Objective To investigate the mechanism of Dangshen (Codonopsis Radix) containing serum (CRCS) on inflammatory factors release and epithelial cell excessive apoptosis in vitro model of ulcerative colitis (UC). Methods Bioinformatics analysis tools were used to predict and regulate the upstream miRNAs of phosphatase and tensin homolog (PTEN). qRT-PCR and Western blotting were used to detect the mRNA and protein expression levels of PTEN and the expressions of miR-21, miR-30a-3p, miR-198, miR-152-3p, miR-301a-3p, miR-320 and miR-3691-5p in colon tissues of UC rats model induced by enema 5% 2,4,6-trinitrobenzenesulfonic acid solution (TNBS)/ethanol composite solution. Lipopolysaccharide (LPS, 10 μg/mL) was used to induce human normal colon epithelial NCM460 cells to construct an in vitro model of UC, 1%, 2%, 4%, 6% and 8% CRCS intervention was administered for 24 h. The cell viability was detected by CCK-8 method. Subsequently, miR-21 mimics and miR-21 inhibitor were transfected into model cells, respectively, which were cultured in 6 % CRCS complete medium for 24 h. The levels of inflammatory factors interleukin-6 (IL-6), IL-17, IL-1β, IL-8, nuclear factor-κB (NF-κB) and tumor necrosis factor-α (TNF-α) in each group were detected by ELISA. The expression of miR-21 in each group was detected by qRT-PCR. Western blotting was used to detect the expressions of PTEN, B-cell lymphoma-2 (Bcl-2), Bcl-2 associated X protein (Bax), cleaved cystein-asparate protease-3 (cleaved Caspase-3) and phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway related proteins. The binding sites of miR-21 and PTEN were predicted by bioinformatics, and the targeting effect of miR-21 on PTEN was verified by dual luciferase reporter gene detection system. Results A total of seven miRNAs that may target PTEN were screened by bioinformatics analysis. Among them, the expression of miR-21 was decreased significantly after CRCS intervention (P < 0.01), while the expressions of miR-30a-3p, miR-198, miR-152-3p, miR-301a-3p, miR-320 and miR-3691-5p had no statistical significance. The results of CCK-8 showed that 2%, 4%, 6% and 8% CRCS could significantly improve cell viability (P < 0.01). Compared with model group, CRCS and miR-21 inhibitor increased the expression levels of PTEN and Bcl-2 proteins in cells (P < 0.01), and decreased the expression levels of miR-21, Bax, cleaved Caspase-3, p-PI3K/PI3K and p-Akt/Akt (P < 0.01). Compared with CRCS group, miR-21 mimics significantly attenuated the up-regulation of PTEN and Bcl-2 expressions by CRCS (P < 0.01) and the down-regulation of miR-21, Bax, cleaved Caspase-3, p-PI3K/PI3K, p-Akt/Akt expressions (P < 0.05, 0.01). The results of dual luciferase reporter assay showed that in the transfected wt-Luc-PTEN plasmocytes, CRCS intervention could significantly increase luciferase activity (P < 0.01), and miR-21 mimics could significantly reduce luciferase activity (P < 0.01). In the combined treatment, miR-21 mimics could partially weaken the effect of CRCS on luciferase activity (P < 0.01). In transfected mut-Luc-PTEN plasmocytes, CRCS intervention and miR-21 mimics did not cause significant changes in luciferase activity. Conclusion CRCS could alleviate UC by inhibiting pro-inflammatory factors release and excessive apoptosis of epithelial cells. The mechanism is at least partially dependent on miR-21/PTEN/PI3K/Akt axis.

