Decision support system for community managed rainwater harvesting: A case study in the salinity-prone coastal region of Bangladesh.

Climate change-induced saline intrusion into both surface and groundwater, extreme weather events, and unregulated water usage are serious threats to the drinking water supply in coastal areas worldwide, especially in least-developed countries. This research developed a data-driven decision-making methodology to evaluate the performance of rainwater harvesting (RWH) systems in the saline-prone southwestern coastal region of Bangladesh. Twenty-five community managed RWH systems, recently piloted in two major coastal districts, were considered the case study to develop and validate this evaluation tool. The evaluation methodology integrates daily water models, lifetime cost analysis, Geographic Information System (GIS)-based parameters supported by the Analytical Hierarchy Process (AHP), and field observation. While the meteorological parameters as well as the hydrological and economic performance were found to be highly suitable, 36 % of the systems showed moderate performance, as challenges remain in ensuring proper operation and maintenance practices at the community level. However, 40 % of the systems showed high performance, with two systems showing very high suitability, which suggests community managed RWH systems as a sustainable adaptation for coastal water supply.

Pharmacological insight of rutin as a potential candidate against peptic ulcer.

Peptic ulcer is a sore on the stomach lining that results from the erosion of the gastrointestinal tract mucosa due to various influencing factors. Of these, Helicobacter pylori infection and non-steroidal anti-inflammatory drugs (NSAIDs) stand out as the most prominent causes. This condition poses a significant global health concern due to its widespread impact on individuals worldwide. While various treatment strategies have been employed, including proton pump inhibitors and histamine-2 receptor antagonists, these have notable side effects and limitations. Thus, there is a pressing need for new treatments to address this global health issue. Rutin, a natural flavonoid, exhibits a range of biological activities, including anti-inflammatory, anticancer, and antioxidant properties. This review explores the potential anti-ulcer effect of rutin in experimental models and how rutin can be a better alternative for treating peptic ulcers. We used published literature from different online databases such as PubMed, Google Scholar, and Scopus. This work highlights the abundance of rutin in various natural sources and its potential as a promising option for peptic ulcer treatment. Notably, the anti-inflammatory properties of rutin, which involve inhibiting inflammatory mediators and the COX-2 enzyme, are emphasized. While acknowledging the potential of rutin, it is important to underscore the necessity for further research to fully delineate its therapeutic potential and clinical applicability in managing peptic ulcers and ultimately improving patient outcomes. This review on the anti-ulcer potential of rutin opened a new door for further study in the field of alternative medicine in peptic ulcer management.

A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms.

Background and objective: Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. Methods: In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. Results: The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. Conclusion: The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

Investigating the role of hypothetical protein (AAB33144.1) in HIV-1 virus pathogenicity: A comparative study with FDA-Approved inhibitor compounds through In silico analysis and molecular docking.

Aim and objective: Due to the a lot of unexplored proteins in HIV-1, this research aimed to explore the functional roles of a hypothetical protein (AAB33144.1) that might play a key role in HIV-1 pathogenicity. Methods: The homologous protein was identified along with building and validating the 3D structure by searching several bioinformatics tools. Results: Retroviral aspartyl protease and retropepsin like functional domains and motifs, folding pattern (cupredoxins), and subcellular localization in cytoplasmic membrane were determined as biological activity. Besides, the functional annotation revealed that the chosen hypothetical protein possessed protease-like activity. To validate our generated protein 3D structure, molecular docking was performed with five compounds where nelfinavir showed (-8.2 kcal/mol) best binding affinity against HXB2 viral protease (PDB ID: 7SJX) and main protease (PDB ID: 4EYR) protein. Conclusions: This study suggests that the annotated hypothetical protein related to protease action, which may be useful in viral genetics and drug discovery.

The portrayal of antimicrobial resistance in Bangladeshi newspapers during 2010-2021: Toward understanding the narrative.

BACKGROUND: Antimicrobial resistance (AMR) is a major global public health crisis and around the last decade, newspapers were one of the main sources of public dissemination of information for so. This study highlights how Bangladeshi mainstream newspapers represented AMR-related news and how they created the narrative of AMR in Bangladesh. METHODS: We conducted both quantitative and qualitative content analysis on 275 AMR-related news articles published in the twelve highest circulated dailies (January 2010 to September 2021). We divided the articles into report, opinion, and editorials and analyzed how their contents built the narrative of AMR in Bangladesh. RESULTS: Bangladeshi newspapers reported misuse of antibiotics by the consumers the most (32.2%), followed by selling without prescriptions (29%), and over-prescription by the health providers (26.1%). There were hardly any news reports describing the impact of pharmaceutical companies in prescribing and selling antibiotics. Around 45% of the news articles were event-oriented. Moreover, they suggested inadequate recommendations to battle AMR. CONCLUSION: Valid, consistent, and reliable AMR news coverage can play a crucial role in creating mass awareness, making providers accountable, and supporting national action plan in mitigating AMR threat. The Bangladeshi journalists interested in reporting AMR-issues should focus on disseminating more Bangla articles with scientific information, and reporting causes and recommendations responsibly.

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.