Correlation of Body Mass Index with Serum Lipid Profile Level in Adolescent Students of Bangladesh.

Obesity is associated with metabolic disorders such as dyslipidaemia, diabetes mellitus (DM), hypertension (HTN) and cardiovascular disease (CVD). There has been rising burden of childhood and adolescent obesity in most developing countries in recent years. Changes in dietary habits, junk and fast food, physical inactivity and smoking habits increases among adolescent students, which causes obesity and simultaneously increases risk of metabolic diseases. The objective of the study is to determine the correlation between Body Mass Index (BMI) and lipid profile among adolescent students of Bangladesh. This cross-sectional observational study was conducted among 79 undergraduate healthy adolescent students, aged 10-18 years who were selected through purposive sampling. The study was conducted from July 2022 to June 2023 in urban and rural areas of Dhaka, Narayanganj and Rangpur. Data was collected using a semi-structured questionnaire. Correlation of dyslipidemia and BMI was analyzed by Pearson Coefficient. Data was analyzed using SPSS version 22.0 with level of statistical significance at p<0.05. Mean age of the respondents was 14.9±4.5 years. Male and female ratio was 2.16:1. Among respondents, 46.8% had BMI 18.5-23.0 (normal), 31.6% had BMI 23.1-25.0 (overweight) and 21.5% had BMI >25.0 (obese). Prevalence of dyslipidaemia was 34.1%. Overweight and obese respondents had raised total cholesterol (TC) level 209.51±48.6 mg/dl and 218.36±80.0 mg/dl respectively. Mean high density lipoprotein (HDL) cholesterol level was 38.91±10.51 mg/dl in overweight and 36.54±10.04 mg/dl in obese. Mean low density lipoprotein (LDL) cholesterol level was 135.23±44.5 mg/dl in overweight and 143.61±56.0 mg/dl in obese. Among obese cases, 94.1% respondents had borderline triglyceride (TG) with mean 164.46±111.0 mg/dl. Among the study respondents, overweight and obesity (higher BMI) tend to have abnormal lipid profile. It is recommended that assessment of BMI should be incorporated into school health programme and those with overweight and obesity should be subjected to routine lipogram in order to apply timely preventive as well as therapeutic measures to save lives.

Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications.

The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytosine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI's capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.

Evaluation of Antimicrobial Efficacy and Clinical Outcomes of Triphala and 2.5% Sodium Hypochlorite as Intraradicular Irrigants in Pulpectomy of Primary Teeth.

A natural irrigation solution with a broad spectrum of antimicrobial coverage, triphala was selected for the pulpectomy procedure. Because of its natural ingredients, it is well-known for promoting tissue healing. It also supposedly has certain additional qualities as compared to usual irrigation solutions that are made chemically. Although 2.5% NaOCl is thought to be perfect since it meets most of the requirements for an irrigation solution but it cannot be optimized for pulpectomy procedure. Primary teeth that were recommended for pulpectomy underwent this randomized controlled experiment. Two groups of eighty-four primary teeth were randomly assigned to receive irrigations: triphala in Group A; 2.5% Sodium hypochlorite in Group B. Sample were taken from infected primary root canals. A sterile test tube with bhi broth as the transport media was used to collect pre- and post-irrigation samples using sterile absorbent paper tips. On agar media, microorganisms were cultivated and their mean colony count was assessed. Following the procedure, the patient's follow-up visits at one, two and three months were used to evaluate the clinical result. The post-microbial colony count was dramatically reduced (p<0.001) by both irrigation treatments. Triphala in Group A is demonstrating desirable efficacy. Clinical success was found satisfactory in both the groups studied (p<0.001). But statistically significant difference was not found (p=0.175). Considering undesirable properties of sodium hypochlorite triphala can be a better alternative as a root canal irrigants in pulpectomy of primary teeth.

Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.

Study of kaempferol in the treatment of COVID-19 combined with Chikungunya co-infection by network pharmacology and molecular docking technology.

