated optimizing its configurations by adjusting numerous parameters and hyperparameters to efficiently manage extensive data and address concerns related to training time. Simulation results indicated that our proposed model outperforms other transfer learning models (Swin Transformer, Vision Transformer, Involutional Neural Network) in terms of accuracy. The test accuracy for the ResNet50, MobileNetv2, DenseNet201, InceptionV3, and VGG16 was 80.77%, 79.98%, 80.23%, 76.89%, and 79.58%, respectively. All of them were surpassed by our proposed KOA-CCTNet model, which had a test accuracy of 94.58% while classifying KOA X-ray images. Furthermore, we reduced the number of images to assess the model’s performance and compared it to existing models. However, by employing a large datahub, our proposed approach provides a unique and effective way to diagnose KOA grades with satisfying results.

Otitis Media (OM), predominantly affecting children, is a significant global health issue, with an estimated 360 million pediatric cases yearly worldwide. OM causes mild and moderate conductive hearing loss which can be disabling for young children, particularly during the first three years of life when brain growth is rapid, resulting in poor speech and language development, poor communication skills, and increased vulnerability on entering school. OM therefore contributes to the global burden of all-cause hearing loss. This systematic review seeks to provide a comprehensive evaluation of pre-trained Artificial Intelligence (AI) models, including both classical Machine Learning (ML) and Deep Learning (DL), in the context of OM. This review proposes six research questions, and it summarizes the body of research across multiple domains, including the diversity and quantity of source material for training and testing models, including otoscopy images, videos, and tympanometry, and the methods used to assess quality and effectiveness in real-time settings. In addition, the review aims to provide insight into the impact and potential of AI in improving OM diagnosis and cast light on the existing challenges, such as model interpretability, limited medical expert involvement, and the need for knowledge discovery and unanswered questions, including the evolving landscape of OM diagnosis within this domain. The findings of this systematic review emphasize the importance of developing more interpretable AI models that incorporate both still images of the tympanic membrane and video recordings (with multiple frames) to maximize sensitivity and specificity of the model. In addition, collaboration with consumers and medical professionals in multiple specialties (general practice, pediatricians, audiologists and ear, nose, throat (ENT) surgeons) is needed to ensure applicability and confidence of these diagnostic digital support systems in real-world healthcare settings.

In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detecting a learner’s preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. Our investigation finds that analyzing learners’ web tracking logs for activity classification and categorizing individual responses for feedback classification are highly effective methods for identifying a learner’s learning styles, such as visual, auditory, and kinesthetic. A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. The blending ensemble technique with the XGBoost meta-learning model accomplished the best performance for learning style detection with an accuracy of 97.56%. Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. Various approaches, such as color coding, audio scripts, mind maps, flashcards, etc., are implemented to adapt the content effectively for the detected categories of learners. The spaCy NLP-based named entity recognition (NER) model demonstrates a 94.16% F1 score and 0.92 exact match ratio for color coding text generation of ten electronic documents comprising 790 distinct individual words. These modifications aim to cater to the unique preferences of learners, fostering a more personalized and engaging educational experience. To the best of our knowledge, this is the first time an integrated learning style detection and content modification system has been developed in this work utilizing efficient AI techniques and a private dataset.

Large Language Models (LLMs) recently demonstrated extraordinary capability in various natural language processing (NLP) tasks including language translation, text generation, question answering, etc. Moreover, LLMs are new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies in a given context. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a plethora of research on LLMs have been appeared within a short time, it is quite impossible to track all of these and get an overview of the current state of research in this area. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. Then the paper provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. The paper also demonstrates the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. The study also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Finally, the paper also explores open issues and challenges to deploy LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.

Vascular Endothelial Growth Factor A (VEGFA) is a glycoprotein that mediates various biological processes, including angiogenesis, vascular permeability, and cellular migration. Aberrant VEGFA signaling is also one of the hallmarks of many types of cancer and has been implicated in various ophthalmological conditions such as diabetic macular edema and age-related macular degeneration. Consequently, a number of therapeutic monoclonal antibodies (mAb) targeting VEGFA have been developed and are widely used to treat these conditions. Bevacizumab (BVZ) and Ranibizumab (RBZ) are two such antibodies that are commercially available and used to treat various cancers and ophthalmological conditions. Nevertheless, a very high rate of non-responsiveness to these mAb treatments has been reported. Therefore, it is important to predict the response to these therapeutic mAb treatments in patients in a personalized approach. This study was aimed at analyzing the impacts of missense variants in the respective VEGFA epitopes for these two therapeutic anti-VEGFA mAbs (BVZ and RBZ) on their interaction with VEGFA through the use of multiple in silico tools. Three missense variants (VEGFAR82W, VEGFAR82Q, and VEGFAG92R) in VEGFA epitopes appear to significantly destabilize VEGFA-BVZ interaction, while only two variants (VEGFAR82W and VEGFAR82Q) affect interaction of VEGFA with RBZ. The VEGFAR82W variant may be pathogenic as well. These missense variants may play roles in the observed heterogeneous response to anti-VEGFA mAb treatments in patients and, therefore, may be used as pharmacogenetic markers for the prediction of responses before administration, and thus for the improvement of therapeutic outcomes. [ J Adv Biotechnol Exp Ther 2023; 6(2.000): 359-374]

