First-principles calculations on monolayer WX2 (X = S, Se) as an effective drug delivery carrier for anti-tuberculosis drugs.

Tuberculosis (TB) remains a major global health concern, necessitating the exploration of novel drug delivery systems to combat the challenges posed by conventional approaches. We investigated the potential of monolayer transition metal dichalcogenides (TMDs) as an innovative platform for efficient and targeted delivery of antituberculosis drugs. Specifically, the electronic and optical properties of prominent TB drugs, isoniazid (INH) and pyrazinamide (PZA), adsorbed on tungsten diselenide (WSe2) and tungsten disulfide (WS2) monolayers were studied using first-principles calculations based on density functional theory (DFT). The investigation revealed that the band gaps of WSe2 and WS2 monolayers remain unaltered upon adsorption of PZA or INH, with negative adsorption energy indicating stable physisorption. We explored different vertical and horizontal configurations, and the horizontal ones were more stable. When INH and PZA drugs were horizontally adsorbed together on WSe2, the most stable configuration was found with an adsorption energy of -2.35 eV. Moreover, the adsorbed drugs could be readily released by light within the visible or near-infrared (NIR) wavelength range. This opened up possibilities for their potential application in photothermal therapy, harnessing the unique properties of these 2D materials. The comprehensive analysis of the band structures and density of states provides valuable insights into how the drug molecules contributed to the conduction and valence bands. The optical responses of anti-TB drugs adsorbed in 2D WSe2 and WS2 were similar to those of pristine 2D WSe2 and WS2. We demonstrated the temperature-dependent release mechanism of our 2D WSe2 and WS2 drug complexes, confirming the feasibility of releasing the discussed anti-tuberculosis drugs by generating heat through photothermal therapy. These findings hold significant promise for developing innovative drug delivery systems that have enhanced efficacy for targeted and low-toxic TB treatment.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal.

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model.

The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5-2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data.