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Abstract Introduction Strengthening and reforming the urban primary healthcare (PHC) system is essential to efficiently deliver need-based healthcare services to the rapidly increasing urban poor population. Such reforms of PHC system need to emphasize the opinion of patients in co-designing services in order that delivery of services can be accessed effectively by the urban population in a timely and low-cost way. Hence, it is important to identify the preference of urban population while choosing healthcare providers. The aim of this proposed protocol is to summarize a planned systematic review of existing evidence on the attributes considered for choosing PHC providers in urban settings of low- and middle-income countries (LMICs), as classified by the World Bank. Methods and analyses An inclusive literature search will be conducted in electronic databases including Pubmed/MEDLINE, Embase, Global Health, Cochrane Library, Web of Science, and Scopus. Databases will be searched from the earliest date of entry until March 30, 2024. Database search will be supplemented by manual search of citations, reference lists, and grey literature sources. Following the pre-set inclusion and exclusion criterion, two researchers will independently screen all the retrieved studies in Covidence. Any discrepancies will be resolved through a discussion between two researchers, and if disagreements persist, a third reviewer will be consulted. The methodological quality of included studies will be appraised using checklist for Conjoint Analysis studies and the Mixed Methods Appraisal Tool (MMAT). An Excel-based data extraction table will be developed, piloted, and refined during the review process. Preference attributes will be identified and analyzed according to their types. The systematic review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta‑Analyses (PRISMA) guidelines. Discussion The identification of attributes, their influence on preference, and heterogeneity with socioeconomic characteristics of the population will help the policymakers and researchers to design targeted PHC interventions. Such evidence will be also useful to design choice experiment studies to quantify the preferred attributes of PHC providers in urban context of LMICs. Systematic review registration PROSPERO CRD42023409720.

Abstract Background Bangladesh is rapidly urbanizing and approximately half of its urban population resides in deprived slums with limited access to basic needs. However, there is a dearth of information on vulnerability levels among slum dwellers. We aimed to assess the level of vulnerability within and between slums via various socioeconomic and health indicators. Methods A cross-sectional survey of 810 randomly selected households was conducted in two purposively selected slums, Korail and Shyampur, in Dhaka from November to December 2021. Data was collected on various indicators, including demographics, education, employment, access to utility, and healthcare services. Principal component factor analysis was employed to identify the key indicators to construct the socioeconomic and health vulnerability index for the urban slums of Bangladesh (SEHVI-BD). Kaiser-Meyer-Olkin, Bartlett’s test, and Cronbach’s alpha coefficient test were used to assess indicators’ suitability. The selected indicators were used to generate an index on a scale of 100, with a higher index value indicating a higher level of vulnerability. The estimated scores were used to categorize the vulnerability status into three levels: mild, moderate, and severe vulnerability. The Mann-Whitney-U test and Kruskal-Wallis test were applied between the generated index and other socioeconomic variables to validate the relationship. Results A total of 27 socioeconomic and health indicators were identified that explained 60% of the variance. The indicators were then grouped into six domains on the basis of their relevance. The prevalence of severe vulnerability in the Korail slum was approximately 9% and moderate vulnerability was 30% whereas these values were approximately 58% and 37%, respectively, in the Shyampur slum. The difference in the vulnerability level between the two slums was also evident across the domains. Households in the poorest wealth quintile, with lower education levels of household heads, and having irregular income experienced higher levels of vulnerability. Conclusions The SEHVI-BD offers a critical tool for policymakers to identify and address vulnerabilities, facilitating more targeted public health interventions in urban Bangladesh and similar low-income settings. This study further emphasizes the importance of integrating comprehensive vulnerability assessments into public health policies to reduce inequalities and improve well-being, especially for the urban marginalized slum population.

BackgroundUpazila Health Complexes (UzHC) serve as the backbone of primary healthcare (PHC) at the sub-district level in Bangladesh, delivering comprehensive healthcare services including both inpatient and outpatient services to the grassroots levels. However, not all the prescribed medicines and diagnostics services are always available at these facilities for outpatient care. This results in out-of-pocket expenditure (OOPE) to the patients for getting prescribed medicines and diagnostics services which has not been properly explored. Thus, we aimed to estimate the overall provider and user costs for outpatient care services at selected UzHCs in Bangladesh.MethodsAn ingredient-based costing approach was applied to estimate the costs for the most commonly reported illnesses at outpatient of UzHCs from a societal perspective. We conducted a health facility survey at four purposively selected UzHCs to estimate provider costs and a patient exit survey among 452 patients of selected illnesses to estimate the user costs. Commonly reported illnesses were identified in consultation with healthcare providers of these facilities. The difference between costs of prescribed and provided medicines at UzHCs was estimated using the market prices. Data was collected between February to March 2021.ResultsThe societal costs of the common outpatient illness or symptoms varied significantly, ranging from BDT 642 to BDT 1,384 per episode. Antenatal care had the highest cost burden at BDT 1,384, followed by respiratory illness at BDT 783 and urinary tract infection at BDT 670. On average, the provider spent BDT 289 for treating an outpatient, while a patient incurred BDT 446 as OOPE. Further, a patient was expected to spend an average of BDT 341 for purchasing medicines not provided from UzHCs.ConclusionOur study found significant gaps between prescribed and provided medicines at UzHCs, leading to higher OOPE for patients. The current healthcare resource allocation strategy does not consider the outpatient load and healthcare demand at PHC facilities, which further exacerbates this gap. Addressing this gap requires a fundamental shift towards a demand-driven resource allocation model within the healthcare financing strategy to improve healthcare access and achieve health for all.

Early detection of colorectal cancer through the proper segmentation of polyps in the colonoscopy images is crucial. Polyps' complex morphology and varied appearances are the greatest obstacles for the segmentation approaches. The paper introduces SAMU-Net, a novel deep learning-based dual-stage architecture consisting of a custom attention-based U-Net and modified Segment Anything Model (SAM) for better polyp segmentation. In our model, we used the custom U-Net architecture with an attention mechanism to obtain polyp segmentation masks as the first stage. This mask is then used to generate a bounding box input for the second stage that contains the modified Segment Anything Model. The modified SAM relies on the use of High-Quality token-based architecture along with global and local properties to segment polyps accurately, even in cases where the shapes and sizes of polyps are diverse and the polyps have different appearances. The efficiency of SAMU-Net generated from four different datasets of colonoscopy images was examined. Our process produced a dice coefficient score of 0.94, which is very impressive and has a considerable improvement over the existing state-of-the-art polyp segmentation methods. Moreover, the qualitative results also visualize that the SAMU-Net is capable of accurately segmenting polyps of wide ranges, thus, it is a relevant tool for computer-aided detection as well as the diagnosis of colorectal cancer.