Analysing the morphometric parameters is the most expedient and parsimonious way of representing the hydrologic and physiographic attributes of river basins. The present study attempts to measure the morphometric parameters for assessing the understanding of morphological, hydrological, and physiographic properties of the south-western part of the Ganges delta. Parameters were analysed from Shuttle Radar Topography Mission's (SRTM) maps and total of eight linear, six areal, eight relief, and five drainage texture parameters have taken with hypsometric analysis for the four major rivers and two tributary river basins. The values of linear parameter denote that most of the streams (82%) fall in first order, and other orders have homogeneous underline materials. The aerial parameters represent low peak discharge and the upper region is less vulnerable to flood. The relief parameter values show that the entire basin has low surface runoff and they are less erosional (slope < 3.97°). The drainage density indicates the coarser nature and circularity ratio (0.08) represents the elongated shape. The southern portion of the basin has a greater flood potential and hypsometric index (0.49) shows the entire basin is in mature stage of formation. These results would be helpful for reckoning the watersheds for drainage management and environmental planning for ecological management and sustainable development. HIGHLIGHTS Diversified hydro-geographic parameters were investigated for one of the major economic regions in Bangladesh.; Rho coefficient values denoting erosion repulsion and less flood potentiality via CR and ER.; Homogeneous baseline material and elongated shape of basins that are easy to control.; HI represents the mature stage where high-resolution DEM data would be effective to calculate the flood potential.;

Endophytic bacteria, recognized as eco-friendly biofertilizers, have demonstrated the potential to enhance crop growth and yield. While the plant growth-promoting effects of endophytic bacteria have been extensively studied, the impact of weed endophytes remains less explored. In this study, we aimed to isolate endophytic bacteria from native weeds and assess their plant growth-promoting abilities in rice under varying chemical fertilization. The evaluation encompassed measurements of mineral phosphate and potash solubilization, as well as indole-3-acetic acid (IAA) production activity by the selected isolates. Two promising strains, tentatively identified as Alcaligenes faecalis (BTCP01) from Eleusine indica (Goose grass) and Metabacillus indicus (BTDR03) from Cynodon dactylon (Bermuda grass) based on 16S rRNA gene phylogeny, exhibited noteworthy phosphate and potassium solubilization activity, respectively. BTCP01 demonstrated superior phosphate solubilizing activity, while BTDR03 exhibited the highest potassium (K) solubilizing activity. Both isolates synthesized IAA in the presence of L-tryptophan, with the detection of nifH and ipdC genes in their genomes. Application of isolates BTCP01 and BTDR03 through root dipping and spraying at the flowering stage significantly enhanced the agronomic performance of rice variety CV. BRRI dhan29. Notably, combining both strains with 50% of recommended N, P, and K fertilizer doses led to a substantial increase in rice grain yields compared to control plants receiving 100% of recommended doses. Taken together, our results indicate that weed endophytic bacterial strains BTCP01 and BTDR03 hold promise as biofertilizers, potentially reducing the dependency on chemical fertilizers by up to 50%, thereby fostering sustainable rice production.

This research article means to academically elaborate the imperativeness of establishing a pragmatic convergence between English Language Teaching (ELT) and Service Learning (SL) as a worthwhile way of ensuring Transformative Education (TE) which, eventually, has a rewarding prospect of buttressing the ensuing as well as the currently-functioning Fourth Industrial Revolution (4IR). The researchers delineate some feasible models for incorporating service-learning into ELT arrangements at the program offering entity, and thereby, illustrate how these SL and ELT combinations might result in TE, and, somehow or other, instigate FIR.

Objectives: The main goal of this research was to develop better tablet formulations by utilizing solid dispersions (SDs) and coprocessing excipients composite to achieve a better release rate of poor water-soluble gliclazide. Methods: The solvent evaporation method made SDs of gliclazide with different carriers carplex 67, carplex 80, and carplex FPS 500 (weight ratio, 1:1). The drug release patterns of the SDs were all evaluated and optimized. The SDs were illustrated by using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), X-ray powder diffraction (PXRD), and Fourier transform infrared spectroscopy (FTIR). Tablet batches FGC-1 to 8 were made using gliclazide-carplex 67 solid dispersions (GC67-SDs) and the co-processed composite of excipients, namely starch-MCC-povidone (SMP) and lactose-MCC-povidone-sodium starch glycolate (LMPS), prepared with coprocessing technology. We evaluated these batches by conducting physicochemical tests and comparing them to the existing commercial brand. Results: In a water medium, the release of gliclazide from SDs peaked within the first 30 min, showing a roughly 5∼6-fold increase compared to plain gliclazide. This quick dissolution rate may be due to the amorphization of the drug, which improved the specific surface area, and increased wettability caused by the hydrophilic properties of carplex particles. This has been confirmed through SEM, DSC, FTIR, and PXRD analysis. All FGC formulations had satisfactory pre-compression factor results, while the post-compression parameters indicated good mechanical strength and homogeneity across the blend. All produced tablets met the weight variation, friability, and disintegration time limit set by the compendia. Through in vitro drug release testing, it was discovered that all FGC tablet batches had consistent and nearly identical release results compared to SDs of gliclazide. However, the FGC-5 to 8 batches containing LMPS composites were determined to be the most effective formulations. In the first 30 min in a water medium, the percentage of drug generated from the FGC-8 tablets involving GC67-SDs and co-processed composite LMPS-4 is approximately 3.5 times higher than the average release of currently marketed products (MPs). After storing the selected FGC tablet batches for three months at 40 °C and 75 % RH, there were no noticeable alterations in the amount of drug and drug release profiles across the batches. Conclusion: Based on these findings, it appears that using the carplex silica-based SDs approach, along with gliclazide and co-processing excipients composite, could result in significant benefits compared to the current commercial brands. This approach could be effectively utilized to create solid dosage forms for drugs that have low solubility in water.