Chikungunya (CHIK) patients may be vulnerable to coronavirus disease (COVID-19). However, presently there are no anti-COVID-19/CHIK therapeutic alternatives available. The purpose of this research was to determine the pharmacological mechanism through which kaempferol functions in the treatment of COVID-19-associated CHIK co-infection. We have used a series of network pharmacology and computational analysis-based techniques to decipher and define the binding capacity, biological functions, pharmacological targets, and treatment processes in COVID-19-mediated CHIK co-infection. We identified key therapeutic targets for COVID-19/CHIK, including TP53, MAPK1, MAPK3, MAPK8, TNF, IL6 and NFKB1. Gene ontology, molecular and upstream pathway analysis of kaempferol against COVID-19 and CHIK showed that DEGs were confined mainly to the cytokine-mediated signalling pathway, MAP kinase activity, negative regulation of the apoptotic process, lipid and atherosclerosis, TNF signalling pathway, hepatitis B, toll-like receptor signaling, IL-17 and IL-18 signaling pathways. The study of the gene regulatory network revealed several significant TFs including KLF16, GATA2, YY1 and FOXC1 and miRNAs such as let-7b-5p, mir-16-5p, mir-34a-5p, and mir-155-5p that target differential-expressed genes (DEG). According to the molecular coupling results, kaempferol exhibited a high affinity for 5 receptor proteins (TP53, MAPK1, MAPK3, MAPK8, and TNF) compared to control inhibitors. In combination, our results identified significant targets and pharmacological mechanisms of kaempferol in the treatment of COVID-19/CHIK and recommended that core targets be used as potential biomarkers against COVID-19/CHIK viruses. Before conducting clinical studies for the intervention of COVID-19 and CHIK, kaempferol might be evaluated in wet lab tests at the molecular level.

A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.

An integrated complete-genome sequencing and systems biology approach to predict antimicrobial resistance genes in the virulent bacterial strains of Moraxella catarrhalis.

Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful; therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance (AMR). To better comprehend the numerous processes that lead to antibiotic resistance in M. catarrhalis, we have adopted a computational method in this study. From the NCBI-Genome database, we investigated 12 strains of M. catarrhalis. We explored the interaction network comprising 74 antimicrobial-resistant genes found by analyzing M. catarrhalis bacterial strains. Moreover, to elucidate the molecular mechanism of the AMR system, clustering and the functional enrichment analysis were assessed employing AMR gene interactions networks. According to the findings of our assessment, the majority of the genes in the network were involved in antibiotic inactivation; antibiotic target replacement, alteration and antibiotic efflux pump processes. They exhibit resistance to several antibiotics, such as isoniazid, ethionamide, cycloserine, fosfomycin, triclosan, etc. Additionally, rpoB, atpA, fusA, groEL and rpoL have the highest frequency of relevant interactors in the interaction network and are therefore regarded as the hub nodes. These genes can be exploited to create novel medications by serving as possible therapeutic targets. Finally, we believe that our findings could be useful to advance knowledge of the AMR system present in M. catarrhalis.

Effect of Adding Gadolinium Oxide Promoter on Nickel Catalyst over Yttrium-Zirconium Oxide Support for Dry Reforming of Methane.

The dry reforming of methane (DRM) was studied for seven hours at 800 °C and 42 L/(g·h) gas hourly space velocity over Ni-based catalysts, promoted with various amounts of gadolinium oxide (x = 0.0, 1.0, 2.0, 3.0, 4.0, and 5.0 wt.%) and supported on mesoporous yttrium-zirconium oxide (YZr). The best catalyst was found to have 4.0 wt.% of gadolinium, which resulted in ∼80% and ∼86% conversions of CH4 and CO2, respectively, and a mole ratio of ∼0.90 H2/CO. The addition of Gd2O3 shifted the diffraction peaks of the support to higher angles, indicating the incorporation of the promoter into the unit cell of the YZr support. The Gd2O3 promoter improved the catalyst basicity and the interaction of NiO with support, which were reflected in the coke resistance (6.0 wt.% carbon deposit on 5Ni+4Gd/YZr; 19.0 wt.% carbon deposit on 5Ni/YZr) and the stability of our catalysts. The Gd2O3 is believed to react with carbon dioxide to form oxycarbonate species and helps to gasify the surface of the catalysts. In addition, the Gd2O3 enhanced the activation of CH4 and its conversion on the metallic nickel sites.

Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh.

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown-including on the transport sector-to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra- and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making.

Recent development of machine learning-based methods for the prediction of defensin family and subfamily.