Increasing crop resilience to drought stress through microorganisms is a sustainable approach. This study evaluated the efficacy of the endophytic fungus Metarhizium anisopliae MetA1 (MA) for improving drought tolerance of barley by analyzing various morphological, physiological, biochemical, and yield factors. Barley grains were treated with MA (1 × 108 spore/ml) and a pot experiment was conducted with three high-yielding barley genotypes: BARI Barley-10, BARI Barley-6, and BARI Barley-9 under three drought conditions: no drought (100 % field capacity, FC), moderate drought (50 % FC), and severe drought (25 % FC). Under drought conditions, MA priming significantly enhanced shoot and root biomass, leaf characteristics, photosynthetic pigment content, and activities of various antioxidant enzymes including superoxide dismutase, catalase, glutathione S-transferase, ascorbate peroxidase, peroxidase, glutathione peroxidase, glutathione reductase, dehydroascorbate reductase, monodehydroascorbate reductase, glyoxalase-I, and glyoxalase-II in all barley genotypes. Furthermore, there was an observed increase in the levels of non-enzymatic antioxidants such as ascorbate and glutathione. MA treatment also led to a significant reduction in stress markers like methylglyoxal, malondialdehyde, lipoxygenase, hydrogen peroxide, and calcium influx along with an increase in proline and potassium content in barley leaves in stressed conditions. Crop growth and yield related attributes in barley were improved, which is evidence of better physiological and biochemical changes under both stress and non-stressed conditions. As such, the study provides evidence suggesting that MA-mediated seed priming is an effective strategy for improving drought tolerance not only in barley but also possibly other crops.

In recent decades, breast cancer has become the most prevalent type of cancer that impacts women in the world, which shows a significant risk to the death rates of women. Early identification of breast cancer might drastically decrease patient mortality and greatly improve the chance of an effective treatment. In modern times, machine learning models have become crucial for classifying cancer and strengthening both the accuracy and efficiency of diagnostic and medical treatment strategies. Therefore, this study is focused on early detection of breast cancer using a variety of machine learning algorithms and desires to identify the most effective feature selection process with an amalgamated dataset. Initially, we evaluated five traditional models and two meta-models on separate datasets. To find the most valuable features, the study used the Least Absolute Shrinkage and Selection Operator (LASSO) as well as SHapley Additive exPlanations (SHAP) selection methods and analyzed them through a wide range of performance regulations. Additionally, we applied these models to the combined dataset and observed that the mergeddataset was significantly beneficial for breast cancer diagnosis. After analyzing the feature selection strategies, it was demonstrated that the majority of models performed more accurately when utilizing SHAP methodologies. Notably, three traditional models and two meta-classifiers obtained an accuracy of 99.82%, demonstrating superior performance compared to state-of-the-art methods. This advancement holds a crucial role as it lays the foundation for refining diagnostic tools and enhancing the progression of medical science in this field.

White blood cells (WBCs) are a vital component of the immune system. The efficient and precise classification of WBCs is crucial for medical professionals to diagnose diseases accurately. This study presents an enhanced convolutional neural network (CNN) for detecting blood cells with the help of various image pre-processing techniques. Various image pre-processing techniques, such as padding, thresholding, erosion, dilation, and masking, are utilized to minimize noise and improve feature enhancement. Additionally, performance is further enhanced by experimenting with various architectural structures and hyperparameters to optimize the proposed model. A comparative evaluation is conducted to compare the performance of the proposed model with three transfer learning models, including Inception V3, MobileNetV2, and DenseNet201.The results indicate that the proposed model outperforms existing models, achieving a testing accuracy of 99.12%, precision of 99%, and F1-score of 99%. In addition, We utilized SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-agnostic Explanations) techniques in our study to improve the interpretability of the proposed model, providing valuable insights into how the model makes decisions. Furthermore, the proposed model has been further explained using the Grad-CAM and Grad-CAM++ techniques, which is a class-discriminative localization approach, to improve trust and transparency. Grad-CAM++ performed slightly better than Grad-CAM in identifying the predicted area's location. Finally, the most efficient model has been integrated into an end-to-end (E2E) system, accessible through both web and Android platforms for medical professionals to classify blood cell.

Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Contaminated water poses a significant public health risk due to waterborne microbial diseases, with typhoid caused by Salmonella Typhi being the most prevalent bacterial waterborne disease in Bangladesh. This study aimed to assess the presence of antibiotic-resistant genes and their patterns in Salmonella Typhi isolated from drinking water sources in Chattogram City, Bangladesh. Out of 150 samples analyzed, 10 isolates were suspected to be Salmonella Typhi by selective plating and biochemical test and 8 were confirmed using PCR amplification of the fliC gene and subsequent sequencing. Phylogenetic analysis provided insights into the genetic diversity and relationships among the identified isolates. The study revealed the presence of Salmonella Typhi in certain drinking water sources, indicating a potential threat to public health. The presence of antibiotic-resistant genes, including sul2, int1, tem, and gyrA, in the genomic and plasmid DNA of Salmonella Typhi isolates emphasizes the role of contaminated water in spreading antibiotic resistance. All 8 isolates were found to contain at least one antibiotic-resistant gene highlighting the widespread presence of resistance elements. This raises serious concerns about the potential impact on the effectiveness of typhoid treatment. Antibiotic susceptibility testing using the disk diffusion assay confirmed multidrug resistance (MDR) in 87.5 % of the Salmonella Typhi isolates, highlighting the urgent need to address the transmission of antibiotic-resistant genes through drinking water sources. The results confirm the widespread environmental transmission of antibiotic resistance in the region and emphasize the potential consequences for the efficacy of typhoid treatment.

