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ABSTRACT The pancreas is involved in digestion and glucose regulation in the human body. Given the recognized link between chronic pancreatitis and pancreatic cancer, addressing pancreatic disorders and pancreatic cancer is particularly challenging. This review aims to highlight the limitations of traditional methods in diagnosing pancreatic disorders and cancer and explore several next‐generation sequencing (NGS) approaches as a promising alternative. There are distinct clinical symptoms that are shared by a number of clinical phenotypes of pancreatic illness induced by particular genetic mutations. Traditional diagnostic methods encompass computed tomography, magnetic resonance imaging, contrast‐enhanced Doppler ultrasound, endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, transabdominal ultrasound, laparoscopy, and positron emission tomography have a prognostic ability of only 5% or less and a 5‐year survival rate. Genetic sequencing can be employed as an alternative to conventional diagnostic techniques. Sanger sequencing and NGS are currently largely operated genome analysis, with no exception for pancreatic disease diagnosis. The NGS methods can sequence millions to billions of short DNA fragments, enabling enormous sample screening in a short amount of time with low‐abundance detection, like in 0.1%–1% mutation prevalence declining approximate cost. Whole‐genome sequencing, whole‐exome sequencing, RNA sequencing, and single‐cell NGS are a few NGS methods utilized to diagnose pancreatic disease. For both research and clinical applications, the NGS techniques can provide a precise diagnosis of pancreatic disorders in a short amount of time at a reasonable expenditure.

A deadly respiratory disease Middle East Respiratory Syndrome (MERS) is caused by a perilous virus known as MERS-CoV, which has a severe impact on human health. Currently, there is no approved vaccine, prophylaxis, or antiviral therapeutics for preventing MERS-CoV infection. Due to its inexorable and integral role in the maturation and replication of the MERS-CoV virus, the 3C-like protease is unavoidly a viable therapeutic target. In this study, 2369 phytoconstituents were enlisted from Japanese medicinal plants, and these compounds were screened against 3C-like protease to identify feasible inhibitors. The best three compounds were identified as Kihadanin B, Robustaflavone, and 3-beta-O- (trans-p-Coumaroyl) maslinic acid, with binding energies of −9.8, −9.4, and −9.2 kcal/mol, respectively. The top three potential candidates interacted with several active site residues in the targeted protein, including Cys145, Met168, Glu169, Ala171, and Gln192. The best three compounds were assessed by in silico technique to determine their drug-likeness properties, and they exhibited the least harmful features and the greatest drug-like qualities. Various descriptors, such as solvent-accessible surface area, root-mean-square fluctuation, root-mean-square deviation, hydrogen bond, and radius of gyration, validated the stability and firmness of the protein-ligand complexes throughout the 100ns molecular dynamics simulation. Moreover, the top three compounds exhibited better binding energy along with better stability and firmness than the inhibitor (Nafamostat), which was further confirmed by the binding free energy calculation. Therefore, this computational investigation could aid in the development of efficient therapeutics for life-threatening MERS-CoV infections.

The rapid expansion of slums with increasing population density are the main barriers to scaling-up sustainable Water, Sanitation, and Hygiene (WASH) facilities in the megalopolis of Dhaka, Bangladesh. The improper arrangement of ‘Cheap WASH’ (i.e., water access, sanitation access, and hygiene practices) and unmanaged disposal of non-biodegradable menstrual hygiene wastes, single-use medical, and plastic usage at slums are making the environment of city surroundings unfavourable and vulnerable posing a great threat to public health. Among the slum-dwellers, particularly, females with a disability are mostly the worst affected and vulnerable due to unfavourable conditions and lack of necessary support. Following the backdrops, this study purposively selected two Dhaka slums, with and without disabled-friendly facilities, to investigate female perspectives on different aspects related to WASH and menstrual hygiene management (MHM) for females with a disability. In total, 30 in-depth interviews, 12 focus group discussions, and 22 key informant interviews were conducted at Korail and Kalyanpur slums. Case studies from the megalopolis of Dhaka, Bangladesh, capture the economic burden added to the budgetary deficits of the family with female-disabled. Discrimination in extra care needed for toilet facilities, MHM education, and transport and overlooking the caretaker's requirement or loss in income are found as additional burdens. HIGHLIGHTS Inaccessibility and inconveniences linked to WASH and MHM are barriers to self-dependency for disabled females.; Economic burdens are adding to disabled families’ budgetary deficits.; Formal education beyond the primary level is commonly denied for menstruating disabled due to a lack of trained caretakers.; High-plinth toilets made to avoid regular flooding exacerbates disabled accessibility to current public latrines.;

