At present,a variety of vaccines have been approved,and existing antiviral drugs are being tested to find an effective treatment for coronavirus disease 2019(COVID-19).However,no standardized treatment has yet been ap...At present,a variety of vaccines have been approved,and existing antiviral drugs are being tested to find an effective treatment for coronavirus disease 2019(COVID-19).However,no standardized treatment has yet been approved by the World Health Organization.The virally encoded chymotrypsin-like protease(3CL^(pro))from severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which facilitates the replication of SARS-CoV in the host cells,is one potential pharmacological target for the development of antiSARS drugs.Online search engines,such as Web of Science,Google Scholar,Scopus and PubMed,were used to retrieve data on the traditional uses of medicinal plants and their inhibitory effects against the SARS-CoV 3CL^(pro).Various pure compounds,including polyphenols,terpenoids,chalcones,alkaloids,biflavonoids,flavanones,anthraquinones and glycosides,have shown potent inhibition of SARS-CoV-2 3CL^(pro) activity with 50% inhibitory concentration(IC_(50))values ranging from 2-44μg/mL.Interestingly,most of these active compounds,including xanthoangelol E(isolated from Angelica keiskei),dieckol 1(isolated from Ecklonia cava),amentoflavone(isolated from Torreya nucifera),celastrol,pristimerin,tingenone and iviperin(isolated from Tripterygium regelii),tannic acid(isolated from Camellia sinensis),and theaflavin-3,3’-digallate,3-isotheaflav1in-3 gallate and dihydrotanshinone I(isolated from Salvia miltiorrhiza),had IC_(50)values of less than 15μg/mL.Kinetic mechanistic studies of several active compounds revealed that their mode of inhibition was dose-dependent and competitive,with K_(i)values ranging from 2.4-43.8μmol/L.Given the significance of plant-based compounds and the many promising results obtained,there is still need to explore the phytochemical and mechanistic potentials of plants and their products.These medicinal plants could serve as an effective inexpensive nutraceutical for the general public to help manage COVID-19.展开更多
Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century.Vacuum freeze-drying(VFD),though invented over a hundred years ago,remains one of the most ad...Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century.Vacuum freeze-drying(VFD),though invented over a hundred years ago,remains one of the most advanced drying techniques,known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state.The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods.However,the qualitative aspects of the dried product are not predictable.In this context,the present study aims to create a deep neural framework(DNF)that predicts the performance of a Vacuum Freeze Drying(VFD)system for kiwifruit,based on its morphology and nutritional value under varying conditions.This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network(GAN)to create diverse,unlabeled datasets.The framework is optimized using Gaussian Process(GP)for hyper-parameter tuning,focusing on minimizing errors like mean square error(MSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).The maximum MSE of 1.243 is found in the prediction of rehydration rate,followed by color(0.725),energy consumption(0.426),moisture content(0.379),texture(0.320),sensory(0.250),and Brix(0.215),respectively.The maximum MAE and MAPE values are recorded 0.833 and 32.99%while the minimum is observed 0.368 and 7.019%in the case of rehydration rate and Brix,respectively.Overall,the R2 value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.展开更多
基金financially supported by President’s International Fellowship Initiative(PIFI)for Postdoctoral Researchers,Chinese Academy of Sciences(No.2020PB0002),China。
文摘At present,a variety of vaccines have been approved,and existing antiviral drugs are being tested to find an effective treatment for coronavirus disease 2019(COVID-19).However,no standardized treatment has yet been approved by the World Health Organization.The virally encoded chymotrypsin-like protease(3CL^(pro))from severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which facilitates the replication of SARS-CoV in the host cells,is one potential pharmacological target for the development of antiSARS drugs.Online search engines,such as Web of Science,Google Scholar,Scopus and PubMed,were used to retrieve data on the traditional uses of medicinal plants and their inhibitory effects against the SARS-CoV 3CL^(pro).Various pure compounds,including polyphenols,terpenoids,chalcones,alkaloids,biflavonoids,flavanones,anthraquinones and glycosides,have shown potent inhibition of SARS-CoV-2 3CL^(pro) activity with 50% inhibitory concentration(IC_(50))values ranging from 2-44μg/mL.Interestingly,most of these active compounds,including xanthoangelol E(isolated from Angelica keiskei),dieckol 1(isolated from Ecklonia cava),amentoflavone(isolated from Torreya nucifera),celastrol,pristimerin,tingenone and iviperin(isolated from Tripterygium regelii),tannic acid(isolated from Camellia sinensis),and theaflavin-3,3’-digallate,3-isotheaflav1in-3 gallate and dihydrotanshinone I(isolated from Salvia miltiorrhiza),had IC_(50)values of less than 15μg/mL.Kinetic mechanistic studies of several active compounds revealed that their mode of inhibition was dose-dependent and competitive,with K_(i)values ranging from 2.4-43.8μmol/L.Given the significance of plant-based compounds and the many promising results obtained,there is still need to explore the phytochemical and mechanistic potentials of plants and their products.These medicinal plants could serve as an effective inexpensive nutraceutical for the general public to help manage COVID-19.
基金support from the National Science and Technology Council Taiwan under the Contract No.NSTC 112-2221-E-027-054-MY2.
文摘Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century.Vacuum freeze-drying(VFD),though invented over a hundred years ago,remains one of the most advanced drying techniques,known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state.The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods.However,the qualitative aspects of the dried product are not predictable.In this context,the present study aims to create a deep neural framework(DNF)that predicts the performance of a Vacuum Freeze Drying(VFD)system for kiwifruit,based on its morphology and nutritional value under varying conditions.This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network(GAN)to create diverse,unlabeled datasets.The framework is optimized using Gaussian Process(GP)for hyper-parameter tuning,focusing on minimizing errors like mean square error(MSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).The maximum MSE of 1.243 is found in the prediction of rehydration rate,followed by color(0.725),energy consumption(0.426),moisture content(0.379),texture(0.320),sensory(0.250),and Brix(0.215),respectively.The maximum MAE and MAPE values are recorded 0.833 and 32.99%while the minimum is observed 0.368 and 7.019%in the case of rehydration rate and Brix,respectively.Overall,the R2 value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.