Malaria is the world’s most fatal and challenging parasitic disease, caused by the Plasmodium parasite and transmitted to humans by the bites of infected female mosquitos. This study presents a stochastic process representing the evolution in time of an unexpected phenomenon. We use different probability techniques to assume the possible outcome in a Malaria model since state variables or parameters are random in a stochastic model. This study considers a deterministic vector-host malaria model and converts this model into the corresponding stochastic model with a Continuous-time Markov chain (CTMC) and Stochastic differential equation (SDE). We prove the positivity and boundedness of solutions and calculate the equilibrium points. The threshold values are evaluated using different approaches. Moreover, the Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC) procedures are used to analyze the sensitivity of each model parameter. Finally, a transient numerical simulation is discussed, and their outcomes strongly correlate with the actual scenario.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.

An analysis was conducted on both freshwater, coastal and marine fish species to evaluate the concentrations of heavy metals, with the aim of assessing their levels and examining the potential health risks for humans linked to the consumption of contaminated fish. This study estimate concentrations of Cr, Fe, Cu, As, Cd and Pb in 60 individuals belonging to 20 species (10 species for freshwater and another 10 for coastal and marine fishes) by using Atomic Absorption Spectrophotometer. Metal concentrations of Cr, Fe, and Pb in freshwater fishes and Cr, Fe, As, and Pb in marine fishes were exceeded the maximum allowable concentration (MAC). The Estimated Daily Intake (EDI), Average Pollution Load Index (APLI), Target Hazard Quotient (THQ), Hazard Index (HI) and Target Cancer Risk (TCR) of heavy metals were determined for the assessment of human health risk. Ranking order of the values of EDI for freshwater fishes, coastal and marine fishes were Cd > Fe > Pb > Cr > Cu > As and Cd > Fe > Pb > Cr > As > Cu. Highest APLI value of 8.14 (Puntius ticto) that is seriously polluted and 3.003 observed in Otolichthoides pama in freshwater and marine fishes, respectively. THQPb and THQAs for all the fish species were exceed the safe limit (THQ>1) suggesting potential health risk to consumers. The hazard index for both the fish samples were exceeded the USEPA (United States Environmental Protection Agency) permitted risk level (HI > 1). The target carcinogenic risk value for Cr and As were crossed the USEPA standard limit (TCR> 1E-04), which denotes that continuous consumption of studied fishes may cause health risk to the consumers. On the other hand, sensitivity analysis of freshwater, coastal and marine fishes indicates that all the metal concentrations were responsible factor for health risk.

The traditional Know Your Customer (KYC) procedure used by banks is deemed unreliable and costly. Therefore, the adoption of emerging technologies is essential for banking firms’ future prospects. One such technology that has gained widespread acceptance is Blockchain, which is known for its reliability and security across various fields. This study aims to investigate how the implementation of Blockchain technology can modify the existing banking business, particularly the KYC document verification process, by storing and monitoring of information. The current need for an optimized KYC system is paramount; one that is coupled with a secure and trustworthy technology like Blockchain that can withstand fraudulent activities while also overcoming scalability and privacy challenges. The article analyzes previous relevant works, which highlight how the implementation of Blockchain technology eliminates the need for intermediaries, thereby reducing the possibility of malicious activities and errors that may occur when there are multiple manual tasks involved.

Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood vessel damage can lead to vision loss and blindness if not recognized at an early stage. Manual DR detection using large fundus image data is time-consuming and error-prone. An effective automatic DR detection system can be significantly faster and potentially more accurate. This study aims to classify fundus images into five DR classes, using deep learning methods, with the highest possible accuracy and the lowest possible computational time. Three distinct DR datasets, APTOS, Messidor2, and IDRiD, are merged, resulting in 5,819 raw images. Before training the model, various image preprocessing techniques are applied to remove artifacts and noise from the images and improve their quality. Three augmentation techniques: geometric, photometric, and elastic deformation, are used to create a balanced dataset. A shallow convolutional neural network (CNN) is developed using three blocks of convolutional layers and maxpool layers with a categorical cross-entropy loss function, Adam optimizer, 0.0001 learning rate, and 64 batch size as a base model, and this is also employed to determine the best data augmentation method for further processing. A study to optimize the performance is then conducted by changing different components and hyperparameters of the base model, resulting in our proposed RetNet-10 model. Six cutting-edge models are employed for comparison. Our proposed RetNet-10 model performed the best, with a testing accuracy of 98.65%. MobileNetV2, VGG16, Xception, VGG19, InceptionV3 and ResNet50 achieved testing accuracies of 91.42%, 90.16%,89.57%, 88.21%, 87.68% and 87.23%, respectively. The model is also trained with several k values to assess its robustness. After image processing and data augmentation, using the combined dataset, and fine-tuning the base model, our proposed RetNet-10 model outperformed other automated methods for DR diagnosis.

Increased exposure to environmental heavy metals and metalloids and their associated toxicities has become a major threat to human health. Hence, the association of these metals and metalloids with chronic, age-related metabolic disorders has gained much interest. The underlying molecular mechanisms that mediate these effects are often complex and incompletely understood. In this review, we summarize the currently known disease-associated metabolic and signaling pathways that are altered following different heavy metals and metalloids exposure, alongside a brief summary of the mechanisms of their impacts. The main focus of this study is to explore how these affected pathways are associated with chronic multifactorial diseases including diabetes, cardiovascular diseases, cancer, neurodegeneration, inflammation, and allergic responses upon exposure to arsenic (As), cadmium (Cd), chromium (Cr), iron (Fe), mercury (Hg), nickel (Ni), and vanadium (V). Although there is considerable overlap among the different heavy metals and metalloids-affected cellular pathways, these affect distinct metabolic pathways as well. The common pathways may be explored further to find common targets for treatment of the associated pathologic conditions.