This article presents in-depth information on the structural and optoelectronic properties of MgLiX3 (X = Cl, Br, and I) perovskites, and it suggests that MgLiX3 perovskites are promising materials for use in a variety of optoelectronic gadgets. The structural and optoelectronic properties of the compounds are determined utilizing first-principles calculations, with the density functional theory applied through the WIEN2k code. The structural stability was verified by computing the formation energy and binding energy. This study investigated the behavior of electronic conductivity and determined the bandgap values by employing TB-mBJ, which are 3.354 eV (MgLiCl3), 1.728 eV (MgLiBr3), and 0.067 eV (MgLiI3). Furthermore, optical properties such as absorption coefficient, reflectivity, conductivity, loss function, dielectric function, refractive index, and extinction coefficient were calculated and analyzed. In the visible range, MgLiBr3 and MgLiI3 exhibit their primary highest peaks of the absorption coefficient, which are 8.8 × 104 cm−1 for MgLiBr3 and 7.7 × 104 cm−1 for MgLiI3. On the other hand, MgLiCl3 demonstrates its initial highest peaks in the UV range, that is, 9 × 104 cm−1. The findings indicate that among the compounds studied, MgLiBr3 shows promise as a candidate for manufacturing solar cell devices based on the SQ limit, bandgap for typical perovskites (within 0.8–2.2 eV), and absorption in the visible range. MgLiCl3 is suitable for manufacturing several optoelectronic devices, such as laser diodes (LDs) and UV sensors due to having a high absorption coefficient in the ultraviolet region. With its low energy bandgap and high absorption coefficient in the IR to VR regions, MgLiI3 is well-suited for manufacturing photodetectors, LEDs, and other optoelectronic devices.

We estimated mud crab (Scylla olivacea) population parameters to determine recruitment patterns, asymptotic carapace width (CW∞), growth coefficient (k), mortality rate (Z, M, and F), exploitation rate (E) and relationship between body weight and carapace width (BW - CW) in the terrestrial edge of the Sundarbans mangrove forest, Bangladesh. Baited long lines and traps were used to collect crab samples from the terrestrial edge of the mangroves (up to 10 km inside of the Sundarbans mangroves) monthly for a year (Jan 2021–Dec 2021). These CW frequency data were utilized to determine population characteristics. Based on the BW × CW relationship, male S. olivacea crabs increased their BW faster (b = 2.89, R2 = 0.86) than females (b = 2.29, R2 = 0.82). Crabs displayed negative growth allometry, b significantly differed from the isometric growth (b = 3). Both male and female CW∞ was estimated to be 126.00 mm and 113.40 mm, with k values of 0.50 yr−1 and 0.48 yr−1, respectively. The total mortality (Z), natural mortality (M), and fishing mortality (F) of male and female crabs were 1.89 yr−1 and 1.84 yr−1, 0.79 yr−1 and 0.80 yr−1 and 1.10 yr−1 and 1.04 yr−1 respectively. Both sexes were recruited using a bimodal recruitment strategy, with a significant peak from March to June. However, young crabs were present year-round. The virtual population analysis (VPA) shows that males and females are caught at smaller sizes (60–90 mm). The estimated exploitation rate (E) exceeds (0.58 for males and 0.57 for females) the optimum exploitation level (E = 0.50) and E values were close to the projected maximum yield per exploitation rate (Emax = 0.655 for males and 0.647 for females) which indicated over-exploitation of the mud crabs from the territorial edge of the Sundarbans mangrove forest of Bangladesh and hence suggested for proper management.

Microorganisms offer a sustainable way to increase crop production and promote eco-friendly farming. The endophytic fungus Metarhizium anisopliae is known for its multiple roles in plant ecosystems, including plant protection, symbiosis, and abiotic stress mitigation. In this study, we evaluated the potential of seed priming with M. anisopliae isolate MetA1 (MA) to enhance germination, photosynthetic efficiency, growth, and yield of two wheat varieties, BARI Gom 26 (BG26) and BARI Gom 33 (BG33) under field conditions. The study demonstrated that MA seed priming significantly improved wheat germination (by 13% and 26.04%) of BG26 and BG33, respectively. Overall, photosynthetic performance, indicated by increased leaf angle, leaf thickness, relative chlorophyll content, and linear electron flow (LEF), quantum yield of Photo System II (Phi2) was increased in MA primed wheat plants, while reducing non-photochemical quenching like NPQt, PhiNO, PhiNPQ of both varieties. These enhancements were attributed to increased shoot biomass (by 215.64% for BG26 and 280.38% for BG33), root biomass (by 141.79% for BG26 and 207.4% for BG33), effective tiller percentage (by 9.17% for BG26 and 5.7% for BG33), spike length (by 25.05% for BG26 and 25.42% for BG33), grain yield parameters such as filled grain percentage (by 23.8% for BG26 and 12.5% for BG33), and grain weight per plant (by 168.62% for BG26 and 119.62% for BG33). The findings of the research demonstrated the potential of M. anisopliae for field use in an agricultural setting, providing a sustainable means of increasing food production.

Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.

Objective The COVID-19 pandemic imposed unprecedented challenges to health systems globally. This study explored slum dwellers’ experience of receiving essential health services during the pandemic and the challenges faced by healthcare providers in urban areas of Bangladesh.Design The study followed a cross-sectional study design using qualitative methods.Setting The study was conducted in Dhaka and Gazipur City Corporations during November 2020–February 2021.Participants 17 key informant interviews were carried out with healthcare providers and policy-makers and 22 in-depth interviews were carried out with slum dwellers. Thematic analysis was performed.Results The study identified challenges to the provision of essential healthcare in selected areas of Dhaka and Gazipur City Corporations during the COVID-19 pandemic. The lack of information on the availability of functional healthcare facilities, fear of contracting COVID-19 and restrictions on movement and transportation, resulted in delays in seeking essential healthcare during a pandemic. Access to healthcare facilities was further hindered by various hospitals’ decision to refuse care to general patients without valid, negative COVID-19 test results. Healthcare providers identified patients’ tendency to hide COVID-19 symptoms as a barrier to providing healthcare services to general patients. Conversely, patients concealed their symptoms to avoid COVID-19 tests and gain access to required treatment. In addition, the reallocation of human resources for COVID-19 treatment disrupted the delivery of essential health services.Conclusion The pandemic affected the accessibility of the slum population to essential healthcare and disrupted health service delivery. The findings of the study have highlighted gaps in the health system during an emergency response period like COVID-19. The study will assist the government and other stakeholders in designing tailored interventions and allocating resources in a more efficient manner to ensure universal health coverage in the face of health emergencies.

