circAtlas 3.0: a gateway to 3 million curated vertebrate circular RNAs based on a standardized nomenclature scheme.

Recent studies have demonstrated the important regulatory role of circRNAs, but an in-depth understanding of the comprehensive landscape of circRNAs across various species still remains unexplored. The current circRNA databases are often species-restricted or based on outdated datasets. To address this challenge, we have developed the circAtlas 3.0 database, which contains a rich collection of 2674 circRNA sequencing datasets, curated to delineate the landscape of circRNAs within 33 distinct tissues spanning 10 vertebrate species. Notably, circAtlas 3.0 represents a substantial advancement over its precursor, circAtlas 2.0, with the number of cataloged circRNAs escalating from 1 007 087 to 3 179 560, with 2 527 528 of them being reconstructed into full-length isoforms. circAtlas 3.0 also introduces several notable enhancements, including: (i) integration of both Illumina and Nanopore sequencing datasets to detect circRNAs of extended lengths; (ii) employment of a standardized nomenclature scheme for circRNAs, providing information of the host gene and full-length circular exons; (iii) inclusion of clinical cancer samples to explore the biological function of circRNAs within the context of cancer and (iv) links to other useful resources to enable user-friendly analysis of target circRNAs. The updated circAtlas 3.0 provides an important platform for exploring the evolution and biological implications of vertebrate circRNAs, and is freely available at http://circatlas.biols.ac.cn and https://ngdc.cncb.ac.cn/circatlas.

Fibrinogen to HDL-Cholesterol ratio as a predictor of mortality risk in patients with acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is characterized by inflammation, oxidative stress, and atherosclerosis, contributing to increased mortality risk. High-density lipoprotein (HDL) takes a crucial part in mitigating atherosclerosis and inflammation through its diverse functionalities. Conversely, fibrinogen is implicated in the development of atherosclerotic plaques. However, the mortality risk predictive capacity of fibrinogen to HDL-cholesterol ratio (FHR) in AMI patients remains unexplored. This research aimed to evaluate the effectiveness of FHR for mortality risk prediction in relation to AMI. METHODS: A retrospective study involving 13,221 AMI patients from the Cardiorenal ImprovemeNt II cohort (NCT05050877) was conducted. Baseline FHR levels were used to categorize patients into quartiles. The assessment of survival disparities among various groups was conducted by employing Kaplan‒Meier diagram. Cox regression was performed for investigating the correlation between FHR and adverse clinical outcomes, while the Fine-Gray model was applied to evaluate the subdistribution hazard ratios for cardiovascular death. RESULTS: Over a median follow-up of 4.66 years, 2309 patients experienced all-cause death, with 1007 deaths attributed to cardiovascular disease (CVD). The hazard ratio (HR) and its 95% confidence interval (CI) for cardiac and all-cause death among individuals in the top quartile of FHR were 2.70 (1.99-3.65) and 1.48 (1.26-1.75), respectively, in comparison to ones in the first quartile, after covariate adjustment. Restricted cubic spline analysis revealed that FHR was linearly correlated with all-cause mortality, irrespective of whether models were adjusted or unadjusted (all P for nonlinearity > 0.05). CONCLUSION: AMI patients with increased baseline FHR values had higher all-cause and cardiovascular mortality, regardless of established CVD risk factors. FHR holds promise as a valuable tool for evaluating mortality risk in AMI patients. TRIAL REGISTRATION: The Cardiorenal ImprovemeNt II registry NCT05050877.

Predictive effect of estimated glomerular filtrate rate by creatinine or cystatin C on mortality in patients with coronary artery disease.

