Introduction: Bangladesh's geriatric health management of the public health facility settings remains quite an unexplored area despite the accelerated aging situation. This study aimed to explore the preparedness of public health facilities and associated challenges, and scopes for geriatric healthcare in Dhaka Division.

Methodology: A mixed-method cross-sectional research design was used to collect data from four purposively selected districts- Dhaka, Tangail, Shariatpur, and Narsingdi. The challenges, and scopes regarding preparedness were assessed through exploratory research design. 21 health facilities from tertiary, secondary, and primary levels were selected for the quantitative survey and 15 were selected for the qualitative interviews.

Findings: The readiness index was 30.54 in our study, indicating a low level of overall preparedness. The readiness score varied across the WHO building blocks: leadership and governance 8.2, financing 13.09, healthcare workforce 18.32, health management and information system 37.69, medicines and technologies 52.8, and service delivery 53.17. The highest readiness score was found for tertiary hospitals (62.28) and the lowest for community clinics (16.94). Dhaka is the best-performing district. The in-depth assessment also found that the tertiary hospitals had advantages but lacked coordination of all the departments, while the root-level facilities lacked effective introduction to the context and innovative primary-level facilities to address geriatric healthcare.

Conclusions: Public healthcare facilities in Bangladesh are not well-prepared to offer services related to geriatric care yet; gaps remain in all the critical pillars of the health systems. In this decade of "Healthy Aging”, the nation must step up its efforts to raise the level of preparedness by recognizing those paucities, addressing them in appropriate plans, programs, and policies, and implementing in a way that not only reduces the gaps in the critical pillars of WHO but also maximizes their effective interaction with one another.

The research intends to assess the psycho-social and health impacts of flooding of 2022 among the vulnerable communities of the north-eastern region of Bangladesh. It also sheds light on some of the life-cycle based health impacts of chronic flash flooding among the population of the area. In addition, some of the tangible health damages from the flooding have also been calculated. Both qualitative and quantitative data have been used during this study. The data has been collected by using the trio of questionnaire survey, group discussions and interviewing. The samples for the survey have been meticulously determined by following the standard statistical procedures. Site selection for data collection has been done on the basis of inundation maps and reconnaissance surveys. The final questionnaire has been approved by the ethical review committee. Most of the data on the psycho-social shocks and life cycle based impacts were collected by using Likert scales. For calculating the damage, data has been collected with open ended questions. The findings from the survey will be finally used to develop an index of the comprehensive health shocks. Policy suggestions on developing community resilience, introducing adaptive social protections and some other community based adaptation practices will be provided on the basis of the findings.

Clay-fired brick production has become a primary environmental concern in Bangladesh. Brick kilns are a vital contributor to the degrading air quality of the country as they emit toxic fumes into the air. Even though there are concerns regarding the well-being of the environment, clay-fired bricks remain the number one building material. Due to the brick's widespread production, efflorescence has become a significant issue. Efflorescence is mainly the buildup of white salt-like particles on brick and other construction materials, and its buildup is a crucial indicator of water intrusion in the bricks. The understanding of efflorescence still needs to be improved amongst owners and engineers of the country. This research intends to identify the leading cause of efflorescence and find a sustainable solution to the problem. To achieve the objectives of this research, five brick samples were collected from 3 physiographic regions in Bangladesh. The names of the brick companies are MMB, PKB, DONE, MOON, and KBM. Each of the bricks was tested for efflorescence and ranked based on the severity of the efflorescence: 1) MMB 90%, 2) MOON and DONE 50%, and 3) PKB 50%. Bricks from MMB were selected for further experiments and testing as they showed the highest level of efflorescence. The soil and brick samples underwent multiple lab experiments, such as leeching and ion extraction, and were tested for cations and mineralogical assemblages using a Scanning Electron Microscope (SEM). The results indicate that calcium was the dominant component and most likely played a significant role in the deposition of salt particles on the brick surface. The findings of this research can be used to reduce brick production with calcium-rich soil to reduce the efflorescence problem in Bangladesh.