Abstract Married women have a higher risk of contracting human immunodeficiency virus (HIV) or develop acquired immune deficiency syndrome (AIDS) than men. Knowledge of HIV/AIDS contributes significantly to describing the prevalence and consequences of such virus/disease. The study aimed to investigate the level of HIV/AIDS knowledge and the socio-demographic variables that influence HIV/AIDS knowledge among married women in Bangladesh. We used three waves of Multiple Indicator Cluster Survey (MICS), which included 33,843, 20,727, and 29,724 married women from 2006, 2012, and 2019 MICS. A score was prepared through their interrogation to determine the level of knowledge and logistic regression models were used for analyzing the data. This study found that the prevalence of knowledge level of HIV/AIDS in different questions increased from 55.20% in 2006 to 58.69% in 2019. In our study, respondents having highest education had 4.03 (95% CI 3.50–4.64) times more chance to obtain “High Score” in 2019 MICS which is 5.30 times in 2012 MICS (95% CI 4.41–6.37) and 2.58 times in 2006 MICS (95% CI 2.28–2.93) compared to illiterate married women. Moreover, respondents from urban area were 1.13 times more likely to obtain “High Score” in 2019 MICS which is 1.14 times in 2012 MICS and 1.16 times in 2006 MICS, respectively than the rural married women. This study also found respondent’s age, division, mass media access, and wealth status have played an important role in HIV/AIDS knowledge. Although a significant proportion of women had adequate knowledge of HIV/AIDS, more knowledge is still required to protect against such viruses/diseases. Thus, we advocate for the implementation of educational program in the curriculum, counselling, particularly in rural areas, and mass media access to ensure quality knowledge throughout the country.

Abstract This study aimed to measure the socio-economic inequalities in having minimum dietary diversity (MDD) among Bangladeshi children aged 6–23 months as well as to determine the factors that potentially contribute to the inequity. The Bangladesh Demographic and Health Survey (BDHS) 2017–2018 data were used in this study. A sample of 2405 (weighted) children aged 6–23 months was included. The overall weighted prevalence of MDD was 37.47%. The concentration index (CIX) value for inequalities in MDD due to wealth status was positive and the concentration curve lay below the line of equality (CIX: 0.1211, p

Aim: The study aims to investigate the implication of employment status on individuals’ mental and physical health status in Bangladesh. Method: Administering a semi-structured interview schedule, data were collected from 320 participants applying the multi-stage random sampling technique from the metropolitan area of Khulna city. An unpaired t-test was executed to observe the comparative scenario of mental health and socio-demographic conditions of different health-bearing classes. Also, logistic regression and the OLS model were executed to assess the association between employment status and physical and mental health status. BMI and Depression Anxiety Stress Scales 21 (DASS-21) were applied to measure the physical and mental health status, respectively. Results: Results revealed that employed individuals were more exposed to physical and mental health disorders compared to unemployed individuals. The employed individuals were at higher risk of being unhealthy, and suffering from back pain and sickness than the unemployed individuals. Results suggested that the employed individuals were expected to get involved more in mental health risk behavior (0.766, p

COVID-19 has undoubtedly absorbed the global public's angst. It has quickly disturbed global life and will have long-term and short-term consequences on several sectors. This comprehensive review study's goal is to discover what research has been done in a year since the beginning of coronavirus in Bangladesh. Consequently, the current study examined the pervasiveness and affiliation of social and economic issues, health and psychological issues and individual perceptions, key challenges, strategies and policy systems, public health, online education, agriculture and food security, criminal activities related to outbreaks, Rohingya refugee issues, and the quality of domestic violence behaviors. The review used electronic databases such as Web of Science, PubMed, PubMed Central, and Scopus to find published material. This study reviewed chosen papers, removing redundancies and including 43 pertinent articles. Among the 43 study items, fourteen were qualitative (32.56%), six mixed (13.95%), and the rest were quantitative (53.49 %). This study helps to identify the issues with current documentation by focusing on interconnected factors and studying COVID-19 events and scenarios. Thus, governments and other stakeholders should reassess these controversial issues to formulate a policy that takes into consideration the situation in Bangladesh and other COVID-19 affected countries.

The global groundwater crisis is a perplexing issue, and for its resolution, it is of the utmost importance for delineating groundwater potential zones. This research aims to create a precise groundwater potential map for Bangladesh's Jashore district by combining geospatial approach and an analytical hierarchy process. Fourteen parameters, namely, lineament density, drainage density, land use and land cover, slope, curvature, topographic position index, topographic wetness index, rainfall, geology, roughness, fractional impervious surface, topsoil texture, soil permeability, and general soil types, were considered for the study after an extensive literature review. The weights of these parameters were determined using an analytical hierarchy process, and the scores of each sub-parameter were assigned based on published literature. The final groundwater potential map was then generated using the weighted overlay analysis tool in ArcGIS 10.3 and categorized into five classes. The analysis reveals that very high, high, moderate, low, and very low groundwater potential zones cover 3.96 km2 (0.16%), 444.75 km2 (17.72%), 1615.51 km2 (64.37%), 441.79 km2 (17.60%), and 3.59 km2 (0.14%) of the study area, respectively. The map removal sensitivity analysis shows that geology is the most significant element in groundwater potential zoning, followed by land use and land cover (LULC), slope, and topsoil texture as moderately sensitive elements. Since the groundwater potentiality zones of the study region are clearly delineated, this research may be valuable for implementing an appropriate water resource management strategy.