Objective To preliminarily explore the correlation between changes in traditional Chinese medicine (TCM) syndromes and peripheral neurotransmitter levels in patients with depression of heart-kidney non-interaction syndrome treated with Jiaotai Pill (交泰丸, JTW) alone or in combination with selective serotonin reuptake inhibitors (SSRIs), and to exploratorily develop efficacy prediction models. Methods This post-hoc analysis utilized data from a randomized controlled trial (n = 91). Patients were divided into the JTW group (n = 31), JTW combined with SSRIs group (n = 30), and SSRIs group (n = 30). Response after eight weeks of treatment was defined as a ≥ 50% reduction rate in the Hamilton depression scale (HAMD) score. Changes in neurotransmitter levels and TCM syndrome scores before and after treatment were compared among the three groups. Spearman correlation analysis was used to assess the associations between changes in individual TCM syndrome scores and changes in neurotransmitter levels of 5-hydroxytryptamine (5-HT), norepinephrine (NE), and dopamine (DA). Firth's penalized-likelihood logistic regression, adjusted for confounders such as age and sex, was employed to analyze the independent associations between baseline characteristics and treatment response within each group, identifying potential predictors. Predictive models were constructed based on statistically significant predictors. Bootstrap internal validation (1 000 repetitions) was performed to correct for overfitting, and the optimism-corrected area under the curve (AUC) was reported to evaluate the discriminatory performance of the models. Results No statistically significant difference in response rates was observed among the three groups (P > 0.05). Comparisons before and after treatment showed that 5-HT and NE levels, as well as most TCM syndrome scores, improved significantly from baseline in all three groups (P < 0.05). Correlation analysis revealed that in the JTW group, Δ5-HT was positively correlated with Δinsomnia/dreamfulness (r = 0.521, P = 0.003) and Δdry throat and mouth (r = 0.438, P = 0.014). Multivariate analysis indicated that in the JTW group, baseline “forgetfulness” was a negative predictor of efficacy (OR = 0.60, P = 0.022). In the combination group, baseline “palpitations” was a positive predictor (OR = 1.62, P = 0.029), while “spermatorrhea or irregular menstruation” was a negative predictor (OR = 0.22, P = 0.007). Notably, the symptom of “spermatorrhea or irregular menstruation” itself did not show significant improvement after treatment. Predictive model analysis showed that in the JTW group, the model based on “forgetfulness” had a corrected AUC of 0.669 (original AUC = 0.838). In the combination group, the composite model including both “palpitations” and “spermatorrhea or irregular menstruation” demonstrated the best predictive performance, with a corrected AUC of 0.809 (original AUC = 0.937), suggesting preliminary discriminatory ability but also indicating potential overfitting. Conclusion By exploring the associations between TCM syndromes and neurotransmitter changes in depressed patients with heart-kidney non-interaction syndrome treated with JTW and by developing efficacy prediction models, this study provides preliminary evidence to guide treatment selection for clinical subtypes predominantly characterized by “exuberance of heart fire” or “kidney essence deficiency”. These findings offer exploratory clues for the precise application of JTW, though its predictive performance requires further validation in prospective and large-scale studies.

Objective To address the issues of strong subjectivity and low efficiency in traditional Chinese medicine origin tracing, we explore the method of Chinese medicine origin tracing based on mid-infrared spectroscopy (MIRS) combined with machine learning algorithm modeling, and provide new technical support for traditional Chinese medicine origin tracing. Methods Based on the mid-infrared spectral dataset, this study performed outlier handling, missing value imputation, first-order difference, high-variance feature selection, distance calculation and clustering analysis, and locally linear embedding (LLE) for dimensionality reduction. Subsequently, we constructed six machine learning models including Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. We then employed the Ivy Algorithm (IVYA) to optimize model parameters. A multidimensional evaluation framework was established, incorporating the area under the receiver operating characteristic (ROC) curve (AUC) alongside accuracy, recall, precision, and F1 score (F1). This framework was used to identify the optimal model for Chinese herbal medicine origin tracing. Results SVM (macro-average AUC = 0.998, F1 = 0.949, accuracy = 0.949, recall = 0.949, precision = 0.956) and ANN (macro-average AUC = 0.999, F1 = 0.940, accuracy = 0.939, recall = 0.939, precision = 0.944) demonstrated optimal performance in identifying the origins of Chinese herbal medicines. They exhibited high consistency in predicting the origins of unknown samples, with core evaluation metrics significantly outperforming RF, LightGBM, KNN, and XGBoost. The SVM model can accurately capture the functional group vibration differences in the medium chemical components of medicinal materials from different origins. ANN models simulate the multifactorial nonlinear coupling effects formed by Chinese herbal medicine origins. Both models not only achieve efficient differentiation among 11 origins but also exhibit complementary strengths: SVM’s globally optimal classification hyperplane excels at processing origins with markedly distinct spectral features, while ANN’s distributed representation capability better suits complex scenarios with high feature overlap. The MIRS method, integrating SVM and ANN, offers rapid, non-destructive, and high-throughput advantages for Chinese herbal medicine traceability. This provides an objective tool for large-scale origin tracing of authentic medicinal materials and delivers a practical technical pathway for the modernization of the Chinese herbal medicine industry. Conclusion MIRS combined with SVM, RF, LightGBM, KNN, XGBoost, and ANN demonstrates effectiveness for Chinese herbal medicine origin tracing. Among these, SVM and ANN emerge as preferred models for this application, offering an interdisciplinary solution. Future integration of multimodal data and deep learning techniques holds promise for enhancing model performance.

