Construction of the prediction model for multiple myeloma based on machine learning.

INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM. METHODS: A total of 465 patients and 150 healthy controls were enrolled in this retrospective study. Based on the variable screening strategy of least absolute shrinkage and selection operator (LASSO), three prediction models, logistic regression (LR), support vector machine (SVM), and random forest (RF), were established combining complete blood count (CBC) and cell population data (CPD) parameters in the training set (210 cases), and were verified in the validation set (90 cases) and test set (165 cases). The performance of each model was analyzed using receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC) were applied to evaluate the models. Delong test was used to compare the AUC of the models. RESULTS: Six parameters including RBC (1012/L), RDW-CV (%), IG (%), NE-WZ, LY-WX, and LY-WZ were screened out by LASSO to construct the model. Among the three models, the AUC of RF model in the training set, validation set, and test set were 0.956, 0.892, and 0.875, which were higher than those of LR model (0.901, 0.849, and 0.858) and SVM model (0.929, 0.868, and 0.846). Delong test showed that there were significant differences among the models in the training set, no significant differences in the validation set, and significant differences only between SVM and RF models in the test set. The calibration curve and DCA showed that the three models had good validity and feasibility, and the RF model performed best. CONCLUSION: The proposed RF model may be a useful auxiliary tool for rapid screening of MM patients.

Insights into the Hormone-Regulating Mechanism of Adventitious Root Formation in Softwood Cuttings of Cyclocarya paliurus and Optimization of the Hormone-Based Formula for Promoting Rooting.

Adventitious root (AR) formation is vital for successful cutting propagation in plants, while the dynamic regulation of phytohormones is viewed as one of the most important factors affecting AR formation. Cyclocarya paliurus, a hard-to-root plant, is faced with the bottleneck of cloning its superior varieties in practice. In this study, ten treatments were designed to figure out the best hormone-based formula for promoting AR formation in softwood cuttings and explore their hormone-regulating mechanisms. Both the rooting process and the rooting parameters of the softwood cuttings were significantly affected by different hormone-based formulas (p < 0.05), while the greatest rooting rate (93%) and root quality index were achieved in the H3 formula (SR3:IR3 = 1:1). Significant differences in the measured phytohormone concentrations, as well as in their ratios, were detected among the cuttings sampled at various AR formation stages (p < 0.05), whereas the dynamics for each phytohormone varied greatly during AR formation. The transcriptome analysis showed 12,028 differentially expressed genes (DEGs) identified during the rooting process of C. paliurus cuttings, while the KEGG enrichment analysis indicated that a total of 20 KEGG terms were significantly enriched in all the comparison samples, with 253 DEGs detected in signal transduction. Furthermore, 19 genes with vital functions in regulating the hormone signaling pathway were identified by means of a WGCNA analysis. Our results not only optimize a hormone-based formula for improving the rooting of C. paliurus cuttings but also provide an insight into the hormonal regulatory network during AR formation in softwood C. paliurus cuttings.

Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. METHODS: Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. RESULTS: We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. CONCLUSIONS: The CRRS model has reliable predictive value for the prognosis of HCC patients.

Whole-genome Duplication Reshaped Adaptive Evolution in A Relict Plant Species, Cyclocarya paliurus.

Cyclocarya paliurus is a relict plant species that survived the last glacial period and shows a population expansion recently. Its leaves have been traditionally used to treat obesity and diabetes with the well-known active ingredient cyclocaric acid B. Here, we presented three C. paliurus genomes from two diploids with different flower morphs and one haplotype-resolved tetraploid assembly. Comparative genomic analysis revealed two rounds of recent whole-genome duplication events and identified 691 genes with dosage effects that likely contribute to adaptive evolution through enhanced photosynthesis and increased accumulation of triterpenoids. Resequencing analysis of 45 C. paliurus individuals uncovered two bottlenecks, consistent with the known events of environmental changes, and many selectively swept genes involved in critical biological functions, including plant defense and secondary metabolite biosynthesis. We also proposed the biosynthesis pathway of cyclocaric acid B based on multi-omics data and identified key genes, in particular gibberellin-related genes, associated with the heterodichogamy in C. paliurus species. Our study sheds light on evolutionary history of C. paliurus and provides genomic resources to study the medicinal herbs.

