Detection of trace components in Xiangdan injection of Dalbergia odorifera based on microextraction and back-extraction along with bar-form-diagram strategy.

Xiangdan Injection are commonly used traditional Chinese medicine formulations for the clinical treatment of cardiovascular diseases. However, the trace components of Dalbergia odorifera in Xiangdan Injection pose a challenge for evaluating its quality due to the difficulty of detection. This study proposes a technology combining dispersive liquid-liquid microextraction and back-extraction (DLLME-BE) along with Bar-Form-Diagram (BFD) to address this issue. The proposed combination method involves vortex-mixing tetradecane, which has a lower density than water, with the sample solution to facilitate the transfer of the target components. Subsequently, a new vortex-assisted liquid-liquid extraction step is performed to enrich the components of Dalbergia odorifera in acetonitrile. The sample analysis was performed on HPLC-DAD, and a clear overview of the chemical composition was obtained by integrating spectral and chromatographic information using BFD. The combination of BFD and CRITIC-TOPSIS strategies was used to optimize the process parameters of DLLME-BE. The determined optimal sample pre-treatment process parameters were as follows: 200 µL extraction solvent, 60 s extraction time, 50 µL back-extraction solvent, and 90 s back-extraction time. Based on the above strategy, a total of 29 trace components, including trans-nerolidol, were detected in the Xiangdan Injection. This combination technology provides valuable guidance for the enrichment analysis of trace components in traditional Chinese medicines.

Improving the geographical origin classification of Radix glycyrrhizae (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition.

Radix glycyrrhizae (licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith). Firstly, the three partitions were separately labeled using the Labelme tool, which was utilized to train the U-Net model. Secondly, the obtained optimal U-Net model was applied to predict three partitions of all samples. Lastly, various machine learning models (LDA, SVM, and PLS-DA) were trained based on segmented hyperspectral data. In addition, a threshold method and a circumcircle method were applied to segment licorice hyperspectral images for comparison. The results revealed that compared with the threshold segmentation method (which yielded SVM classifier accuracies of 99.17%, 91.15%, and 92.50% on the training set, validation set, and test set, respectively), the U-Net segmentation method significantly enhanced the accuracy of origin classification (99.06%, 94.72% and 96.07%). Conversely, the circumcircle segmentation method did not effectively improve the accuracy of origin classification (99.65%, 91.16% and 92.13%). By integrating Raman imaging of licorice, it can be inferred that the U-Net model, designed for region segmentation based on the inherent tissue structure of licorice, can effectively improve the accuracy origin classification, which has positive significance in the development of intelligence and information technology of Chinese medicine quality control.

Network pharmacology and molecular docking to explore the mechanism of Sheng Xue Bao mixture against iron deficiency anemia.

Based on network pharmacology and molecular docking, we investigated the mechanism of action of Sheng Xue Bao mixture (SXBM) in treating iron deficiency anemia (IDA). We screened the HERB and traditional Chinese medicine systems pharmacology database and analysis platform databases to identify the active ingredients and targets of SXBM. The targets associated with "iron deficiency anemia" were collected from GeneCards, TTD, and OMIM databases. A component-target interaction network was constructed using Cytoscape 3.8.2. The protein-protein interaction network of candidate targets was generated using the STRING database and visualized with Cytoscape 3.8.2 software. Core modules obtained from clustering analysis were subjected to Gene Ontology and Kyoto encyclopedia of genes and genomes enrichment analysis. Finally, molecular docking validation of key targets and active components was performed using Autodock Vina software. A total of 174 active components and 111 genes were identified as potential active components and targets for IDA treatment, including quercetin, kaempferol, luteolin, beta-sitosterol, and other flavonoids as main active components. Gene Ontology enrichment analysis show that interleaved genes are enriched in 2328 biological processes, 71 cellular component expression processes, and 157 molecular function processes. Kyoto encyclopedia of genes and genomes analysis mainly envolved Prostate cancer, Hepatitis B, Kaposi sarcoma-associated herpesvirus infection, Endocrine resistance, Lipid and atherosclerosis, Central carbon metabolism in cancer, Human cytomegalovirus infection and HIF-1 signaling pathway. STAT3, SRC, PIK3R1, and GRB2 were selected as core targets. The molecular docking results demonstrated strong interactions between key components and their respective target proteins. Network pharmacological analysis suggested that SXBM could treat IDA by regulating various biological processes and related signaling pathways. It laid the foundation for further elucidating the molecular mechanism of SXBM treatment of IDA.

