ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

Objective:To evaluate the clinical value of an obstetric intelligent assistant in predicting postpartum hemorrhage (PPH) after vaginal delivery.Methods:This retrospective cohort study included 4 832 women who delivered vaginally at ≥26 weeks of gestation at the Third Affiliated Hospital, Guangzhou Medical University between May 2023 and April 2025. Participants were categorized into PPH (382 cases, blood loss ≥500 ml within 24 h after delivery) and non-PPH groups (4 450 cases). Using traditional statistical methods combined with machine learning approaches, including support vector machines and extreme gradient boosting, supplemented with deep learning techniques, we developed a novel artificial neural network model—the obstetric intelligent assistant. This model provides a refined classification of PPH occurrence and estimated blood loss volume. The obstetric intelligent assistant integrates 70 antenatal and intrapartum risk factors through hospital information system interfacing to generate visualized risk probability outputs. Predictive performance was compared between the obstetric intelligent assistant and four conventional prediction tools (Chinese Labor Room Traffic Light System; Association of Women's Health, Obstetric and Neonatal Nurses; American College of Obstetrics and Gynecology Safe Motherhood Initiative; and California Maternal Quality Care Collaborative prediction tools) using receiver operating characteristic curve.Results:(1) For antenatal prediction, the obstetric intelligent assistant achieved an area under the curve of 0.826 (95% CI: 0.774-0.838), with sensitivity of 0.794 and specificity of 0.712, while the four conventional prediction tools showed area under the curve ranging from 0.569 to 0.586. (2) For intrapartum prediction, the obstetric intelligent assistant achieved an area under the curve of 0.786 (95% CI: 0.751-0.820), with sensitivity of 0.837 and specificity of 0.762, whereas the conventional tools showed area under the curve between 0.600 and 0.613. Conclusion:The obstetric intelligent assistant demonstrates superior performance in predicting PPH compared to conventional prediction tools.

OBJECTIVE To explore the effect of Astragali Radix-Curcuma Zedoaria-Paridis Rhizoma(Qi-Zhu-Zao)combina-tion on inhibiting the growth and metastasis of colon cancer based on the PINK1/Parkin/EMT signaling pathway.METHODS Thirty male BALB/c mice were randomly assigned to five groups:sham operation group,model group,positive control group,high-dose Qi-Zhu-Zao group(5.85 g·kg-1),and low-dose Qi-Zhu-Zao group(2.925 g·kg-1),with six mice in each group.An orthotopic colon cancer model was established in the mice using CT26.WT cells.After 15 days of treatment,tumor and liver tissues were collected from each group.Hematoxylin and eosin(HE)staining was performed to assess tumor metastasis,and transmission electron microscopy was used to observe mitochondrial autophagy in tumor tissues.The expression of mitochondrial autophagy-related proteins PINK1,Parkin,p62,and LC3-Ⅱ/LC3-Ⅰ was analyzed using Western blot and immunohistochemistry(IHC).Additionally,the expression levels of epithelial-mesenchymal transition(EMT)-related proteins and mRNA,including E-cadherin,N-cadherin,Vimentin,and Snail,were detected using Western blot,qPCR,and IHC staining.RESULTS Compared to the model group,mice in the treatment groups exhibited significantly reduced tumor volumes and fewer metastatic foci.Additionally,liver tissues showed pathological changes,and the overall growth condition of the mice was markedly improved;the tumor tissues in the treatment groups displayed selective mitochon-drial autophagy,accompanied by the formation of autophagosomes.The treatment influenced the PINK1/Parkin pathway-mediated mi-tochondrial autophagy biological process,with PINK1,Parkin,p62,and LC3-Ⅱ/LC3-Ⅰ levels being significantly upregulated(P<0.05,P<0.01),the high-dose group exhibited a more significant impact than the low-dose group(P<0.05,P<0.01).Furthermore,the treatment groups also showed significant reductions in the protein and mRNA levels of N-cadherin,Vimentin,and Snail(P<0.05,P<0.01),along with significant increases in the protein and mRNA levels of E-cadherin(P<0.05,P<0.01),these effects were more pronounced in the high-dose group compared to the low-dose group(P<0.05,P<0.01).CONCLUSION The herbal combination of Qi-Zhu-Zao inhibits tumor growth and metastasis to a certain extent in a mouse model of orthotopic transplantation of colon cancer.The underlying mechanism may involve the restoration of mitochondrial function through the PINK1/Parkin signaling pathway and the inhibition of the epithelial-mesenchymal transition(EMT)process,thereby achieving a therapeutic effect on colon cancer.