Healthy skin reflects a healthy microbiome and vice versa. The contemporary society, marked by a sharp increase in skin irritation cases, has compelled researchers, dermatologists, and the cosmetics industry to investigate the correlation between skin microbiomes and the use of skincare products. Different cosmetics can change skin's normal flora to a varying degree –some changes can be detrimental, there are also instances where these alterations aid in restoring the skin microbiome. Previous studies using artificial skin models, metagenomic analysis, and culture-based approaches have suggested that skincare products play an important role in skin microbial alteration. This article assessed current knowledge on microbial shifts from daily use of various personal and skincare products. We have also introduced a readily applicable framework, synthesized from various observations, which can be employed to identify the normal skin microbiome and evaluate the impact of personal care and skincare products on it. We also discussed how lifestyle choice remake skin microbial makeup. Future studies are warranted to examine the effect of personal and skincare product usage on skin microbiome across various age groups, genders, and body sites with a multi-study approach.

Graph neural network (GNN) is a formidable deep learning framework that enables the analysis and modeling of intricate relationships present in data structured as graphs. In recent years, a burgeoning interest has arisen in exploiting the latent capabilities of GNN for healthcare-based applications, capitalizing on their aptitude for modeling complex relationships and unearthing profound insights from graph-structured data. However, to the best of our knowledge, no study has systemically reviewed the GNN studies conducted in the healthcare domain. This study has furnished an all-encompassing and erudite overview of the prevailing cutting-edge research on GNN in healthcare. Through analysis and assimilation of studies, current research trends, recurrent challenges, and promising future opportunities in GNN for healthcare applications have been identified. China emerged as the leading country to conduct GNN-based studies in the healthcare domain, followed by the USA, UK, and Turkey. Among various aspects of healthcare, disease prediction and drug discovery emerge as the most prominent areas of focus for GNN application, indicating the potential of GNN for advancing diagnostic and therapeutic approaches. This study proposed research questions regarding diverse aspects of GNN in the healthcare domain and addressed them through an in-depth analysis. This study can provide practitioners and researchers with profound insights into the current landscape of GNN applications in healthcare and can guide healthcare institutes, researchers, and governments by demonstrating the ways in which GNN can contribute to the development of effective and efficient healthcare systems.

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.

A novel automated multi-classification approach is proposed for the anticipation of lung abnormalities using chest X-ray and CT images. The study leverages a publicly accessible dataset with an insufficient and unbalanced number of images, addressing this issue by employing the data augmentation approach DCGAN to balance the dataset. Various preprocessing procedures are applied to improve features and reduce noise in lung pictures. As the base for the model, the vision trans-former and convolution-based compact convolutional transformer (CCT) model is utilized. To determine the best model configuration, an ablation study is performed on the original CCT model using a CT scan dataset with image dimensions of $32\times32$ . Following that, this model is trained on the X-ray dataset to evaluate performance on an entirely other modality. The performances are compared to six pre-trained models with $32\times 32$ images. While traditional models achieved modest performance, with test accuracies ranging from 43% to 77% and 49% to 73% requiring lengthy training times, the suggested model performed exceptionally well, obtaining test accuracies of 99.77% and 95.37% for CT and X-ray, respectively with a short training duration of 10–12 and 40–42 seconds/epoch. Robustness is demonstrated through the progressive reduction of the number of training images, with findings indicating that the model maintains good performance even on a reduced dataset. An explainable AI technique Grad-CAM is used to explain the model’s judgment. Grad-CAM-based color visualization is shown to explain model assessments and help health specialists make quick, confident decisions. This study used image preprocessing and deep learning techniques to detect lung anomalies, and it addressed the challenges of training time and computational complexity.

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.

Different physical properties of ternary intermetallic La-based superconducting materials LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) are examined through the self consistent ab-initio scheme depending on the DFT replication. The main purpose of this study is to comprehend several different crucial characteristics of these compounds, including their crystal phase, stability manner, bulk features, band configuration, dielectric, conductivity as well as thermal features. Good accordance in the structural analysis of superconducting materials LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) has been observed between the optimized and synthesized lattice parameters. The phonon dispersion curve indicates that the compounds LaCo2Si2, LaCu2Si2, LaRh2Si2, LaPd2Si2, and LaIr2Si2 are dynamically stable, but LaAg2Si2, LaPt2Si2, and LaAu2Si2 are dynamically unstable in nature. Mechanical crystal constancy of the selected materials is verified from the investigated elastic constants. Ductility behaviors of these compounds are ensured by the values of Pugh's and Poisson's ratios. High Young's modulus of LaIr2Si2 ensured its elevated hardness along with huge melting temperature compared than other phases. Anisotropic character of these materials can be ensured through the calculations of elastic anisotropy. The electrical properties of the superconducting LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) compounds were also studied in details. Metallic character of the selected solids is observed through the investigation of DOS and band structure calculations. The investigated dielectric function, conductivity, and absorption also reflect the conductive behavior of superconducting LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) materials. The optical phenomena ensured their huge absorption as well as optical conductive nature at the site of ultraviolet energy region. Moreover, high reflectivity in the UV region makes these superconductors good solar reflectors for this energy site. Very low minimum thermal conductivity of these compounds especially of LaPt2Si2 and LaAu2Si2 makes them useful and fascinating for thermal barrier coating (TBC) materials.

BackgroundSince 2016, the government of Bangladesh has been piloting a health protection scheme known as Shasthyo Surokhsha Karmasuchi (SSK), which specifically targets households living below the poverty line. This noncontributory scheme provides enrolled households access to inpatient health care services for 78 disease groups. Understanding patients’ experiences with health care utilization from the pilot SSK scheme is important for enhancing the quality of health care service delivery during the national-level scale-up of the scheme. ObjectiveWe aimed to evaluate patient satisfaction with the health care services provided under the pilot health protection scheme in Bangladesh. MethodsA cross-sectional survey was conducted with the users of the SSK scheme from August to November 2019. Patients who had spent a minimum of 2 nights at health care facilities were selected for face-to-face exit interviews. During these interviews, we collected information on patients’ socioeconomic characteristics, care-seeking experiences, and level of satisfaction with various aspects of health care service delivery. To measure satisfaction, we employed a 5-point Likert scale (very satisfied, 5; satisfied, 4; neither satisfied nor dissatisfied, 3; dissatisfied, 2; very dissatisfied, 1). Descriptive statistics, statistical inferential tests (t-test and 1-way ANOVA), and linear regression analyses were performed. ResultsWe found that 55.1% (241/438) of users were either very satisfied or satisfied with the health care services of the SSK scheme. The most satisfactory indicators were related to privacy maintained during diagnostic tests (mean 3.91, SD 0.64), physicians’ behaviors (mean 3.86, SD 0.77), services provided at the registration booth (mean 3.86, SD 0.62), confidentiality maintained regarding diseases (mean 3.78, SD 0.72), and nurses’ behaviors (mean 3.60, SD 0.83). Poor satisfaction was identified in the interaction of patients with providers about illness-related information (mean 2.14, SD 1.40), availability of drinking water (mean 1.46, SD 0.76), cleanliness of toilets (mean 2.85, SD 1.04), and cleanliness of the waiting room (mean 2.92, SD 1.09). Patient satisfaction significantly decreased by 0.20 points for registration times of 16-30 minutes and by 0.32 points for registration times of >30 minutes compared with registration times of ≤15 minutes. Similarly, patient satisfaction significantly decreased with an increase in the waiting time to obtain services. However, the satisfaction of users significantly increased if they received a complete course of medicines and all prescribed diagnostic services. ConclusionsMore than half of the users were satisfied with the services provided under the SSK scheme. However, there is scope for improving user satisfaction. To improve the satisfaction level, the SSK scheme implementation authorities should pay attention to reducing the registration time and waiting time to obtain services and improving the availability of drugs and prescribed diagnostic services. The authorities should also ensure the supply of drinking water and enhance the cleanliness of the facility.