Characteristics and Functional Application of Cellulose Fibers Extracted from Cow Dung Wastes.

The widespread use of petroleum-based products has led to increasing environmental and ecological problems, while the extraction and application of various natural cellulose fibers have received increasing attention. This research focuses on the extraction of cellulose fibers from cow dung using different treatments: hot water, hydrogen peroxide (H2O2), sodium hydroxide (NaOH) and potassium hydroxide (KOH) boilings, as well as a selection of the best quality cow dung fibers for papermaking with quality control. The study's objective is to find a sustainable method to extract as much material as possible from renewable biomass feedstock. The results show that the best extraction rate is obtained by KOH boiling with 42% cellulose fibers extracted. Corresponding handmade paper has a burst index of 2.48 KPam2/g, a tear index of 4.83 mNm2/g and a tensile index of 26.72 Nm/g. This project expands the sources of natural cellulose fibers to an eco-friendly and sustainable one and opens up new applications for cow dung.

Comparison of ischemic stroke diagnosis models based on machine learning.

Background: The incidence, prevalence, and mortality of ischemic stroke (IS) continue to rise, resulting in a serious global disease burden. The prediction models have a great value in the early prediction and diagnosis of IS. Methods: The R software was used to screen the differentially expressed genes (DEGs) of IS and control samples in the datasets GSE16561, GSE58294, and GSE37587 and analyze DEGs for enrichment analysis. The feature genes of IS were obtained by several machine learning algorithms, including the least absolute shrinkage and selector operation (LASSO) logistic regression, the support vector machine-recursive feature elimination (SVM-RFE), and the Random Forest (RF). The IS diagnostic models were constructed based on transcriptomics by machine learning and artificial neural network (ANN). Results: A total of 69 DEGs, mainly involved in immune and inflammatory responses, were identified. The pathways enriched in the IS group were complement and coagulation cascades, lysosome, PPAR signaling pathway, regulation of autophagy, and toll-like receptor signaling pathway. The feature genes selected by LASSO, SVM-RFE, and RF were 17, 10, and 12, respectively. The area under the curve (AUC) of the LASSO model in the training dataset, GSE22255, and GSE195442 was 0.969, 0.890, and 1.000. The AUC of the SVM-RFE model was 0.957, 0.805, and 1.000, respectively. The AUC of the RF model was 0.947, 0.935, and 1.000, respectively. The models have good sensitivity, specificity, and accuracy. The AUC of the LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 1.000, 0.995, and 0.997, respectively, in the training dataset. However, the AUC of LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 0.688, 0.605, and 0.619, respectively, in the GSE22255 dataset. The AUC of the LASSO+ANN and RF+ANN models was 0.740 and 0.630, respectively, in the GSE195442 dataset. In the training dataset, the sensitivity, specificity, and accuracy of the LASSO+ANN model were 1.000, 1.000, and 1.000, respectively; of the SVM-RFE+ANN model were 0.946, 0.982, and 0.964, respectively; and of the RF+ANN model were 0.964, 1.000, and 0.982, respectively. In the test datasets, the sensitivity was very satisfactory; however, the specificity and accuracy were not good. Conclusion: The LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.

Association Analysis Between Genotype and Environment: Differentiation Between Cyclocarya paliurus Resources That Accumulate Triterpenoids.