Objective:To analyze trends in urticaria incidence among children aged 0 - 14 years in China from 1990 to 2021, to explore its changing patterns in different age, period, and cohort groups, and to investigate the impact of age and air pollutants on the incidence trends.Methods:Data were obtained from the Global Burden of Disease Database (GBD2021) , including the number of urticaria cases, crude incidence rates, and age-standardized incidence rates among children aged 0 - 14 years of different genders in China from 1990 to 2021. The Joinpoint regression model was used to calculate the annual percentage change (APC) and average annual percentage change (AAPC) to assess temporal trends in incidence rates. An age-period-cohort model was applied to assess the effects of age, period, and cohort on urticaria incidence. Data on the annual emissions of 4 air pollutants (SO 2, CO, PM 2.5, and PM 10) in China from 1990 to 2021 were obtained from the Emissions Database for Global Atmospheric Research (EDGAR) , and a multivariable meta-regression model was used to explore the relationship between air pollutants and urticaria incidence. Results:From 1990 to 2021, the age-standardized incidence rate of urticaria among children aged 0 - 14 years in China demonstrated a slight overall downward trend (AAPC = -0.03%, P < 0.01) . The incidence rate was generally higher in female children than in male children, and the decline in incidence rates was greater in female children than in male children (female AAPC = -0.02%, male AAPC = -0.01%, both P < 0.01) . The age-period-cohort model indicated that the risk of urticaria decreased with advancing age: with the age group of 0 - 4 years as the reference ( RR = 1.000) , the risk of urticaria significantly decreased in the age group of 5 - 9 years ( RR = 0.790, 95% CI: 0.789 - 0.791) and further declined in the age group of 10 - 14 years ( RR = 0.711, 95% CI: 0.710 - 0.711) ; the period effect analysis showed that the risk of urticaria gradually decreased after the baseline period of 1992 - 1996 ( RR = 1.000) , and dropped to 0.995 (95% CI: 0.994 - 0.997) in the period of 2017 - 2021; in the cohort effect analysis of the overall population aged 0 - 14 years, with the 1988 - 1992 birth cohort as the base cohort, an earlier birth cohort 1978 - 1982 exhibited the highest risk of urticaria ( RR = 1.006, 95% CI: 1.004 - 1.009) , while the 2013 - 2017 cohort showed the lowest risk ( RR = 0.996, 95% CI: 0.994 - 0.997) . The multivariable meta-regression analysis indicated a significant association between PM 2.5 exposure and urticaria incidence ( β = 0.319, 95% CI: 0.022 - 0.616, P = 0.033) , although this association was not statistically significant in different age groups. Conclusions:From 1990 to 2021, children aged 0 - 4 years in China were the highest-risk group for urticaria; the decline in the incidence rate of urticaria was more pronounced in female children than in male children, and earlier birth cohorts exhibited higher risks of urticaria. Exposure to PM 2.5 appeared to be associated with the incidence of urticaria.

Objective:To analyze serum inflammatory factors associated with antihistamine resistance in patients with chronic spontaneous urticaria (CSU) .Methods:A total of 88 CSU patients were enrolled from Guangzhou Dermatology Hospital from January 2022 to December 2024. All patients received antihistamine treatment according to the "Guideline for diagnosis and treatment of urticaria in China (2022) " . Based on the 7-day urticaria activity score (UAS7) after 4-week treatment, these patients were divided into an antihistamine-sensitive group and an antihistamine-resistant group. Serum levels of inflammatory factors at the initial visit were analyzed using the Olink-targeted proteomics technology. Specific biomarkers associated with antihistamine resistance were identified, and Spearman correlation analysis was carried out to analyze correlations among differentially expressed proteins. A logistic regression model was constructed based on the Olink proteomics data, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Measurement data were expressed as mean ± standard deviation or median (lower quartile, upper quartile) .Results:The 88 CSU patients aged 12 to 81 (38.78 ± 13.89) years, with the disease duration being 18 (7.00, 60.00) months. There were 32 patients in the antihistamine-sensitive group and 56 in the antihistamine-resistant group. No significant differences were found between the two groups in terms of age, disease duration, gender, or history of allergic diseases (all P > 0.05) . After 4 weeks of antihistamine treatment, the UAS7 score was significantly higher in the antihistamine-resistant group (25.00 [15.25, 31.00] points) than in the antihistamine-sensitive group (0.50 [0.00, 4.00] points; Z = -7.08, P < 0.001) . The Olink-targeted proteomics identified 5 differentially expressed proteins between the two groups: compared with the antihistamine-sensitive group, the antihistamine-resistant group showed > 2-fold higher expression of fibroblast growth factor 19 (FGF19) , interleukin-15 receptor subunit alpha (IL-15RA) , eotaxin (CCL11) , and monocyte chemoattractant protein-1 (MCP-1) ; in contrast, the expression of sulfotransferase 1A1 (ST1A1) in the antihistamine-sensitive group was 2.54 times that in the antihistamine-resistant group. Among the differentially expressed proteins, MCP-1 showed the highest specificity (1.00) for predicting antihistamine resistance, followed by CCL11 (0.97) . Correlation analysis revealed a significant positive correlation between MCP-1 and CCL11, and a significant negative correlation between IL-15RA and ST1A1. ROC curve analysis showed that MCP-1 and CCL11 had area under the curve values of 0.603 and 0.630, respectively, in predicting antihistamine resistance. Conclusion:MCP-1 and CCL11 may be potential biomarkers for predicting antihistamine resistance in CSU patients.