Objective To explore the cross-regional and multi-dimensional cooperation of global traditional Chinese medicine (TCM) researchers in the field of research on the TCM Corydalis yanhusuo from 2000 to 2024, and to sort out the research hotspots, frontier trends and regional distribution of research topics, so as to provide reference for the subsequent research on C. yanhusuo. Methods Literature on C. yanhusuo was retrieved from databases such as China National Knowledge Infrastructure (CNKI), Wanfang, VIP, Web of Science (WOS), and PubMed. The bibliometric software Coocation (COOC20.0) combined with VOSviewer 1.6.20 was used to conduct a visual analysis from multiple dimensions, including the number of publications, core authors, prolific institutions, keyword co-occurrence, and new word discovery, to construct a knowledge map of research hotspots and frontiers for C. yanhusuo. Results Since the 21st century, the number of papers published on C. yanhusuo research at home and abroad has gradually increased, and China is the core country for C. yanhusuo research. Major universities and research institutes in China are the main institutions for C. yanhusuo research, and the scope of cooperation exists in various TCM research units and related enterprises. English publications on C. yanhusuo are mostly contributed by Chinese research institutions, with hotspots concentrating on isolation and structural elucidation of alkaloid active components, establishment of quality assessment methods, and evaluation of alkaloid activity mechanisms. Conclusion Research on C. yanhusuo has entered a stage of diversified and in-depth development. In the future, it is expected that cutting-edge technologies such as artificial intelligence and multi-omics will be applied to the research on C. yanhusuo targets, mechanisms of action, and compound medication rules.

Objective To identify the genome-wide family members of type III polyketide synthase (PKS III) and conduct bioinformatics analysis and expression profile analysis and clarify the relationship between their expression and the content of active components of Huzhang (Polygonum cuspidatum). Methods Based on the genome data of P. cuspidatum, the members of PKS III gene family were identified. Subsequently, the characteristics of these members were analyzed, including the physicochemical properties of their encoded proteins, secondary structure, phylogeny, and conserved motifs. Transcriptome data were further used to analyze their expression profiles, and the correlation between the expression levels of these PKS III genes and the active components was investigated. Results A total of 73 PKS III members were identified in the whole genome of P. cuspidatum, which were unevenly distributed on 20 chromosomes. Subcellular localization prediction indicated that most PKS III members were localized in the cytoplasm, while a small portion were localized on the plasma membrane. Phylogenetic analysis revealed that they were clustered into three major groups, and members within the same group shared identical conserved domains. Analysis of promoter cis-acting elements demonstrated that the PKS III genes contained various elements responsive to hormones, light, and growth and development. Expression profile analysis showed significant differences in the expression of PKS III genes among five tissues of roots and rhizomes of P. cuspidatum. Specifically, the expression levels of PKS32 and PKS42 were significantly higher than those of other genes in all tissues, with the highest expression detected in the rhizome pith. Correlation analysis indicated that PKS3, PKS8, PKS21, PKS44, PKS58, and PKS61 exhibited a significant positive correlation with the contents of polydatin and physcion, and PKS27, PKS56, and PKS69 showed a significant positive correlation with the contents of resveratrol and emodin; and PKS32 and PKS42 displayed a significant positive correlation with the content of physcion. Conclusion A total of 73 PKS III family members were identified, and the expression levels of some members correlated significantly with the contents of active components in P. cuspidatum roots and rhizomes. This study provides a foundation for subsequent research on the functions of PKS III genes and for elucidating the biosynthetic mechanisms of anthraquinones, stilbenes, and flavonoids in P. cuspidatum.