Rampant slum growths add more barriers to scaling up sustainable WASH facilities coupled with safe MHM amenities for megacities. On top of inequalities in accessing to WASH provision, MHM sensitivity to the needs of disabled girls or women is commonly denied. Low-income community case studies from the megalopolis of Dhaka, Bangladesh, capture multi-faceted links between disabled-female's agony and hygiene issues. Layers of discrimination are faced starting from no education after puberty to becoming a family burden. An overall assessment was further extended to identify SWOT for water and sanitation access to hygiene and MHM practices from the gender perspectives applying several mixed-method participatory approaches. Insufficient government support amount, inadequate pipeline water supplies, inconvenient infrastructures, and pricey hygiene products are considered as key weaknesses. Immediate demands include policies for empowerment via training, education coupled with caring services, and adequate, affordable, and disabled inclusive MHM amenities at public places. Finally, the analyses suggest community choices classifying recommendations into three broad categories, namely policy, intervention, and research to serve policymakers and gender experts in identifying potential measures to achieve the global targets of SDGs 3, 5, 6, and 10. Among many solutions, income opportunity via non-governmental loans for caretakers was proposed by most. HIGHLIGHTS Available amenities are not designed to tackle the complex support needs of disabled-females.; Expensive menstrual commodities are often barriers to self-dependency.; Immediate demand includes empowerment via training and/or entrepreneurship.; A facilitative support worker at school is highly recommended.; Running Theory of Change Workshops is necessary to break social taboos towards MHM and females with disability.;

Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the β-oxidation of fatty acids, and the Krebs’s cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.

Forensic anthropologists and medico-legal professionals encounter a range of cases in which the body has been severely disintegrated, dismembered, or mutilated beyond recognition. The most critical goal of any medico-legal inquiry is to be able to identify unknown human remains. To establish the biological profile in forensic investigation, estimations of stature and sex from a range of human body dimensions are fundamental issues. This study developed formulae to determine the stature and sex from a range of body measurements in Bangladeshi adult populations based on a sample of 300 healthy young Bangladeshi individuals (150 males and 150 females) aged ranging from 21 to 30 years. Stature and four body measurements (sitting shoulder height, shoulder breadth, sitting popliteal height, and sitting knee height) were measured for each individual. Linear and stepwise multiple regression analyses were utilized in stature estimation and logistics regression analysis was used to determine the sex of the individuals. This study found a good correlation between the stature and all parameters except for shoulder breadth. The left sitting knee height was the significant parameter in stature estimation because of the higher value of R, R2, and a lower SEE value. Moreover, the right-sitting popliteal height revealed a higher sex estimation accuracy rate than other parameters. The sex estimation accuracy varied from 78.000% to 97.000%. The findings of this study have great value in forensic science and research in determination of stature and sex estimation in the adult population of Bangladesh.

The emergence of several novel SARS-CoV-2 variants regarded as variants of concern (VOCs) has exacerbated pathogenic and immunologic prominences, as well as reduced diagnostic sensitivity due to phenotype modification-capable mutations. Furthermore, latent and more virulent strains that have arisen as a result of unique mutations with increased evolutionary potential represent a threat to vaccine effectiveness in terms of incoming and existing variants. As a result, resisting natural immunity, which leads to higher reinfection rates, and avoiding vaccination-induced immunization, which leads to a lack of vaccine effectiveness, has become a crucial problem for public health around the world. This study attempts to review the genomic variation and pandemic impact of emerging variations of concern based on clinical characteristics management and immunization effectiveness. The goal of this study is to gain a better understanding of the link between genome level polymorphism, clinical symptom manifestation, and current vaccination in the instance of VOCs.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic due to the high transmission and mortality rate of this virus. The world health and economic sectors have been severely affected by this deadly virus, exacerbated by the lack of sufficient efficient vaccines. The design of effective drug candidates and their rapid development is necessary to combat this virus. In this study, we selected 23 antimicrobial peptides from the literature and predicted their structure using PEP-FOLD 3.5. In addition, we docked them to the SARS-CoV-2 spike protein receptor-binding domain (RBD) to study their capability to inhibit the RBD, which plays a significant role in virus binding, fusion and entry into the host cell. We used several docking programs including HDOCK, HPEPDOCK, ClusPro, and HawkDock to calculate the binding energy of the protein-peptide complexes. We identified four peptides with high binding free energy and docking scores. The docking results were further verified by molecular dynamics (MD) simulations to characterize the protein-peptide complexes in terms of their root-mean-square fluctuation (RMSF), root-mean-square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond formation. Allergenicity and toxicity predictions suggested that the peptides we identified were non-allergenic and non-toxic. This study suggests that these four antimicrobial peptides could inhibit the RBD of SARS-CoV-2. Future in vitro and in vivo studies are necessary to confirm this.