Nearly all living species comprise of host defense peptides called defensins, that are crucial for innate immunity. These peptides work by activating the immune system which kills the microbes directly or indirectly, thus providing protection to the host. Thus far, numerous preclinical and clinical trials for peptide-based drugs are currently being evaluated. Although, experimental methods can help to precisely identify the defensin peptide family and subfamily, these approaches are often time-consuming and cost-ineffective. On the other hand, machine learning (ML) methods are able to effectively employ protein sequence information without the knowledge of a protein's three-dimensional structure, thus highlighting their predictive ability for the large-scale identification. To date, several ML methods have been developed for the in silico identification of the defensin peptide family and subfamily. Therefore, summarizing the advantages and disadvantages of the existing methods is urgently needed in order to provide useful suggestions for the development and improvement of new computational models for the identification of the defensin peptide family and subfamily. With this goal in mind, we first provide a comprehensive survey on a collection of six state-of-the-art computational approaches for predicting the defensin peptide family and subfamily. Herein, we cover different important aspects, including the dataset quality, feature encoding methods, feature selection schemes, ML algorithms, cross-validation methods and web server availability/usability. Moreover, we provide our thoughts on the limitations of existing methods and future perspectives for improving the prediction performance and model interpretability. The insights and suggestions gained from this review are anticipated to serve as a valuable guidance for researchers for the development of more robust and useful predictors.

Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.

Admission Plasma Glucose as In-Hospital Outcome Predictor in First Attack of Non-ST Segment Elevation Myocardial Infarction in Non Diabetic Patient.

Coronary artery disease is the leading cause of death and disability globally. The presentation of Non-ST segment elevation myocardial infarction (NSTEMI) is heterogeneous, with different risk levels in terms of death, infarction and recurrence of infarction. Current evidence suggests that plasma glucose level or hyperglycemia is a mediator of worse prognosis of MI. The objective of the study was to correlate on admission plasma glucose level in non-diabetic patient with in-hospital outcome of patients after first attack of NSTEMI. This prospective analytical study was conducted among purposively selected 280 patients with NSTEMI admitted in coronary care unit of Mymensingh Medical College Hospital during the period of June 2016 to May 2017. Data were collected from the informant by face to face interview, clinical examination and investigations using a pretested semi-structured case record form. Data were analyzed by SPSS. Patients were categorized into two groups; Group A: NSTEMI with admission plasma glucose level below 7.8mmol/l, (n=150, Male-110, Female-40). Group B: NSTEMI with admission plasma glucose level ≥7.8mmol/l, (n=130, Male-95, Female-35). Group B (n=130) is divided into two subgroups. Subgroup-I: NSTEMI with Hyperglycemia (7.8-9.3mmol/l), n = 67 (male 44, female 23), Subgroup-II: NSTEMI with Hyperglycemia (≥9.4mmol/l), n = 63 (male 51, female 12). All Patients were non diabetic excluded by HbA1c. The mean left ventricular ejection fraction (LVEF) of Group B, Subgroup-II was significantly less than that of Subgroup-I (p<0.05). Correlation between LVEF levels and on admission plasma glucose level showed statistically significant moderate negative correlation, suggesting that the higher was the level of on admission plasma glucose level; the lower was the LV ejection fraction level in first attack of NSTEMI patients. Correlation coefficient between Troponin-I and plasma glucose level on admission of the study population (r=0.030) suggesting that the higher was the level of admission plasma glucose level the higher was the Troponin-I level in first attack of NSTEMI patients. The more was the plasma glucose level, less was LVEF, more was the heart failure and prolonged hospital stay. The study showed a strong predictor of adverse in-hospital outcome in the various levels of plasma glucose and NSTEMI. There was association between the concentration of the plasma glucose and the extent, severity of disease in the means of mean LVEF, the rate of heart failure and duration of hospital stay. The importance of this finding is even clear that RBS is a standard, valuable diagnostic tool for evaluation of severity and prediction of outcome of patients with NSTEMI.

Modification of CeNi0.9Zr0.1O3 Perovskite Catalyst by Partially Substituting Yttrium with Zirconia in Dry Reforming of Methane.

Methane Dry Reforming is one of the means of producing syngas. CeNi0.9Zr0.1O3 catalyst and its modification with yttrium were investigated for CO2 reforming of methane. The experiment was performed at 800 °C to examine the effect of yttrium loading on catalyst activity, stability, and H2/CO ratio. The catalyst activity increased with an increase in yttrium loading with CeNi0.9Zr0.01Y0.09O3 catalyst demonstrating the best activity with CH4 conversion >85% and CO2 conversion >90% while the stability increased with increases in zirconium loading. The specific surface area of samples ranged from 1−9 m2/g with a pore size of 12−29 nm. The samples all showed type IV isotherms. The XRD peaks confirmed the formation of a monoclinic phase of zirconium and the well-crystallized structure of the perovskite catalyst. The Temperature Program Reduction analysis (TPR) showed a peak at low-temperature region for the yttrium doped catalyst while the un-modified perovskite catalyst (CeNi0.9Zr0.1O3) showed a slight shift to a moderate temperature region in the TPR profile. The Thermogravimetric analysis (TGA) curve showed a weight loss step in the range of 500−700 °C, with CeNi0.9Zr0.1O3 having the least carbon with a weight loss of 20%.

DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins.

Accurate identification of DNA-binding proteins (DBPs) is critical for both understanding protein function and drug design. DBPs also play essential roles in different kinds of biological activities such as DNA replication, repair, transcription, and splicing. As experimental identification of DBPs is time-consuming and sometimes biased toward prediction, constructing an effective DBP model represents an urgent need, and computational methods that can accurately predict potential DBPs based on sequence information are highly desirable. In this paper, a novel predictor called DeepDNAbP has been developed to accurately predict DBPs from sequences using a convolutional neural network (CNN) model. First, we perform three feature extraction methods, namely position-specific scoring matrix (PSSM), pseudo-amino acid composition (PseAAC) and tripeptide composition (TPC), to represent protein sequence patterns. Secondly, SHapley Additive exPlanations (SHAP) are employed to remove the redundant and irrelevant features for predicting DBPs. Finally, the best features are provided to the CNN classifier to construct the DeepDNAbP model for identifying DBPs. The final DeepDNAbP predictor achieves superior prediction performance in K-fold cross-validation tests and outperforms other existing predictors of DNA-protein binding methods. DeepDNAbP is poised to be a powerful computational resource for the prediction of DBPs. The web application and curated datasets in this study are freely available at: http://deepdbp.sblog360.blog/.

Overtaking risk modeling in two-lane two-way highway with heterogeneous traffic environment of a low-income country using naturalistic driving dataset.

INTRODUCTION: Driver behavior related to overtaking maneuvers, which are considered a major safety risk determinant on two-lane two-way highway in low- and middle-income countries (LMIC), are an important subject of further analysis. This study evaluates safety risk in terms of nature and severity of probable conflicts during overtaking maneuvers on a bi-directional undivided two-lane highway in a heterogeneous traffic environment of a low-income country. Nature and severity of probable conflicts were defined with the application of surrogate safety proximity indicators in real-world naturalistic driving environment. METHOD: A risk severity model for overtaking maneuver was developed to better understand the significant factors associated with the probability of conflict and its severity during overtaking maneuver using discrete choice modeling approaches. The relevance of three alternate discrete outcome frameworks, namely multinomial logit (ML), ordered probit (OP), and mixed logit (MXL) models are addressed. The best fitted model is identified and estimated. The impact of the significant attributes was also evaluated. The study collected data from a section of two-lane highway in Bangladesh using naturalistic driving from both observational and computer vision techniques. A total of 46 explanatory variables related to overtaking maneuver are assessed. RESULTS: Speed differential between overtaking and overtaken vehicles have a significant impact on the probability of severe conflicts. Moreover, the presence of a bus as an overtaking vehicle was found to contribute significantly to the severity of conflicts. CONCLUSIONS: The study makes substantial research contributions related to overtaking behavior and safety risk evaluation during overtaking in mixed traffic environment in low-income countries. The results can be used as a proactive tool for the evaluation of overtaking maneuvers and associated safety risk, and making policy decisions reducing safety risk during overtaking maneuver as well as overall safety, while acknowledging the limited resources and facilities in low-income countries.

An Uninformed Decision-Making Process for Cesarean Section: A Qualitative Exploratory Study among the Slum Residents of Dhaka City, Bangladesh.

The decision-making process and the information flow from physicians to patients regarding deliveries through cesarean section (C-section) has not been adequately explored in Bangladeshi context. Here, we aimed to explore the extent of information received by mothers and their family members and their involvement in the decision-making process. We conducted a qualitative exploratory study in four urban slums of Dhaka city among purposively selected mothers (n = 7), who had a cesarean birth within one-year preceding data collection, and their family members (n = 12). In most cases, physicians were the primary decision-makers for C-sections. At the household level, pregnant women were excluded from some crucial steps of the decision-making process and information asymmetry was prevalent. All interviewed pregnant women attended at least one antenatal care visit; however, they neither received detailed information regarding C-sections nor attended any counseling session regarding decisions around delivery type. In some cases, pregnant women and their family members did not ask health care providers for detailed information about C-sections. Most seemed to perceive C-sections as risk-free procedures. Future research could explore the best ways to provide C-section-related information to pregnant women during the antenatal period and develop interventions to promote shared decision-making for C-sections in urban Bangladeshi slums.