Diclofenac (DCF) is a non-steroidal anti-inflammatory drug (NSAID), commonly used for the treatment of pain. It can inhibit prostaglandin synthesis by blocking cyclooxygenase (COX). It shows some crucial side effects like gastrointestinal, cardiovascular, renal, and liver injury. The gastrointestinal injury occurred due to the presence of a carboxylic (COOH) group at the core of DCF. The hydroxyl (OH) portion of COOH was replaced by inserting some new functional groups (CH3, OCH3, CH2NH2, NH2, NHCOCH3, NHCONH2, Cl, CF3) considering the retrosynthetic strategy which reduces gastrointestinal side effects with improved chemical and biological activity. Herein, we have investigated the physicochemical, spectral, molecular dynamics, biological and pharmacokinetic properties of the mentioned analogues. Density functional theory (DFT) and time-dependent DFT along with the B3LYP/6-31g (d,p) basis set have been utilized to calculate their geometrical, chemical, and spectral properties. Molecular docking and non-bonding interactions have been performed against human prostaglandin synthase protein (PDB ID: 5F1A) to investigate their binding affinities, modes, and stability. ADMET and PASS prediction studies were performed to investigate their microbial inhibition and toxicological profile. Quantum chemical calculations and spectral characterization support the geometry of newly designed analogues. From the molecular docking simulation, most of the analogues exhibited better binding affinity than the parent drug except for DCF-2. ADMET calculations predict the improved pharmacokinetic and non-carcinogenic properties of all DCF analogues. Biological activities and drug-likeness indicate that all the analogues of DCF have been exposed to comparatively lower action of the gastrointestinal hemorrhage than DCF and exhibit antipyretic, analgesic, and anti-inflammatory actions as well as performed to overcome the poor absorption or permeation of drugs. Finally, based on the above investigation, this study can be helpful to design a potential drug candidate with improved medicinal action and reduced selective adverse effects.

Abstract Diabetes mellitus is a complex and heterogeneous metabolic disorder which is often pre- or post-existent with complications such as cardiovascular disease, hypertension, inflammation, chronic kidney disease, diabetic retino- and nephropathies. However, the frequencies of these co-morbidities vary among individuals and across populations. It is, therefore, not unlikely that certain genetic variants might commonly contribute to these conditions. Here, we identified four single nucleotide polymorphisms (rs5186, rs1800795, rs1799983 and rs1800629 in AGTR1, IL6, NOS3 and TNFA genes, respectively) to be commonly associated with each of these conditions. We explored their possible interplay in diabetes and associated complications. The variant allele and haplotype frequencies at these polymorphic loci vary among different super-populations (African, European, admixed Americans, South and East Asians). The variant alleles are particularly highly prevalent in different European and admixed American populations. Differential distribution of these variants in different ethnic groups suggests that certain drugs might be more effective in selective populations rather than all. Therefore, population specific genetic architectures should be considered before considering a drug for these conditions.

Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present DR screening method requires the skill of ophthalmologists and is time-consuming. In this study, we present an automated system for DR severity classification employing the fine-tuned Compact Convolutional Transformer (CCT) model to overcome these issues. We assembled five datasets to generate a more extensive dataset containing 53,185 raw images. Various image pre-processing techniques and 12 types of augmentation procedures were applied to improve image quality and create a massive dataset. A new DR-CCTNet model is proposed. It is a modification of the original CCT model to address training time concerns and work with a large amount of data. Our proposed model delivers excellent accuracy even with low-pixel images and still has strong performance with fewer images, indicating that the model is robust. We compare our model’s performance with transfer learning models such as VGG19, VGG16, MobileNetV2, and ResNet50. The test accuracy of the VGG19, ResNet50, VGG16, and MobileNetV2 were, respectively, 72.88%, 76.67%, 73.22%, and 71.98%. Our proposed DR-CCTNet model to classify DR outperformed all of these with a 90.17% test accuracy. This approach provides a novel and efficient method for the detection of DR, which may lower the burden on ophthalmologists and expedite treatment for patients.

A B S T R A C T Systemic lupus erythematosus (SLE) is a chronic, inflammatory, autoimmune, multisystem connective multi system connective tissue disease characterized by various autoantibodies to nuclear and cytoplasmic antigens and commonly affects the joints and a variety of organs due to an over activation of the body's immune system. There is wide heterogeneity in presentation of SLE patients, including lung, central nervous system, skin, kidney, and hematologic manifestations. The presence of antinuclear antibodies (ANA) in serum is generally considered a decisive diagnostic sign of SLE. However, a small subset of SLE patients who had the typical clinical features of SLE was reported to show persistently negative ANA tests. Our report describes a 24-yr-old female who presented with the clinical manifestations of SLE such as malar rash, photosensitivity, arthritis, oral ulcer, and proteinuria. The serum autoantibodies were all negative except anti ribosomal P. She was also positive for lupus anticoagulantanti - coagulant. She was treated with oral prednisolone , hydroxychloroquine with topical tacrolimus, and improved significantly. Three months after, repeat ANA, and anti-ds DNA showed persistent negativity, but lupus anti - coagulant remained positive. This case suggests that ANA may not be required in the pathogenesis of SLE.

Various anthropogenic and natural events over the years have gradually increased human exposure to various heavy metals. Several of these heavy metals including cadmium, mercury, nickel, chromium, and the metalloid arsenic among others, have created major public health concerns for their high level of toxicities. Identification of the general as well as the differentially affected cellular metabolic pathways will help understanding the molecular mechanism of different heavy metal-induced toxicities. In this study, we analyzed 25 paired (control vs. treated) transcriptomic datasets derived following treatment of various human cells with different heavy metals and metalloid (arsenic, cadmium, chromium, iron, mercury, nickel and vanadium) to identify the affected metabolic pathways. The effects of these metals on metabolic pathways depend not only on the metals per se, but also on the nature of the treated cells. Tissue of origin, therefore, must be considered while assessing the effects of any particular heavy metal or metalloid. Among the metals and metalloid, arsenic appears to have relatively more pleiotropic influences on cellular metabolic pathways including those known to have association with diabetes. Although only two stem cell derived datasets are included in the current study, effects of heavy metals on these cells appear to be different from other mature cells of similar tissue origin. This study provides useful information about different heavy metal affected pathways, which may be useful in further exploration using wet-lab based techniques.