Cyclocarya paliurus is mainly distributed in subtropical areas of China. Its leaves are rich in beneficial triterpenoids that have bioactivities against human diseases, including hyperlipemia, diabetes, and hypertension. In this study, data on the genetic diversity, distributing environment, and triterpenoids of C. paliurus samples were collected from a wide area in China. The data covered 316 C. paliurus germplasms collected from 26 distinct populations. Association analysis between genotype and triterpenoids was carried out to describe triterpenoids accumulation pattern. Based on our analyses, we identified the important trend that genotypes with higher triterpenoid contents belonged to a unique genotype subgroup. The results showed that pterocaryoside B and pterocaryoside A significantly vary among the genotypic subgroups. In addition, the different genotypic subgroups showed distinct geographical distributing areas. These findings provide information about the relationship between genetic and environmental factors and how this affects triterpenoids accumulation. This information will be valuable for targeted breeding and for further germplasm selection of C. paliurus resources.

Nitrogen addition modifies the relative gene expression level and accumulation of carbon-based bioactive substances in Cyclocarya paliurus.

In China, lots of Cyclocarya paliurus plantations have been established for tea and functional food production on nitrogen (N)-limited land. The optimum N levels require for biosynthesis and accumulation of carbon-based bioactive substances vary among plant species. This study integrated field trial with hydroponic culture to assess impact of nitrogen addition on accumulation and relative gene expression level of carbon-based secondary metabolites in C. paliurus. N addition significantly influenced not only contents of polyphenols, flavonoids and triterpenoids and relative gene expression levels of their biosynthetic pathway in C. paliurus leaves but also leaf biomass production and the bioactive substance accumulations. An intermediate N addition induced the highest contents of polyphenols, flavonoids and triterpenoids in leaves, but the optimized accumulation of these bioactive substances in the leaves was the trade-off between their contents and leaf biomass production. Correlation analysis showed that related gene expression levels were closely correlated with contents of their leaf corresponding secondary metabolites. Compared with ratios of carbon/N (C/N) and carbon/phosphorus (C/P) in the soil, ratios of C/N and C/P in the leaves were more strongly related to the contents and accumulations of polyphenols, flavonoids and triterpenoids. To obtain higher yields of targeted phytochemicals, the threshold ratios of C/N and C/P in the leaves are recommended for N and P fertilization at similar sites. Overall, our findings would provide the theoretical basis and technical support for manipulating N fertilization in C. paliurus plantations to obtain higher accumulations of targeted bioactive substances.

Cotton disease identification method based on pruning.

Deep convolutional neural networks (DCNN) have shown promising performance in plant disease recognition. However, these networks cannot be deployed on resource-limited smart devices due to their vast parameters and computations. To address the issue of deployability when developing cotton disease identification applications for mobile/smart devices, we compress the disease recognition models employing the pruning algorithm. The algorithm uses the γ coefficient in the Batch Normalization layer to prune the channels to realize the compression of DCNN. To further improve the accuracy of the model, we suggest two strategies in combination with transfer learning: compression after transfer learning or transfer learning after compression. In our experiments, the source dataset is famous PlantVillage while the target dataset is the cotton disease image set which contains images collected from the Internet and taken from the fields. We select VGG16, ResNet164 and DenseNet40 as compressed models for comparison. The experimental results show that transfer learning after compression overall surpass its counterpart. When compression rate is set to 80% the accuracies of compressed version of VGG16, ResNet164 and DenseNet40 are 90.77%, 96.31% and 97.23%, respectively, and the parameters are only 0.30M, 0.43M and 0.26M, respectively. Among the compressed models, DenseNet40 has the highest accuracy and the smallest parameters. The best model (DenseNet40-80%-T) is pruned 75.70% of the parameters and cut off 65.52% of the computations, with the model size being only 2.2 MB. Compared with the version of compression after transfer learning, the accuracy of the model is improved by 0.74%. We further develop a cotton disease recognition APP on the Android platform based on the model and on the test phone, the average time to identify a single image is just 87ms.