Objective To explore the mechanism of malt alcohol extract improving depression-like behavior induced by CUMS in rats by regulating gut microbiota.Methods The depression model of rats was established using an 8-weeks CUMS procedure,and the administration group was given low(59.6 mg·kg-1)and high(178.8 mg·kg-1)doses of malt alcohol extract,respectively.The depression-like behavior of rats was evaluated by classic behavioral test.The composition of intestinal microbiota of rats was analyzed by 16S rRNA sequencing.The morphological changes of colon were observed by hematoxylin and eosin(HE),the expression of ZO-1 and Occludin in colon was detected by immunofluorescence(IF),and the expression of IL-10,IL-1βand 5-HT were detected by ELISA.Results The low dose of malt alcohol extract attenuated the depressive behavior and restored the expression of 5-HT in the brain of CUMS rats.16S rRNA sequencing results showed that the diversity and relative abundance of gut microbiota changed after treatment with the low dose of malt alcohol extract.ELISA results showed that the low dose of malt alcohol extract significantly reversed the CUMS-induced reduction of IL-10 and elevation of IL-1 β.HE results showed that the low dose of malt alcohol extract significantly ameliorated CUMS-induced structural damage in colon.IF results showed increased protain expression of intestinal epithelial barrier tight junction proteins ZO-1 and Occludin by the low dose of malt alcohol extract.Conclusion The low dose of malt alcohol extract can ameliorate CUMS-induced depressive-like behavior in rats by modulating intestinal flora,restoring 5-HT expression in the brain,inhibiting inflammation,and repairing the intestinal barrier.

Objective To explore the mechanism of malt alcohol extract improving depression-like behavior induced by CUMS in rats by regulating gut microbiota.Methods The depression model of rats was established using an 8-weeks CUMS procedure,and the administration group was given low(59.6 mg·kg-1)and high(178.8 mg·kg-1)doses of malt alcohol extract,respectively.The depression-like behavior of rats was evaluated by classic behavioral test.The composition of intestinal microbiota of rats was analyzed by 16S rRNA sequencing.The morphological changes of colon were observed by hematoxylin and eosin(HE),the expression of ZO-1 and Occludin in colon was detected by immunofluorescence(IF),and the expression of IL-10,IL-1βand 5-HT were detected by ELISA.Results The low dose of malt alcohol extract attenuated the depressive behavior and restored the expression of 5-HT in the brain of CUMS rats.16S rRNA sequencing results showed that the diversity and relative abundance of gut microbiota changed after treatment with the low dose of malt alcohol extract.ELISA results showed that the low dose of malt alcohol extract significantly reversed the CUMS-induced reduction of IL-10 and elevation of IL-1 β.HE results showed that the low dose of malt alcohol extract significantly ameliorated CUMS-induced structural damage in colon.IF results showed increased protain expression of intestinal epithelial barrier tight junction proteins ZO-1 and Occludin by the low dose of malt alcohol extract.Conclusion The low dose of malt alcohol extract can ameliorate CUMS-induced depressive-like behavior in rats by modulating intestinal flora,restoring 5-HT expression in the brain,inhibiting inflammation,and repairing the intestinal barrier.

