Daffodil International University

Optical coherence tomography (OCT) scanning is crucial for the diagnosis of widespread ophthalmic diseases. Traditionally, experts manually identify diseases and biomarkers from OCT scans. Recently, modern medical imaging practices have increasingly utilized deep learning techniques to speed up and improve diagnostic accuracy in ophthalmology. However, obtaining accurately labeled datasets is a significant challenge in medical imaging due to the expertise required for precise annotation by trained professionals. This article presents a novel method, named OBoctNet, with a new two-stage training strategy to improve the identification of ophthalmic biomarkers using OCT scans from the OLIVES dataset, which contains only 12% labeled data. This approach leverages a robust methodology that effectively uses labeled and unlabeled data to enhance biomarker identification accuracy, achieving a cumulative performance increase of 23% across 50% of the biomarkers when compared to the previous studies. To better identify biomarkers, the OBoctNet employs an active learning strategy that uses unlabeled data and dynamically ensembles models based on their performance within each experimental setup. Additionally, the usage of Gradient weighted Class Activation Mapping (GradCAM) helps identify regions of interest associated with relevant biomarkers, enhancing interpretability and transparency for potential clinical adoption.

Introduction This study presents a comprehensive exploration of the biomedical potential of the synthesized metal-organic framework Zn₁O(BDC)₀, focusing on its applications in cancer and diabetes treatment and its advanced drug delivery capabilities. Methods The structural and physicochemical properties of Zn₁O(BDC)₀ were characterized using FTIR, TGA, ¹H NMR, PXRD, and elemental analysis, revealing its exceptional stability and coordination properties. Molecular docking, molecular dynamics simulations (100 ns), and MM-GBSA calculations were performed to assess binding affinities and stability. Results The interactions of Zn₁O(BDC)₀ with salmon sperm DNA (SSDNA) and bovine serum albumin (BSA) demonstrated significant anticancer potential, evidenced by binding constant values of 6.0 × 10ⁱ M⁸¹ and Gibbs free energy changes of -17.93 and -19.61 kcal/mol, respectively, highlighting its ability to suppress tumor cell proliferation. With doxorubicin (DOX) loading and reloading efficiencies of 88% and 87.5%, Zn₁O(BDC)₀ exhibited superior drug delivery capabilities. The anti-diabetic potential was validated by the formation of human insulin (HI) hexamers with ΔG values of 0.8 ± 0.1 and a significant decrease in absorption intensity (5.8 to 0.05 at 250 nm). Molecular docking studies revealed moderate to high binding affinities (-10.0 to -5.3 kcal/mol) with biomolecular targets, supported by molecular dynamics simulations over 100 ns and MM-GBSA calculations indicating robust stability (ΔG = -33.31 kcal/mol). Conclusion These in-silico and in-vitro analyses underscore the significant pharmacological promise of Zn₁O(BDC)₀ as a multifunctional agent for anticancer, antidiabetic, and drug delivery applications.

Background Diabetes mellitus (DM) is a prevalent chronic metabolic condition characterized by high blood sugar levels, resulting from insufficient insulin production or ineffective insulin use, posing substantial global health issues. Research on the relationship between glycemic status and the ratio of neutrophils to lymphocytes (NLR) and monocytes to lymphocytes (MLR) is limited. This study aimed to fill these knowledge gaps by examining the connection between DM and inflammatory markers within the Asir region. Methods Data from 3545 participants were retrospectively analyzed. The dataset, gathered between 2021 and 2023, comprises 38 laboratory tests obtained from the Future Lab Pioneer database. The study's inclusion criteria focused on diabetes profile tests (glycated hemoglobin [HbA1c] and fasting blood glucose [FBG]) and manually computed inflammatory markers (NLR and MLR), which were stratified by age and sex. Results This study demonstrated significant differences in NLR levels compared with FBG levels across all adult age groups and adult female participants (p < 0.0001), as well as among all elderly age groups (p = 0.0006) and elderly women (p = 0.01). MLR levels were significant in all adult age groups (p = 0.04) and in adult women (p = 0.02). When NLR and MLR were compared to HbA1c levels, a significant difference in the mean NLR was found in adult women (p = 0.005). Additionally, the mean MLR levels were significant in all adult age groups (p = 0.04) and adult women (p = 0.02). Conclusion Although a larger sample size is necessary for this research, the results indicate that NLR and MLR could serve as valuable indicators for evaluating inflammation in people with disrupted glucose metabolism, particularly in adult and female populations.

