OBJECTIVE To establish fingerprint， chemical pattern recognition and multi-component quantification analysis of Sanzi powder， and evaluate its quality. METHODS HPLC method was adopted. The fingerprints of 15 batches of Sanzi powder were established by using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were also conducted. The variable importance in projection （VIP） value greater than 1 was used as the index to screen the differential markers， and the contents of the differential markers were determined by the same HPLC method. RESULTS A total of 21 common peaks in the HPLC fingerprints of 15 batches of Sanzi powder were calibrated， and the similarities of them were 0.994- 0.999； 6 common peaks were identified， including gallic acid （peak 3）， garminoside （peak 10）， corilagin （peak 11）， chebulinic acid （peak 16）， ellagic acid （peak 18）， crocin Ⅰ （peak 19）. According to the results of cluster analysis， YKD2024LH005，No.YKD2023LH062） principal component analysis and orthogonal partial least squares-discriminant analysis， 15 batches of samples could be clustered into two categories： S1， S5， S7， S9， S14 were clustered into one category； S2-S4， S6， S8， S10-S13， S15 were clustered into one category. VIP values of 11 differential components such as corilagin， chebulinic acid and ellagic acid were higher than 1. Among 15 batches of samples， the contents of corilagin， chebulinic acid and ellagic acid ranged 2.667-5.152， 9.506- 13.522， 0.891-1.811 mg/g. CONCLUSIONS Established HPLC fingerprint and multi-component quantification analysis of Sanzi powder are rapid and simple， and can be used for quality evaluation of Sanzi powder by combining with chemical pattern recognition. Eleven components such as corilagin， chebulinic acid and ellagic acid are differential markers affecting the quality of Sanzi powder.

Background As one of the key populations in the prevention and treatment of iodine deficiency disorders, it is important to continuously monitor the iodine nutritional level of school-age children. The current reference interval for thyroid volume in China is based on age only, without taking into account differences in individual developmental levels, and the distribution of thyroid volume may vary regionally due to economic, demographic, and environmental factors. The current reference cut-off points for thyroid volume proposed by the World Health Organization are not based on the Chinese population. Objective To understand the iodine nutritional status and distribution of thyroid volume (Tvol) among children aged 8-10 years in Huangpu District, Shanghai, China, to identify impact factors of Tvol, and to propose a reference range upper limit for local thyroid health surveillance, so as to provide a basis for goiter control and prevention. Methods Six hundred children aged 8-10 years in Huangpu District were recruited in 2017, 2020, and 2023, and body height, weight, thyroid volume, urinary iodine, and iodine content of household edible salt were determined. A multilevel model was constructed using population density and area as regional variables, and age, body surface area (BSA), and body mass index (BMI) as potential impact factors for at the individual level, to assess their effects on thyroid volume. Quantile regression of thyroid volume was performed, and the 98th percentile (P₉₈) of thyroid volume was predicted based on age and BSA. Results The iodized salt coverage in the households of surveyed children in 2017, 2020, and 2023 was 72.0%, 57.0%, and 48.0%, respectively, and the iodized salt coverage decreased by year (χ²=24.31, P<0.001). The urinary iodine level of children in 2017 was higher than that in 2020 and 2023 (χ²=18.77, P<0.001). The Tvol medians of children in 2017, 2020, and 2023 were 2.29, 2.49, and 2.97 mL, respectively, and the Tvol increased by year (χ²=60.04, P<0.001). The proportion of goiter was higher in children in 2023 than in 2017 and 2020 (χ²=6.57, P<0.05). Sex differences were not statistically significant for urinary iodine levels, thyroid volume, and goiter. The median Tvol was 2.26, 2.58, and 2.76 mL in children of 8, 9, and 10 years old respectively, and the Tvol increased with age (χ²=49.02, P <0.001). Tvol was positively correlated with age, BSA, and BMI with correlation coefficients of 0.2846, 0.3723, and 0.2950, respectively. The final quantile regression model showed that the strongest effect of BSA was associated with the 98th percentile of Tvol. Multilevel models considering population density or area as regional variables failed to achieve convergence. Conclusion To establish the upper limit of the reference interval for thyroid volume of healthy children in Huangpu District, the effects of age and BSA should be considered at the individual level, and the upper limit of the normal interval for goiter screening is suggested to be the 98th percentile value of BSA at the same age. In addition, the adaptability of population density and regional area as indicators of regional aggregation of thyroid volume distribution still needs to be further explored.