BACKGROUND: Renal dysfunction leads to poor prognosis of patients with coronary artery disease (CAD). Current studies have reported the prognosis or mortality of various diseases using different estimated glomerular filtrate rate (eGFR) formulas, while the performance of these equations is unclear in CAD patients. We aim to evaluate the predict effect of creatinine-based eGFR (eGFRcr), cystatin C-based eGFR (eGFRcys), and both creatinine and cystatin C-based eGFR (eGFRcr-cys) in CAD patients. METHODS: A total of 23,178 patients with CAD were included from CIN-II cohort study. The association of eGFRcr, eGFRcys and eGFRcr-cys with cardiovascular and all-cause mortality was detected by Cox regression analysis. The predictive effect of eGFRcr, eGFRcys and eGFRcr-cys on mortality was assessed. RESULTS: During a median follow up of 4.3 years, totally 2051 patients (8.8%) experience all-cause mortality, of which 1427 patients (6.2%) died of cardiovascular disease. For the detection of cardiovascular mortality among CAD patients, eGFRcr-cys had high discriminatory capacity with area under the curve (AUC) in receiver operator characteristic analysis of 0.730, which was significantly better than eGFRcr (AUC = 0.707, p < 0.001) and eGFRcys (AUC = 0.719, p < 0.001). Similar results were observed in all-cause mortality. Restricted cubic spline showed a U-shaped association between eGFRcr and all outcomes in patients with both reduced and supranormal eGFR levels, while a L-shaped association in eGFRcys and eGFRcr-cys. CONCLUSIONS: Estimated GFR based on both creatinine and cystatin C has highest predictive effect for cardiovascular and all-cause mortality among CAD patients. Meanwhile, supranormal eGFRcr may indicate a higher risk of mortality.

Multi-omics profiling of PC-3 cells reveals bufadienolides-induced lipid metabolic remodeling by regulating long-chain lipids synthesis and hydrolysis.

INTRODUCTION: Lipid metabolism participates in various biological processes such as proliferation, apoptosis, migration, invasion, and maintenance of membrane homeostasis of prostate tumor cells. Bufadienolides, the active ingredients of Chansu, show a robust anti-proliferative effect against prostate cancer cells in vitro, but whether bufadienolides could regulate the lipid metabolism in prostate cancer has not been evaluated. OBJECTIVES: Our study explored the regulatory effects of bufadienolides on lipid metabolism in human prostate carcinoma cells (PC-3). METHODS: Untargeted lipidomics and transcriptomics were combined to study the effect of different bufadienolides interventions on lipid and gene changes of PC-3 cells. The key genes related to lipid metabolism and prostate cancer development were verified by qPCR and western blotting. RESULTS: Lipidomic analysis showed that the active bufadienolides significantly downregulated the content of long-chain lipids of PC-3 cells. Based on transcriptomic and qPCR analyses, many genes related to lipid metabolism were significantly regulated by active bufadienolides, such as ELOVL6, CYP2E1, GAL3ST1, CERS1, PLA2G10, PLD1, SPTLC3, and GPX2. Bioinformatics analysis of the Cancer Genome Atlas database and literature retrieval showed that elongation of very long-chain fatty acids protein 6 (ELOVL6) and phospholipase D1 (PLD1) might be important regulatory genes. Western blot analysis revealed that active bufadienolides could downregulate PLD1 protein levels which might promote anti-prostate cancer effect. CONCLUSIONS: All these findings support that bufadienolides might induce lipid metabolic remodeling by regulating long-chain lipids synthesis and phospholipid hydrolysis to achieve an anti-prostate cancer effect, and PLD1 would probably be the key protein.

An improved strategy for identification and annotation of easily in-sourced dissociation diterpene lactones from plant natural products: Taking Andrographis paniculata (Burm. f.) as an example.

RATIONALE: Diterpene lactones (DL) in Andrographis paniculata (AP) are known as "natural antibiotics" for their excellent antibacterial activity. During mass spectrometry (MS) analysis, the hydroxyl groups in the AP DL skeleton are prone to neutral loss of H2 O, producing high in-source fragment peaks and affecting the characterization of these components. METHODS: Mass tags were applied during the MS data acquisition step, and special adduct ion form was used to guide the data processing and characterization steps. Besides, the total number of characterized AP DLs significantly increased when combining the number of neutrally lost H2 O from AP DLs, incorporating information on the diagnostic ions, and adopting molecular networks generated with the Global Natural Products Social Molecular Networking database. RESULTS: Ninety-nine DLs, comprising 6 monohydroxyl groups, 20 dihydroxyl groups, 27 trihydroxy groups, and 46 DLs with more than 3 hydroxyl groups, were characterized from AP. In addition, based on the characteristic fragments in the product ions (C3 H4 , Δm/z = 40.03 Da), it could be assumed that 90 DLs had the C19-OH structure among the identified DLs. The current study provides a new approach for collecting, processing, and characterizing MS analysis of natural DLs prone to in-source fragmentation. CONCLUSIONS: MS characterization of AP DLs was significantly improved, and many potential new compounds were identified in AP. This characterization provides new methods for the purification and identification of AP DLs.