The current coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus 2 (SARS-CoV-2), involves severe acute respiratory syndrome and poses unprecedented challenges to global health. Structure-based drug design techniques have been developed targeting the main protease of the SARS-CoV-2, responsible for viral replication and transcription, to rapidly identify effective inhibitors and therapeutic targets. Herein, we constructed a phytochemical dataset of 1154 compounds using deep literature mining and explored their potential to bind with and inhibit the main protease of SARS-CoV-2. The three most effective phytochemicals Cosmosiine, Pelargonidin-3-O-glucoside, and Cleomiscosin A had binding energies of -8.4, -8.4, and -8.2 kcal/mol, respectively, in the docking analysis. These molecules could bind to Gln189, Glu166, Cys145, His41, and Met165 residues on the active site of the targeted protein, leading to specific inhibition. The pharmacological characteristics and toxicity of these compounds, examined using absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses, revealed no carcinogenicity or toxicity. Furthermore, the complexes were simulated with molecular dynamics for 100 ns to calculate the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen profiles from the simulation trajectories. Our analysis validated the rigidity of the docked protein-ligand. Taken together, our computational study findings might help develop potential drugs to combat the main protease of the SARS-CoV-2 and help alleviate the severity of the pandemic.

Male infertility is significantly influenced by the plasma-protein sex hormone-binding globulin (SHBG). Male infertility, erectile dysfunction, prostate cancer, and several other male reproductive system diseases are all caused by reduced testosterone bioavailability due to its binding to SHBG. In this study, we have identified 345 phytochemicals from 200 literature reviews that potentially inhibit severe acute respiratory syndrome coronavirus 2. Only a few studies have been done using the SARS-CoV-2 inhibitors to identify the SHBG inhibitor, which is thought to be the main protein responsible for male infertility. In virtual-screening and molecular-docking experiments, cryptomisrine, dorsilurin E, and isoiguesterin were identified as potential SHBG inhibitors with binding affinities of −9.2, −9.0, and −8.8 kcal/mol, respectively. They were also found to have higher binding affinities than the control drug anastrozole (−7.0 kcal/mol). In addition to favorable pharmacological properties, these top three phytochemicals showed no adverse effects in pharmacokinetic evaluations. Several molecular dynamics simulation profiles’ root-mean-square deviation, radius of gyration, root-mean-square fluctuation, hydrogen bonds, and solvent-accessible surface area supported the top three protein–ligand complexes’ better firmness and stability than the control drug throughout the 100 ns simulation period. These combinatorial drug-design approaches indicate that these three phytochemicals could be developed as potential drugs to treat male infertility.

Coronavirus disease 2019 (COVID-19) is a potentially lethal and devastating disease that has quickly become a public health threat worldwide. Due to its high transmission rate, many countries were forced to implement lockdown protocols, wreaking havoc on the global economy and the medical crisis. The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative virus for COVID-19, represent an effective target for the development of a new drug/vaccine because it is well-conserved and plays a vital role in viral replication. Mpro inhibition can stop the replication, transcription as well as recombination of SARS-CoV-2 after the infection and thus can halt the formation of virus particles, making Mpro a viable therapeutic target. Here, we constructed a phytochemical dataset based on a rigorous literature review and explored the probability that various phytochemicals will bind with the main protease using a molecular docking approach. The top three hit compounds, medicagol, faradiol, and flavanthrin, had binding scores of −8.3, −8.6, and −8.8 kcal/mol, respectively, in the docking analysis. These three compounds bind to the active groove, consisting of His41, Cys45, Met165, Met49, Gln189, Thr24, and Thr190, resulting in main protease inhibition. Moreover, the multiple descriptors from the molecular dynamics simulation, including the root-mean-square deviation, root-mean-square fluctuation, solvent-accessible surface area, radius of gyration, and hydrogen bond analysis, confirmed the stable nature of the docked complexes. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed a lack of toxicity or carcinogenicity for the screened compounds. Our computational analysis may contribute toward the design of an effective drug against the main protease of SARS-CoV-2.