Abstract Background Rapid weight gain (RWG) in infants is associated with overweight and obesity in childhood and beyond, highlighting the need for early intervention. Methods Data from a birth cohort of Australian Aboriginal and Torres Strait Islander children living in an urban area were analysed to determine the prevalence of RWG in infancy and the association between RWG and overweight and obesity, categorised using both body mass index and waist to height ratio from birth to 9 years. Results The prevalence of overweight and obesity is higher in this cohort (at 47%) than the population average. The Australian population as a whole has seen steady increases. In this cohort although the prevalence of combined overweight and obesity remained relatively stable between 2 and 9 years, the proportion of children categorized as obese using BMI has increased. 42% of children who were overweight or obese at 9 years had experienced RWG in infancy. Children were 2.7 and 3.9 times more likely to be overweight at 9 years if they experienced RWG or were overweight at 2 years, respectively. Conclusion RWG was common in this cohort and the strongest predictor of excess weight at 2 years and at 9 years. Early intervention is crucial in the first year of life across the whole population to prevent obesity in children. Culturally appropriate interventions developed with the community are required for Aboriginal and Torres Strait Islander babies and their parents.

Osteosarcoma is a genetically complex cancer, thus there are increasing efforts to identify biomarkers and regulators within the epigenome that can better predict patient outcomes and provide new therapeutic targets. In this commentary, we have evaluated the work of Peng and colleagues on the identification and function of the signaling axis circ_0088212/miR_520 h/APOA1 in osteosarcoma. We provide the context of how novel it is to demonstrate the anti-tumor functions of APOA1 and not just correlative gene expression with patient outcome. We further postulate why some studies involving circRNAs in osteosarcoma contradict one another. We conclude that the study performed by Peng and colleagues was performed with enough rigor to give confidence that circ_0088212 is a bona fide tumor suppressor in osteosarcoma.

Sulfotransferase 1A1 (SULT1A1), a major cytosolic sulfotransferase, can catalyze the sulfonation of both endogenous and exogenous compounds. It participates in the metabolism of several drugs and pro-carcinogens. Although certain variants of the SULT1A1 are known to alter responses to drugs and increase susceptibility to various cancers, the effects of the majority of SULT1A1 variants are still unknown. In the present study, we predicted the potential pathogenicity of 119 missense variants at 96 functionally important sites of SULT1A1, and examined the effects of these variants on the enzyme's structure and stability using state-of the-art in silico tools. Additionally, we applied molecular docking and protein dynamics simulation techniques to assess the impacts of missense variants on SULT1A1-cofactor binding affinity and SULT1A1 residue fluctuation profile, respectively. Based on the extent of reduction in SULT1A1 stability, alteration in structure, decrease in cofactor binding affinity and changes in the flexibility of cofactor and substrate-interacting loops, we suggest that further experimental studies should particularly focus on 25 variants (P47L, K48T, S49A, G50R, G50S, W53R, C70G, C70R, A86D, H108D, H108N, S138F, M145I, Y169D, Y193F, T227P, M232L, V243D, M248T, G259A, G259V, A261S, G262A, G262E, F268S). Besides, our study sheds light on the possible mechanism of altered functionality of some already known detrimental variants of SULT1A1. Future studies may look into the prospects of using these variants as pharmacogenetic markers.

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.

Diclofenac, a nonsteroidal anti-inflammatory drug, is commonly prescribed for the analgesic, antipyretic and anti-inflammation treatment. It is effectively used in acute and chronic pain. It inhibits the prostaglandin synthesis by blocking cyclooxygenase (COX). However, it shows some crucial side effects like renal, gastrointestinal and cardiovascular injury to the human and other living things. It also causes gastrointestinal side effects like bleeding and perforation if taken at higher dose for a long period of time. Liver toxicity is also another issue related to oral diclofenac. To improve its safety profile, an attempt has been taken to design some new potential drug to reduce the side effects with better medicinal action. The insertion of new functional groups such as CH3, OCH3, F, CF3, OCF3, Cl, OH, COOH, NH2, CH2NH2, CONH2, NHCOCH3 at the different positions of its core structure significantly enhance the chemical and biological performance. Chemical, physical and spectral calculation has been performed by geometry optimization to characterize the newly designed structures. Molecular docking and dynamics simulation have been performed against human prostaglandin synthase protein (PDB ID: 5F19) to predict binding affinity, bonding interaction and stability of protein-drug complex. ADMET predictions have been studied to search for their pharmacokinetic properties like absorption, metabolism and toxicity. Physicochemical and spectral data support the new structural conformation. Molecular docking and dynamics studies disclose improved medicinal effect and pharmacokinetic prediction suggest their reduced side effects and non-carcinogenic properties than the parent drug which lead to develop the novel drug with improved clinical safety.

