[Application of near infrared spectroscopy to predict contents of various lactones in chromatographic process of Ginkgo Folium].

This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.

Algae-Derived Anti-Inflammatory Compounds against Particulate Matters-Induced Respiratory Diseases: A Systematic Review.

Air pollution has recently become a subject of increasing concern in many parts of the world. The World Health Organization (WHO) estimated that nearly 4.2 million early deaths are due to exposure to fine particles in polluted air, which causes multiple respiratory diseases. Algae, as a natural product, can be an alternative treatment due to potential biofunctional properties and advantages. This systematic review aims to summarize and evaluate the evidence of metabolites derived from algae as potential anti-inflammatory agents against respiratory disorders induced by atmospheric particulate matter (PM). Databases such as Scopus, Web of Science, and PubMed were systematically searched for relevant published full articles from 2016 to 2020. The main key search terms were limited to "algae", "anti-inflammation", and "air pollutant". The search activity resulted in the retrieval of a total of 36 publications. Nine publications are eligible for inclusion in this systematic review. A total of four brown algae (Ecklonia cava, Ishige okamurae, Sargassum binderi and Sargassum horneri) with phytosterol, polysaccharides and polyphenols were reported in the nine studies. The review sheds light on the pathways of particulate matter travelling into respiratory systems and causing inflammation, and on the mechanisms of actions of algae in inhibiting inflammation. Limitations and future directions are also discussed. More research is needed to investigate the potential of algae as anti-inflammatory agents against PM in in vivo and in vitro experimental models, as well as clinically.

[Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine].

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Cycloartane triterpenoids from Actaea vaginata with anti-inflammatory effects in LPS-stimulated RAW264.7 macrophages.

Five undescribed cycloartane triterpenoids, including two cycloartane trinor-triterpenoids, were isolated from a 70% ethanol extract of the whole plant of Actaea vaginata (Ranunculaceae), together with thirteen known cycloartane triterpenoids. Their structures were determined by spectroscopic techniques and quantum chemical calculations for intramolecular noncovalent interactions with reduced density gradient method. All compounds were evaluated for their anti-inflammatory effects by a lipopolysaccharide (LPS)-stimulated nitric oxide (NO) production model in RAW264.7 macrophage cells, and some showed potent inhibitory effects with IC50 values ranging from 5.0 to 24.4 µM. Further mechanism studies showed that one compound dose-dependently suppressed LPS-induced NO production and pro-inflammatory cytokines secretion, and decreased the expression of iNOS, through inhibiting NF-κB activation.

Triterpenoids from Cyclocarya paliurus that Enhance Glucose Uptake in 3T3-L1 Adipocytes.

Four previously undescribed compounds, including three rarely occurring seco-dammarane triterpenoid glycosides and a pentacyclic triterpenic acid, were isolated from a 70% ethanol extract of the leaves of Cyclocarya paliurus (Juglandaceae), along with eleven known triterpenoids. Their structures were determined by spectroscopic techniques, including 2D NMR and HRESIMS, as well as chemical methods. Among them, several triterpenoids enhanced insulin stimulated glucose uptake in both 3T3-L1 adipocytes and C2C12 myotubes. Furthermore, compound 1 dose-dependently increased glucose uptake through activating AMP-activated protein kinase (AMPK)-p38 pathway. Collectively, triterpenoids from C. paliurus could be developed as insulin sensitizers, which might have therapeutic potential for insulin resistance and hyperglycemia.

[On-line detection of concentration process of Ganmaoling granules by near infrared spectroscopy combined with automatic control system].

Our research was designed for on-line detection of multi-index in the concentration process of Ganmaoling granules by integration of near infrared spectroscopy and automatic control system. First, on-line detection system was set up in the concentration tank for Ganmaoling granules production. Spectra were scanned and values of chlorogenic acid, linarin, solid content and relative density were measured. Models of partial least squares regression were built and imported into near infrared workstation. By connecting the control system, real-time multi-index values were determined automatically in the concentration process. Results showed that correlation coefficients of chlorogenic acid, linarin, solid content and relative density models were 0.963, 0.989, 0.993 and 0.918, respectively. Relative standard errors of prediction were 3.71%, 4.28%, 4.17% and 0.24%, respectively, indicating a good performance and high accuracy of the models. Real-time data collection during the whole process was measured by the near infrared detecting system in the control system. In conclusion, the near infrared detection system is able to perform real-time automatic determination of multiindex in the concentration process of Ganmaoling granules with significant advantages.