Infrastructure alone cannot ensure resilience to weather events in drinking water supplies.

Climate resilient water supplies are those that provide access to drinking water that is sustained through seasons and through extreme events, and where good water quality is also sustained. While surface and groundwater quality are widely understood to vary with rainfall, there is a gap in the evidence on the impact of weather and extremes in rainfall and temperature on drinking water quality, and the role of changes in water system management. A three-country (Bangladesh, Nepal and Tanzania) observational field study tracked 2353 households clustered around 685 water sources across seven different geographies over 14 months. Water quality (E. coli) data was modelled using GEE to account for clustering effects and repeated measures at households. All types of infrastructure were vulnerable to changes in weather, with differences varying between geographies; protected boreholes provided the greatest protection at the point of collection (PoC). Water quality at the point of use (PoU) was vulnerable to changes in weather, through changes in PoC water quality as well as changes in management behaviours, such as safe storage, treatment and cleaning. This is the first study to demonstrate the impact of rainfall and temperature extremes on water quality at the PoC, and the role that weather has on PoU water quality via management behaviours. Climate resilience for water supplies needs to consider the infrastructure as well as the management decisions that are taking place at a community and household level.

Effectiveness of a multi-modal capacity-building initiative for upgrading biomedical waste management practices at healthcare facilities in Bangladesh: a 21st century challenge for developing countries.

INTRODUCTION: Biomedical waste management (BMWM) has attracted attention across the world as improper management can pose a serious threat for healthcare workers (HCWs), the general population and the environment. This study aimed to analyse the effectiveness of a multi-modal intervention (MMI) to upgrade BMWM practices at healthcare facilities across Bangladesh. METHODS: This quasi-experimental study, with a pre- and post-test design, was undertaken at nine healthcare facilities (five public, three private and one autonomous) over three phases, and concluded in 2019. The MMI included various strategies including: (i) system change; (ii) education and training; (iii) visual reminders; (iv) monitoring and feedback; and (v) ensuring sustainability at the study hospitals. Data collected from 2726 HCWs and waste handlers through direct observation were analysed using Statistical Package for Social Sciences Version 24. RESULTS: Significant improvements were seen in waste segregation practices using colour-coded bins (from 1% to 79%). The use of personal protective equipment during transportation and final management/disposal increased from 3% to 55%. Compliance with the use of standardized methods for collecting and transporting biomedical waste (BMW) increased substantially from 0% to 78%, while compliance with standardized methods for final management/disposal of BMW improved by 39%. CONCLUSION: Compliance with BMWM practices is very poor in Bangladesh due to a lack of knowledge, manpower and resources. Nevertheless, this MMI can be used as a tool to significantly improve BMWM practices in healthcare facilities. Initiatives such as this MMI will help the Government of Bangladesh to achieve Sustainable Development Goal 3.3 and universal health coverage by 2030.

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases.

The Coronavirus Disease 2019 (COVID-19) still tends to propagate and increase the occurrence of COVID-19 across the globe. The clinical and epidemiological analyses indicate the link between COVID-19 and Neurological Diseases (NDs) that drive the progression and severity of NDs. Elucidating why some patients with COVID-19 influence the progression of NDs and patients with NDs who are diagnosed with COVID-19 are becoming increasingly sick, although others are not is unclear. In this research, we investigated how COVID-19 and ND interact and the impact of COVID-19 on the severity of NDs by performing transcriptomic analyses of COVID-19 and NDs samples by developing the pipeline of bioinformatics and network-based approaches. The transcriptomic study identified the contributing genes which are then filtered with cell signaling pathway, gene ontology, protein-protein interactions, transcription factor, and microRNA analysis. Identifying hub-proteins using protein-protein interactions leads to the identification of a therapeutic strategy. Additionally, the incorporation of comorbidity interactions score enhances the identification beyond simply detecting novel biological mechanisms involved in the pathophysiology of COVID-19 and its NDs comorbidities. By computing the semantic similarity between COVID-19 and each of the ND, we have found gene-based maximum semantic score between COVID-19 and Parkinson's disease, the minimum semantic score between COVID-19 and Multiple sclerosis. Similarly, we have found gene ontology-based maximum semantic score between COVID-19 and Huntington disease, minimum semantic score between COVID-19 and Epilepsy disease. Finally, we validated our findings using gold-standard databases and literature searches to determine which genes and pathways had previously been associated with COVID-19 and NDs.