As global climate challenges intensify, corporate carbon disclosure quality (CDQ) has emerged as a vital indicator of corporate environmental transparency and accountability. However, corporate governance (CG) mechanisms remain contested: are they primarily legitimation tools to meet external expectations or governance instruments to ensure substantive environmental performance? This study conducts a systematic literature review (SLR) utilizing the PRISMA framework, methodically screening and synthesizing 64 studies published between 2011 and 2024. Key findings encompass (1) Persistent suboptimal CDQ across both developed and emerging markets; (2) A trend towards theoretical integration, with the CG‐CDQ relationship increasingly reflecting the interplay of multiple theoretical frameworks; (3) Dynamic duality of CG's role, wherein its primary function depends on institutional pressure, governance architecture, and stakeholder oversight; (4) Bridging institutional voids in emerging economies, as demonstrated by China's state‐owned enterprises (SOEs) surpassing non‐state‐owned firms under policy incentives. This study also identifies critical research gaps: over two‐thirds of CG‐CDQ studies concentrate on the US, Europe, and China, while ASEAN and Africa—carbon‐intensive regions—remain underexplored, constituting a 90% research void. The cultural dimension is frequently neglected, with only one study examining informal institutions, such as Confucianism. These findings highlight that governments should mandate carbon disclosure standards, implement cross‐border accreditation mechanisms, and enhance social oversight; corporations should bolster governance, incorporate environmental expertise, and ensure that 30% of their executives are female; and investors should advocate for ESG performance agreements that correlate carbon disclosure with investment returns, fostering sustainable development.

Strontium-based perovskite-type proton-conducting SrCe 0.5 Zr 0.3 Y 0.1 Gd 0.05 Zn 0.05 O 3−δ (SrCZYGdZn), SrCe 0.5 Zr 0.3 Y 0.1 Sm 0.05 Zn 0.05 O 3−δ (SrCZYSmZn), and SrCe 0.5 Zr 0.3 Y 0.1 Yb 0.05 Zn 0.05 O 3−δ (SrCZYYbZn) have been synthesized by a solid-state reaction method. The studied materials were characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and electrochemical impedance spectroscopy (EIS). All the materials showed tetragonal symmetry with the P4mm space group. SEM image analysis confirms the well-crystallized high-density materials with good grain size, and TGA shows the thermal stability up to 800°C. In wet 5% H 2 , the ionic conductivities of SrCZYGdZn, SrCZYSmZn, and SrCZYYbZn were 2.36 × 10 −4 Scm −1 , 9.33 × 10 −5 Scm −1 , and 1.17 × 10 −3 Scm −1 , respectively, at 700°C. On the other hand, in a dry H 2 atmosphere, the ionic conductivities were 2.33 × 10 −4 Scm −1 , 8.97 × 10 −5 Scm −1 , and 9.17 × 10 −4 Scm −1 , respectively, at the same temperature. The calculated activation energies of SrCZYGdZn, SrCZYSmZn, and SrCZYYbZn were 0.90 eV, 0.41 eV, and 0.31 eV, respectively, in wet 5% H 2. Reasonable conductivity and low activation energy makes these electrolytes promising for IT-SOFC.

Skin diseases are a significant public health challenge in Bangladesh, with prevalence rates soaring from 11.16 to 63% in recent years. The lack of access to dermatological expertise and resource constraints in rural areas exacerbate delayed or inaccurate diagnoses, leading to worsening conditions and higher treatment costs. This study addresses this critical issue by developing a robust and accurate system for classifying Bangladesh’s ten most common skin diseases using convolutional neural networks (CNNs)-based transfer learning models. Six pre-trained CNN models are implemented, and a novel ensemble model (i.e., SkinIncept) is proposed. Data collection incorporates primary data from the Damien Foundation Hospital and the Bangladesh Institute of Dermatology, STD, and AIDS (BIDSA), supplemented by additional images from reliable web portals. Before model training, extensive preprocessing techniques such as cropping, resizing, filtering, contrast enhancement, histogram equalization, CLAHE, gamma correction, segmentation, and data augmentation are applied to ensure the quality and consistency of the images. The quality of processed images is validated using statistical methods, including MSE, PSNR, SSIM, and RMSE. The performance of each model is rigorously evaluated using several performance metrics, instilling confidence in the study’s methodology and results. Among the six pre-trained models, InceptionResNet-V2 achieved 93.65% accuracy, and the proposed ensemble model achieved a remarkable classification accuracy of 96.52% based on six ablation studies to fine-tune the model and optimize the hyperparameters. These findings form the foundation for “Skin Medicare,” a mobile application providing AI-driven, accessible, and accurate skin disease diagnosis, offering a scalable solution to improve healthcare delivery in underserved and resource-constrained regions.

