A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu.

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

Dan-shen Yin promotes bile acid metabolism and excretion to prevent atherosclerosis via activating FXR/BSEP signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Dan-shen Yin (DSY), a traditional prescription, has been demonstrated to be effective in decreasing hyperlipidemia and preventing atherosclerosis (AS), but its mechanism remains unknown. We hypothesized that DSY activates farnesoid X receptor (FXR) to promote bile acid metabolism and excretion, thereby alleviating AS. AIM OF THE STUDY: This study was designed to explore whether DSY reduces liver lipid accumulation and prevents AS by activating FXR and increasing cholesterol metabolism and bile acid excretion. MATERIALS AND METHODS: The comprehensive chemical characterization of DSY was analyzed by UHPLC-MS/MS. The AS models of ApoE-/- mice and SD rats was established by high-fat diet and high-fat diet combined with intraperitoneal injection of vitamin D3, respectively. The aortic plaque and pathological changes were used to evaluate AS. Lipid levels, H&E staining and oil red O staining were used to evaluate liver lipid accumulation. The cholesterol metabolism and bile acid excretion were evaluated by enzyme-linked immunosorbent assay, UPLC-QQQ/MS. In vitro, the lipid and FXR/bile salt export pump (BSEP) levels were evaluated by oil red O staining, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting. RESULTS: A total of 36 ingredients in DSY were identified by UPLC-MS/MS analysis. In vivo, high-dose DSY significantly inhibited aortic intimal thickening, improved arrangement disorder, tortuosity, and rupture of elastic fibers, decreased lipid levels, and reduced the number of fat vacuoles and lipid droplets in liver tissue in SD rats and ApoE-/- mice. Further studies found that high-dose DSY significantly reduced liver lipid and total bile acids levels, increased liver ursodeoxycholic acid (UDCA) and other non-conjugated bile acids levels, increased fecal total cholesterol (TC) levels, and augmented FXR, BSEP, cholesterol 7-alpha hydroxylase (CYP7A1), ATP binding cassette subfamily G5/G8 (ABCG5/8) expression levels, while decreasing ASBT expression levels. In vitro studies showed that DSY significantly reduced TC and TG levels, as well as lipid droplets, while also increasing the expression of ABCG5/8, FXR, and BSEP in both HepG2 and Nr1h4 knockdown HepG2 cells. CONCLUSION: This study demonstrated that DSY promotes bile acid metabolism and excretion to prevent AS by activating FXR. For the prevent of AS and drug discovery provided experimental basis.

Characterization of moisture migration and diffusion in two types of tobacco biomass during the dehydration process by the TG-NMR analysis.

The tobacco waste generated from the tobacco agriculture and industry, including the discarded stem and leaf, often needs dehydration pretreatment before thermal conversion utilization. In order to study the water activity and migration of tobacco waste during the pretreatment process, TG-NMR (Thermogravimetric Nuclear Magnetic Resonance) was used to obtain the drying curves and LF-NMR (Low Field Nuclear Magnetic Resonance) T2 inversion spectrum at each stage of tobacco drying. Meanwhile, the variation pattern of pore distribution during the dehydration process of two types of tobacco waste has been obtained. Combined with the pore distribution changes, a possible spatial migration mode of water was proposed. The change of adsorption energy of water during tobacco drying was calculated, and verified the above hypothesis. This study results provide reference for the optimization of dehydration pretreatment process for different tobacco waste in order to reduce energy consumption during recycling of tobacco biomass.

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.

Clinical text and documents contain very rich information and knowledge in healthcare, and their processing using state-of-the-art language technology becomes very important for building intelligent systems for supporting healthcare and social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs). The experimental results on three sub-tasks including (1) clinical case (CC), (2) clinical terminology (CT), and (3) ontological concept (OC) show that our models achieved top-level performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrate that the small-sized pre-trained language model (PLM) outperformed the other two extra-large language models by a large margin in the clinical domain fine-tuning, which finding was never reported in the field. Finally, the transfer learning method works well in our experimental setting using the WMT21fb model to accommodate a new language space Spanish that was not seen at the pre-training stage within WMT21fb itself, which deserves more exploitation for clinical knowledge transformation, e.g. to investigate into more languages. These research findings can shed some light on domain-specific machine translation development, especially in clinical and healthcare fields. Further research projects can be carried out based on our work to improve healthcare text analytics and knowledge transformation. Our data is openly available for research purposes at: https://github.com/HECTA-UoM/ClinicalNMT.

Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis.

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.

Dosage optimization for reducing tumor burden using a phenotype-structured population model with a drug-resistance continuum.

Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.

A comprehensive investigation on the chemical changes of traditional Chinese medicine with classic processing technology: Polygonum multiflorum under nine cycles of steaming and sunning as a case study.

The processing of traditional Chinese medicine (TCM) plays an important role in the clinical application, which usually has the function of "increasing efficiency and reducing toxicity". Polygonum multiflorum (PM) has been reported to induce hepatotoxicity, while it is believed that the toxicity is reduced after processing. Studies have shown that the hepatotoxicity of PM is closely related to the changes in chemical components before and after processing. However, there is no comprehensive investigation on the chemical changes of PM during the processing progress. In this research, we established a comprehensive method to profile both small molecule compounds and polysaccharides from raw and different processed PM samples. In detail, an online two-dimensional liquid chromatography coupled with quadrupole-orbitrap mass spectrometry (2D-LC/Q-Orbitrap MS) was utilized to investigate the small molecules, and a total of 150 compounds were characterized successfully. After multivariate statistical analysis, 49 differential compounds between raw and processed products were screened out. Furthermore, an accurate and comprehensive method for quantification of differential compounds in PM samples was established based on ultra-high performance liquid chromatography/Q-Orbitrap-MS (UHPLC/Q-Orbitrap-MS) within 16 min. In addition, the changes of polysaccharides in different PM samples were analyzed, and it was found that the addition of black beans and steaming times would affect the content and composition of polysaccharides in PM significantly. Our work provided a reference basis for revealing the scientific connotation of the processing technology and increasing the quality control and safety of PM.

A Semi-Markov Approach to Study a Group of Kinesin Motors.

We propose a general mathematical and computational approach to study cellular transport driven by a group of kinesin motors. It is a framework for multi-scale modeling that integrates kinetic models of single kinesin motors, including detachment and reattachment events, to study group behaviors of several motors. By formulating the problem as a semi-Markov process and applying a central limit theorem, asymptotic velocity and diffusivity can be readily calculated, which offers considerable computational advantage over Monte Carlo simulations in tasks such as parameter sensitivity analysis and model selection. We demonstrate the method with some examples. The importance of incorporating the intrinsic microscopic-level dynamics of individual motors is illustrated by showing how changes at the microscopic level propagate to the motor-cargo complex at a mesoscopic level. Particularly, we showcase an example in which changes in the second moment of single-motor characteristics gives rise to different first moment characteristics of the motor group.

Confinement Effect and 3D Design Endow Unsaturated Single Ni Atoms with Ultrahigh Stability and Selectivity toward CO2 Electroreduction.

Developing single-atomic catalysts with superior selectivity and outstanding stability for CO2 electroreduction is desperately required but still challenging. Herein, confinement strategy and three-dimensional (3D) nanoporous structure design strategy are combined to construct unsaturated single Ni sites (Ni-N3) stabilized by pyridinic N-rich interconnected carbon nanosheets. The confinement agent chitosan and its strong interaction with g-C3N4 nanosheet are effective for dispersing Ni and restraining their agglomeration during pyrolysis, resulting in ultrastable Ni single-atom catalyst. Due to the confinement effect and structure advantage, such designed catalyst exhibits a nearly 100% selectivity and remarkable stability for CO2 electroreduction to CO, exceeding most reported state-of-the-art catalysts. Specifically, the CO Faradaic efficiency (FECO) maintains above 90% over a broad potential range (-0.55 to -0.95 V vs. RHE) and reaches a maximum value of 99.6% at a relatively low potential of -0.67 V. More importantly, the FECO is kept above 95% within a long-term 100 h electrolyzing. Density functional theory (DFT) calculations explain the high selectivity for CO generation is due to the high energy barrier required for hydrogen evolution on the unsaturated Ni-N3. This work provides a new designing strategy for the construction of ultrastable and highly selective single-atom catalysts for efficient CO2 conversion.

Comprehensive quality control of Qingjin Yiqi granule based on UHPLC-Q-Orbitrap-MS and UPLC-QQQ-MS.