Dissecting chemical markers for controlling "Paozhi" method of traditional Chinese medicine with untargeted metabolomics: Vinegar-baked Euphorbia kansui as a case study.

The processing of Traditional Chinese Medicine requires the appropriate parameters, while the specific chemical markers are still absent to obtain the optimized processing. In this study, we used vinegar-baked Euphorbia kansui as a case to dissect the chemical markers for the baking process using untargeted metabolomics. The robust chemical markers were selected based on the three rules, correlation, significant difference, and controllability. All the differential features were categorized based on their mass defects. After the differential analysis, 310 differential compounds were screened out and could be mainly divided into six categories: diacylglycerols and triacylglycerols demonstrated increasing trends with the baking time in the discriminant model, while ingenane-type diterpenes, jatrophane-type diterpenes, fatty acid esters, and fatty acids had decreasing trends. It was unexpected to find that the diterpenes did not correlate with the baking time. Only very few compounds meet the three rules. They were validated with a high-performance liquid chromatography method. Finally, only 13-Hydroxy-9,11-octadecadienoic acid and its isomer 9-Hydroxy-10,12-octadecadienoic acid could be used further to differentiate the commercial vinegar-baked Euphorbia kansui. It would be of interest to evaluate whether these two compounds could be utilized as markers to control more processing methods in future studies.

Comparison of cardiac biomarkers on risk assessment of contrast-associated acute kidney injury in patients undergoing cardiac catheterization: A multicenter retrospective study.

AIM: Cardiac biomarkers' predictive value of contrast-associated acute kidney injury (CA-AKI) remains unclear. We analysed whether creatine kinase isoenzyme-MB (CKMB), cardiac troponin I (cTnI) and preoperative N-terminal pro-brain natriuretic peptide (NT-proBNP) are tied to CA-AKI patients undergoing cardiac catheterization. METHODS: In the multi-center study, we included 3553 people underwent cardiac catheterization for analysis. CA-AKI was defined as the absolute increase of over 0.3 mg/dL or an increase of more than 50% compared with the baseline serum creatinine within 48 hours following cardiac catheterization. Logistic regression model and receiver operating characteristic (ROC) curves were used to examine the association between cardiac biomarkers and CA-AKI and the efficacy of Mehran risk score (MRS) model on CA-AKI prediction with and without cardiac biomarkers. RESULTS: Among 3553 people, 200 people eventually developed CA-AKI. The logistic regression model showed that log10 CKMB (odds ratio (OR): 1.97, 95%CI:1.51-2.57, p < .001), cTnI (OR: 1.03, 95%CI: 1.02-1.04, p < .001) and log10 NT-proBNP (OR: 3.19, 95%CI: 2.46-4.17, p < .001) were independent predictors of CA-AKI. The ROC curve demonstrated that area under the curve (AUC) of MRS was 0.733. CKMB, cTnI and NT-proBNP all significantly improved the AUC value in combination with MRS model. (NT-proBNP: 0.798, p < .001; CKMB: 0.758, p = .003; cTnI: 0.755, p = .002), among which the NT-proBNP had the best predictive efficacy improvement. CONCLUSION: Cardiac biomarkers of CKMB, cTnI and NT-proBNP are all independently associated with CA-AKI among patients undergoing cardiac catheterization while NT-proBNP remains the best indicator. Adding CKMB, cTnI and NT-proBNP to MRS improved the prognostic efficacy and may be considered effective tools to predict the risk of CA-AKI in clinical practice.

Development and preliminary testing of the cancer-related fatigue comprehensive assessment scale in cancer survivors.