Hydrogen sulfide alleviates salinity stress in Cyclocarya paliurus by maintaining chlorophyll fluorescence and regulating nitric oxide level and antioxidant capacity.

Cyclocarya paliurus is commonly used to treat diabetes in China. However, the natural habitats of C. paliurus are typically affected by salt stress. Hydrogen sulfide (H2S) is a growth regulator that is widely used to enhance plant stress tolerance, but the possible mechanism underlying H2S-alleviated salt stress in C. paliurus remains unclear. C. paliurus seedlings pretreated with NaHS (an H2S donor) were exposed to salt stress, and then, the leaf and total biomass, chlorophyll fluorescence parameters, nitric oxide (NO) content, oxidative damage, and proline and phenolic content were investigated to test the hypothesis that H2S and NO were involved in the salt tolerance of C. paliurus. The results showed that H2S pretreatment maintained chlorophyll fluorescence and attenuated the loss of plant biomass. We also found that H2S pretreatment further increased the endogenous NO content and nitrate reductase activity compared with salt treatment. Moreover, H2S pretreatment alleviated salt-induced oxidative damage, as indicated by lowered lipid peroxidation, through an enhanced antioxidant system including more proline and phenolic accumulation and increased antioxidant enzyme activities. However, C. paliurus leaves treated with the NO scavenger significantly diminished H2S-mediated NO production and alleviation of membrane lipid peroxidation. Thus, we concluded that H2S-induced NO was involved in C. paliurus salt tolerance.

Immunological analysis and differential genes screening of venous thromboembolism.

PURPOSE: To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. METHODS: The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. RESULTS: Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. CONCLUSIONS: Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

Astragalus membranaceus Injection Suppresses Production of Interleukin-6 by Activating Autophagy through the AMPK-mTOR Pathway in Lipopolysaccharide-Stimulated Macrophages.

Astragalus membranaceus (AM), used in traditional Chinese medicine, has been shown to enhance immune functions, and recently, its anti-inflammatory effects were identified. However, the mechanisms of action remain unclear. Most studies have shown that autophagy might be involved in the immune response of the body, including inflammation. Here, we developed an inflammatory model by stimulating macrophages with lipopolysaccharides (LPS) to explore the anti-inflammatory effect and mechanisms of AM injection from the perspective of the regulation of autophagy. Immunoblot, immunofluorescence, and ELISA were used to determine the effects of AM injection on the production of interleukin-6 (IL-6) and alterations of autophagy markers. It was found that AM injection reduced the expression of IL-6 in LPS-stimulated macrophages and reversed the LPS-induced inhibition of cellular autophagy. After treatment with inhibitors of signaling pathways, it was shown that LPS downregulated autophagy and upregulated the production of IL-6 in macrophages via the protein kinase B (Akt)/mammalian target of rapamycin (mTOR) pathway. AM injection reversed the effects of LPS by activating the AMP-activated protein kinase (AMPK) instead of inhibiting Akt. These results were further confirmed by testing activators and siRNA silencing of AMPK. Hence, these 2 distinct signaling molecules appear to exert opposite effects on mTOR, which integrates information from multiple upstream signaling pathways, negatively regulating autophagy. In addition, we demonstrated that autophagy might play a key role in regulating the production of IL-6 by testing activators and inhibitors of autophagy and siRNA silencing of ATG5. These findings showed that AM injection might enhance autophagy by activating AMPK and might further play a repressive effect on the LPS-stimulated expression of IL-6. This study explored the relationship between autophagy, signaling pathways, and the production of inflammatory factors in a model of endotoxin infection and treatment with AM injection.

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

Exploring the Pathological Mechanism of Bladder Cancer Based on Tumor Mutational Burden Analysis.