INTRODUCTION: Qingjin Yiqi granule (QYG) is a prescription medicine of traditional Chinese medicine which is widely used clinically for the recovery of coronavirus patients. However, there is currently limited research on the quality control of QYG. OBJECTIVE: To evaluate the quality of QYG qualitatively and quantitatively by making full use of advanced chromatography-mass spectrometry techniques. METHODS: Firstly, a multicomponent characterisation of QYG was performed by ultrahigh-performance liquid chromatography coupled with a Q Exactive™ hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS) system using a rapid negative/positive switching mode. Secondly, the co-condition fingerprint analysis of constituted herbal medicines of QYG was performed to unveil active ingredients as the quality markers of QYG. Thirdly, the marker compounds in 10 batches of QYG were quantified by ultrahigh-performance liquid chromatography coupled with a Waters Xevo TQ-S triple quadrupole mass spectrometry (UPLC-QQQ-MS) system. RESULTS: A comprehensive method that combined the inclusion list and data-dependent acquisition (DDA) to achieve a systematic characterisation of QYG was established by UHPLC-Q-Orbitrap-MS. After analysis based on Compound Discoverer software and Global Natural Products Social (GNPS) platform, a total of 332 compounds were detected. Eleven Q-markers were determined for the quality evaluation of QYG by comparison with the fingerprint of nine constituted herbal medicines. An adjusted multiple reaction monitoring (MRM) quantification method was further established to simultaneously determine the 11 Q-markers for holistic quality evaluation of QYG. CONCLUSION: This is the first study to report comprehensive multicomponent characterisation, identification, and quality assessment of QYG, which could be used for effective guarantee of the quality of QYG.

Comprehensive multicomponent characterization and quality assessment of Xiaoyao Wan by UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS and HS-GC-IMS.

Xiaoyao Wan (XYW) is a prescription medicine of traditional Chinese medicine (TCM) with the effects of "soothing the liver and relieving depression," and "strengthening spleen and nourishing blood". XYW has been widely concerned in the treatment of depression and has become one of the commonly used classic formulas in clinical practice. However, the pharmacodynamic substance basis and the quality control studies of XYW are hitherto quite limited. Here, we aim to fully utilize an advanced ultra - performance liquid chromatography-quadrupole - Orbitrap mass spectrometry (UPLC-Q-Orbitrap-MS), headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) technique to deep characterization of the pharmacological substance basis and quantitatively evaluate the quality of XYW. Firstly, 299 compounds were identified or tentatively characterized, including 198 non-volatile organic compounds (n-VOCs) and 101 volatile organic compounds (VOCs). Secondly, principal component analysis (PCA) and hierarchical cluster analysis (HCA) was used to analyze quality differences in XYW at different manufacturers. Thirdly, a parallel reaction monitoring (PRM) method was established and validated to quantify the fourteen major effective substances in different manufacturers of XYW, which were chosen as the benchmarked substances to evaluate the quality of XYW. In conclusion, this study shows that the strategy provides a useful method for quality control of TCM and offers a practical workflow for exploring the quality consistency of TCM.

An Exploration of Dynamic Changes in the Mulberry Growth Process Based on UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS, and HS-GC-IMS.

This work was designed to investigate the dynamic changes process of non-volatile organic compounds (n-VOCs) and volatile organic compounds (VOCs) in mulberries during different growth periods using UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS, and HS-GC-IMS. A total of 166 compounds were identified, including 68 n-VOCs and 98 VOCs. Furthermore, principal component analysis (PCA), random forest analysis (RFA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze differences in mulberries at different ripening stages. A total of 74 compounds appeared or disappeared at different ripening periods and 24 compounds were presented throughout the growth process. Quantitative analysis and antioxidant experiments revealed that as the mulberries continued to mature, flavonoids and phenolic acids continued to increase, and the best antioxidant activity occurred from stage IV. Conclusively, an effective strategy was established for analyzing the composition change process during different growth periods, which could assist in achieving dynamic change process analysis and quality control.

Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid.

Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 "identity markers" (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.

Discovery and evaluation of active compounds from Xuanfei Baidu formula against COVID-19 via SARS-CoV-2 Mpro.