Afforestation and reforestation using native and exotic tree species in degraded land are considered to be one of the popular suggestive measures for carbon storage and potential flood magnitude reduction. However, oftentimes, it ignores quantitative evaluation of their impacts on catchment hydrology, specially in water-limited regions with consequences for downstream users. Though the science of the effect of plantations on catchment water yield is well established and understood, understanding changes in water yield or stream flow pattern following plantation activities is crucial for developing plans for water resources management. This review is an attempt to address this issue by comparing the effect of native forest and plantation forest on the water yield of forested catchments conducted on tropical climate condition. A rigorous database developed by Hua et al. (2022) was used for this review. This database considered catchment studies comprising native and plantation forest from 1960-2021. Primary forest, secondary forest and generic forests were considered as “native” forest whereas mixed-plantation, mono-plantation and abandoned plantation were considered as “plantation” forest. The amount of water discharge or draining annually from forested catchments/plots, expressed as % of rainfall is considered as water yield of a catchment or plot. The results revealed that native forest water yield is 4% higher (37±19%) when it was compared with plantations (33±20%). In case of various types of plantations, catchment with mono-plantation has yielded more water (35%) compared to mixed (26%) and abandoned plantations (10%). In contrast, among native forest types, secondary forest has yielded more water (41%) compared to generic native (38%) and primary forest (34%). Native forests age and plantation forest age does not have significant effect on water yield. When native forest water yield was compared with plantation it was found that water yield has decreased up to of 2.30%. Both mono-plantation and mixed plantation methods showed a negative log response ratio indicates water yield decreased because of plantation intervention. Temperature and rainfall doesn’t have significant impact on both native and plantation forest water yield. Overall, this review suggests that native forests are much more productive in terms of catchment water yield compared to catchments with plantations. Therefore, policy makers need to take more measures to protect mature native forests from human disturbances.

Rapid unplanned industrialization and urbanization in Bangladesh have led to significant pollution and degradation of freshwater sources, highlighting the need for preservation as a fundamental human right. Untreated wastewater discharged from industries contributes to heavy metal toxicity, posing a significant risk to aquatic organisms, wildlife, and human health. The Meghna River, a potential source of freshwater for the capital city, Dhaka, and the sustainer of diverse aquatic ecosystems and habitats, also serves as a sink for untreated industrial effluents containing heavy metal contaminants. Hence, to mitigate heavy metal contamination and preserve the ecosystem and human health, it is crucial to regularly monitor the water quality and heavy metal concentration of the river. However, using conventional techniques to evaluate heavy metal pollution in large areas like rivers comes with challenges, including inaccessibility, time, and cost. With the advent of advanced remote sensing technologies, using Satellite Earth Observation Data (EO) and correlating high-resolution satellite images and in-situ data offers the potential to assess the pollution level of the study area effectively. In this study, sentinel-2 satellite images were utilized to assess the heavy metal concentration of the Meghna River. Samples were collected during the Monsoon and Post Monsoon period within the proximity of industrial areas near Meghna Bridge, Narayanganj. The laboratory analysis of heavy metal concentration was conducted using the ICP mass spectrometer. Multiple linear regression (MLR) models were established between measured heavy metal concentrations and spectral reflectances of different bands or band ratios at the corresponding sampling points. The equations generated using the MLR were further applied to the satellite images for zoning the concentration level of heavy metal in the study area. The accuracy of the developed models was examined by Pearson correlation coefficients, and the estimated values generated from the model were further compared with the observed values. The results indicated a significant correlation for Hg, Co, Zn, and As with the spectral reflectance data.