The recent coronavirus outbreak has changed the world’s economy and health sectors due to the high mortality and transmission rates. Because the development of new effective vaccines or treatments against the virus can take time, an urgent need exists for the rapid development and design of new drug candidates to combat this pathogen. Here, we obtained antiviral peptides obtained from the data repository of antimicrobial peptides (DRAMP) and screened their predicted tertiary structures for the ability to inhibit the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using multiple combinatorial docking programs, including PatchDock, FireDock, and ClusPro. The four best peptides, DRAMP00877, DRAMP02333, DRAMP02669, and DRAMP03804, had binding energies of −1125.3, −1084.5, −1005.2, and −924.2 Kcal/mol, respectively, as determined using ClusPro, and binding energies of −55.37, −50.96, −49.25, −54.81 Kcal/mol, respectively, as determined using FireDock, which were better binding energy values than observed for other peptide molecules. These peptides were found to bind with the active cavity of the SARS-CoV-2 main protease; at Glu166, Cys145, Asn142, Phe140, and Met165, in addition to the substrate-binding sites, Domain 2 and Domain 3, whereas fewer interactions were observed with Domain 1. The docking studies were further confirmed by a molecular dynamics simulation study, in which several descriptors, including the root-mean-square difference (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), and hydrogen bond formation, confirmed the stable nature of the peptide–main protease complexes. Toxicity and allergenicity studies confirmed the non-allergenic nature of the peptides. This present study suggests that these identified antiviral peptide molecules might inhibit the main protease of SARS-CoV-2, although further wet-lab experiments remain necessary to verify these findings.

Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD.

The recently emerged coronavirus (SARS-CoV-2) has created a crisis in world health, and economic sectors as an effective treatment or vaccine candidates are still developing. Besides, negative results in clinical trials and effective cheap solution against this deadly virus have brought new challenges. The viral protein, the main protease from SARS-CoV-2, can be effectively targeted due to its viral replication and pathogenesis role. In this study, we have enlisted 88 peptides from the AVPdb database. The peptide molecules were modeled to carry out the docking interactions. The four peptides molecules, P14, P39, P41, and P74, had more binding energy than the rest of the peptides in multiple docking programs. Interestingly, the active points of the main protease from SARS-CoV-2, Cys145, Leu141, Ser139, Phe140, Leu167, and Gln189, showed nonbonded interaction with the peptide molecules. The molecular dynamics simulation study was carried out for 200 ns to find out the docked complex’s stability where their stability index was proved to be positive compared to the apo and control complex. Our computational works based on peptide molecules may aid the future development of therapeutic options against SARS-CoV-2.

Every year thousands of people are being displaced in coastal areas of Bangladesh due to natural calamities associated with climate change, known as Internally Displaced Peoples (IDPs). Climate change adaptation measures play a significant role in coping with the alteration of climatic components, while various forms of barriers hinder the sustainability of adaptation. This research was conducted to understand the perception of IDPs on climate change impact on health in the coastal areas of Bangladesh, including the adaptation practices and barriers to the coping strategies. To fulfill the objective, 420 individual surveys were conducted randomly in two Sub-districts of Khulna district in Bangladesh. The findings reveal that the riverbank erosion and cyclones were the primary reasons for displacement, and the social relationships were hampered in the new places of living. Also, the temperature in summer and winter, and the rainfall intensity increased, whereas rainfall slightly decreased over the last ten years. Differences of opinion were identified about the effects of the changing climatic variables on the respondents' health between the previous and present locations. Despite practicing different adaptive strategies, the weak financial condition and a lack of access to health care information are mostly hindering the sustainability of adaptation. This research may help policymakers in taking proper initiatives to ensure sustainable adaptation practices in the coastal areas.