ObjectiveTo establish the fingerprints of 15 batches of Hengzhi Kechuan capsules， to quantitatively analyze 10 index components， and to evaluate the quality uniformity of samples from different batches. MethodsThe fingerprints and quantitative analysis of Hengzhi Kechuan capsules were established by a combination method of high performance liquid chromatography coupled with diode array detector and charged aerosol detector（HPLC-DAD-CAD）， adenosine， guanosine， vanillic acid， safflomin A， agarotetrol， naringin， hesperidin， militarine， ginsenoside Rb₁， and glycyrrhizic acid were selected as quality attribute indexes. A total of 15 batches of Hengzhi Kechuan capsules from 2022 to 2024（3 boxes per batch） were qualitatively and quantitatively analyzed， and the quality uniformity level of the manufacturers was characterized by parameters of intra-batch consistency（P_A） and inter-batch consistency（P_B）. The homogeneity and difference of quality attribute indexes of samples from different years were analyzed by heatmap clustering analysis. ResultsHPLC fingerprints and quantitative method of Hengzhi Kechuan capsules were established， and the methods could be used for qualitative and quantitative analysis of this preparation， which was found to be stable and reliable by method validation. The similarity of fingerprints of 15 batches of samples was 0.887-0.975， a total of 13 common peaks were calibrated， and 10 common peaks were designated， all of which were quality attribute index components. The results of quantitative analysis showed that the contents of the above 10 ingredients in the samples were 0.038-0.078， 0.115-0.251， 0.007-0.018， 0.291-0.673， 0.122-0.257， 0.887-1.905， 1.841-3.364， 1.412-2.450， 2.207-3.112， 0.650-1.161， respectively. And the contents of ginsenoside Rb₁ and glycyrrhizic acid met the limit requirements in the 2020 edition of Chinese Pharmacopoeia. For the samples from 15 batches， the P_A values of the 10 index components were all <10%， indicating good intra-batch homogeneity， and the P_B values ranged from 33.86% to 92.97%， suggesting that the inter-batch homogeneity was poor. Heatmap clustering analysis showed that the samples from different years were clustered into separate categories， and adenosine， guanosine， safflomin A， naringin， hesperidin and agarotetrol were the main differential components. ConclusionThe intra-annual quality uniformity of Hengzhi Kechuan capsules is good and the inter-annual quality uniformity is insufficient， which may be related to the quality difference of Pinellinae Rhizoma Praeparatum， Carthami Flos， Citri Sarcodactylis Fructus， Citri Reticulatae Pericarpium， Aquilariae Lignum Resinatum， Citri Fructus， etc. In this study， the fingerprint and multi-indicator determination method of Hengzhi Kechuan capsules was established， which can be used for more accurate and efficient quality control and standardization enhancement.