This present study assessed the overall flood hazards of Sylhet and Sunamganj districts based on a 1D–2D coupled hydraulic model for the recent flood event in 2022 in the Sylhet region. The model was developed utilizing discharge, water level, cross sections, and rainfall data collected from the BWDB and digital elevation model (DEM), as well as Sentinel imagery data from the USGS and Copernicus Hub, respectively. The 1D model was calibrated and validated using the water level datasets of 2004 and 2010 respectively. Manning’s roughness coefficient was applied as a tuning parameter for model calibration, ranging from 0.013 to 0.015. In addition, the 2D model was also validated by a qualitative comparison between the simulated and remote sensing-based (Sentinel-1) flood inundation maps. From the unsteady flow simulation, the flood hazard parameters (flood depth and velocity) were produced. In the depth map, the maximum value for the entire modeled area is greater than 5 m. Furthermore, it is observed that the ranges of the maximum and minimum flood velocity for the whole modeled area are greater than 2.09 m/s and lower than 0.1 m/s, respectively. The result also shows that Sulla upazilla of Sunamganj district and Balaganj upazilla of Sylhet are the most inundated upazillas of the two districts, respectively. Hence, it can be said that the areas with a higher depth, higher velocity, and longer duration are the most flood-prone and flood-susceptible areas within the study area for the 2022 Sylhet flood event. The findings of the study are expected to aid floodplain management authorities in limiting future flood damage and fatalities.

The frequent mutation capacity of SARS-CoV-2 and lack of effective therapy methods have put the world on high alert. In this work, novel compound searching was carried out for SARS-CoV-2 protein showing low mutation susceptibility. The probability of undergoing viral escape mutation to the effects of drug is lower if conserved proteins are targeted by drugs. Mutation rate analysis of all SARS-CoV-2 proteins was carried out to identify proteins which are less mutation prone. Because thearubigin was discovered to have inhibitory effects against viral 3-chymotrypsin-like-proteases involved in coronavirus replication, virtual screening method was used to look for chemicals with similar structures to thearubigin that could have effects against SARS-CoV-2 NSP10-16 complex. Out of 1000 compounds having thearubigin-like structures located using PubChem, 31 were filtered out using Lipinski's rule of five. ADMET profiling, molecular docking, and molecular dynamics simulation investigations of hit compounds were carried out. Four compounds (PubChem IDs 90950326, 54764514, 124086159, and 122450369) showed probable molecular interactions with NSP10-16 after docking experiments. Furthermore, molecular dynamics simulations were run for 100 ns to determine SASA, RMSD, RMSF, and Rg values of thearubigin and hits in order to reveal the binding kinetics, structural behavior, and flexibility of these compounds. All ligands exhibit drug-like qualities and can be subjected to further lead optimization in order to be produced as medications. The absorption profiles of the hit ligands were analyzed computationally in human models using GastroPlus software. This research adds to the body of knowledge and guidelines for developing effective SARS-CoV-2 treatments.

Deltas like the Ganges-Brahmaputra Delta (GBD) experience significant land subsidence due to overlaying load, consolidation, sediment compaction, regional tectonics, and human activity. The southwestern region of Bangladesh, marked by its low-lying floodplain with compressible sediments, experiences significant land subsidence. However, accurate assessments for these areas are very scarce, often relying on localized measurements rather than comprehensive data. This study employed InSAR (Interferometric Synthetic Aperture Radar) analysis, spanning from 2014 to 2022, to effectively measure land subsidence, and visualize it spatially. In the study area, subsidence rates were found to be ranging from 3 to 20 mm/year, with most areas experiencing 3-10 mm/year. While some areas show higher rates of 10-13 mm/year, very few exceeded 14 mm/year. The average subsidence rate was 5.98 mm/year, demonstrating significant spatial variability across the region. The findings were validated based on existing Global navigation satellite system (GNSS) data points, affirming their reliability. Despite minor discrepancies with other measurements at specific points, the overall results align well with existing data. This comprehensive InSAR analysis provides a detailed assessment of land subsidence in the southwestern region of Bangladesh, highlighting significant spatial variability and offering valuable insights for future coastal development and risk mitigation strategies.

Mangrove ecosystem, such as the Sundarbans Mangrove Forest (SMF) in Bangladesh, plays a significant role in the conservation of coastal biodiversity, give protection to the communities against harsh impacts of climate change, as well as ecosystem services benefits from its diverse habitat. However, with the current trend in climate extreme events which is further exacerbate by continuous anthropogenic activities and natural disasters, this beautiful landscape faces an increasing threat to habitat degradation and change in habitat status due to these factors.