BACKGROUND: The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) is still a widespread concern. As one of the effective traditional Chinese medicine (TCM) formulae, Xuanfei Baidu formula (XFBD) shows significant efficacy for treatment of COVID-19 patients. However, its antiviral active compounds and mechanism are still unclear. PURPOSE: In this study, we explored the bioactive compounds of XFBD and its antiviral mechanism by integrating computational analysis and experimental testing. METHODS: Focusing on the SARS-CoV-2 main protease (Mpro), as a key target in virus transcription and replication, the fluorescence resonance energy transfer (FRET) assay was built to screen out satisfactory natural inhibitors in XFBD. The surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) were undertaken to verify the binding affinity of ligand-Mpro. Omicron BA.1.1 and BA.2.3 variants were used to evaluate the antiviral activity of the focused compounds in non-cytotoxicity concentrations. For introducing the molecular mechanism, computational modeling and NMR spectra were employed to characterize the ligand-binding modes and identify the ligand-binding site on Mpro. RESULTS: From a library of 83 natural compounds, acteoside, licochalcone B, licochalcone D, linoleic acid, and physcion showed the satisfactory inhibition effects on Mpro with IC50 ranging from 1.93 to 42.96 µM, which were further verified by SPR. Showing the excellent binding affinity, acteoside was witnessed to gain valuable insights into the thermodynamic signatures by ITC and presented antiviral activity on Omicron BA.1.1 and BA.2.3 variants in vitro. The results revealed that acteoside inhibited Mpro via forming the hydrogen bond between 7-H of acteoside and Mpro. CONCLUSION: Acteoside is regarded as a representative active natural compound in XFBD to inhibit replication of SARS-CoV-2, which provides the antiviral evidence and some insights into the identification of SARS-CoV-2 Mpro natural inhibitors.

Quantification of nimetazepam and its metabolite in human hair samples using liquid chromatography-tandem mass spectrometry: Case report.

Nimetazepam (marketed brand names; Erimin and Lavol) is an intermediate acting benzodiazepine derivative, which was widely used mainly in East and Southeast Asian region countries including Japan, Malaysia, Brunei, the Philippines, Thailand, Indonesia, Hong Kong, Singapore and China. Nimetazepam and its metabolite 7-aminonimetazepam were quantified from human hair samples by liquid chromatography tandem-mass spectrometry (LC-MS/MS), under selective reaction monitoring mode. Using diazepam-d5 as an internal standard, the concentration of nimetazepam and its metabolite 7-aminonimetazepam could be determined by matrix matched calibration method. Extraction of the target compounds was performed by using methanol, followed by evaporation and being concentrated with nitrogen. The Limit of quantification concentrations of nimetazepam and its metabolite 7-aminonimetazepam in hair samples were both 25 pg/mg by established method. The concentrations of nimetazepam in hair samples obtained from 2 users were 27.4, and 22.0 pg/mg, respectively; the concentrations of 7-animonimetazepam in hair samples were 54.2 and 29.1 pg/mg, respectively. In our study, the 7-aminonimetazepam concentrations in hair was higher than those of nimetazepam in the authentic hair samples. To our knowledge, this is the first report to establish the detailed procedure for quantificating nimetazepam and 7-aminonimetazepam in human hair by LC-MS/MS.

RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems.

Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.

Extensive evaluation of plasma metabolic sample preparation process based on liquid chromatography-mass spectrometry and its application in the in vivo metabolism of Shuang-Huang-Lian powder injection.

Shuang-Huang-Lian powder injection (SHLPI) is a natural drug injection made of honeysuckle, scutellaria baicalensis and forsythia suspensa. It has the characteristics of complex chemical composition and difficult metabolism research in vivo. LC-MS platform has been proven to be an important analytical technology in plasma metabolomics. Unfortunately, the lack of an effective sample preparation strategy before analysis often significantly impacts experimental results. In this work, twenty-one extraction protocols including eight protein precipitation (PPT), eight liquid-liquid extractions (LLE), four solid-phase extractions (SPE), and one ultrafiltration (U) were simultaneously evaluated using plasma metabolism of SHLPI in vivo. In addition, a strategy of "feature ion extraction of the multi-component metabolic platform of traditional Chinese medicine" (FMM strategy) was proposed for the in-depth characterization of metabolites after intravenous injection of SHLPI in rats. The results showed that the LLE-3 protocol (Pentanol:Tetrahydrofuran:H2O, 1:4:35, v:v:v) was the most effective strategy in the in vivo metabolic detection of SHLPI. Furthermore, we used the FMM strategy to elaborate the in vivo metabolic pathways of six representative substances in SHLPI components. This research was completed by ion migration quadrupole time of flight mass spectrometer combined with ultra high performance liquid chromatography (UPLC/Vion™-IMS-QTof-MS) and UNIFI™ metabolic platform. The results showed that 114 metabolites were identified or preliminarily identified in rat plasma. This work provides relevant data and information for further research on the pharmacodynamic substances and in vivo mechanisms of SHLPI. Meanwhile, it also proves that LLE-3 and FMM strategies could achieve the in-depth characterization of complex natural drug metabolites related to Shuang-Huang-Lian in vivo.