OBJECTIVE To evaluate the quality of Mongolian medicine Sendeng-4 based on qualitative and quantitative analysis combined with chemical pattern recognition， in order to provide the reference for its quality control. METHODS The chemical components in Sendeng-4 were analyzed qualitatively by HPLC-Q-Exactive-MS. The contents of 16 components （methyl gallate， ethyl gallate， epicatechin， dihydromyricetin， genipin-1-O-β-D-gentiobioside， caffeic acid， catechin， corilagin， deacetylasperulosidic acid methyl ester， rutin， geniposide， luteolin， myricetin， quercetin， ferulic acid， and toosendanin） in 15 batches of Sendeng-4 （sample S1-S15） were quantitatively analyzed by HPLC-MS/MS. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares discriminant analysis were conducted and variable importance projection （VIP） value greater than 1 was used as the index to screen the differential components. RESULTS A total of 73 chemical components were identified in Sendeng-4， including 20 flavonoids， 16 tannins， 14 organic acids， etc. According to the quantitative analysis， the results exhibited that the average contentsthe of above 16 components in 15 batches of Sendeng-4 were 3.683-7.730， 2.391-6.952， 2 275.538-4 377.491， 2 699.188-3 537.924， 858.266-1 377.393， 3.366-11.003， 140.624-315.683，414.629-978.334， 285.501-1 510.457， 27.799-48.325， 3 625.415-6 309.563， 0.506-0.656， 442.337-649.283， 47.093-59.736， 12.942-15.822， 127.738-326.649 μg/g， respectively. According to the results of CA and PCA， 15 batches of samples could be clustered into two categories: S1-S3， S5-S6， S9-S10 and S13 were clustered into one category； S4， S7-S8， S11-S12， S14-S15 were clustered into one category. VIP values of geniposide， epicatechin， deacetylasperulosidic acid methyl ester and genipin-1-O- β-D-gentiobioside were all greater than 1. CONCLUSIONS HPLC-Q-Exactive-MS and HPLC-MS/MS techniques are employed for the qualitative and quantitative analysis of Sendeng-4. Through chemical pattern recognition analysis， four differential components are identified: geniposide， epicatechin， deacetylasperulosidic acid methyl ester， and genipin-1-O-β-D-gentiobioside.

Periodontitis is a chronic inflammatory disease of the periodontal supporting tissues caused by plaque microorganisms, whereas inflammatory bowel disease (IBD) is a chronic inflammatory disease characterized by gastrointestinal tract damage. Studies have revealed a close association between periodontitis and IBD, and gut microbiota has been shown to play an important role in the development of IBD. When the gut microbiota is disturbed, it leads to intestinal barrier disruption, triggers immune-inflammatory responses, and influences IBD progression. There are significant differences between the salivary microbiota of periodontitis patients and healthy individuals, and periodontal pathogens can enter the intestinal tract with saliva and participate in the development of IBD by influencing the interactions between gut microbiota composition, immune responses, metabolite production, and intestinal barrier function. Current gut microbiota-targeted intervention strategies, such as fecal microbiota transplantation (FMT) and probiotic supplementation, have shown potential therapeutic value in the treatment of periodontitis. These approaches may exert synergistic effects on both periodontitis and IBD through microbiota modulation. This review summarizes research progress on the relationship between periodontitis and IBD to provide a foundation for the prevention and treatment of these two diseases.

The interactions between microorganisms and medicinal plants are crucial to the quality improvement of medicinal plants. Medicinal plants attract microorganisms to colonize by secreting specific compounds and provide niche and nutrient support for these microorganisms, with a symbiotic network formed. These microorganisms grow in the rhizosphere, phyllosphere, and endophytic tissues of plants and significantly improve the growth performance and medicinal component accumulation of medicinal plants by promoting nutrient uptake, enhancing disease resistance, and regulating the synthesis of secondary metabolites. Microorganisms are also widely used in the ecological planting of medicinal plants, and the growth conditions of medicinal plants are optimized by simulating the microbial effects in the natural environment. The interactions between microorganisms and medicinal plants not only significantly improve the yield and quality of medicinal plants but also enhance their geoherbalism, which is in line with the concept of green agriculture and eco-friendly development. This study reviewed the research results on the interactions between medicinal plants and microorganisms in recent years and focused on the analysis of the great potential of microorganisms in optimizing the growth environment of medicinal plants, regulating the accumulation of secondary metabolites, inducing systemic resistance, and promoting the ecological planting of medicinal plants. It provides a scientific basis for the research on the interactions between medicinal plants and microorganisms, the research and development of microbial agents, and the application of microorganisms in the ecological planting of medicinal plants and is of great significance for the quality improvement of medicinal plants and the green and sustainable development of TCM resources.
Plants, Medicinal/metabolism* ; Bacteria/genetics* ; Symbiosis