Background Maternal mental health and other underlying factors might affect a child’s nutritional status. This study assesses child undernutrition and its associated characteristics, including maternal mental health, in low-income settings in Dhaka, Bangladesh. Methods A community-based cross-sectional study was conducted among 397 lactating mothers with children aged 6–23 months from low-income settings in Dhaka. Anthropometric measurements were taken following standard protocols, and Z-scores for weight-for-age, height-for-age, and BMI-for-age were calculated. Maternal depression and anxiety were assessed using the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder 7-Item Scale, respectively. The child feeding index was developed based on breastfeeding, dietary diversity, and meal frequency. Multivariate logistic regression models explored the relationship between child undernutrition and maternal mental health and other risk factors. Results In low-income regions of Dhaka, the prevalence was 31.9% for stunting, 14.0% for wasting, and 24.1% for underweight children. Approximately half of the mothers experienced depression (55%) and anxiety (50%). High maternal depression levels were associated with increased odds of stunted (AOR = 1.80, 95% CI = 1.10–2.94, p < 0.05) and wasted (AOR = 2.70, 95% CI = 1.38–5.28, p < 0.05) children. Similarly, anxiety was linked to a higher risk of underweight children (AOR = 1.77, 95% CI = 1.04–3.11, p < 0.05). Female children had approximately twice the risk of stunting than boys (AOR = 2.13, 95% CI = 1.32–3.44, p < 0.01). Younger maternal age also doubled the risk of stunting (AOR = 1.97, 95% CI = 1.20–3.22, p < 0.01). Low adherence to a feeding index increased the odds of stunting (AOR = 3.21, 95% CI = 1.99–5.16, p < 0.001) and underweight (AOR = 4.20, 95% CI = 2.50–7.07, p < 0.01). Children born to underweight mothers were almost twice as likely to become underweight (AOR = 2.01, 95% CI = 1.01–4.03, p < 0.05) compared to those born to normal/overweight mothers. Conclusion Maternal depression and anxiety adversely affect the nutrition of their children. Sociodemographic factors such as the child’s sex, maternal age, maternal health, and child feeding practices significantly contribute to child undernutrition. Policy initiatives should prioritize maternal mental health and address child undernutrition in these settings.

Cervical cancer remains a major global health challenge, largely driven by persistent infections with high-risk human papillomavirus (HPV). Although preventive vaccines have reduced cervical cancer incidence in some settings, effective therapeutic strategies for established HPV-associated malignancies remain limited. High-risk HPV types (particularly 16 and 18) utilize their E6 oncoprotein to promote ubiquitin-mediated degradation of the tumor suppressor p53, thereby facilitating uncontrolled cell proliferation and immune evasion. Targeting E6 has thus emerged as a key strategy to counteract HPV-driven carcinogenesis. In this work, we employed a comprehensive in silico framework—encompassing density functional theory (DFT), ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling, molecular docking (including refinement and validation), and molecular dynamics (MD) simulations—to evaluate a series of chemically modified lupenone derivatives as potential HPV oncoprotein inhibitors. Initially, lupenone was modified with different functional groups, and each derivative was screened for drug-likeness via ADMET analysis to confirm pharmacological viability. Concurrently, pharmacophore mapping highlighted key alignments between ligand functional groups and pharmacophoric sites, while DFT calculations elucidated each compound’s electronic structure, conformational stability, and chemical reactivity. Subsequent docking assessments against E6 oncoprotein and molecular dynamics simulations further confirmed structural robustness in several top-performing compounds, indicating minimal conformational fluctuations over time. These findings demonstrate the potential of lupenone derivatives as promising scaffolds for anti-HPV therapy. However, in vitro and in vivo investigations are necessary to confirm their efficacy, toxicity profiles, and clinical relevance in mitigating HPV-related cervical cancer.