Background: Xanthomonas oryzae pv. oryzae is a plant pathogen responsible for causing one of the most severe bacterial diseases in rice, known as bacterial leaf blight that poses a major threat to global rice production. Even though several experimental compounds and chemical agents have been tested against X. oryzae pv. oryzae, still no approved drug is available. In this study, a subtractive genomic approach was used to identify potential therapeutic targets and repurposible drug candidates that could control of bacterial leaf blight in rice plants. Results: The entire proteome of the pathogen underwent an extensive filtering process which involved removal of the paralogous proteins, rice homologs, non-essential proteins. Out of the 4382 proteins present in Xoo proteome, five hub proteins such as dnaA, dnaN, recJ, ruvA, and recR were identified for the druggability analysis. This analysis led to the identification of dnaN-encoded Beta sliding clamp protein as a potential therapeutic target and one experimental drug named [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid that can be repurposed against it. Molecular docking and 100 ns long molecular dynamics simulation suggested that the drug can form stable complexes with the target protein over time. Conclusion: Findings from our study indicated that the proposed drug showed potential effectiveness against bacterial leaf blight in rice caused by X. oryzae pv. oryzae. It is essential to keep in consideration that the procedure for developing novel drugs can be challenging and complicated. Even the most promising results from in silico studies should be validated through further in vitro and in vivo investigation before approval.

The adsorption performance, kinetics, and thermodynamic parameters of reactive dyes on chitosan-treated cotton fabric are described in this research. The batch experiment was carried out to investigate the influence of pH, time, temperature, dye concentration, and material to liquid ratio of Remazol Red RR and Remazol Yellow RR, two widely used reactive dyes. The adsorption of red and yellow dyes was observed at λmax 517 and 419 nm respectively. For both Remazol Red RR and Remazol Yellow RR reactive dyes with starting dye concentrations of 0.0667 and 40 mg/L, the greatest dye adsorption was reported at pH 11 (0.0009 and 0.1284 mg/g) and pH 4 (0.0004 and 0.1038 mg/g). The adsorption performance was investigated using the Langmuir and Freundlich isotherms. The adsorption behaviours of the two reactive dyes closely matched to the Langmuir adsorption isotherm (R2>0.98) than Freundlich isotherm (R2>0.92). The pseudo-second-order kinetic model (R2>0.99) was followed by the dyeing kinetics of modified cotton sample. Thermodynamic investigation was covered with enthalpy change (ΔH⁰), Gibbs free energy (ΔG⁰) and entropy change (ΔS⁰) where all the values were reported positive confirming that the adsorption process was non-spontaneous, endothermic and the higher affinity of reactive dye on chitosan treated cotton.

Farmers in Bangladesh are enthusiastic in dragon fruit farming as a new and promising crop that offers both difficulties and potential. The purpose of the study was to examine the advantages and disadvantages of this novel fruit from the perspective of growers. It is a descriptive survey research which was conducted in purposively selected four Upazilas of Chapainawabganj region of Bangladesh including Chapainawabganj Sadar, Gomastapur, Nachol, and Godagari from 16th April to 25th May, 2022. Pearson’s correlation test was administered to determine the relationship between socio-demographic characteristics of the growers and their knowledge on dragon fruit production. Socio-demographic profile of the randomly selected growers of the study revealed that majority of the respondents were in their middle age, highly educated, had a medium annual income with medium sized land, little farming experience, less training and higher cosmopolitan behavior. Majority of the dragon fruit growers showed high level of understanding regarding dragon fruit cultivation and their fellow farmers and mass media were determined to be the most effective sources of information. According to the study’s findings, the majority of farmers were inspired to grow dragon fruit because it is more resistant to pests and diseases, promotes excellent health, yields for a long time, and can be grown from both seeds and cuttings. Furthermore, the fruit has a higher profit margin than other field crops. Weeds, bird invasions, high labor expenses, lack of technical knowledge of farmers on dragon fruit farming were recognized as major obstacles to fruit production. According to a correlation analysis, farmers’ age, extension contacts, and cosmopolitan nature were significantly and positively correlated with their knowledge of growing dragon fruit. More government support, especially from the Department of Agricultural Extension (DAE), and fixing the issues described above could increase the fruit’s future prospects in the country.

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.

Introduction: Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose: The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study. Method: Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images. Result: The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset. Conclusion: The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.

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.

This article represents a research study about the effect of compression temperature on the performance of Zinc Oxide (ZnO)-based dye-sensitized solar cell (DSSC). To find the optimum compression temperature, the electrodeposited photoanodes are subjected to compression at 60 MP with various compression temperatures ranging from room temperature to 80 °C. The performance analysis involved the analysis of Electrochemical Impedance Spectroscopy (EIS) and photocurrent-voltage (I-V) data under dark and illuminated conditions. The EIS data are examined to gain insights into the electron transport mechanism and validate the cell's performance under optimum compression temperature. The findings of this study demonstrate that cells prepared at 60 MP with a compression temperature of 70 °C show the most favorable photovoltaic performance compared to cells prepared at other compression temperatures. Thickness measurement confirms that increasing the compression temperature ensures a compact layer of photoelectrode. A compression temperature greater than 70 °C causes several defects on the photoelectrode surface, as confirmed by the Scanning Electron Microscopy Image. EIS and I-V data confirm that the cell prepared at 60 MP and 70 °C heating gives comparatively lower series resistance and higher shunt resistance. Though the series and shunt resistance exhibited different values under dark and illumination conditions, their trends remained consistent. Under this optimized compression temperature the cells achieved a maximum efficiency (η) of 2.78%, accompanied by an open circuit voltage (Voc) of 0.58 V, a photocurrent density (Jsc) of 8.87 mA/cm2, and a fill factor of 0.54.

