Transcriptome-Wide Identification and Integrated Analysis of a UGT Gene Involved in Ginsenoside Ro Biosynthesis in Panax ginseng.

Panax ginseng as a traditional medicinal plant with a long history of medicinal use. Ginsenoside Ro is the only oleanane-type ginsenoside in ginseng, and has various pharmacological activities, including anti-inflammatory, detoxification, and antithrombotic activities. UDP-dependent glycosyltransferase (UGT) plays a key role in the synthesis of ginsenoside, and the excavation of UGT genes involved in the biosynthesis of ginsenoside Ro has great significance in enriching ginsenoside genetic resources and further revealing the synthesis mechanism of ginsenoside. In this work, ginsenoside-Ro-synthesis-related genes were mined using the P. ginseng reference-free transcriptome database. Fourteen hub transcripts were identified by differential expression analysis and weighted gene co-expression network analysis. Phylogenetic and synteny block analyses of PgUGAT252645, a UGT transcript among the hub transcripts, showed that PgUGAT252645 belonged to the UGT73 subfamily and was relatively conserved in ginseng plants. Functional analysis showed that PgUGAT252645 encodes a glucuronosyltransferase that catalyzes the glucuronide modification of the C3 position of oleanolic acid using uridine diphosphate glucuronide as the substrate. Furthermore, the mutation at 622 bp of its open reading frame resulted in amino acid substitutions that may significantly affect the catalytic activity of the enzyme, and, as a consequence, affect the biosynthesis of ginsenoside Ro. Results of the in vitro enzyme activity assay of the heterologous expression product in E. coli of PgUGAT252645 verified the above analyses. The function of PgUGAT252645 was further verified by the result that its overexpression in ginseng adventitious roots significantly increased the content of ginsenoside Ro. The present work identified a new UGT gene involved in the biosynthesis of ginsenoside Ro, which not only enriches the functional genes in the ginsenoside synthesis pathway, but also provides the technical basis and theoretical basis for the in-depth excavation of ginsenoside-synthesis-related genes.

Comprehensive Evaluation of Appreciation of Rhododendron Based on Analytic Hierarchy Process.

Qinting Lake Park has effectively imported Rhododendron varieties from Zhejiang Province. The analytic hierarchy process was employed to devise an evaluation framework to evaluate the ornamental and adaptive features of these species. Subsequently, we conducted a standardized evaluation of 24 species for their ornamental and adaptive traits under controlled cultivation conditions. The findings indicated that the percentage of ornamental flowers in the first-level index was significantly greater than the other two factors, indicating that the ornamental value of flowers was the most important in the evaluation of Rhododendron ornamental value. Among the secondary indicators, the proportion of flower color and flower weight was significantly higher than that of other factors, which had the greatest impact on the evaluation results. The 24 Rhododendron species were classified into two grades based on their ornamental value, as determined by index weights and scoring standards. Rhododendron 'Xueqing', Rhododendron 'Big Qinglian', and Rhododendron 'Jinyang No. 9' exhibited superior ornamental value and demonstrated more favorable suitability for garden applications.

Genomic analysis quantifies pyroptosis in the immune microenvironment of HBV-related hepatocellular carcinoma.

Pyroptosis, a way of pro-inflammatory death, plays a significant part in the tumor microenvironment (TME). A recent study has shown that the hepatitis C virus changes the TME by inducing pyroptosis against hepatocellular carcinoma (HCC). However, compared to TME in hepatitis C virus-infected HCC, the exploration of immune characteristics and response to immunotherapy associated with the pyroptosis phenotype is still insufficient in hepatitis B virus-related HCC (HBV-HCC). Our study constructed pyroptosis-score (PYS) via principal-component analysis (PCA) to unveil the link between pyroptosis and tumor immunity in 369 HBV-HCC patients. Compared with the low-PYS group, subjects with higher PYS were associated with poor prognosis but were more susceptible to anti-PD-L1 treatment. In addition, we found that PYS can serve independently as a prognostic factor for HBV-HCC, making it possible for us to identify specific small molecule drugs with a potential value in inhibiting tumors via targeting pyroptosis. Also, the target genes predicted by the Weighted gene co-expression network analysis (WGCNA) and pharmacophore model were enriched in the HIF-1 signaling pathway and NF-kB transcription factor activity, which were related to the mechanism of inflammation-driven cancer. The PYS is extremely important in predicting prognosis and responses to immunotherapy. New treatment strategies for inflammation-driven cancers may be found by targeting pyroptosis.

Modeling clinical activities based on multi-perspective declarative process mining with openEHR's characteristic.