Quite a number of research has been conducted to assessed the impact of threats to the SMF, however, most of this research make use of indirect approach of ecological study which only assess single threat, habitat, and a very small spatial coverage. Therefore, the purpose of this study is to explore the use of remote sensing and Ecosystem modeling to assess habitat quality and degradation from interplay between multiple threats. Using TERRSET2020 Ecosystem Habitat Model as a modeling tool, and Google Earth Engine for Habitat classification, this study aims to determine (i) how landcover in SMF and surrounding communities changed overtime, (ii) what are the threats causing habitat degradation and their sources, (iii) what are habitats experiencing quality and degradation changes, and (iv) how is the distribution of habitat quality and degradation overtime in the SMF.

Our methodology involves four analytical steps with first three steps serving as an input for the final. Firstly, we will use Remote sensing data, and Google Earth Engine (GEE) platform to analyze Landcover as the habitat type for our model. Landsat and Sentinel-2A (2000, 2011, 2023) data will be assessed through GEE cloud and supervised classification techniques will apply to generate comprehensive Landcover history. In the second step, we will identify various threat that are causing habitat degradation from both anthropogenic activities, climate and environmental impact. Relative impact of each threat will be assessed by giving weight to each of them and then determine maximum distance to habitat. Also, all threats will be organized in a table and map representing each will be generated. Next is the analysis of habitat suitability to each threat. A range of suitability score will be assigned between 0 and 1 to each habitat with the use of Analytical Hierarchical Process for decision making and organize in table. Finally, all results including, Land cover map, threat maps and table, and habitat suitability table will be fed into the Habitat Quality Model to generate habitat degradation and habitat quality map for each selected year over the study area.

This research will provide valuable understanding of multiple threats impact to habitat quality and degradation in the Sundarbans Mangrove. The results are expected to support and inform government, stakeholder, and Non-Governmental Organization sustainable management practices, ecosystem conservation, Natural resources management, and contribute to climate change mitigation and adaptation strategies.

This present study assessed the overall flood hazards of Sylhet city and adjacent floodplain areas based on a 1D–2D coupled hydraulic model for the recent flood event in 2022 in the Sylhet region. The model was developed utilizing discharge, water level, cross sections, and rainfall data collected from the BWDB and digital elevation model (DEM), as well as Sentinel imagery data from the USGS and Copernicus Hub, respectively. The 1D model was calibrated and validated using the water level datasets of 2004 and 2010. Manning’s roughness coefficient was applied as a tuning parameter for model calibration, ranging from 0.013 to 0.015. In addition, the 2D model was also validated by a qualitative comparison between the simulated and remote sensing-based (Sentinel-1) flood inundation maps. From the unsteady flow simulation, the flood hazard parameters (extent of flood, depth, arrival time, velocity, and duration of the flood) were produced. The produced flood extent map shows that about 50% and 20% of the land areas of the entire modelled area and Sylhet city were inundated during that flood event, respectively. In the depth map, the maximum value for the entire modelled area and Sylhet city is greater than 5 m. The modelling results suggest that the maximum and minimum flood arrival times for a 1 m-depth flood in the entire modelled area are more than 50 days and less than 5 days, respectively. It also suggests that the maximum and minimum flood arrival times for 1 m of depth in Sylhet city are more than 20 days and less than 10 days, respectively. Furthermore, it is observed that the ranges of the maximum and minimum flood velocity for the whole modelled area and the city of Sylhet are greater than 0.4 ms^-1 and lower than 0.1 ms^-1, respectively. Finally, the flood duration map shows that the maximum and minimum flood duration times for a 1 m-deep flood in the modelled area and Sylhet city are more than 115 days and less than 60 days, respectively. Hence, it can be said that the areas with a higher depth, lower arrival time, higher velocity, and longer duration are the most flood-prone and flood-susceptible areas within the study area for the 2022 Sylhet flood event. The study’s findings are expected to aid floodplain management authorities in limiting future flood damage and fatalities.