Objectives The Three Delays Model has been commonly used to understand and prevent maternal mortality but has not been systematically applied to emergency medical conditions more generally. The objective of this study was to identify delays in emergency medical care seeking and delivery in rural Bangladesh and factors contributing to these delays by using the Three Delays Model as a framework.Design A qualitative approach was used. Data were collected through focus group discussions and in-depth interviews using semistructured guides. Two analysts jointly developed a codebook iteratively and conducted a thematic analysis to triangulate results.Setting Six unions in Raiganj subdistrict of Bangladesh.Participants Eight focus group discussions with community members (n=59) and eight in-depth interviews with healthcare providers.Results Delays in the decision to seek care and timely receipt of care on reaching a health facility were most prominent. The main factors influencing care-seeking decisions included ability to recognise symptoms and decision-making power. Staff and resource shortages and lack of training contributed to delays in receiving care. Delay in reaching care was not perceived as a salient barrier. Both community members and healthcare providers expressed interest in receiving training to improve management of emergency conditions.Conclusions The Three Delays Model is a practical framework that can be useful for understanding barriers to emergency care and developing more tailored interventions. In rural Bangladesh, training community members and healthcare providers to recognise symptoms and manage acute conditions can reduce delays in care seeking and receiving adequate care at health facilities.

Maternal mortality remains a major population health problem in the developing world due in part to inadequate healthcare before, during, and after childbirth. Mass media has the potential to disseminate information about maternal healthcare that can improve well-being for mothers and infants, particularly among women with limited educational attainment. This study examines the impact of mass media exposure (e.g., television, radio, and newspaper) and sociodemographic factors on maternal healthcare utilization in four South Asian countries: India, Bangladesh, Nepal, and Pakistan. Analyses use 2014–2017 Demographic and Health Surveys, which are nationally representative of women aged 15–49 years. Results show that maternal healthcare utilization is significantly higher among women exposed to mass media across countries, even after controlling for mother's, husband's, and household sociodemographic factors. Women exposed to mass media are 46–86% more likely to receive antenatal care, 24–53% more likely to deliver their babies by skilled birth attendants, and 36–94% more likely to receive postpartum check-ups across countries. Mother's educational attainment moderates the association between mass media exposure and some maternal healthcare services in three of the four countries. Governments and public health organizations can consider mass media as a key intervention in promoting maternal health in developing contexts.

Introduction: Early identification of chronic kidney disease (CKD) provides valuable opportunities for effective interventions that reduce the risk of outcomes, particularly renal failure. Objectives: This study aimed to screen the Bangladeshi asymptomatic adult population for CKD to identify potential risk factors for its development. Patients and Methods: The screening program was carried out among the 400 subjects in the Thakurgaon district of Bangladesh to identify people with the risk of CKD. All the subjects were asymptomatic and previously been never diagnosed with kidney diseases. Demographic data were collected by a structured questionnaire. Urinary protein was tested by dipstick method, and serum creatinine was measured by an auto-analyzer. Estimated glomerular filtration rate (eGFR) was calculated by using standard formula. CKD was diagnosed and classified according to the National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines. Results: A total of 18.2% respondents were found to have likely CKD to whom 82% were in stage 1 and 18% were in stage 2. The majority of the likely CKD respondents (30.1%) were in age >60 years. The prevalence of proteinuria was significantly (P=0.0001) higher among previously documented CKD patients compared to the control group. Logistic analysis revealed that after adjustments, CKD showed a significant association with diabetes mellitus (ORs: 7.46, P=0.00), smoking (ORs: 2.36, P=0.02), obesity (ORs: 3.98, P=0.00) and hypertension (ORs: 1.16, P=0.66) compared to control. Conclusion: A substantial number of adults were found to be unaware of the existence of CKD hence, large-scale prevention programs should be undertaken to reduce the classical risk factors of these disorders.

Abstract Background Patterns of opioid dispensing often exhibit substantial temporal and geographical variability, which has implications for public health policy decisions and interventions. The study examined recent trends in prescription opioid dispensing and identified high dispensing areas and factors associated with the doses dispensed. Methods Three years (1 January 2013–31 December 2015) of dispensing data of prescription opioids in local government areas (LGAs) for New South Wales (NSW), Australia’s most populous state, were analyzed. The proportion of individuals who were dispensed opioids was computed for four age-groups. A Chi-square test was used to examine trends over time in proportions of the population who were dispensed opioids in four age-groups. The number of prescriptions over time and quantities in daily defined dose/1000 people/day (denoted DDD) were also examined. LGAs with relatively high levels of dispensing were identified and mapped. A multivariate regression model was used to identify factors associated with DDD. Results Overall, codeine, oxycodone and tramadol were the main opioids in terms of DDD, number of prescriptions and number of individuals who were dispensed these medications. Quantity (in DDD), and population dispensed to were consistently higher for women than men over time. Proportions of individuals who were dispensed opioids increased significantly over time in all four age-groups. In the multivariate model, age, urbanization, sex and socio-economic indexes for areas were significantly associated with doses dispensed among opioid users. All areas with very high dispensing were outside major metropolitan areas. Conclusions Given that over-use of opioids is a major public health problem and that long-term use has substantial side effects including dependence, it is important to understand spatial patterns of opioid prescribing to enable targeted interventions. Nationwide implementation of real-time drug-monitoring programs and access to monitoring databases from both doctor and pharmacy point-of-care sources may potentially reduce excessive and undue use of opioid.