Objective To screen, identify, and analyze the FsUGTs family from Forsythia suspensa based on transcriptomic data. Methods The FsUGTs gene family of F. suspensa was identified from transcriptomic and genomic data. Bioinformatics techniques were applied for comprehensive analysis to explore the physicochemical properties and subcellular localization of the proteins encoded by these genes. The evolutionary relationships among these gene family members were further revealed through phylogenetic tree construction, chromosomal localization, cis-acting element analysis, conserved motif analysis, domain analysis, and gene structure analysis. In addition, RT-qPCR was used to detect the expression patterns of FsUGTs in different tissues (roots, stems, leaves, and fruits), and correlation analysis was performed by integrating these results with the tissue distribution of phillyrin. Results After screening and identification in the F. suspensa transcriptome data, a total of 82 FsUGTs were finally obtained. Their amino acid lengths ranged from 385 to 558 amino acids (aa), relative molecular masses from 43 140—60 750, and theoretical isoelectric points (pI) from 4.81 to 8.26. These proteins are primarily localized to the plasma membrane and all contain the PSPG box domain. Phylogenetic analysis classified the 82 FsUGTsinto 17 groups: Group L contained the highest number of UGTs (16 members), followed by Groups A and E with ten and nine respectively, while no FsUGTs were distributed in Group Q. Chromosomal localization results showed that 16 FsUGTs were located on Chr10, with seven each on Chr11, Chr12, and Chr13, while Chr3 contained the fewest FsUGTs (only two). Analysis of cis-acting elements indicated that the expression of FsUGTs was most strongly influenced by light, followed by methyl jasmonate. Domain analysis revealed that all 82 FsUGTs contained three conserved domains, namely GT1_Gtf-like, PLN02448, and Glycosyltransferase_GTB-type superfamily. Correlation analysis between tissue-specific expression of FsUGTs and phillyrin content in different tissues indicated that FsUGT1-FsUGT3, FsUGT9, FsUGT12-FsUGT16, FsUGT18, FsUGT21-FsUGT24, and FsUGT31 may play roles in the glycosylation of phillygenin to form phillyrin. Conclusion This study identified and analyzed the physicochemical properties, cis-acting elements, protein structures, phylogenetic relationships, and tissue-specific expression of the FsUGTs family from F. suspensa. It also screened FsUGTs that may be involved in the glycosylation of phillygenin to phillyrin, laying a foundation for the further identification of phillygenin glycosyltransferases and the understanding of the regulatory mechanisms underlying the synthesis of medicinal components in F. suspensa.

Objective To initially analyze and determine the levels of potential quality Markers (Q-Markers) in four medicinal parts (spikes, stems, leaves, and roots) of Prunella vulgaris based on fingerprinting, network pharmacology, and molecular docking techniques.Methods Fingerprint profiles of the spikes, stems, leaves, and roots of P. vulgaris were established for eight different origins. The common and non-common peaks in the fingerprints of different parts were identified. To predict Q-Markers of P. vulgaris, similarity analysis, principal component analysis (PCA), TOPSIS analysis, and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed, utilizing network pharmacology. Additionally, quantitative analysis was conducted to provide a comprehensive quality evaluation. Results A total of 17 common peaks were identified in the spikes of eight batches of P. vulgaris, 22 in the stems, 21 in the leaves, and 16 in the roots. Peaks 5, 11, 12, 13, 14, 16, and 20 were identified as caffeic acid, rutin, hyperoside, isoquercitrin, salviaflaside, rosmarinic acid, and luteolin, respectively. Caffeic acid and rosmarinic acid were present in all four parts, with similarity values exceeding 0.97 within the same medicinal part, while the fingerprints of different medicinal parts showed significant differences. Network pharmacology screening identified 14 core targets, including PTGS2 and EGFR. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed involvement in protein polysaccharides in cancer, COVID-19, lipid metabolism, and atherosclerosis. A “component-target-pathway” network diagram was developed. For molecular docking, 14 core targets and two components were chosen. The results indicated that the components exhibited strong binding properties with the proteins. Conclusion The established fingerprinting method is both accurate and reliable. Combined with network pharmacology and molecular docking technology, it predicted the activity of two potential Q-Markers in the four medicinal parts of P. vulgaris. This study provides a theoretical foundation for the comprehensive quality evaluation of P. vulgaris and the exploration of new medicinal parts.

