Phytochemical and Biological Investigation of an Indigenous Plant of Bangladesh, Gynura procumbens (Lour.) Merr.: Drug Discovery from Nature.

Gynura procumbens (Lour.) Merr. (Family: Asteraceae) is a tropical Asian medicinal plant found in Thailand, China, Malaysia, Indonesia, and Vietnam. It has long been utilized to treat a variety of health concerns in numerous countries around the world, such as renal discomfort, constipation, diabetes mellitus, rheumatism, and hypertension. The chemical investigation resulted in the isolation and characterization of six compounds from the methanol (MeOH) extract of the leaves of Gynura procumbens, which were identified as phytol (1), lupeol (2), stigmasterol (3), friedelanol acetate (4), ß-amyrin (5), and a mixture of stigmasterol and ß-sitosterol (6). In-depth investigations of the high-resolution 1H NMR and 13C NMR spectroscopic data from the isolated compounds, along with comparisons to previously published data, were used to clarify their structures. Among these, the occurrence of Compounds 1 and 4 in this plant are reported for the first time. The crude methanolic extract (CME) and its different partitionates, i.e., petroleum ether (PESF), chloroform (CSF), ethyl acetate (EASF), and aqueous (AQSF) soluble fractions, were subjected to antioxidant, cytotoxic, thrombolytic, and anti-diabetic activities. In a DPPH free radical scavenging assay, EASF showed the maximum activity, with an IC50 value of 10.78 µg/mL. On the other hand, CSF displayed the highest cytotoxic effect with an LC50 value of 1.94 µg/mL compared to 0.464 µg/mL for vincristine sulphate. In a thrombolytic assay, the crude methanolic extract exhibited the highest activity (63.77%) compared to standard streptokinase (70.78%). During the assay for anti-diabetic activity, the PESF showed 70.37% of glucose-lowering activity, where standard glibenclamide showed 63.24% of glucose-reducing activity.

Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh.

Land use land cover change (LULC) significantly impacts urban sustainability, urban planning, climate change, natural resource management, and biodiversity. The Chattogram Metropolitan Area (CMA) has been going through rapid urbanization, which has impacted the LULC transformation and accelerated the growth of urban sprawl and unplanned development. To map those urban sprawls and natural resources depletion, this study aims to monitor the LULC change using Landsat satellite imagery from 2003 to 2023 in the cloud-based remote sensing platform Google Earth Engine (GEE). LULC has been classified into five distinct classes: waterbody, build-up, bare land, dense vegetation, and cropland, employing four machine learning algorithms (random forest, gradient tree boost, classification & regression tree, and support vector machine) in the GEE platform. The overall accuracy (kappa statistics) and the receiver operating characteristic (ROC) curve have demonstrated satisfactory results. The results indicate that the CART model outperforms other LULC models when considering efficiency and accuracy in the designated study region. The analysis of LULC conversions revealed notable trends, patterns, and magnitudes across all periods: 2003-2013, 2013-2023, and 2003-2023. The expansion of unregulated built-up areas and the decline of croplands emerged as primary concerns. However, there was a positive indication of a significant increase in dense vegetation within the study area over the 20 years.