This study aims to understand and explore how the female floating sex workers in Bangladesh have experienced the vulnerability of COVID-19 pandemic-induced lockdown and what sort of coping strategies they have adopted to fight the crisis. By employing the qualitative interpretative phenomenological approach (IPA), this study has collected data from both primary (17 sex workers were interviewed twice) and secondary sources. For data analysis, the authors followed inductive thematic analysis using the Graneheim approach and NVivo-12 software. The findings of the study suggest that floating sex workers have faced some significant challenges during the pandemic, which include financial crisis, food insecurity, difficulty in availing healthcare facilities leading to mental challenges, harassment cases and exclusion from emergency assistance from various sectors. The study also revealed some coping strategies, for instance, taking loans, borrowing food, selling properties and belongings and concealing their own identity, which were taken by sex workers for fighting out the emerging situations. Finally, the article has come up with some pragmatic policy recommendations that are expected to guide the concerned policymakers and related agencies in devising COVID-19 protection and welfare policies for the vulnerable sex worker community.

Institutional delivery, defined as giving birth in a health facility with skilled delivery assistants, is essential for reducing maternal mortality in low‐ and middle‐income countries. Bangladesh has historically had high maternal mortality rates, with 452 maternal deaths per 100,000 live births in 1993, which declined to 123 per 100,000 live births in 2020. Despite this progress, the maternal mortality rate remains high, and achieving the Sustainable Development Goal (SDG) target of 70 per 100,000 live births requires further improvements in maternal healthcare, particularly in institutional delivery services. This study aimed to assess wealth‐related disparities in the use of institutional delivery services in Bangladesh using data from the Multiple Indicator Cluster Survey (MICS) 2019. We applied a Wagstaff‐type decomposition approach using the Erreygers‐corrected concentration index (CIX) to explore wealth‐related inequality in institutional delivery. Multiple logistic regression was used to identify factors associated with institutional delivery, and a CIX measured wealth‐related disparities. Decomposition analysis helped identify key contributors to these disparities. Results showed that 52% of deliveries were institutional deliveries. Women from rich‐ and middle‐income households had a 94% adjusted odds ratio (AOR = 1.94; 95% confidence intervals [CI]: 1.62–2.34) and 32% (AOR = 1.32; 95% CI: 1.12–1.56) higher likelihood, respectively, of delivering in a health institution compared to poor women. The CIX value of 0.170 indicated institutional delivery was more common among wealthier women. Decomposition analysis revealed that antenatal care (ANC) visits (33.1%), parity (11.8%), and wealth index (11.1%) were significant contributors to wealth‐related disparities. In conclusion, institutional delivery remains underutilized in Bangladesh, with only half of all births occurring in health facilities. To accelerate progress in reducing maternal mortality, targeted pro‐poor strategies are essential, particularly in rural and underserved areas like Mymensingh. Efforts should focus on expanding healthcare access, improving maternal education, and strengthening ANC services.

Health crises, particularly in the form of pandemics, have a long history of destroying and disrupting the political, social, and economic order of organized human societies. There is a continuous endeavor to derive lessons from these historical events to better comprehend, prepare for, and mitigate the impacts of future pandemics. This review article, adopting the perspectives of crisis management and crisis communication, seeks to operationalize historical human experiences in the realm of policy-making. By engaging with both academic and popular literature on past pandemic events, the article endeavors to apply these insights to the COVID-19 pandemic while the crisis was still unfolding. The findings indicate that the most pertinent lessons from past health crises are often not accurately learned or applied, potentially due to political pressures that emerge during such crises�

Kyasanur Forest Disease (KFD) is a zoonotic disease primarily found in India, spread by Haemaphysalis ticks and caused by the Kyasanur Forest Disease Virus (KFDV) from the Flavivirus genus. Signs and symptoms of the KFD are high body temperature, muscle pain, gastrointestinal issues, and hemorrhages, with a mortality rate of 3% to 5%. Changes in the environment, such as deforestation and climate change, have resulted in further spread of KFDV to different regions in India. The spread of the virus occurs through ticks and different vertebrates, with humans being occasional hosts in the transmission cycle. The current method of prevention relies on a vaccine that has been inactivated with formalin, but its effectiveness is still a concern. This indicates the pressing need for vaccinations, tick management, and study on the molecular biology of KFDV, specifically focusing on the involvement of bats as potential hosts. These approaches could improve the overall effectiveness of containment and preventive programme of the disease in areas where it is common or increasingly present.

