Multi-Evaluating Strategy for Siji-kangbingdu Mixture: Chemical Profiling, Fingerprint Characterization, and Quantitative Analysis

Yao, Zhuoru; Yu, Jin-Gao; Tang, Zhishu; Liu, Hongbo; Ruan, Kaihua; Song, Zhong-Xing; Liu, Yanru; Ke, Yan; Tang, Yuping; Ma, Huqiang

doi:10.3390/molecules24193545

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…The standard value of RFP should be S m ≥ 0.95 and P m ≈ 100. Yao et al applied a multi evaluation method for a non-prescription herbal drug mixture using ultra-performance liquid chromatography (UPLC) fingerprinting at 254 nm, compound identification via UPLC-triple time of flight (Triple-TOF)-MS analysis, and quantitative determination of the compounds via UPLC-UV-254 nm ( Yao et al, 2019 ). Although similarity levels of the fingerprints were 0.935–0.984, PCA using 7 detected compounds showed 2 clusters.…”

Section: Applications Of Fingerprinting Methodsmentioning

confidence: 99%

Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines

2022

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Herbal drugs or herbal medicines (HMs) have a long-standing history as natural remedies for preventing and curing diseases. HMs have garnered greater interest during the past decades due to their broad, synergistic actions on the physiological systems and relatively lower incidence of adverse events, compared to synthetic drugs. However, assuring reproducible quality, efficacy, and safety from herbal drugs remains a challenging task. HMs typically consist of many constituents whose presence and quantity may vary among different sources of materials. Fingerprint analysis has emerged as a very useful technique to assess the quality of herbal drug materials and formulations for establishing standardized herbal products. Rather than using a single or two marker(s), fingerprinting techniques take great consideration of the complexity of herbal drugs by evaluating the whole chemical profile and extracting a common pattern to be set as a criterion for assessing the individual material or formulation. In this review, we described and assessed various fingerprinting techniques reported to date, which are applicable to the standardization and quality control of HMs. We also evaluated the application of multivariate data analysis or chemometrics in assisting the analysis of the complex datasets from the determination of HMs. To ensure that these methods yield reliable results, we reviewed the validation status of the methods and provided perspectives on those. Finally, we concluded by highlighting major accomplishments and presenting a gap analysis between the existing techniques and what is needed to continue moving forward.

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Section: Applications Of Fingerprinting Methodsmentioning

confidence: 99%

Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines

2022

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“…Glycyrrhizic acid metabolizes into glycyrrhetinic acid, which suppresses the expression of androgen target genes TMPRSS2 (133). Moreover, some of important antiviral drugs commonly used in China contain Glycyrrhizin ingredients, such as Lianhua Qingwen capsule, Maxing Shigan decoction and Sijikangbingdu herbal mixture (23,(134)(135)(136). A case of severe COVID-19 patient with a negative PCR result but a positive antibody test recovers after receiving diammonium glycyrrhizinate and vitamin C replacement therapy (137).…”

Section: Antiviral Effectmentioning

confidence: 99%

A narrative review of COVID-19: magnesium isoglycyrrhizinate as a potential adjuvant treatment

et al. 2021

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Coronavirus disease 2019 (COVID-19) has become a global pandemic affecting more than 200 countries with 87 million patients worldwide as of January 7, 2021. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replicates in a large amount and reaches high-titer levels in a short time after the infection. COVID-19 caused by SARS-CoV-2 shows clinical symptoms mainly including fever, fatigue, dry cough, and dyspnea. In more severe COVID-19 patients, viral pneumonia characterized by bilateral ground glass or patchy opacity, may lead to acute respiratory distress syndrome (ARDS), cytokine storm, multi-organ damage, and even death. Unfortunately, there is no effective therapy for COVID-19 until now.Magnesium isoglycyrrhizinate (MgIG), a magnesium salt of 18-α glycyrrhizic acid stereoisomer, belongs to the fourth generation of glycyrrhizic acid preparation. MgIG has various pharmacological activities including anti-inflammation, anti-oxidation, anti-virus, and immunoregulation, showing the protection against the injury of the vital organs (such as kidney, heart, and lung). Clinically, MgIG injection is usually used as a hepatoprotective agent to treat liver diseases. This narrative review summarizes the research and application of MgIG, and provides the evidence supporting the recommended MgIG as supportive therapy in the "Management Standard for Mild and Common Patients of Coronavirus Disease 2019

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“…The trace detection of multitargets qualitatively and quantitatively is one of the consecutive hotspots in a variety of fields such as biomedicine, − environmental science, and food safety. − Surface-enhanced Raman spectroscopy (SERS), originating from Raman spectroscopy and being ignited by localized surface plasmon resonance, has attracted extensive attention owing to the near-single-molecule-level fingerprint information. − In particular, the qualification and quantitation of SERS, including Raman spectroscopy, have been greatly improved with the rapid development of modern chemometrics.…”

Section: Introductionmentioning

confidence: 99%

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

et al. 2022

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Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.

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Multi-Evaluating Strategy for Siji-kangbingdu Mixture: Chemical Profiling, Fingerprint Characterization, and Quantitative Analysis

Cited by 16 publications

References 25 publications

Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines

Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines

A narrative review of COVID-19: magnesium isoglycyrrhizinate as a potential adjuvant treatment

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

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