Potential bioactive phytochemicals, antioxidant properties and anticancer pathways of Nymphaea nouchali

Objective:To investigate bioactive phytochemicals and antioxidant activities of Nymphaea nouchali and to explore its anticancer pathways by a network pharmacology approach.Methods:Using a spectrophotometer and high-performance liquid chromatography-diode array detector(HPLC-DAD),we quantified bioactive phytochemicals in methanolic extract of Nymphaea nouchali tuber.The extracts were investigated for in vitro antioxidant properties.Targets of these bioactive phytochemicals were predicted and anticancer-associated pathways were analyzed by a network pharmacology approach.Moreover,we identified the predicted genes associated with cancer pathways and the hub genes in the protein-protein interaction network of predicted genes.Results:Quantitative results indicated the total phenolics,total flavonoids,and total proanthocyanidins in the methanolic extract of Nymphaea nouchali tuber.HPLC-DAD analysis showed rutin(39.44 mg),catechin(39.20 mg),myricetin(30.77 mg),ellagic acid(11.05 mg),gallic acid(3.67 mg),vanillic acid(0.75 mg),rosmarinic acid(4.81 mg),p-coumaric acid(3.35 mg),and quercetin(0.90 mg)in 1 g of dry extract.The extract showed the radical scavenging activities of 2,2-diphenyl-1-picrylhydrazyl,2,2’-azinobis(3-ethylbenzothiazoline-6-sulfonic acid)and N,N-dimethyl-pphenylenediamine.By using network pharmacology,we predicted 130 target genes associated with cancer pathways.The top hub genes(IL6,AKT1,EGFR,JUN,PTGS2,MAPK3,CASP3,and CXCL8)were also identified,which were associated with cancer pathways and interacted with bioactive phytochemicals of the methanolic extract of Nymphaea nouchali tuber.Conclusions:Our study provides insights into the mechanism of anticancer activities of the methanolic extract of Nymphaea nouchali tuber.

The effects of temperature on decomposition and allelopathic phytotoxicity of boneseed litter

Decomposition of plant litter is a fundamental process in ecosystem function, carbon and nutrient cycling and, by extension, climate change. This study aimed to investigate the role of temperature on the decomposition of water soluble phenolics（WSP）, carbon and soil nutrients in conjunction with the phytotoxicity dynamics of Chrysanthemoides monilifera subsp. monilifera（boneseed） litter. Treatments consisted of three factors including decomposition materials（litter alone, litter with soil and soil alone）, decomposition periods and temperatures（5-15, 15-25and 25-35°C（night/day））. Leachates were collected on 0, 5, 10, 20, 40 and 60 th days to analyse physico-chemical parameters and phytotoxicity. Water soluble phenolics and dissolved organic carbon（DOC） increased with increasing temperature while nutrients like SO-24 and NO-13 decreased. Speed of germination, hypocotyl and radical length and weight of Lactuca sativa exposed to leachates were decreased with increasing decomposition temperature. All treatment components had significant effects on these parameters. There had a strong correlation between DOC and WSP, and WSP content of the leachates with radical length of test species. This study identified complex interactivity among temperature, WSP, DOC and soil nutrient dynamics of litter occupied soil and that these factors work together to influence phytotoxicity.

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases

The coronavirus disease that outbreak in 2019 has caused various health issues.According to the WHO,the first positive case was detected in Bangladesh on 7th March 2020,but while writing this paper in June 2021,the total confirmed,recovered,and death cases were 826922,766266 and 13118,respectively.Due to the emergence of COVID-19 in Bangladesh,the country is facing a major public health crisis.Unfortunately,the country does not have a comprehensive health policy to address this issue.This makes it hard to predict how the pandemic will affect the population.Machine learning techniques can help us detect the disease's spread.To predict the trend,parameters,risks,and to take preventive measure in Bangladesh;this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory.Here,we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh.We extracted the data for daily confirmed,recovered,and death cases from March 2020 to August 2021.The obtained Root Mean Square Error(RMSE)values of confirmed,recovered,and death cases indicates that our result is more accurate than other contemporary techniques.This study indicates that the LSTM model could be used effectively in predicting contagious diseases.The obtained results could help in explaining the seriousness of the situation,also mayhelp the authorities to take precautionary steps to control the situation.