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.

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

Abstract Background Financing healthcare through out-of-pocket (OOP) payment is a major barrier in accessing healthcare for the poor people. The Health Economics Unit (HEU) of the Ministry of Health and Family Welfare of the government of Bangladesh has developed Shasthyo Suroksha Karmasuchi (SSK), a health protection scheme, with the aim of reducing OOP expenditure and improving access of the below-poverty-line (BPL) population to healthcare. The scheme started piloting in 2016 at Kalihati sub-district of Tangail District. Our objective was to assess healthcare utilization by the enrolled BPL population and to identify the factors those influencing their utilization of the scheme. Method A cross-sectional household survey was conducted from July to September 2018 in the piloting sub-district. A total of 806 households were surveyed using a semi-structured questionnaire. Information on illness and sources of healthcare service were captured for the last 90 days before the survey. Multiple logistic regression models were applied to determine the factors related to utilization of healthcare from the SSK scheme and other medically trained providers (MTPs) by the SSK members for both inpatient and outpatient care. Result A total of 781 (24.6%) people reported of suffering from illness of which 639 (81.8%) sought healthcare from any sources. About 8.0% (51 out of 639) of them sought healthcare from SSK scheme and 28.2% from other MTPs within 90 days preceding the survey. Households with knowledge about SSK scheme were more likely to utilize healthcare from the scheme and less likely to utilize healthcare from other MTPs. Non-BPL status and suffering from an accident/injury were significantly positively associated with utilization of healthcare from SSK scheme. Conclusion Among the BPL population, healthcare utilization from the SSK scheme was very low compared to that of other MTPs. Effective strategies should be in place for improving knowledge of BPL population on SSK scheme and the benefits package of the scheme should be updated as per the need of the target population. Such initiative can be instrumental in increasing utilization of the scheme and ultimately will reduce the barriers of OOP payment among BPL population for accessing healthcare.

Natural colourants were widely employed in the ancient world to colour cotton garments. With the revolution of synthetic dyes, natural dyes have lost their position in respect of some performance. However, nowadays the demand for sustainable and eco-friendly products is increasing worldwide due to the awareness of ecological balance. The purpose of this study is to utilize natural colourants in printing on cotton fabric. In this regard, the colourants were extracted from myrobalan fruit, pomegranate peel and catechu, which were eventually applied to the fabric by screen printing technique. The print paste was prepared with carboxymethyl cellulose (CMC) as a natural thickener and polyvinyl alcohol (PVA) as a synthetic thickener. The stability of the print paste was studied with respect to time elapse by viscosity measurement. The depth of shade and colour fastness of the printed fabric were measured. K/S value was evaluated with respect to the stability of print paste. The viscosity was changed slightly in both the CMC and PVA thickener with respect to the stability of the print paste. The washing fastness grades for colour staining wash ranged from 4 to 5, rubbing fastness grades of dry rubbing also ranged from 4 to 5, wet rubbing fastness was 3/4 to 4/5 as well as perspiration fastness grades in alkaline, and in acidic it was 3/4 to 5. Thus, myrobalan fruit, pomegranate peel and catechu have shown excellent performance in printing on cotton textiles, which could be an alternative to the synthetic counterpart.

This article addresses the response of GLAss fiber REinforced aluminum to hypervelocity impacts of micrometeoroid analogs at impact velocities of 7 km/s and beyond. In relation, the damage modes of different GLAss fiber REinforced aluminum configurations have been exemplified. The GLAss fiber REinforced aluminum configurations comprised six to twelve variably thick aluminum layers and up to four plies of glass fiber reinforced epoxy per composite laminate. Hypervelocity impact experiments have been conducted with the help of a two-stage light-gas gun, wherein aluminum- and stainless steel projectiles were launched at velocities up to 7.15 km/s. Visual inspection of the damage area suggested the dissipation of impact energy in elastic-plastic deformation, petalling, delamination, debonding, tensile failure of fibers, and pyrolysis of epoxy. A prevailing damage mode was not apparent albeit. The quasi-isotropic ply orientation of S2-glass/FM94-epoxy laminates promoted the interference of shock- and rarefaction waves and suppressed the damage area of GLAss fiber REinforced aluminum. To discriminate between the impact performance of different GLAss fiber REinforced aluminum configurations, the energy dissipated in different damage modes of GLAss fiber REinforced aluminum has been assessed quantitatively. In terms of normalized energy, the cross-ply GLAss fiber REinforced aluminum dissipated higher energy in petal formation than in other primary damage modes. The normalized petalling energy was found to decline with the increase of impact energy. The outcomes of this study will help to optimize the GLAss fiber REinforced aluminum laminate, which will be employed as a bumper shield to prevent the fatal damage and the unzipping of a spacecraft pressure bulkhead.

Identification of brain tumors at an early stage is crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges of tumor biopsies, three dimensional (3D) Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors using deep learning. In this study, three BraTS datasets are employed to classify brain tumor into two classes where each of the datasets contains four 3D MRI sequences for a single patient. This research is composed of two approaches. In the first part, we propose a hybrid model named TimeDistributed-CNN-LSTM (TD- CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) where each layer is wrapped with a TimeDistributed function. The objective is to consider all the four MRI sequences of each patient as a single input data because every sequence contains necessary information of tumor. Therefore, the model is developed with optimal configuration performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each of the MRI sequences to compare the performance. Moreover, the datasets are preprocessed to ensure highest performance. Results demonstrate that the TD-CNN-LSTM network outperforms 3D CNN achieving the highest test accuracy of 98.90%. Later, to evaluate the performance consistency, the TD-CNN-LSTM model is evaluated with K-fold cross validation. The approach of putting together all the MRI sequences at a time with good generalization capability can be used in future medical research which can aid radiologists in tumor diagnostics effectively.