Objective To establish the HPLC fingerprint of water-soluble components for different habitat‑processed products of Fritillaria thunbergii, and evaluate the quality of F. thunbergii piece and Huibei from different origins. Methods Inertsil ODS-SP-C₁₈column (250 mm × 4.6 mm, 5.0 μm) was used with methanol (A)-water (B) as mobile phase for gradient elution. The detection wavelength was 260 nm, the column temperature was 25 °C, the volume flow rate was 1.0 mL/min, and the injection volume was 15 μL. The HPLC fingerprint was established and the similarity was analyzed. The chemical constituents were identified and quantitatively determined by comparing with the reference substances. Hierarchical cluster analysis (HCA), principal components analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) were used to evaluate the quality of F. thunbergii from different producing areas. Results A total of 15 common peaks were matched in the fingerprints of F. thunbergii piece and Huibei, and ten components were identified including peak 3-uracil, peak 5-hypoxanthine, peak 6-guanine, peak 8-thymine, peak 9-inosine, peak 10-guanosine, peak 11-adenine, peak 12-thymidine, peak 14-adenosine and peak 15-2-deoxyadenosine. The areas of 15 common peaks were further analyzed by HCA, PCA and OPLS-DA. The results can distinguish F. thunbergii piece and Huibei well. With VIP > 1 as the index, a total of eight differential components with large contribution rate were obtained, which were peak 3,7,10,5,12,14,6 and 8. Conclusion The established fingerprint and multi-component content determination methods of water-soluble components of F. thunbergii piece and Huibei are simple and reproducible, and can be used for the quality evaluation and quality control of different processing methods of F. thunbergii from different producing areas.

Objective To achieve accurate, nondestructive and low-cost identification of Panax ginseng age, a P. ginseng age identification method was established in this study based on hyperspectral imaging technology combined with machine learning. Methods Hyperspectral images of 84 P. ginseng samples and hyperspectral data of 1 680 regions of interest were obtained by acquiring P. ginseng hyperspectral images in the visible-near infrared (VNIR) and short-wave infrared (SWIR) bands from one to seven years old in Tonghua, Jilin, China, respectively. The hyperspectral data of ginseng samples were preprocessed with multiple scattering correction (MSC), standard normal variation (SNV), Savitzky-Golay smoothing, first-order derivative (FD) and second-order derivative (SD) in the VNIR, SWIR and VNIR + SWIR fusion bands, and then combined with partial least squares discriminant analysis (PLS-DA), linear Then, we combined PLS-DA and LinearSVC discriminant analysis methods to establish the identification models of P. ginseng years in two classification scales distinguished by “medicinal food”, three classification scales distinguished by greater than, less than, and equal to five years, and seven classification scales distinguished by seven years, respectively. Results In the VNIR 410—720 nm band range, there was an overall trend of sequential decrease in the average spectral reflectance of P. ginseng from one year to seven years at the same wavelength. The results of confusion matrix evaluation of different classification recognition models showed that the LinearSVC model with FD preprocessing in SWIR band and fusion band had better classification and higher accuracy at three annual scales, and the prediction set accuracy of 2, 3 and 7 classification models were 99.60%, 98.41% and 95.24%, respectively. The recognition models built using the feature bands screened by the continuous projection algorithm (SPA) have higher accuracy at 2 and 3 classifications, and use fewer bands for more efficient classification and recognition. Conclusion Hyperspectral imaging technology combined with machine learning and feature band screening methods can better achieve the identification of the age of P. ginseng of specific origin, and provide a reference for realizing the practical applications of this technology in P. ginseng age identification and quality control.

Chinese medicine formulas (CMF) are the core means of clinical treatment in traditional Chinese medicine (TCM). As a key component of CMF, dosage forms can exert a decisive impact on the exertion of efficacy by altering the absorption, distribution, metabolism, and excretion processes of drugs. “Same prescription, different dosage forms” refers to the diverse dosage forms formed by processing the same prescription through different preparation technologies, and its phenomenon of “different effects” has become a research hotspot in the field of TCM and a focus of clinical attention. Centering on “same prescription, different dosage forms, different effects”, this paper first constructs a complete logical chain of “differences in dosage form and preparation technology-changes in chemical composition profiles-differentiation of in vivo pharmacokinetic-differences in pharmacodynamic and clinical efficacy”. It systematically sorts out the differences of TCM with “same prescription, different dosage forms” in chemical composition, pharmacokinetics, in vitro activity, pharmacodynamics, clinical application and adverse reactions. It clarifies that the core of these differences lies in the fact that preparation technologies affect chemical composition profiles and dissolution behaviors, thereby significantly altering pharmacokinetic characteristics and tissue distribution patterns, and ultimately leading to the differentiation of pharmacodynamic and clinical application. By establishing this logical chain, this paper aims to provide theoretical support for the precise design of TCM dosage forms, the improvement of quality standards, and the rational clinical drug selection, so as to promote the high-quality development of TCM with “same prescription, different dosage forms” in the process of precision medicine and internationalization.