Although immunotherapy has progressed in the treatment of bladder cancer, some patients still have poor prognosis. New therapeutic targets are eager to be discovered to improve the outcomes of bladder cancer. With the development of high-throughput sequencing and tumor profiling, potential tumor biomarkers were identified. Through the interpretation of related data from the Cancer Genome Atlas database (TCGA), some key genes have been discovered to drive the development and prognosis of urinary bladder neoplasm. On account of the success of immunotherapy in many cancer types, we established the relationship between tumor mutation burden and immune microenvironment of bladder cancer and found the changes of several immune cells in this disease. Based on the understanding of the bladder tumor genome and immune environment, this study is supposed to provide new therapies for the treatment of bladder neoplasm.

Prevalence of Hyperuricemia and its Correlation with Serum Lipids and Blood Glucose in Physical Examination Population in 2015 - 2018: a Retrospective Study.

BACKGROUND: Hyperuricemia (HUA) is a metabolic disease caused by a disorder of purine metabolism, which increases the risk of cardio-cerebrovascular disease. Serum lipids and blood glucose are risk factors for metabolic syndrome. Thus, this study aimed to assess the prevalence of HUA and its relationship with serum lipids and blood glucose. METHODS: A total of 59,074 cases (32,623 males and 26,451 females) from three hospitals in Lanzhou city from January 2015 to December 2018 were grouped according to serum uric acid (SUA) level to analyze the differences in age, total cholesterol (TC), triacylglycerol (TG), low density lipoprotein (LDL), high density lipoprotein (HDL), fasting blood glucose (FBG), creatinine (Cr), and blood urea nitrogen (BUN). The changes of prevalence of HUA among different age and gender groups was analyzed. Pearson's correlation analysis was used to analyze the correlation of SUA level with clinical indicators. The risk factors of HUA were analyzed by using binary logistic regression analysis. ROC curve was used to analyze the independent risk factors of elevated SUA. RESULTS: The overall prevalence rate of HUA was 19.87% and the prevalence rate of males was significantly higher than that of females (28.35% vs. 9.41%, χ2 = 3,289.143, p < 0.01). The prevalence rates of HUA from 2015 - 2018 were 19.54%, 19.31%, 18.64%, and 21.81%, respectively. Compared with the normal SUA group, TC, TG, and LDL significantly increased in the HUA group. The correlation analysis showed that SUA was negatively correlated with gender and HDL, and positively correlated with age, FBG, TC, TG, and LDL. The logistic regression analysis revealed that TG, TC, and LDL were risk factors for HUA. The ROC curve analysis showed that the risk of HUA significantly increased when TG was above 1.645 mmol/L. CONCLUSIONS: The overall prevalence of HUA in physical examination population has generally been at a high level in the past 4 years. Serum lipids and blood glucose may be independent risk factors for predicting HUA.

[Bioinformatics analysis of COL1A1 regulated by miR-129-5p as a potential therapeutic target for gastric cancer].

OBJECTIVE: To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer. METHODS: The differentially expressed genes (DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes (Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients. RESULTS: A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein- protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5p was found to bind to the 3'UTR of COL1A1 mRNA, and compared with that in normal tissues, miR-129-5p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients. CONCLUSIONS: COL1A1 under regulation by MiR-129-5p is a potential therapeutic target for gastric cancer.

Antidiabetic Effect of Cyclocarya paliurus Leaves Depends on the Contents of Antihyperglycemic Flavonoids and Antihyperlipidemic Triterpenoids.

Cyclocarya paliurus has been used commonly to treat diabetes in China. However, the effective components and the effect of plant origin remain unclear. In this study, C. paliurus leaves with different chemical compositions were selected from five geographical locations, and their effects on streptozotocin (STZ)-induced diabetic mice were evaluated with both ethanol and aqueous extracts. Glucose levels, lipid levels, and biomarkers of liver and kidney function were measured. The principal components of both C. paliurus ethanol and aqueous extracts from different geographical locations differed quantitatively and qualitatively. Results showed that C. paliurus extracts with better antihyperglycemic effects were characterized by higher contents of total flavonoids, especially quercetin-3-O-glucuronide and kaempferol-3-O-glucuronide. Furthermore, significantly negative correlations were found between triterpenoids contents and lipid levels. These results revealed the potential antihyperglycemic capacity of C. paliurus flavonoids and the antihyperlipidemic effect of C. paliurus triterpenoids. Thus, we suggest that the composition of C. paliurus compounds might help to design therapeutic alternatives for the treatment of diabetes mellitus. However, geographic origins and the extraction solvents can also affect the effectiveness of the treatment as these factors influence the chemical compositions and thereby the biological activities.