An intelligent extraction approach of feature information from three-dimensional DAD chromatogram for integrated quality control of traditional Chinese medicines: Gardenia jasminoides root as an example.

Traditional Chinese medicine (TCM) fingerprinting, which has the characteristics of holism and ambiguity, is a conventional strategy for the holistic quality control of TCMs. However, the fingerprinting of TCMs at the current stage generally adopts a single wavelength or few wavelengths, lacking the effective utilization of diode-array detector (DAD) chromatogram data. This study proposes an intelligent extraction approach of feature information from a three-dimensional DAD chromatogram to establish a novel bar-form-diagram (BFD) for integrated quality control of TCMs. The BFD was automatically established by the chromatographic and spectral information of a complex hybrid system in a DAD chromatogram. This covered the peak areas of target compositions at the optimal absorption wavelength. Taking 27 batches of Gardenia jasminoides root as samples, the BFD combined with chemometrics was applied for assessing the quality of samples completely, which improved the accuracy of origin classification using hierarchical cluster analysis, principal component analysis, soft independent modeling of class analogy and orthogonal partial least squares discriminant analysis. Single-wavelength fingerprinting and BFD used 23 and 38 common peaks as variables respectively, and the adjusted rand index results of the single wavelength and BFD were 0.559 and 0.819, respectively. Compared with the ergodic methods of each single wavelength, the peak recognition method in this study improved the operation speed from 180 s to 4 s and the computational complexity. The established BFD approach performed more abundant characteristic information of chemical components of TCMs and more accurate origin classification ability, and it had great advantages in the overall quality control of TCMs.

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review.

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms.

Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.

Multi-wavelength fusion column fingerprint technology combined with chemometric analysis to evaluate the overall quality of the Gardenia jasminoides root.

Chromatographic fingerprinting provides effective technical means for quality evaluation of traditional Chinese medicine. In this work, a novel multi-wavelength fusion column fingerprint was obtained by intelligent selection of chromatographic peaks from different wavelengths, which displayed the maximum peak area information under the optimal wavelength at the same retention time. Here, the Gardenia jasminoides root was selected as a sample. The multi-wavelength fusion column fingerprint graph of the Gardenia jasminoides root was constructed from five wavelengths (203 nm, 210 nm, 238 nm, 250 nm and 330 nm). The peak capacity, peak resolution, the number of common peaks and similarity were used to evaluate the performance. The 19 batches of Gardenia jasminoides root were classified into three categories with clear distinction between origin categories based on the multi-wavelength fusion column fingerprint combined with chemometrics, including hierarchical cluster analysis and principal component analysis. Nine markers of variation that led to differences between batches were screened by orthogonal partial least squares discriminant analysis. This study demonstrated that the classification model based on the multi-wavelength fusion column fingerprint was better than that on a single-wavelength, and the fusion fingerprint was suitable for the identification and quality control of traditional Chinese medicine with more comprehensive chemical composition information and more accurate prediction ability.

An integrated strategy using LC-MS/MS combined with in vivo microdialysis for the simultaneous determination of lignans of Schisandra chinensis (Turcz.) Baill. Fructus and endogenous neurotransmitters: application in pharmacokinetic and pharmacodynamic studies.