BACKGROUND: Tailored management of cancer-related fatigue (CRF) is important for effective coping; however, it has been hindered by the lack of a comprehensive tool that assesses both symptoms and treatable influencing factors. AIMS AND OBJECTIVES: The aim was to develop a cancer-related fatigue comprehensive assessment scale (CRF-CAS) and assess its psychometric properties. DESIGN: This was a mixed-method study. METHODS: The study included two phases which were conducted in Zhejiang Province, China. In phase one, a literature search, brainstorming sessions, Delphi studies, cognitive interviews and a pilot study were conducted to construct and revise CRF-CAS indicators. In phase two, a questionnaire-based survey was conducted among cancer survivors. Item analysis was used to select and optimize indicators. Cronbach's α was calculated for reliability analysis. Validity analysis included concurrent validity and structural validity. RESULTS: A 93-item tool was initially constructed. Phase one ended with revision and optimization. The preliminary scale included five dimensions (CRF symptoms, physical activity, cognitive-emotional status, sleep status, nutritional status) and 30 items. The mean item-content validity index (I-CVI) and scale-level CVI universal agreement (S-CVI/UA) were .98, and the adjusted mean values of Kappa for indicators ranged from .91-1, as evaluated by the expert group. The Pearson correlation coefficient between the CRF-CAS and criterion scales ranged from .337-.862. Cronbach's α coefficient ranged from .624-.728. Respondents agreed that the scale was acceptable for administration and that it contributed to decision-making in fatigue management. Confirmatory factor analysis (CFA) indicated that the CRF-CAS fit well. CONCLUSIONS: The construction process of the CRF-CAS, involving panel discussion and expert and participant evaluations, was shown to be scientific and feasible. The CRF-CAS had relatively good validity and reliability in version 5 of its preliminary scale, which requires further improvement in future studies.

Information Entropy-Based Strategy for the Quantitative Evaluation of Extensive Hyperspectral Images to Better Unveil Spatial Heterogeneity in Mass Spectrometry Imaging.

Hyperspectral images can be generated from mass spectrometry imaging (MSI) data for the intuitive data visualization purpose. However, hundreds of HSIs can be generated by different dimensionality reduction methods, which poses great challenges in selecting the high-quality images with the best intuitive visualization results of the MSI data. Here, we presented a novel approach that objectively evaluates the image quality of the hyperspectral images. The applicability of this method was demonstrated by analyzing the MSI data acquired from human prostate cancer biopsy samples and mouse brain tissue section, which harbored an intrinsic tissue heterogeneity. Our method was based on the information entropy and contrast measured from image information content and image definition, respectively. The heterogeneity of the MSI data from high-dimensional space was reduced to three-dimensional embeddings and thoroughly evaluated to achieve satisfactory visualization results. The application of information entropy and contrast can be used to choose the optimized visualization results rapidly and objectively from an extensive number of hyperspectral images and be adopted to evaluate and optimize different dimensionality reduction algorithms and their hyperparameter combinations. In conclusion, the information entropy-based strategy could be a bridge between chemometrician and biologists.

Policy-Driven Potential for Deploying Carbon Capture and Sequestration in a Fossil-Rich Power Sector.

In 2020, the Wyoming Legislature enacted House Bill No. 0200 (HB0200), which requires utilities to generate a percentage of dispatchable and reliable low-carbon electricity by 2030. This state requirement must take into consideration "any potentially expiring federal tax credits", such as the federal Section 45Q tax credit. This study aims to examine the potential role of economic and policy incentives that facilitate carbon capture and sequestration (CCS) deployment. A unit-level retrofit analysis shows that deploying CCS at existing coal-fired power plants in Wyoming to meet the HB0200 emission limit would decrease the net efficiency by 29% and increase the levelized cost of electricity by 237% on the fleet average. The CO2 avoidance cost varies by unit from $65/t to 201/t, which reveals economic challenges for CCS retrofits. However, the current tax credit of $50 per metric ton of CO2 for saline-reservoir storage can lower the avoidance cost by 47% on the fleet average. The proposed enhancement of the tax credit to $85/t would offset the added cost for CCS deployment for a total capacity of 3.4 GW. Joint policy and economic incentives can encourage fossil fuel abatement to play a firm role in energy transition.

Quantitative imaging of natural products in fine brain regions using desorption electrospray ionization mass spectrometry imaging (DESI-MSI): Uncaria alkaloids as a case study.