Light quality affects flavonoid production and related gene expression in Cyclocarya paliurus.

Understanding the responses of plant growth and secondary metabolites to differential light conditions is very important to optimize cultivation conditions of medicinal woody plants. As a highly valued and multiple function tree species, Cyclocarya paliurus is planted and managed for timber production and medical use. In this study, LED-based light including white light (WL), blue light (BL), red light (RL), and green light (GL) were used to affect leaf biomass production, flavonoid accumulation and related gene expression of one-year C. paliurus seedlings in controlled environments. After the treatments of 60 days, the highest leaf biomass appeared in the treatment of WL, while the lowest leaf biomass was found under GL. Compared to WL, the total flavonoid contents of C. paliurus leaves were significantly higher in BL, RL, and GL, but the highest values of selected flavonoids (kaempferol, isoquercitrin and quercetin) were observed under BL. Furthermore, the greatest yields of total and selected flavonoids in C. paliurus leaves per seedling were also achieved under BL, indicating that blue light was effective for inducing the production of flavonoids in C. paliurus leaves. Pearson's correlation analysis showed that there were significantly positive correlations between leaf flavonoid content and relative gene expression of key enzymes (phenylalanine ammonia lyase, PAL; 4-coumaroyl CoA-ligase, 4CL; and chalcone synthase, CHS) in the upstream, which converting phenylalanine into the flavonoid skeleton of tetrahydroxy chalcone. It is concluded that manipulating light quality may be potential mean to achieve the highest yields of flavonoids in C. paliurus cultivation, however this needs to be further verified by more field trials.

Chromanopyrones and a flavone from Hypericum monogynum.

Six new compounds, including three pyrone derivatives (2-4), one new flavone (5), and two new naturally-occurring compounds (1 and 6), together with 16 known compounds were isolated from the leaves and twigs of Hypericum monogynum. In addition, compounds 2-4 are racemates. Their structures and absolute configurations were determined on the basis of extensive analysis of spectroscopic data and ECD calculation. All compounds were evaluated for the inhibitory effects on α-glucosidase, compounds 1, 5, and 7 showed moderate inhibitory activities with IC50 values of 161.46, 257.78, and 11.54µg/ml, respectively. Compound 8 exhibited weak anti-oxidant activity with IC50 value of 12.55µg/ml.

Monoterpenoid indole alkaloids from Gardneria multiflora.

Six new monoterpenoid indole alkaloids, 19(E)-9-demethoxy-16-dehydroxylchitosenine-17-O- ß-d-glucopyranoside (1), 19(E)-9,10-didemethoxy-16-dehydroxylchitosenine-17-O-ß-d-gluco-pyranoside (2), 19(E)-9,10-didemethoxy-16-dehydroxyl-11-methoxychitosenine (3), 19(E)-9,10-didemethoxy-16-dehydroxyl-11-methoxychitosenine-17-O-ß-d-glucopyranoside (4), 19(Z)-18-carboxylgardneramine (5), and 19(E)-18-demethoxygardneramine-N (4)-oxide (6), along with four known alkaloids, were isolated from Gardneria multiflora, and their structures were elucidated by spectroscopic analysis. Compounds 1, 2 and 4 are the first example of Gardneria alkaloids whose glucose units were attached to C-17. None of the compounds were cytotoxic to any of five human cancer cell lines.