Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides).

Accurate characterization of Panax herb ginsenosides is challenging because of the isomers and lack of sufficient reference compounds. More structural information could help differentiate ginsenosides and their isomers, enabling more accurate identification. Based on the VionTM ion-mobility high-resolution LC-MS platform, a multidimensional information library for ginsenosides, namely GinMIL, was established by predicting retention time (tR) and collision cross section (CCS) through machine learning. Robustness validation experiments proved tR and CCS were suitable for database construction. Among three machine learning models we attempted, gradient boosting machine (GBM) exhibited the best prediction performance. GinMIL included the multidimensional information (m/z, molecular formula, tR, CCS, and some MS/MS fragments) for 579 known ginsenosides. Accuracy in identifying ginsenosides from diverse ginseng products was greatly improved by a unique LC-MS approach and searching GinMIL, demonstrating a universal Panax saponins library constructed based on hierarchical design. GinMIL could improve the accuracy of isomers identification by approximately 88%.

A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction.

To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.

[Content determination of ten flavonoids and alkaloids in Gleditsiae Sinensis Fructus, Gleditsiae Fructus Abnormalis, and Gleditsiae Spina].

To study the quality control of three traditional Chinese medicines derived from Gleditsia sinensis [Gleditsiae Sinensis Fructus(GSF), Gleditsiae Fructus Abnormalis(GFA), and Gleditsiae Spina(GS)], this paper established a multiple reaction monitoring(MRM) approach based on ultra-high performance liquid chromatography-triple quadrupole-linear ion-trap mass spectrometry(UHPLC-Q-Trap-MS). Using an ACQUITY UPLC BEH C_(18) column(2.1 mm × 100 mm, 1.7 µm), gradient elution was performed at 40 ℃ with water containing 0.1% formic acid-acetonitrile as the mobile phase running at 0.3 mL·min~(-1), and the separation and content determination of ten chemical constituents(e.g., saikachinoside A, locustoside A, orientin, taxifolin, vitexin, isoquercitrin, luteolin, quercitrin, quercetin, and apigenin) in GSF, GFA, and GS were enabled within 31 min. The established method could quickly and efficiently determine the content of ten chemical constituents in GSF, GFA, and GS. All constituents showed good linearity(r>0.995), and the average recovery rate was 94.09%-110.9%. The results showed that, the content of two alkaloids in GSF(2.03-834.75 µg·g~(-1)) was higher than that in GFA(0.03-10.41 µg·g~(-1)) and GS(0.04-13.66 µg·g~(-1)), while the content of eight flavonoids in GS(0.54-2.38 mg·g~(-1)) was higher than that in GSF(0.08-0.29 mg·g~(-1)) and GFA(0.15-0.32 mg·g~(-1)). These results provide references for the quality control of G. sinensis-derived TCMs.

Strategies and applications of covalent organic frameworks as promising nanoplatforms in cancer therapy.

Cancer nanomedicine is the best option to face the limits of conventional chemotherapy and phototherapy methods, and thus the intensive quest for new nanomaterials to improve therapeutic efficacy and safety is still underway. Owing to their low density, well-defined structures, large surface area, finely tunable pore size, and metal ion free features, covalent organic frameworks (COFs) have been extensively studied in many research fields. The recent great interest in nanoscale COFs to improve the properties of bulk COFs has led to broadening of their applicability in the biomedical field, such as nanocarriers with an outstanding loading capacity and efficient delivery of therapeutic agents, smart theranostic nanoplatforms with excellent stability, high ROS generation, light-to-heat conversion capabilities, and different response and diagnostic characteristics. The COFs and related nanoplatforms with a wide variety of designability and functionalization have opened up a new avenue for exciting opportunities in cancer therapy. Herein we review the state-of-the-art technical and scientific developments in this emerging field, focusing on the overall progress addressed so far in building versatile COF-based nanoplatforms to enhance chemotherapy, photodynamic/photothermal therapy, and combination. Future perspectives for achieving the synergistic effect of cancer elimination and clinical translation are further discussed to motivate future contributions and explore new possibilities.

Acupuncture for type 2 diabetes mellitus: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Acupuncture has been used to treat type 2 diabetes (T2DM) for 2000 years and there are emerging clinical evidence and animal studies for its efficacy. However, we are unable to conclude the effectiveness and safety on this issue yet. OBJECTIVES: To assess the effects and safety of acupuncture for T2DM. METHODS: We systematically searched 5 databases and 2 clinical registry platforms from inception to 2018-6-4. RCTs for acupuncture or its variants compared with sham acupuncture or no acupuncture controls for T2DM were included. The primary outcomes were glycemic control and adverse events. RESULTS: A total of 21 studies, which comprised a total of 1943 participants, were included in the final meta-analysis. Compared with sham acupuncture or no acupuncture plus baseline treatments, acupuncture plus baseline treatments yield reduction in FBG(MD 1.21 mmol/l, 95%CI 1.56 to 0.87), 2 h BG(MD 2.13 mmol/l, 95%CI 2.79 to 1.46), HA1c (MD 1.12%, 95%CI 1.62 to 0.62). Our results also show acupuncture can improve blood lipids and blood pressure control, and reduce weight. CONCLUSIONS: As one type of multifactorial intervention, acupuncture could be recommended as a supplementary treatment in the management of T2DM, especially in those with obesity or metabolic disorders. However, due to the small sample size, poor methodological quality of trials reviewed, the amount of evidence is not fully convincing. There is a need for well-planned, long-term studies. REGISTRATION: International Prospective Register of Systematic Reviews (Number CRD42018094573).