The several physical features including structural, mechanical, electronic, optical and thermal properties of Pt3Ti and Pt3Cu have investigated here hypothetically for the first time. The possible results of Pt3Ti and Pt3Cu have been compared to other pt-based compounds for reliability of this work. The DFT simulation depends on ab-initio manner through CASTEP regulation is utilized for all of the investigation. The structural properties of these materials are in better agreement with the test data already available. The calculated elastic constants demonstrate mechanical stability for all the compounds. The computed Poisson's as well as Pugh's ratios demonstrate the behavior of ductility of these phases, with Pt3Cu is being far more ductile over Pt3Ti. The large Young’s modulus and hardness value of Pt3Ti ensured that Pt3Ti is harder material than Pt3Cu. The high machinability index of Pt3Cu ensured that this phase has high damage-tolerant capability and useful application in industry. The anisotropic factor and universal index of anisotropy ensure the elastic nature of anisotropic for all of these phases. The study of density of states (both TDOS and PDOS) as well as electronic band structures evidences the metallic behavior for these materials. The optical features of compounds Pt3Ti and Pt3Cu have also been investigated through CASTEP code for the first time and recommended that these phases have huge application in solar cell to reduce extra solar heat. The calculated Debye and melting temperature ensured that Pt3Ti is more thermally conductive and can be used in high temperature structural materials. The very low minimum thermal conductivity ensured that Pt3Cu can be used as a better Thermal Barrier Coating (TBC) material than Pt3Ti.

BackgroundBangladesh National Tuberculosis (TB) Control Programme (NTP) has deployed improved diagnostic technologies which may drive up the programme costs. We aimed to estimate the supply-side costs associated with the delivery of the NTP and the funding gap between the cost of implementation and available funding for the Bangladesh NTP.MethodsAn ingredient-based costing approach was applied using WHO's OneHealth Tool software. We considered 2016, as the base year and projected cost estimates up to 2022 using information on NTP planned activities. Data were collected through consultative meetings with experts and officials/managers, review of documents and databases, and visits to five purposively selected TB healthcare facilities. The estimated costs were compared with the funds allocated to the NTP between 2018 and 2022 to estimate the funding gap.FindingsThe estimated total cost of NTP was US$ 49.22 million in 2016, which would increase to US$ 146.93 million in 2022. Human resources (41.1%) and medicines and investigations/ supplies (38.0%) were the major two cost components. Unit costs were highest for treating extensively drug-resistant TB at US$ 7,422.4 in 2016. Between 2018-2022, NTP would incur US$ 536.8 million, which is US$ 235.18 million higher than the current allocation for NTP.ConclusionOur results indicated a funding gap associated with the NTP in each of the years between 2018-2022. Policy planners should advocate for additional funding to ensure smooth delivery of TB services in the upcoming years. The cost estimates of TB services can also be used for planning and budgeting for delivering TB services in similar country contexts.

Shigellaa Gram-negative, non-motile bacillus, is the primary causative agent of the infectious disease shigellosis, which kills 1.1 million people worldwideevery year. The children under the age of five are primarily the victims of this disease. This study has been conducted to assess the prevalence of shigellosis through selective plating, biochemical test and conventional PCR assays, where the samples were collected from suspected diarrheoal patients. Invasive plasmid antigen H (ipaH) and O-antigenic rfc gene were used to identify Shigella spp. and S. flexneri respectively. For validation of these identification, PCR product of ipaH gene of a sample (Shigella flexneri MZS 191) has been sequenced and submitted to NCBI database (GenBank accession no- MW774908.1). Further this strain has been used as positive control. Out of 204, around 14.2% (n = 29)(P> 0.01) pediatric diarrheoal cases were screened as shigellosis. Another interesting finding was that most of shigellosis affected children were 7 months to 1 year (P> 0.01).The significance of this study lies in the analyses of the occurrenceand the molecular identification of Shigellaspp. and S. flexneri that can be utilized in improving the accurate identification and the treatment of the most severe and alarming shigellosis.

RNA interference (RNAi) regulates a variety of eukaryotic gene expressions that are engaged in response to stress, growth, and the conservation of genomic stability during developmental phases. It is also intimately connected to the post-transcriptional gene silencing (PTGS) process and chromatin modification levels. The entire process of RNA interference (RNAi) pathway gene families mediates RNA silencing. The main factors of RNA silencing are the Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families. To the best of our knowledge, genome-wide identification of RNAi gene families like DCL, AGO, and RDR in sunflower (Helianthus annuus) has not yet been studied despite being discovered in some species. So, the goal of this study is to find the RNAi gene families like DCL, AGO, and RDR in sunflower based on bioinformatics approaches. Therefore, we accomplished an inclusive in silico investigation for genome-wide identification of RNAi pathway gene families DCL, AGO, and RDR through bioinformatics approaches such as (sequence homogeneity, phylogenetic relationship, gene structure, chromosomal localization, PPIs, GO, sub-cellular localization). In this study, we have identified five DCL (HaDCLs), fifteen AGO (HaAGOs), and ten RDR (HaRDRs) in the sunflower genome database corresponding to the RNAi genes of model plant Arabidopsis thaliana based on genome-wide analysis and a phylogenetic method. The analysis of the gene structure that contains exon-intron numbers, conserved domain, and motif composition analyses for all HaDCL, HaAGO, and HaRDR gene families indicated almost homogeneity among the same gene family. The protein-protein interaction (PPI) network analysis illustrated that there exists interconnection among identified three gene families. The analysis of the Gene Ontology (GO) enrichment showed that the detected genes directly contribute to the RNA gene-silencing and were involved in crucial pathways. It was observed that the cis-acting regulatory components connected to the identified genes were shown to be responsive to hormone, light, stress, and other functions. That was found in HaDCL, HaAGO, and HaRDR genes associated with the development and growth of plants. Finally, we are able to provide some essential information about the components of sunflower RNA silencing through our genome-wide comparison and integrated bioinformatics analysis, which open the door for further research into the functional mechanisms of the identified genes and their regulatory elements.