Abstract Background Increased awareness amongst large population groups is a major determinant for the prevention of diabetes and its complications as well as related metabolic disorders. Knowledge and attitude are the principal markers of awareness that need to be studied in various population groups in specific racial and cultural contexts. The present study was undertaken to explore knowledge, attitude and practice (KAP) regarding -diabetes mellitus (DM) among nondiabetic (nonDM) and type 2 diabetes mellitus (T2DM) patients in Bangladesh. Methods A cross-sectional study was conducted among 18,697 adults (aged 18 years and above; 7796 male and 10,901 female; 6780 nonDM and 11,917 T2DM) selected purposively from the OPD of 19 healthcare centres in and around Dhaka and in northern parts of Bangladesh. KAP were assessed by a pre-structured, interviewer-administered questionnaire and categorised using predefined scores of poor (mean + 1 SD). Univariate and bivariate statistical analysis were done as appropriate. Multivariate linear regression was done to examine the association between diabetes related KAP and other covariates. Results The mean (±SD) age (years) of all the study participants was 46 ± 14, mean BMI 24.4 ± 4.1 and mean waist-hip ratio (WHR) was 0.93 ± 0.07. The proportion of poor, average and good knowledge scores among T2DM subjects were 17%, 68% and 15% respectively. The corresponding values for attitude score were 23%, 67% and 10% respectively. The KAP regarding diabetes was found to be better among people who were living with diabetes compared to their counterparts. DM males showed better knowledge and practice regarding diabetes, compared to nonDM counterparts (M ± SD; 44.18 ± 16.13 vs 40.88 ± 15.62, p =

Background: Hepatitis C virus (HCV) infection and chronic kidney disease are common and potentially serious medical problems throughout the world. In recent years, it has become clear that these two conditions are linked in several important ways. Indeed, some forms of renal diseases are precipitated by HCV infection and patients with end-stage renal disease (ESRD) are at increased risk for acquiring HCV infection. Patients with chronic kidney disease typically show an impaired immune response compared with healthy individuals and also other risk factors related with treatment and management. CKD patients ultimately undergo end stage renal therapy like dialysis for their treatment and survival. Risk factors for the infections are more in dialysis period than in predialytic stages. Like other developing countries CKD patients with HCV infection are very common in our country. For this reason the CKD patients should be properly diagnosed knowing the infection status before dialysis which would help both the patient and doctor to choose their proper treatment approach. Objective: This cross-sectional study was done to know the prevalence of HCV infection in the CKD patients before starting dialysis therapy. Materials and Methods: A total of 197 patients with chronic kidney disease stage five (CKD-V) before starting dialysis therapy were included as subjects of this study. Among the CKD patients anti-HCV was detected to see prevalence of hepatitis C virus infection. The patients were also tested for HBsAg to assess co-infection. After collecting all the data of different test results analyses were done by SPSS version 15.0. Results: In this study 195 (99%) patients were anti-HCV negative and only two patients (1%) were found positive. Conclusion: HCV infection in CKD patients before dialysis should be taken into account so that HCV negative CKD patients would not get the infection during dialysis and standard screening procedures should be taken to prevent transmission of infection.

This study investigates the socio-economic conditions of the fish farmers and aquaculture status in Parbatipur of Dinajpur district during May to October 2016. Majority of the fish farmers belonged to the age group of 31 to 40 years (44.3%) and represented by 68.6% Muslims. Among them 40% had secondary level of education and most of the families were nuclear (64.3%). Farmers were involved in fish culture as their primary (24%) and secondary occupation (53%). Over 80% of the farmers had electricity facilities and 41% of them received health services from the village doctors. Only 9% of the fish farmers received formal training on fish culture. The average aquaculture pond size was 0.18 ha, where 84% ponds were perennial. The average stocking density was 23208 fingerlings ha–1 whereas organic fertilizer, urea and TSP were applied at 8665.4 kg ha–1 yr–1, 210.1 kg ha–1 yr–1 and 133.5 kg ha–1 yr–1 respectively. Pre and post stocking liming doses were 205.7 kg ha–1 yr–1 and 138.4 kg ha–1 yr–1 respectively. Necessary training facilities with institutional supports, credit facilities and extension services could play an important role in improving the fish production.

Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (R0) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.

Synovial sarcoma, an uncommon cancer, typically affects young adults. Survival rates range from 36% to 76%, decreasing significantly when metastases are present. Synovial sarcomas form in soft tissues, often near bones, with about 10% demonstrating ossification in the tumor. The literature is inconclusive on whether the presence of ossification portends a worse prognosis. To this end, we analyzed our genetic mouse models of synovial sarcoma to determine the extent of ossification in the tumors and its relationship with morbidity. We noted higher ossification within our metastatic mouse model of synovial sarcoma. Not only did we observe ossification within the tumors at a frequency of 7%, but an even higher frequency, 72%, of bone reactivity was detected by radiography. An enrichment of bone development genes was associated with primary tumors, even in the absence of an ossification phenotype. In spite of the ossification being intricately linked with the metastatic model, the presence of ossification was not associated with a faster or worse morbidity in the mice. Our conclusion is that both metastasis and ossification are dependent on time, but that they are independent of one another.

Sphingolipid metabolism is an important process in sustaining the growth needs of rapidly dividing cancer cells. Enzymes that synthesize sphingolipids have become attractive targets in cancer pharmacology. Ceramide is a precursor for synthesizing sphingolipids such as sphingomyelin, sphingosine-1-phosphate, and glucosylceramide. Sphingomyelin synthase (SMS) is the enzyme that transfers a phosphatidylcholine to ceramide to generate sphingomyelin. To test the inhibition of SMS, scientists assess the buildup of ceramide in the cell, which is cytotoxic. Because ceramide is a small lipid molecule, there are limited tools like antibodies to detect its presence. Alternatively, designated machines for small-molecule separation coupled with mass spectrometry detection can be used; however, these can be cost-prohibitive. We used a commercially available NBD-ceramide to apply to human cancer cell lines in the presence or absence of a known SMS inhibitor, jaspine B. After short incubation times, we were able to collect cell lysates and using solvent extraction methods, run the cellular material on a thin-layer chromatography plate to determine the levels of intact fluorescently labeled ceramide. Brighter fluorescence on the TLC plate correlated to greater SMS inhibition. Small molecules can then be screened quantifiably to determine the biological impact of inhibiting the sphingolipid metabolism pathways involving ceramide.