Schisandra chinensis (Turcz.) Baill Fructus (SCF) is the ripe fruit of Schisandra chinensis (Turcz.) Baill, and is often used as a neuroprotective drink. Modern pharmacological studies have shown that lignans are the main bioactive components responsible for neuroprotection and have potential in the treatment of Alzheimer's disease (AD). However, the mechanism of action of SCF in the treatment of AD from the pharmacokinetics-pharmacodynamics (PK-PD) perspective remains not well established. The purpose of this study is to investigate and compare the pharmacokinetic differences of lignans in normal and AD rats, as well as to investigate their effects on neurotransmitters and their role in the treatment of AD. To achieve this goal, an integrated strategy using LC-MS/MS combined with in vivo microdialysis for the simultaneous determination of lignans of SCF and endogenous neurotransmitters has been developed and validated. The results show that the pharmacokinetic behaviors of ten lignans in the AD group were significantly different from those in the normal group. The AD group had better absorption and slower elimination than the normal group. In addition, the pharmacodynamic results of the Morris water maze (MWM) test, biochemical tests, histopathological examination, as well as immunohistochemistry analysis showed that lignans could improve the learning and memory of AD rats. The oral administration of SCF could restore the levels of the neurotransmitter parameters; seven neurotransmitters showed clockwise or counterclockwise changes with the four lignans in the hippocampal region. Taken together, the PK and PD studies based on in vivo microdialysis sampling might offer novel insights into the mechanisms of action of SCF against AD.

Mass spectrometry-based serum lipidomics strategy to explore the mechanism of Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. leaves in the treatment of ischemic stroke.

Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. leaves (ESL) were reported to have neuroprotective function and are also used to treat cranial and cerebral traumas as a traditional Chinese medicine and food herbage plant. However, there has been no previous study on ESL treatment for stroke at the level of lipid disorders. To clarify the mechanism of ESL in treating ischemic stroke, this study was carried out from 3 aspects, namely, the regulation of lipid disorders, protection of the nervous system, as well as anti-inflammatory and antioxidant actions. This study established a lipidomics research strategy that was developed by UPLC-Q-TOF/MS analysis. The quantification of neurotransmitters in the serum and brain tissue of rats was performed using UPLC-TQ/MS. Also, we quantified the oxidative stress and inflammatory reaction by measuring the contents of SOD, MDA, TNF-α, IL-6, and IL-10 via the ELISA kits for serum and brain tissue. According to UPLC-Q-TOF/MS-based lipidomics analysis, 27 lipidomics biomarkers were identified in this study, including PC, PE, SM, and TG, which were distributed in various lipid metabolic pathways, including glycerophospholipid, linoleic acid, alpha-linolenic acid, glycerolipid, sphingolipid, and arachidonic acid metabolism pathways. By reversing the changes in the lipid content caused by the disease, ESL has a therapeutic effect on ischemic stroke. Furthermore, quantitative results of neurotransmitters indicated that they can be regulated by ESL. Finally, the results of ELISA showed that ESL can treat ischemic stroke to a certain extent by reducing the oxidative and inflammatory damage. Therefore, ESL may play a therapeutic role in the treatment of ischemic stroke in different ways. This research preliminarily revealed the mechanism of ESL in the treatment of ischemic stroke and provided support for the further application of ESL.

A rapid protocol to distinguish between Citri Exocarpium Rubrum and Citri Reticulatae Pericarpium based on the characteristic fingerprint and UHPLC-Q-TOF MS methods.

Citri Exocarpium Rubrum (CER) and Citri Reticulatae Pericarpium (CRP) are used as common functional foods and traditional Chinese medicine (TCM). As different parts of the same fruit, CER and CRP have different effects in clinical applications. However, they are commonly confused due to the similarity of the chemical compounds and a lack of scientific method to distinguish them in the finished product. In this study, an ultra-high performance liquid chromatography-diode array detector (UHPLC-DAD) technique and an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) method were employed to generate the characteristic fingerprint under the optimum analytical conditions. Principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) were applied to represent different chemical markers for CER and CRP. 44 potential markers including 15 polymethoxylated flavanones (PMFs), 5 flavone-C-glycosides, 6 flavanone-O-glycosides, 3 flavonoid-O-glycosides, 8 organic acids, 5 limonoids and 2 alkaloids were successfully identified by using UNIFI software. The heat map showed that there were significant differences in the CER and CRP samples. Furthermore, 12 potential markers were screened out by characteristic fingerprint and UHPLC-Q-TOF MS methods and were analyzed by quantitative analysis of multicomponents by a single marker (QAMS) method. Finally, a prediction model based on the discovered chemical markers was established for discrimination between CER and CRP. Using these markers can significantly distinguish the unknown processed products of CER and CRP. In conclusion, an effective way to quickly and easily distinguish CER and CRP was successfully established based on the characteristic fingerprint and UHPLC-Q-TOF MS. It could also be a new strategy for analysis of different processed products of the same plant source.