[Determination of 10 mycotoxin contaminants in Panax notoginseng by ultra performance liquid chromatography-tandem mass spectrometry].

To ensure the quality and safety of Panax notoginseng, a method for the simultaneous determination of 10 mycotoxins in Panax notoginseng was developed using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The sample was extracted with acetonitrile and purified by HLB multifunction cleanup column. The separation was performed on a Phenomenex Kinetex XB-C18 column by gradient elution using methanol and 5 mmol·L(-1) ammonium acetate as mobile phase. The targeted compounds were detected in MRM mode by mass spectrometry with electrospray ionization (ESI) source operated in both positive and negative ionization modes. The linear relationships of the 10 mycotoxins were good in their respective linear ranges. The correlation coefficients (r) ranged from 0.9981 to 1.0000. The LOQs of the 10 mycotoxins were between 0.15 and 8.6 µg·kg(-1). The average recoveries ranged from 73.8% to 107.0% with relative standard deviations (RSDs) of 0.10%-10.9%. The results demonstrated that the proposed method was sensitive and accurate, and suitable for the mycotoxins quantification in Panax notoginseng.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Three-in-one agonists for PPAR-α, PPAR-γ, and PPAR-δ from traditional Chinese medicine.

Nowadays, the occurrence of metabolic syndrome, which is characterized by obesity and clinical disorders, has been increasing rapidly over the world. It induces several serious chronic diseases such as cardiovascular disease, dyslipidemia, gall bladder disease, hypertension, osteoarthritis, sleep apnea, stroke, and type 2 diabetes mellitus. Peroxisome proliferator-activated receptors (PPARs), which have three isoforms: PPAR-α, PPAR-γ, and PPAR-δ, are key regulators of adipogenesis, lipid and carbohydrate metabolism, and are potential drug targets for treating metabolic syndrome. The traditional Chinese medicine (TCM) compounds from TCM Database@Taiwan ( http://tcm.cmu.edu.tw/ ) were employed to virtually screen for potential PPAR agonists, and structure-based pharmacophore models were generated to identify the key interactions for each PPAR protein. In addition, molecular dynamics (MD) simulation was performed to evaluate the stability of the PPAR-ligand complexes in a dynamic state. (S)-Tryptophan-betaxanthin and berberrubine, which have higher Dock Score than controls, form stable interactions during MD, and are further supported by the structure-based pharmacophore models in each PPAR protein. Key features include stable H-bonds with Thr279 and Ala333 of PPAR-α, with Thr252, Thr253 and Lys331 of PPAR-δ, and with Arg316 and Glu371 of PPAR-γ. Hence, we propose the top two TCM candidates as potential lead compounds in developing agonists targeting PPARs protein for treating metabolic syndrome.

[Comprehensive quality control method for total flavonoid of Fructus Aurantii].

OBJECTIVE: To establish a comprehensive quality control method for total flavonoid of Fructus Aurantii. METHODS: RP-HPLC and spectrophotometry were applied for the quantitative and fingerprint analysis of total flavonoid of Fructus Aurantii. The contents of naringin and neohesperidin were determined on an Agilent SB-C18column (4.6 mm × 250 mm, 5 µm). The mobile phase was composed of 0.02 % H3PO4 and CH3CN (80:20). The flow rate was 1 ml/min with DAD detected at 280 nm. The column temperature was maintained at 35°C. The fingerprints were developed on an Agilent SB-C18 column (4.6 mm × 250 mm, 5 µm). The mobile phase was composed of 0.5 % HAc and CH3OH with a linear gradient elution. The ratio of 0.5 % HAc and CH3OH was: 0 min, 80:20; 10 min, 60:40; 35 min, 30:70; 50 min, 0:100. The flow rate was 1 ml/min with DAD detected at 320 nm. The column temperature was maintained at 30 degree. Meanwhile, the contents of total flavonoid were determined at 283 nm. RESULT: The contents range of naringin, neohesperidin and total flavonoid were 38.3 %- 47.2%, 21.0 %- 28.5% and 79.9%-88.6 %, respectively. The fingerprints of the effective fractions showed 12 common peaks and the fingerprint similarity was all above 98.0 % compared with the standard chromatogram. CONCLUSION: The method reported in this paper can be used effectively for the quality control of total flavonoid of Fructus Aurantii.