Background The COVID-19 pandemic has impacted the mental health of people across the world, including those with disabilities in Bangladesh. However, very little research exists that has explored the mental health problems experienced by persons with disabilities in rural and urban areas of Bangladesh. This study aimed to investigate the prevalence and associated factors of common mental health problems in persons with disabilities in rural and urban areas of Bangladesh. Methods A cross-sectional survey using the Bangla Depression Anxiety Stress Scale-21 (BDASS-21) with sociodemographic was conducted among 950 participants with varying types of disabilities in Dhaka, Narayanganj, and Gazipur. Descriptive and inferential statistical analyses were used to measure the effects. Results The prevalence of moderate to extremely severe depression, anxiety, and stress among participants was 67.6%, 72.6%, and 49.5%, respectively. Urban participants exhibited significantly higher levels of depression (76.6% in Dhaka), anxiety (86.1% in Dhaka), and stress (32.1% in Dhaka) compared to their rural counterparts (depression: 86.16%, anxiety: 91.07%, stress: 97.77% in Gazipur). Gender differences were observed in anxiety, with females reporting higher anxiety levels than males (p<0.05). Age and geographical location were significantly associated with stress (p<0.042 and p<0.001, respectively), with those reporting higher anxiety also experiencing greater stress (p<0.001). Specific disabilities, such as visual disabilities, were linked to higher stress levels, while depression and anxiety did not show significant associations with demographic factors or disability type. Conclusion Results highlight the prevalence of common mental health problems among persons with disabilities in Bangladesh. The findings can contribute to the development of appropriate public health intervention plans taking into consideration persons with disabilities, especially during emergencies.

Background and Aims Internet‐related disorders for example, internet addiction (IA) seem to be frequent among adolescents all over the world. However, there could be a possible link between body mass index (BMI) and the risk of eating disorders (EDs) in connection with IA. This study aimed to determine the relationship between risk of EDs, BMI, and IA among Bangladeshi adolescents, as well as factors associated with IA. Methodology A cross‐sectional study was carried out among 2147 individuals, using a stratified random sampling method, aged 13–19, from various selected schools and colleges spread across Bangladesh using a Google form questionnaire consisting of sections on socio‐demographic factors, body mass index (BMI), eating attitude test scale, and internet addiction test scale. Descriptive analysis, Pearson Chi‐square test, logistic regression model, and a bivariate correlation analysis were fit to determine the relationship and factors. Results We found that 24.1% of students had IA, 23.2% were at risk of EDs, 6.6% were underweight, 1.9% were overweight, 24% were obese and the remainder were normal. The relationship between IA, BMI, and risk of EDs was significant and positively co‐related. Moreover, we found gender, the purpose of internet use, daily internet usage, physical exercise, literature reading habits, and victims of bullying were significantly associated with IA. Conclusion The findings highlight the need for further research and strategies to diagnose and treat EDs and IA, among adolescents. Promoting physical activity, healthy habits, and awareness at the institutional and parental levels is crucial for mitigating these risks and addressing sociodemographic, internet usage, and emotional health factors.

