Material basis and pharmacodynamic mechanism of YangshenDingzhi granules in the intervention of viral pneumonia:Based on serum pharmacochemistry and network pharmacology

Background:YangshenDingzhi granules(YSDZ)are clinically effective in preventing and treating COVID-19.The present study elucidates the underlying mechanism of YSDZ intervention in viral pneumonia by employing serum pharmacochemistry and network pharmacology.Methods:The chemical constituents of YSDZ in the blood were examined using ultraperformance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry(UPLC-Q-Exactive Orbitrap MS).Potential protein targets were obtained from the SwissTargetPrediction database,and the target genes associated with viral pneumonia were identified using GeneCards,DisGeNET,and Online Mendelian Inheritance in Man(OMIM)databases.The intersection of blood component-related targets and disease-related targets was determined using Venny 2.1.Protein-protein interaction networks were constructed using the STRING database.The Metascape database was employed to perform enrichment analyses of Gene Ontology(GO)functions and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathways for the targets,while the Cytoscape 3.9.1 software was utilized to construct drug-component-disease-target-pathway networks.Further,in vitro and in vivo experiments were performed to establish the therapeutic effectiveness of YSDZ against viral pneumonia.Results:Fifteen compounds and 124 targets linked to viral pneumonia were detected in serum.Among these,MAPK1,MAPK3,AKT1,EGFR,and TNF play significant roles.In vitro tests revealed that the medicated serum suppressed the replication of H1N1,RSV,and SARS-CoV-2 replicon.Further,in vivo testing analysis shows that YSDZ decreases the viral load in the lungs of mice infected with RSV and H1N1.Conclusion:The chemical constituents of YSDZ in the blood may elicit therapeutic effects against viral pneumonia by targeting multiple proteins and pathways.

Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating

The aim of this study was in-line,rapid,and non-destructive detection for soluble solid content(SSC)in pomelos using visible and near-infrared spectroscopy(Vis-NIRS).However,the large size and thick rind of pomelo affect the stability of spectral acquisition and the biological variabilities affect the robustness of models.Given these issues,in this study,an efficient prototype in-line detection system in transmittance mode was designed and evaluated in comparison with an off-line detection system.Data from the years 2019 and 2020 were used for modeling and the external validation data were obtained by the inline detection system in 2021.The wavelength selection methods of changeable size moving window(CSMW),random frog(RF),and competitive adaptive reweighted sampling(CARS)were used to improve the prediction accuracy of partial least squares regression(PLSR)models.The best performance of internal prediction was obtained by CARS-PLSR and the determination coefficient of prediction(),root mean square error of prediction(RMSEP),and residual predictive deviation(RPD)were 0.958,0.204%,and 4.821,respectively.However,all models obtained large prediction biases in external validation.The latent variable updating(LVU)method was proposed to update models and improve the performance in external validation.Ten samples from the external validation set were randomly selected to update the models.Compared with the recalibration method,LVU could effectively modify the original models which matched the SSC range of the external validation set.The CSMW-PLSR models were more robust in external validations.The off-line model with LVU performed best with a root mean square error of validation(RMSEV)of 0.599%and the in-line model with recalibration obtained RMSEV of 0.864%.These results demonstrated the application potential of the transmittance Vis-NIRS for in-line rapid prediction of SSC in pomelos and the modeling and updating methods could be applied to samples with biological variabilities.

Promising real-time fruit and vegetable quality detection technologies applicable to manipulator picking process

In recent years,worldwide research on fruit and vegetable quality detection technology includes machine vision,spectroscopy,acoustic vibration,tactile sensors,etc.These technologies have also been gradually applied to fruit and vegetable grading and sorting lines in recent years,greatly improving the income of farmers.There have been numerous reviews of these techniques.Most of the published research on fruit and vegetable quality detection technology is still carried out in the laboratory.The emphases have been on quality feature extraction,model establishment and experimental verification.The successful application in the fruit and vegetable sorting production line proves that these studies have high application potential and value,and we look forward to the performance of these sensing technologies in the fruit and vegetable picking field.Therefore,in this paper,based on the future highly automated fruit and vegetable picking mode,we will focus on three kinds of fruit and vegetable quality detection technologies including machine vision,tactile sensor and spectroscopy,to provide some reference for future research.Since there are currently limited cases of detecting quality during the fruit and vegetable picking,experiments performed on prototypes of manipulator,or devices such as Nanocilia sensors,portable spectrometers,etc.,which are compact and convenient to mount on manipulator will be reviewed.Several tables and mosaics showing the performance of the three technologies in the detection of fruit and vegetable quality over the past five years have been listed.The performance of each sensing technology was relatively satisfactory in the laboratory in general.However,in the picking scenario,there are still many challenges to be solved.Different from industrial environments,agricultural scenarios are complex and changeable.Fragile and vulnerable agricultural products pose another challenge.The development of portable devices and nanomaterials have become important breakthroughs.Optical and tactile detection methods,as well as the integration of different quality detection methods,are expected to be the trends of research and development.