Uncaria species (Rubiaceae) are used as traditional Chinese medicines (TCMs) to treat central nervous system (CNS) diseases, and monoterpene indole alkaloids are the main bioactive constituents. Localization and quantification of CNS drugs in fine brain regions are important to provide insights into their pharmacodynamics, for which quantitative mass spectrometry imaging (MSI) has emerged as a powerful technique. A systematic study of the quantitative imaging of seven Uncaria alkaloids in rat brains using desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was presented. The distribution of the alkaloids in thirteen brain regions was quantified successfully using the calibration curves generated by a modified on-tissue approach. The distribution trend of different Uncaria alkaloids in the rat brain was listed as monoterpene indole alkaloids > monoterpene oxindole alkaloids, R-configuration epimers > S-configuration epimers. Particularly, Uncaria alkaloids were detected directly in the pineal gland for the first time and their enrichment phenomenon in this region had an instructive significance in future pharmacodynamic studies.

Mass spectrometry imaging: new eyes on natural products for drug research and development.

Natural products (NPs) and their structural analogs represent a major source of novel drug development for disease prevention and treatment. The development of new drugs from NPs includes two crucial aspects. One is the discovery of NPs from medicinal plants/microorganisms, and the other is the evaluation of the NPs in vivo at various physiological and pathological states. The heterogeneous spatial distribution of NPs in medicinal plants/microorganisms or in vivo can provide valuable information for drug development. However, few molecular imaging technologies can detect thousands of compounds simultaneously on a label-free basis. Over the last two decades, mass spectrometry imaging (MSI) methods have progressively improved and diversified, thereby allowing for the development of various applications of NPs in plants/microorganisms and in vivo NP research. Because MSI allows for the spatial mapping of the production and distribution of numerous molecules in situ without labeling, it provides a visualization tool for NP research. Therefore, we have focused this mini-review on summarizing the applications of MSI technology in discovering NPs from medicinal plants and evaluating NPs in preclinical studies from the perspective of new drug research and development (R&D). Additionally, we briefly reviewed the factors that should be carefully considered to obtain the desired MSI results. Finally, the future development of MSI in new drug R&D is proposed.

A simple and effective method for identification of Fraxini Cortex from different sources by multi-mode fingerprint combined with chemometrics.

Fraxini Cortex has a long history of being used as a medicinal plant in traditional Chinese medicine. However, it is challenging to differentiate and make quality evaluations for Fraxini Cortex from different origins due to their similarities in morphological features, as well as general chemical composition using traditional chemical analytical methods. In this study, a simple and effective method was developed to identify Fraxini Cortex from different origins by multi-mode fingerprint combined with chemometrics. Digital images of the high-performance thin-layer chromatography profiles were converted to grayscale intensity, and the common patterns of high-performance thin-layer chromatography fingerprints were generated with ChemPattern software. Authentication and quality assessment were analyzed by similarity analysis, hierarchical cluster analysis, principal component analysis, and multivariate analysis of variance. The ultra-high-performance liquid chromatography fingerprints were analyzed by similarity analysis, principal component analysis, and orthogonal partial least square-discriminant analysis. When combined with chemometrics, high-performance thin-layer chromatography and ultra-high-performance liquid chromatography fingerprint provided a simple and effective method to evaluate the comprehensive quality of Fraxini Cortex, and to distinguish its two original medicinal materials (Fraxinus chinensis Roxb. and Fraxinus rhynchophylla Hance.) recorded in the Chinese Pharmacopeia and its three adulterants (Fraxinus mandschurica Rupr., Fraxinus pennsylvanica Marsh., and Juglans mandshurica Maxim.). A similar workflow may be applied to establish a differentiation method for other medicinal and economic plants.

Authentication of Shenqi Fuzheng Injection via UPLC-Coupled Ion Mobility-Mass Spectrometry and Chemometrics with Kendrick Mass Defect Filter Data Mining.