BACKGROUND: It is significant to model clinical activities for process mining, which assists in improving medical service quality. However, current process mining studies in healthcare pay more attention to the control flow of events, while the data properties and the time perspective are generally ignored. Moreover, classifying event attributes from the view of computers usually are difficult for medical experts. There are also problems of model sharing and reusing after it is generated. METHODS: In this paper, we presented a constraint-based method using multi-perspective declarative process mining, supporting healthcare personnel to model clinical processes by themselves. Inspired by openEHR, we classified event attributes into seven types, and each relationship between these types is represented in a Constrained Relationship Matrix. Finally, a conformance checking algorithm is designed. RESULTS: The method was verified in a retrospective observational case study, which consists of Electronic Medical Record (EMR) of 358 patients from a large general hospital in China. We take the ischemic stroke treatment process as an example to check compliance with clinical guidelines. Conformance checking results are analyzed and confirmed by medical experts. CONCLUSIONS: This representation approach was applicable with the characteristic of easily understandable and expandable for modeling clinical activities, supporting to share the models created across different medical facilities.

Using machine learning models to improve stroke risk level classification methods of China national stroke screening.

BACKGROUND: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency. METHOD: Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels. RESULT: The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year. CONCLUSION: Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

Physical, chemical and biological behaviour of fumigants on cottonseed.

Fumigation is required to protect cottonseed in storage and pre-shipment from insect pests and/or microorganisms. Fumigation of cottonseed with carbon disulphide (CS2), carbonyl sulphide (COS), ethanedinitrile (C2N2), ethyl formate (EF), methyl bromide (MB) and phosphine (PH3) showed that >85% of the fumigants disappeared within 5 h of exposure. COS maintained >20 mg L-1 for 24 h. After 1 day of aeration, 75%-85% of the absorbed COS and MB and 20%-40% of the absorbed CS2, EF and PH3 were released from treated cottonseed. The fumigant residues were reduced by 80% for COS, 50% for EF or MB and 25% for CS2 after 1 day of aeration. After 13 days of aeration, fumigant residues were reduced by 95% for MB, 65% for EF, 55% for CS2 and to natural levels in the COS residue. Carbon disulphide, COS, PH3, EF and C2N2 had no effect on the germination of cottonseed, but germination was reduced to 50% by MB. COS has potential as a fumigant for control of insect pests in cottonseed because it dissipates quickly and does not negatively impact germination. On the other hand, MB appears to strongly absorb and requires an extended period for residues to dissipate, and it negatively impacts germination.

Using machine learning models to classify stroke risk level based on national screening data.

With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke risk factors and conducts high-risk population interventions for people aged over 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, atrial fibrillation, smoking, lack of exercise, apparently overweight or obese and family history of stroke. People with more than two risk factors or with a history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk level for people with "unknown" values in the fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in the statistical results at the national level. In this paper, firstly, we construct the training set and test set and process the imbalanced training set based on oversampling and undersampling method. Then, we develop logistic regression model, decision tree model, neural network model and random forest model for stroke risk classification, and evaluate these models based on the recall and precision. The results show that the model based on random forest achieves best performance considering recall and precision. The models constructed in this paper can improve the screening efficiency and avoid unnecessary rescreening and intervention expenditures.

QRStream: A Secure and Convenient Method for Text Healthcare Data Transferring.

With the increasing of health awareness, the users become more and more interested in their daily health information and healthcare activities results from healthcare organizations. They always try to collect them together for better usage. Traditionally, the healthcare data is always delivered by paper format from the healthcare organizations, and it is not easy and convenient for data usage and management. They would have to translate these data on paper to digital version which would probably introduce mistakes into the data. It would be necessary if there is a secure and convenient method for electronic health data transferring between the users and the healthcare organizations. However, for the security and privacy problems, almost no healthcare organization provides a stable and full service for health data delivery. In this paper, we propose a secure and convenient method, QRStream, which splits original health data and loads them onto QR code frame streaming for the data transferring. The results shows that QRStream can transfer text health data smoothly with an acceptable performance, for example, transferring 10K data in 10 seconds.

CrowdMapping: A Crowdsourcing-Based Terminology Mapping Method for Medical Data Standardization.

Standardized terminology is the prerequisite of data exchange in analysis of clinical processes. However, data from different electronic health record systems are based on idiosyncratic terminology systems, especially when the data is from different hospitals and healthcare organizations. Terminology standardization is necessary for the medical data analysis. We propose a crowdsourcing-based terminology mapping method, CrowdMapping, to standardize the terminology in medical data. CrowdMapping uses a confidential model to determine how terminologies are mapped to a standard system, like ICD-10. The model uses mappings from different health care organizations and evaluates the diversity of the mapping to determine a more sophisticated mapping rule. Further, the CrowdMapping model enables users to rate the mapping result and interact with the model evaluation. CrowdMapping is a work-in-progress system, we present initial results mapping terminologies.