Rhizophora mucronata Lam., a halophyte from Mauritius Island, inhibits the entry of Zika virus in human cells (A549)- an in vitro and in silico analysis.

The recent appearance of Zika virus (ZIKV) in Brazil should serve as a wake-up call to international authorities, as it poses a threat to global public health. In the present study, we investigated whether a mangrove plant, Rhizophora mucronata Lam. (R. mucronata) collected in Mauritius, possesses anti-ZIKV activity at the non-cytotoxic doses. ZIKVMC-MR766NIID (ZIKVGFP) was used for assessing anti ZIKV activity. In silico docking (Autodock 4) and molecular simulation were performed on collected data. Using a recombinant ZIKV expressing reporter green fluorescent protein(GFP) protein, we discovered that fruit and root methanolic, decocted fruit and root extracts were effective inhibitors of ZIKV infection in human epithelial A549 cells at negligible cytotoxicity. The mechanisms by which such extracts prevented ZIKV infection are linked to the inability of the virus to attach to the host cell surface. The outcomes of this study were supported by the docking calculations in which some of the dominant compounds have shown high binding affinity against ZIKV. The scientific data gathered in this study might pave the way for the future development of possible R. mucronata inhibitors to combat ZIKV.fCommunicated by Ramaswamy H. Sarma.

Unveiling the Antioxidant, Clinical Enzyme Inhibitory Properties and Cytotoxic Potential of Tambourissa peltata Baker-An Understudied Endemic Plant.

This study documents for the first time the phytochemical composition and biological activities of Tambourissa peltata Baker, an endemic plant from Mauritius. Phytochemical extraction was performed using ethyl acetate, methanol and distilled water as solvents. The phytochemical composition was determined through HPLC-MS and other standard assays. The DPPH, ABTS, FRAP, CUPRAC and phosphomolybdenum assays were employed for the determination of the antioxidant potential, whereas cell viability assays were used to determine the cytotoxicity. The highest phenolic and phenolic acid contents were obtained in the aqueous extract (179.91 ± 0.67 gallic acid equivalents/g and 55.74 ± 1.43 caffeic acid equivalents/g). The highest quantity of flavonoids was obtained in the ethyl acetate extract (28.97 ± 0.46 rutin equivalents/g). The methanolic extract was the highest source of flavonols (33.71 ± 0.13 mg catechin equivalents/g). A total of 34 phytochemicals were identified, mainly proanthocyanidins and flavonoid glycosides. The highest antioxidant activity in DPPH (973.40 ± 5.65 mg TE (Trolox equivalents)/g), ABTS (2030.37 ± 40.83 mg TE/g), FRAP (1461.39 ± 5.95 mg TE/g), CUPRAC (1940.99 ± 20.95 mg TE/g) and phosphomolybdenum (8.37 ± 0.23 mmol TE/g) assays was recorded for the aqueous extract. The ethyl acetate extract was the most active metal chelator. The highest acetylcholinesterase inhibitor was the methanolic extract, whereas the ethyl acetate extract was the most active against BChE. The tyrosinase enzyme was most inhibited by the methanolic extract. Alpha-amylase and glucosidase were most inhibited by the aqueous extract. The methanolic extract was capable of inducing cell cytotoxicity to the human colorectal carcinoma without damaging normal cells. T. peltata warrants further attention from the scientific community given its multifaceted biological properties.

Above the Invasive and Ornamental Attributes of the Traveler's Palm: An In Vitro and In Silico Insight into the Anti-Oxidant, Anti-Enzymatic, Cytotoxic and Phytochemical Characterization of Ravenala madagascariensis.

Ravenala madagascariensis is a widely known ornamental and medicinal plant, but with a dearth of scientific investigations regarding its phytochemical and pharmacological properties. Hence, these properties were appraised in this study. The DPPH (154.08 ± 2.43 mgTE/g), FRAP (249.40 ± 3.01 mgTE/g), CUPRAC (384.57 ± 1.99 mgTE/g), metal chelating (29.68 ± 0.74 mgEDTAE/g) and phosphomolybdenum assay (2.38 ± 0.07 mmolTE/g) results demonstrated that the aqueous extract had the most prominent antioxidant activity, while the methanolic extract displayed the best antioxidant potential in the ABTS assay (438.46 ± 1.69 mgTE/g). The HPLC-ESI-Q-TOF-MS-MS analysis allowed the characterization of 41 metabolites. The methanolic extract was the most active against acetylcholinesterase. All extracts were active against the alpha-amylase and alpha-glucosidase enzymes, with the ethyl acetate extract being the most active against the alpha-amylase enzyme, while the methanolic extract showed the best alpha-glucosidase inhibition. A plethora of metabolites bonded more energetically with the assayed enzymes active sites based on the results of the in silico studies. R. madagascariensis extracts used in this study exhibited cytotoxicity against HT29 cells. The IC50 of the methanolic extract was lower (506.99 ug/mL). Based on the heat map, whereby flavonoids were found to be in greater proportion in the extracts, it can be concluded that the flavonoid portion of the extracts contributed to the most activity.

Modeling the susceptibility of an uneven-aged broad-leaved forest to snowstorm damage using spatially explicit machine learning.

Snowstorms are disturbance agents that have received relatively little research attention rather than significant disturbances that they pose to forest ecosystems. In this study, we modeled the interactions between snowstorms and different characteristics of a forest stand in northern Iran and spatially visualized the susceptibility of the stand to damage caused by snowstorms using the random forest (RF) and logistic regression (LR) methods. After a severe snowstorm in November 2021 that caused stem breakage and uprooting of individual trees, the location of 185 damaged trees was identified via field surveys and used for generating an inventory map of snowstorm damage. The thematic maps of fourteen explanatory variables representing the characteristics of damaged trees and the study forest were produced. The models were trained with 70% of the damaged trees and validated with the remaining 30% based on the area under the receiver operating characteristic curve (AUC). The results indicated the better performance of RF compared to LR in both training (0.934 vs. 0.896) and validation (0.925 vs. 0.894) phases. The RF model identified slope, aspect, and wind effect as the variables with the greatest impacts on the forest stand sustainability to snowstorm damage. Approximately 30% of the study area was categorized as high and very high susceptible to snowstorms. Our results can enable forest managers to tailor more informed adaptive forest management plans in readiness for snowstorm seasons and recovery from their damage.