Efficiency of Sophora flavescens-Fructus Ligustri Lucidi Drug Pairs in the Treatment of Liver Fibrosis Based on the Response Surface Method.

The pairing of Sophora flavescens and Fructus Ligustri lucidi is taken from Shi Jinmo Medicine. The idea behind this pairing was inspired by the similarity in pharmacological effects of the two herbal drugs, both of which are known to be effective in the treatment and protection against liver fibrosis. To quantitatively study the extent of the interaction between these drugs and the effect of pairing on the treatment of liver fibrosis, an animal model of liver fibrosis mice was established by intraperitoneal injection of low-dose carbon tetrachloride. The drugs were then administered individually, or in predefined compatibility ratio pairs, by gavage, and the effects on indexes of liver fibrosis were observed. The multisynthetic index method was adopted using Matlab software in order to construct a three-dimensional response surface map of the integration effect and conduct interaction analysis of Sophora flavescens and Fructus Ligustri lucidi. The quadratic surface fitting pattern was designed by quadratic regression to determine the optimal range of each drug. The obtained results show that when the compatibility ratio of Sophora flavescens-Fructus Ligustri lucidi drug pairs is less than or equal to 1:1, their therapeutic effect is enhanced by synergy (interaction value ranging between -0.2 and -1). Overall, the synergy of the high-dose drug pairs is stronger than that of the low-dose drug pairs. The optimal dose ranges are 6~12 g and 8~17 g for Sophora flavescens and Fructus Ligustri lucidi, respectively.

Neurochemical effects of the R form of α-lipoic acid and its neuroprotective mechanism in cellular models of Parkinson's disease.

OBJECTIVE: Parkinson's disease (PD) is a common neurodegenerative disease. This study aimed to investigate the effects of the R form of α-lipoic acid (RLA) in cellular models of PD induced by 6-hydroxydopamine (6-OHDA) or 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). METHODS: Cell viability and apoptosis were detected using CCK8 and Annexin V-FITC assays, respectively. Intracellular reactive oxygen species (ROS) were detected by fluorescence staining. ELISA assays were performed to detect the levels of dopamine and α-synuclein. To evaluate the effects of RLA on mitochondrial function, cytotoxicity, ATP levels, and mitochondrial gene expression were assayed. Additionally, the expression levels of autophagy-related proteins, including Parkin, PINK1, p62, ATG12, and LC3, were analyzed by western blot, and cell autophagy was visualized by immunofluorescence. RESULTS: RLA increased cell viability and decreased apoptosis, intracellular ROS, and cytotoxicity, and induced cell autophagy in PD models induced by 6-OHDA and MPTP. RLA also reversed the decreased dopamine and increased α-synuclein expression induced by 6-OHDA and MPTP. The mitochondrial regulatory protein PGC-1α was significantly up-regulated by RLA. The expression levels of autophagy-related proteins, including Parkin, PINK1, p62, and ATG12, were down-regulated after RLA treatment, while LC3 expression was up-regulated. CONCLUSIONS: RLA has a protective effect against cellular damage induced by 6-OHDA and MPTP. The neuroprotective mechanism of RLA may be associated with improvement of mitochondrial function and autophagy. Therefore, RLA may serve as a promising potential adjuvant for PD treatment.