OBJECTIVE To comprehensively evaluate the quality of Sanzi powder from different batches based on 12 components quantitative analysis combined with chemical pattern recognition and entropy weight-TOPSIS method. METHODS The contents of 12 components in 15 batches of Sanzi powder （No. S1-S15） were determined by HPLC-MS/MS， such as ethyl gallate， gallic acid， ferulic acid， corilagin， genipin-1-O-β-D-gentiobioside， toosendanin， geniposide， caffeic acid， methyl deacetylated coumarinate， tannic acid， rutin， quercetin. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were conducted on the assay results. Using variable importance projection （VIP） value＞1 and P＜0.05 as the evaluation criteria， the quality differential markers in Sanzi powder were screened. The entropy weight method was used to calculate the weight value， and TOPSIS method was used to rank the quality of 15 batches of Sanzi powder from superior to inferior. RESULTS The contents of the 12 components were 13.494-24.292， 2 069.608-3 188.100， 1.410-3.616， 1 065.030-2 630.584， 1 404.704-1 838.078， 101.640-354.268， 9 193.720-14 777.854， 1.240-5.060， 148.028-5 541.990， 4 261.422-5 607.438， 107.560- 195.512， 2.226-4.192 μg/g， respectively. The results of CA， PCA and OPLS-DA indicated that 15 batches of Sanzi powder could be clustered into two groups. Specifically， batches S3， S7， S10 and S15 were grouped into one category， and remaining batches were grouped into one category. VIP values of geniposide， quercetin， caffeic acid， and methyl deacetylated coumarinate were all greater than 1， with corresponding P-values less than 0.05. The results of the entropy weight-TOPSIS analysis revealed that methyl deacetylate exhibited the smallest information entropy and the highest weight. The relative closeness degrees of samples S3， S7， S10 and S15 ranged from 0.789 to 0.973， while the remaining samples ranged from 0.054 to 0.172. CONCLUSIONS The contents of 12 components in Sanzi powder could be determined accurately by using HPLC-MS/MS technology. Methyl deacetylated coumarinate， geniposide， quercetin and caffeic acid were identified as the quality differential markers. It was found that the overall quality of samples S3， S7， S10 and S15 were superior to that of other batches. Notably， the quality of Gardeniae Fructus decoction pieces emerges as a critical factor in ensuring the consistency of the preparation’s quality.

Notum, a negative feedback regulator of the Wnt signaling, has emerged as a promising target for treating glucocorticoid-induced osteoporosis (GIOP). This study showcases an efficient strategy for discovering the anti-Notum constituents from herbal medicines (HMs) as novel anti-GIOP agents. Firstly, a rapid-responding near-infrared fluorogenic substrate for Notum was rationally engineered for high-throughput identifying the anti-Notum HMs. The results showed that Bu-Gu-Zhi (BGZ), a known anti-osteoporosis herb, potently inhibited Notum in a competitive-inhibition manner. To uncover the key anti-Notum constituents in BGZ, an efficient strategy was adapted via integrating biochemical, phytochemical, computational, and pharmacological assays. Among all identified BGZ constituents, three furanocoumarins were validated as strong Notum inhibitors, while 5-methoxypsoralen (5-MP) showed the most potent anti-Notum activity and favorable safety profiles. Mechanistically, 5-MP acted as a competitive inhibitor of Notum via creating strong hydrophobic interactions with Trp128 and Phe268 in the catalytic cavity of Notum. Cellular assays showed that 5-MP remarkably promoted osteoblast differentiation and activated Wnt signaling in dexamethasone (DXMS)-challenged MC3T3-E1 osteoblasts. In dexamethasone-induced osteoporotic mice, 5-MP strongly elevated bone mineral density (BMD) and improved cancellous and cortical bone thickness. Collectively, this study constructs a high-efficient platform for discovering key anti-Notum constituents from HMs, while 5-MP emerges as a promising anti-GIOP agent.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.