Two-birds with one stone: Improving both cathode and anode electrochemical performances via two-dimensional Te-CoTe2/rGO ultrathin nanosheets as sulfur hosts in lithium-sulfur batteries.

A Te-doped CoTe2 film could be grown in situ on reduced graphene oxide (rGO) to develop a Te-CoTe2/rGO composite with an ultrathin layered structure, which has multiple protective effects on both the sulfur positive electrode and lithium negative electrode in lithium sulfur (Li-S) batteries. The Te-CoTe2/rGO composite as a sulfur host not only shows a strong adsorbing ability for lithium polysulfides (LiPSs) but can also accelerate the conversion reaction of active material sulfur during the charging/discharging process. More importantly, this host can turn the shuttle effect from an unfavorable factor to a favorable factor, which could improve the electrochemical performance of the lithium anode with uniform lithium plating/stripping resulting from the intermediate polytellurosulfide species (Li2TexSy), which could be generated on the cathode surface via Te reacting with soluble Li2Sn (4 ≤ n ≤ 8). As a result, the S@Te-CoTe2/rGO cathode shows a discharge capacity of 970.0 mA h g-1 in the first cycle at 1 C and retains a high capacity of 545.5 mA h g-1 after 1000 cycles, corresponding to a low capacity decay rate of only 0.043% per cycle. In addition, in situ X-ray diffraction (XRD) and in situ Raman were used to explore the sulfur conversion process. This study not only demonstrates that a two-dimensional (2D) ultrathin Te-CoTe2/rGO composite is successfully developed with multiple effects on Li-S batteries but also opens a new pathway for designing unique sulfur hosts to promote the electrochemical performance of Li-S batteries.

Integration of UHPLC/Q-OrbitrapMS-based metabolomics and activities evaluation to rapidly explore the anti-inflammatory components from lasianthus.

Lasianthus, belonging to Rubiaceae, has been verified to improve clinical syndrome in immune diseases (e.g., hepatitis, nephritis, and rheumatoid arthritis). Both the anti-inflammatory function and chemical composition of Lasianthus vary considerably between different species but few studies focus. So essential it is to explore lasianthus and further search for anti-inflammatory substances. The target of this artical is to analyze the anti-inflammatory activity and chemical composition of lasianthus of different species. And the subsequent active compounds were explored. Primary, the anti-inflammatory activity among seven species of lasianthus (e.g., L. fordii., L. wallichii., L. hookeri C., L. verticillatus., L. sikkimensis., L. appressihirtus., and L. hookeri var) were evaluated by vitro experiments (RAW 264.7 cells). Next, UHPLC/Q-Orbitrap-MS-based metabolomics and the mass defect filter (MDF) algorithm were performed to explore metabolites. In addition, principal component analysis (PCA) was to screen out differential compounds in seven species. Finally, the correlation analysis between activities and composition to rapidly discover the active compounds (compounds were verified pharmacologically). Among the 7 species of lasianthus, the L. fordii. and L. hookeri C indicated the best anti-inflammatory activity. Untargeted metabolomics and MDF show 112 compounds, classified into six dominant types (e.g., flavonoids, phenolic acids, alkaloids, iridoids, coumarins, and anthraquinones). Furthermore, 33 differential metabolites were confirmed by PCA. Then according to correlation analysis and pharmacological validation, 7 compounds IC50＜100 (e.g., scopoletin, asperulosidic acid, chlorogenic acid, ferulic acid, betaine, syringic acid, and emodin) were verified as anti-inflammatory compounds and conduct quantitative analysis. Metabolomics integrated with activities evaluation might be a rapid and effective strategy to explore the active compounds from natural products.