Currently, a worldwide pandemic has been declared in response to the spread of coronavirus disease 2019 (COVID-19), a fatal and fast-spreading viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The low availability of efficient vaccines and treatment options has resulted in a high mortality rate, bringing the world economy to its knees. Thus, mechanistic investigations of drugs capable of counteracting this disease are in high demand. The main protease (Mpro) expressed by SARS-CoV-2 has been targeted for the development of potential drug candidates due to the crucial role played by Mpro in viral replication and transcription. We generated a phytochemical library containing 1672 phytochemicals derived from 56 plants, which have been reported as having antiviral, antibacterial, and antifungal activity. A molecular docking program was used to screen the top three candidate compounds: epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate, which had respective binding affinities of −8.4, −8.5, and −8.8 kcal/mol. Several active sites in the targeted protein, including Cys145, His41, Met49, Glu66, and Met165, were found to interact with the top three candidate compounds. The multiple simulation profile, root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and solvent-accessible surface area values supported the inflexible nature of the docked protein–compound complexes. The toxicity and carcinogenicity profiles were assessed, which showed that epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate had favorable pharmacological properties with no adverse effects. These findings suggest that these compounds could be developed as part of an effective drug development pathway to treat COVID-19.

Plankton is essential to estuarine food webs, providing key food for species like fish larvae, and understanding their dynamics helps us comprehend ecological interactions and energy transfer in aquatic ecosystems. This study examined the plankton community structure, diversity, variability and the environmental variables of water that influenced these communities in a freshwater-dominated estuary along the Northern Bay of Bengal during the dry season. A variety of tools were employed, including a dissecting microscope, a Sedgwick-Rafter cell, and a Hanna multi-parameter meter, to obtain detailed and comprehensive data. From the analyses, in total 37 genera of phytoplankton belonging to 11 classes such as Bacillariophyceae (66%), Coscinodiscophyceae (28%), Fragilariophyceae (1%), Euglenophyceae (1%), Cyanophyceae (1%), others (3%) were recorded. Zooplankton, consisting of multicellular organisms, was represented by 11 genera across five classes, with Monogononta (53%) and Insecta (29%) being the most prevalent. Phytoplankton exhibited a mean abundance of 180,000 ± 20,200 cells l ^−1 whereas zooplankton showed a mean abundance of 3,000 ± 200 cells l ^−1 . The diversity indices (H′), evenness (J′) and dominance (D) ranged from 1.188 ± 0.015 to 0.19 ± 0.006, 0.9377 ± 0.002 to 0.5118 ± 0.006, 0.365 ± 0.001 to 0.143 ± 0.002 respectively, suggested low to moderate plankton diversity with a suppressed community and moderate to high dominance in the river water. Based on the similarity percentage (SIMPER) and analysis of similarity (ANOSIM), Phytoplankton and zooplankton varied significantly between stations while non-metric multi-dimensional scaling (nMDS) indicated greater consistency in phytoplankton communities across stations compared to zooplankton. Furthermore, Pearson’s correlation coefficient (rs) and canonical correspondence analysis (CCA) identified transparency, sulfates, dissolved oxygen, conductivity, TDS, pH, and salinity as major influencers of plankton abundance; nitrate had a moderate effect. Spearman’s rank correlations showed significant positive (p < 0.05) correlations between diversity indices and environmental variables. Future research should focus on long-term monitoring and the potential impacts of environmental changes on plankton communities.

Biometric indices, i.e. i) length-weight relationships (LWRs), ii) form factor (a3.0), iii) length-frequency distributions (LFDs), and iv) condition factors (relative KR and Fulton’s KF) are considered to be very crucial in the assessment of fishery studies as they provide information on fish population growth and coastal habitat well-being. The study of biometric indices of mangrove fish has, however, received little attention. Our research investigates the LFDs, LWRs, a3.0, KR and KF of 395 individuals from nine families (Latidae, Engraulidae, Gobiidae, Mugilidae, Synbranchidae, Schilbeidae, Scatophagidae, Plotosidae, and Terapontidae). The LFDs showed that the lowest total length (TL) was 4.57 cm for Stolephorus tri, and highest TL was 56.20 for Monopterus cuchia. The LWRs showed that the b (allometric coefficient) values ranging from 2.01 (Plotosus canius) to 3.29 (Terapon jarbua), appeared as highly significant (P