The use of traditional knowledge in indigenous healthcare systems is vital for the conservation of floral diversity. The study was conducted between 2021 and 2023 to document and investigate the traditional utilization of medicinal plants in the Upper District Dir. A total of 79 species belonging to 34 different families were used to treat various diseases. Our investigations emphasize the importance of domestic consumption of plant resources for treating various human ailments. The dominant families were Lamiaceae (7 species), Asteraceae, and Amaranthaceae (5 species each). Herbaceous plants constituted the majority (73.15%), followed by trees (17.72%) and shrubs (10.12%). The study used descriptive ethnobotanical indices to evaluate the importance and conservation status of the species. These indices included Relative Frequency of Citations (RFC), Use Values (UV), Use Reports (UR), Fidelity Levels (FL), Informant Consensus Factors (ICF), Cultural Importance Values (CIV), Family Importance Values (FIV), and Medical Importance (MI). The UV varied from 0.90 (Taraxacum officinale L.) to 0.16 (Plantago lanceolata L. and Platanus orientalis L.) while RFC ranged from 0.75 (Teucricum stocksianum Boiss.) to 0.19 (Conyza canadensis L.). The species with the highest FL (95.69%) was Isodon rugosus Wall. where the lowest FL (0.44%) was recorded for Ocimum basilicum L. Similarly, CIV ranged from 3.74 to 0.04 for Teucricum stocksianum Boiss. and Bunium persicum (Bioss). The Family importance value ranged from 92.30 to 17.69, while the highest ICF of 0.62 was recorded for gastrointestinal diseases and urogenital problems. According to the results of the study, 24 plant species were classified as vulnerable, followed by rare (20 species), infrequent (13 species), and dominant (12 species), while 10 species were endangered. Practical usage of plant resources necessitates the implementation of conservation policies and further comprehensive research to optimize their sustainable utilization. Graphical abstract

Atherosclerosis is a chronic disease caused by inflammation in the blood vessel wall, which leads to plaque formation in the endothelial lining. Preexisting atherosclerosis contributes to various cardiovascular problems, including myocardial infarction, congestive cardiac failure, arrhythmias, and acute coronary syndrome, all of which are associated with higher postoperative mortality and morbidity. Rupturing of atherosclerosis poses a significant health concern as triggering a heart attack. Therefore, it is essential to identify atherosclerosis early to prevent health complications and their subsequent consequences. Atherosclerosis is typically diagnosed by angiograms, computerized tomography scans and stress tests. Additionally, various biomarkers are released by inflammatory cells in the surrounding tissue and artery walls during the pathogenesis of atherosclerosis. Quantifying these biomarkers can aid in diagnosing and determining the severity of atherosclerosis in relation to cardiovascular diseases. This review focuses on the recent advancements in understanding the formation of atherosclerosis and the implementation of biomarkers in nano biosensors.

Objectives Infections with bacteria and viruses have been linked to an increase in the risk of Coronary Heart Disease (CHD). The development of CHD may be influenced by Natural Killer cells (NK), which are the first defense system of the body in opposition to these infections. This study aimed to compare scRNA-seq and transcriptomics data to find natural killer cell-linked genetic biomarkers that might potentially be used for the diagnosis and assessment of CHD. Methods Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing datasets were analyzed to bring out Differentially Expressed Genes (DEGs). To ascertain the function of these DEGs, we used gene set enrichment analysis with Gene Ontology (GO) and metabolic pathways using FunRich, DAVID, and SRplot tools. Hub proteins were identified from Protein-Protein Interaction (PPI) network analysis and also discovered transcription factors, post-transcription factors, and chemical compounds. Biomarker validation of CHD was carried out via probability analysis through the Receiver Operating Characteristic (ROC) curve. Results A total of 106 shared DEGs were identified after integrating genes and cross-comparative analysis. Signal transduction, cell-to-cell communication, immune activation, immune responses, cytoplasm pathway, plasma membrane, transcription factor, MHC class II, and receptor activity are the most enrichment GO term, while cell adhesion molecules, immune system, TYROBP, LCK, FYN, and T-cell receptor signaling pathways are key metabolic pathways. An interconnected system of Protein- Protein Interactions (PPI) identified the top hub proteins, including CDK1 and PTPRC, which could serve as potential biomarkers through network topology analysis. Furthermore, several transcription factors (YY1, PPARG, NFIC, FOXC1, GATA2, NFKB1, RELA and TP53) and translation factors (hsa-mir-195-5p, hsa-mir-34a-5p, hsa-mir-16-5p and hsa-mir-124-3p) were discovered in this investigation. It was also anticipated that some drug-like compounds might be advantageous against CHD. Conclusion To have a deeper understanding of molecular pathways and to create effective treatments for CHD, this study provides molecularly predicted biomarkers at the gene expression level and protein basis.