Nearly 5% of the Shenqi Fuzheng Injection's dry weight comes from the secondary metabolites of Radix codonopsis and Radix astragali. However, the chemical composition of these metabolites is still vague, which hinders the authentication of Shenqi Fuzheng Injection (SFI). Ultra-high performance liquid chromatography with a charged aerosol detector was used to achieve the profiling of these secondary metabolites in SFI in a single chromatogram. The chemical information in the chromatographic profile was characterized by ion mobility and high-resolution mass spectrometry. Polygonal mass defect filtering (PMDF) combined with Kendrick mass defect filtering (KMDF) was performed to screen potential secondary metabolites. A total of 223 secondary metabolites were characterized from the SFI fingerprints, including 58 flavonoids, 71 saponins, 50 alkaloids, 30 polyene and polycynes, and 14 other compounds. Among them, 106 components, mainly flavonoids and saponins, are contributed by Radix astragali, while 54 components, mainly alkaloids and polyene and polycynes, are contributed by Radix codonopsis, with 33 components coming from both herbs. There were 64 components characterized using the KMDF method, which increased the number of characterized components in SFI by 28.70%. This study provides a solid foundation for the authentification of SFIs and the analysis of its chemical composition.

The Obesity Amelioration Effect in High-Fat-Diet Fed Mice of a Homogeneous Polysaccharide from Codonopsis pilosula.

A homogeneous polysaccharide coded as CPP-1 was extracted and purified from the root of Codonopsis pilosula (Franch.) Nannf. by water extraction, ethanol precipitation, and column chromatography. Its structure was analyzed by HPGPC-ELSD, HPLC, GC-MS, FT-IR, and NMR techniques. The results indicated that CPP-1 was composed of mannose (Man), glucose (Glc), galactose (Gal), and arabinose (Ara) at a molar ratio of 5.86 : 51.69 : 34.34 : 8.08. The methylation analysis revealed that the main glycosidic linkage types of CPP-1 were (1→)-linked-Glc residue, (1→3)-linked-Glc residues, (1→4)-linked-Gal residue, (1→2,3,4)-linked-Glc residue, (1→)-linked-Man residue, (1→3,4)-linked-Glc residue, and (1→)-linked-Ara residue. In vivo efficacy trial illustrated that CPP-1 supplements could alleviate HFD-induced mice obesity significantly, as well as improve obesity-induced disorders of glucose metabolism, alleviate insulin resistance, and improve the effects of lipid metabolism. The findings indicate that this polysaccharide has the potential for the treatment of obesity.

Optimal Control of Background-Based Uncertain Systems with Applications in DC Pension Plan.

In this paper, we propose a new optimal control model for uncertain systems with jump. In the model, the background-state variables are incorporated, where the background-state variables are governed by an uncertain differential equation. Meanwhile, the state variables are governed by another uncertain differential equation with jump, in which both the background-state variables and the control variables are involved. Under the optimistic value criterion, using uncertain dynamic programming method, we establish the principle and the equation of optimality. As an application, the optimal investment strategy and optimal payment rate for DC pension plans are given, where the corresponding background-state variables represent the salary process. This application in DC pension plans illustrates the effectiveness of the proposed model.

Evolution of the adaptogenic concept from traditional use to medical systems: Pharmacology of stress- and aging-related diseases.

Adaptogens comprise a category of herbal medicinal and nutritional products promoting adaptability, resilience, and survival of living organisms in stress. The aim of this review was to summarize the growing knowledge about common adaptogenic plants used in various traditional medical systems (TMS) and conventional medicine and to provide a modern rationale for their use in the treatment of stress-induced and aging-related disorders. Adaptogens have pharmacologically pleiotropic effects on the neuroendocrine-immune system, which explain their traditional use for the treatment of a wide range of conditions. They exhibit a biphasic dose-effect response: at low doses they function as mild stress-mimetics, which activate the adaptive stress-response signaling pathways to cope with severe stress. That is in line with their traditional use for preventing premature aging and to maintain good health and vitality. However, the potential of adaptogens remains poorly explored. Treatment of stress and aging-related diseases require novel approaches. Some combinations of adaptogenic plants provide unique effects due to their synergistic interactions in organisms not obtainable by any ingredient independently. Further progress in this field needs to focus on discovering new combinations of adaptogens based on traditional medical concepts. Robust and rigorous approaches including network pharmacology and systems pharmacology could help in analyzing potential synergistic effects and, more broadly, future uses of adaptogens. In conclusion, the evolution of the adaptogenic concept has led back to basics of TMS and a new level of understanding of holistic approach. It provides a rationale for their use in stress-induced and aging-related diseases.