Early identification and adequate treatment can help prevent lung disorders from becoming chronic, severe, and life-threatening. X-ray images are commonly used and an automated and effective method involving deep learning techniques can potentially contribute to quick and accurate diagnosis of lung disorders. However, in the study of medical imaging using deep learning, two obstacles limit interpretability. One is an insufficient and imbalanced number of training samples in most medical datasets. The other is excessive training time. Although training time can be reduced by decreasing the number of pixels in the images, training with low resolution images tends to result in poor performance. This study represents a solution to overcome these impediments by balancing the number of images and reducing overall processing time while preserving accuracy. The dataset used in this research contains an unequal number of images in the different classes. The quantity of data in the classes is balanced by creating synthetic images based on the patterns and characteristics of the original images, using a Deep Convolutional Generative Adversarial Network (DCGAN). Unwanted regions are removed from the X-ray images, the brightness and contrast of the images are enhanced, and the abnormalities are highlighted by using different artifact removal, noise reduction, and enhancement techniques. We propose a Modified Compact Convolutional Transformer (MCCT) model using 32 × 32 sized images for the categorization of lung disorders into four classes. An ablation study of eleven cases is employed to adjust several hyper parameters and layer topologies. This reduces training time while preserving accuracy. Six transfer learning models, VGG19, VGG16, ResNet152, ResNet50, ResNet50V2, and MobileNet are applied with the same image size the performance is compared with the proposed MCCT model. Our MCCT model records the greatest test accuracy of 95.37%, requiring a short training time, 10-12 s/epoch, whereas the other models only reach near-moderate performance with accuracies ranging from 43% to 79% and training times of 80-90 s/epoch. The robustness of the model with regards to the number of training samples is validated by training the model multiple times reducing the number of training images gradually from 49621 images to 6204 images. Results suggest that even with a smaller dataset, the performance is sustained. Our proposed approach may contribute to an effective CAD based diagnostic system by addressing the issues of insufficient and imbalanced numbers of medical images, excessive training times and low-resolution images.

Background: Diarrhea is a leading cause of morbidity and mortality among under-five children in Bangladesh. Hospitalization for diarrhea can pose a significant burden on households and health systems. The aim of this study was to estimate the cost of illness due to diarrhea from the healthcare facility, caregiver, and societal perspectives in Bangladesh. Method: A cross-sectional study with an ingredient-based costing approach was conducted in 48 healthcare facilities in Bangladesh. In total, 899 caregivers of under-five children with diarrhea were interviewed face-to-face between August 2017 and May 2018, followed up over phone after 7–14 days of discharge, to capture all expenses and time costs related to the entire episode of diarrhea. Results: The average cost per episode for caregivers was US$62, with $29 direct and $34 indirect costs. From the societal perspective, average cost per episode of diarrhea was $71. In 2018, an estimated $79 million of economic costs were incurred for treating diarrhea in Bangladesh. Using 10% of income as threshold, over 46% of interviewed households faced catastrophic expenditure from diarrheal disease. Conclusion: The economic costs incurred by caregivers for treating per-episode of diarrhea was around 4% of the annual national gross domestic product per-capita. Investment in vaccination can help to reduce the prevalence of diarrheal diseases and avert this public health burden.

The key work of this research is to implement the ‘Fourth Industrial Revolution’ by using the internet of things (IoT) which can be applied to flammable gas condition monitoring and detection of gas leakage and activate fire extinguisher in case of fire accidents. Internet of things enabled the sensors to transmit LPG concentration value reading to the mobile app or cloud server and control actuators by connecting all in the same network. In case of a fire accident, the solenoid valve will be opened automatically or can be opened by an android application manually which will be pre-installed in mobile phones. Another advantage of this system is, the gas cylinder or flammable particle source can be closed by closing the solenoid valves attached to their outlets. The first challenge of industry 4.0 is to develop a cyber-physical system where all physical entities can be monitored and controlled over the internet or another way remotely or from a single point. This fire extinguisher system can be used everywhere and in all types of fire-fighting because all types of fire extinguishers are commercially available in cylinders where solenoid valves can be used instead of conventional valves. Use of robust automatic control system will eliminate the effort of human for continuous monitoring of LPG condition, leakage and in the activation of fire extinguishers by cyber-physical system.

Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

Abstract Background This study estimated the economic cost of treating measles in children under-5 in Bangladesh from the caregiver, government, and societal perspectives. Method We conducted an incidence-based study using an ingredient-based approach. We surveyed the administrative staff and the healthcare professionals at the facilities, recording their estimates supported by administrative data from the healthcare perspective. We conducted 100 face-to-face caregiver interviews at discharge and phone interviews 7 to 14 days post-discharge to capture all expenses, including time costs related to measles. All costs are in 2018 USD ($). Results From a societal perspective, a hospitalized and ambulatory case of measles cost $159 and $18, respectively. On average, the government spent $22 per hospitalized case of measles. At the same time, caregivers incurred $131 and $182 in economic costs, including $48 and $83 in out-of-pocket expenses in public and private not-for-profit facilities, respectively. Seventy-eight percent of the poorest caregivers faced catastrophic health expenditures compared to 21% of the richest. In 2018, 2263 cases of measles were confirmed, totaling $348,073 in economic costs to Bangladeshi society, with $121,842 in out-of-pocket payments for households. Conclusion The resurgence of measles outbreaks is a substantial cost for society, requiring significant short-term public expenditures, putting households into a precarious financial situation. Improving vaccination coverage in areas where it is deficient (Sylhet division in our study) would likely alleviate most of this burden.

Antibiotic-resistance genes carried by coliforms in drinking water is a concerning issue for public health in Bangladesh. This research was carried out to identify coliforms in drinking water and to understand the importance of the int1 gene of coliforms in the spread of resistance to bacterial antibiotics through consumption of contaminated water. A total of 31 drinking water samples were collected from restaurants ( n = 18), health center ( n = 9), and residences ( n = 4) located in Chattogram City, Bangladesh. The isolation and identification of coliforms was performed on selective media with a combination of biochemical and molecular analysis. PCR amplification of the LacZ, uidA and int1 genes was carried out for the identification of the coliform and fecal coliform and antibiotic resistant gene, respectively. Antimicrobial susceptibility test was performed according to the Kirby-Bauer disk diffusion method with McFarland standard against three selective antibiotics including co-trimoxazole, ciprofloxacin, and ampicillin. Of 31 drinking water samples, coliforms were detected within 32% ( n = 10) of the water samples, nine samples were collected in restaurants and one sample in a residence. But no coliform was detected in the drinking water of the health center. Among the identified coliforms, the prevalence of fecal coliforms and the int1 gene was 60% ( n = 6) and 40% ( n = 4), relatively. All isolates containing the int1 microbial-resistance gene were resistant to ampicillin.This study shows that drinking water consumed in different restaurants located in Chattogram, Bangladesh is contaminated by antibiotic-resistant gene bearing coliforms that not only increase the risk of water-borne disease, but also may be the major cause of antibiotic resistance transmission in this part of Bangladesh.