"Force iteration molecular designing" strategy for the systematic characterization and discovery of new protostane triterpenoids from Alisma Rhizoma by UHPLC/LTQ-Orbitrap-MS.

Comprehensive analysis and identification of chemical components are of great significance for evaluating the efficacy and safety of herbal medicines, as well as for drug exploitation and development. Here we developed a "force iteration molecular designing" strategy, by combing a database-based in-house software for a precursor ion list (PIL) and PIL-triggered collision-induced dissociation-MS2 and high-energy C-trap dissociation-MS2 (PIL-CID/MS2-HCD/MS2) on an LTQ-Orbitrap mass spectrometer, aiming for the systematic characterization and discovery of new protostane triterpenoids (PTs) from Alisma Rhizoma (AR). AR was a well-known herbal remedy widely used for diarrhea, but its systematic characterization and comparison between two botanical origins have not been reported. Firstly, in-house software was developed based on force iteration, to generate a PIL that contains 483 accurate precursor ions. Secondly, to facilitate the acquisition of rich fragments and diagnostic ions sufficient for the structural elucidation of different types of PTs, a hybrid data acquisition method, namely PIL-CID/MS2-HCD/MS2, was generated. Thirdly, a total of 473 PTs were rapidly characterized from two botanical origins of AR according to an established four-step interpretation method, and the common constituents were 277 with ratio 70% (277/395) and 78% (277/355) in the rhizome of Alisma plantago-aquatica and A. orientale, respectively. Finally, two new PTs were isolated and unambiguously identified by NMR verifying the feasibility of this combined data acquisition strategy. This integrated strategy could improve the efficiency in the detection of new compounds in a single run and is practical to comprehensively characterize the complex components in herbal medicines.

Comprehensive feature-based molecular networking and metabolomics approaches to reveal the differences components in Cinnamomum cassia and Cinnamomum verum.

Cinnamon was been a widely used plant in medicinal and spices for a long time and has spread all over the world. However, the differences in the components of the bark from Cinnamomum cassia and Cinnamomum verum, the two most common types of cinnamon, have not been thoroughly investigated. In the present experiment, ultra-high-performance liquid chromatography LTQ-Orbitrap Velos Pro hybrid mass spectrometer-based metabolomics coupled with chemometrics and feature-based molecular networking were employed to dramatically distinguish and annotate Cinnamomum cassia Bark and Cinnamomum verum bark. As a consequence, principal component analysis, orthogonal projection to latent structures discriminates analysis, and heat map analysis demonstrated clear discrimination between the profiles of metabolites in cinnamon. Besides, as the known compounds, proanthocyanidins (cinnamtannin B1 and procyanidin B2) and alkaloids (norboldine, norisoboldine) with variable importance in the projection scores >6, and an unknown alkaloid (formula C24 H33 NO6 ) were selected as the best markers to discriminate cinnamon. Furthermore, large numbers of proanthocyanidins and alkaloids components were identified through feature-based molecular networking for the first time. Our investigation provides new ideas for the discovery of quality markers and identification of unknown components in natural products.

Untargeted metabolomics coupled with chemometric analysis deducing robust markers for discrimination of processing procedures: Wine-processed Angelica sinensis as a case study.

Wine-processed Angelica Sinensis is a widely used Chinese medicinal decoction piece in China. However, there are hardly any robust markers indicating the processing procedure of wine-processed Angelica Sinensis, including the amount of rice wine and processing degree. A strategy integrating untargeted metabolomics and chemometric analysis for deducing robust markers was provided and applied to the discrimination of processing procedure. First, 86 compounds were tentatively identified in wine-processed Angelica Sinensis by ultra-high-performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry. Second, 93 potential chemical markers were selected using multivariate analysis, among which nine robust chemical markers were selected by verification with commercial samples. Finally, the effects of processing temperature, time, and amount of rice wine on the three selected chemical markers were investigated through a rapid analytical method. It was demonstrated that both m/z 258.1097 and 238.1189 were positively correlated with the amount of rice wine and processing degree. In summary, this study introduced two candidate processing markers as robust markers for discriminating the processing procedures of wine-processed Angelica sinensis. It also proposed a strategy to provide the reference for the research of other decoction pieces.