Objective:To analyze trends in urticaria incidence among children aged 0 - 14 years in China from 1990 to 2021, to explore its changing patterns in different age, period, and cohort groups, and to investigate the impact of age and air pollutants on the incidence trends.Methods:Data were obtained from the Global Burden of Disease Database (GBD2021) , including the number of urticaria cases, crude incidence rates, and age-standardized incidence rates among children aged 0 - 14 years of different genders in China from 1990 to 2021. The Joinpoint regression model was used to calculate the annual percentage change (APC) and average annual percentage change (AAPC) to assess temporal trends in incidence rates. An age-period-cohort model was applied to assess the effects of age, period, and cohort on urticaria incidence. Data on the annual emissions of 4 air pollutants (SO 2, CO, PM 2.5, and PM 10) in China from 1990 to 2021 were obtained from the Emissions Database for Global Atmospheric Research (EDGAR) , and a multivariable meta-regression model was used to explore the relationship between air pollutants and urticaria incidence. Results:From 1990 to 2021, the age-standardized incidence rate of urticaria among children aged 0 - 14 years in China demonstrated a slight overall downward trend (AAPC = -0.03%, P < 0.01) . The incidence rate was generally higher in female children than in male children, and the decline in incidence rates was greater in female children than in male children (female AAPC = -0.02%, male AAPC = -0.01%, both P < 0.01) . The age-period-cohort model indicated that the risk of urticaria decreased with advancing age: with the age group of 0 - 4 years as the reference ( RR = 1.000) , the risk of urticaria significantly decreased in the age group of 5 - 9 years ( RR = 0.790, 95% CI: 0.789 - 0.791) and further declined in the age group of 10 - 14 years ( RR = 0.711, 95% CI: 0.710 - 0.711) ; the period effect analysis showed that the risk of urticaria gradually decreased after the baseline period of 1992 - 1996 ( RR = 1.000) , and dropped to 0.995 (95% CI: 0.994 - 0.997) in the period of 2017 - 2021; in the cohort effect analysis of the overall population aged 0 - 14 years, with the 1988 - 1992 birth cohort as the base cohort, an earlier birth cohort 1978 - 1982 exhibited the highest risk of urticaria ( RR = 1.006, 95% CI: 1.004 - 1.009) , while the 2013 - 2017 cohort showed the lowest risk ( RR = 0.996, 95% CI: 0.994 - 0.997) . The multivariable meta-regression analysis indicated a significant association between PM 2.5 exposure and urticaria incidence ( β = 0.319, 95% CI: 0.022 - 0.616, P = 0.033) , although this association was not statistically significant in different age groups. Conclusions:From 1990 to 2021, children aged 0 - 4 years in China were the highest-risk group for urticaria; the decline in the incidence rate of urticaria was more pronounced in female children than in male children, and earlier birth cohorts exhibited higher risks of urticaria. Exposure to PM 2.5 appeared to be associated with the incidence of urticaria.

Objective:To analyze serum inflammatory factors associated with antihistamine resistance in patients with chronic spontaneous urticaria (CSU) .Methods:A total of 88 CSU patients were enrolled from Guangzhou Dermatology Hospital from January 2022 to December 2024. All patients received antihistamine treatment according to the "Guideline for diagnosis and treatment of urticaria in China (2022) " . Based on the 7-day urticaria activity score (UAS7) after 4-week treatment, these patients were divided into an antihistamine-sensitive group and an antihistamine-resistant group. Serum levels of inflammatory factors at the initial visit were analyzed using the Olink-targeted proteomics technology. Specific biomarkers associated with antihistamine resistance were identified, and Spearman correlation analysis was carried out to analyze correlations among differentially expressed proteins. A logistic regression model was constructed based on the Olink proteomics data, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Measurement data were expressed as mean ± standard deviation or median (lower quartile, upper quartile) .Results:The 88 CSU patients aged 12 to 81 (38.78 ± 13.89) years, with the disease duration being 18 (7.00, 60.00) months. There were 32 patients in the antihistamine-sensitive group and 56 in the antihistamine-resistant group. No significant differences were found between the two groups in terms of age, disease duration, gender, or history of allergic diseases (all P > 0.05) . After 4 weeks of antihistamine treatment, the UAS7 score was significantly higher in the antihistamine-resistant group (25.00 [15.25, 31.00] points) than in the antihistamine-sensitive group (0.50 [0.00, 4.00] points; Z = -7.08, P < 0.001) . The Olink-targeted proteomics identified 5 differentially expressed proteins between the two groups: compared with the antihistamine-sensitive group, the antihistamine-resistant group showed > 2-fold higher expression of fibroblast growth factor 19 (FGF19) , interleukin-15 receptor subunit alpha (IL-15RA) , eotaxin (CCL11) , and monocyte chemoattractant protein-1 (MCP-1) ; in contrast, the expression of sulfotransferase 1A1 (ST1A1) in the antihistamine-sensitive group was 2.54 times that in the antihistamine-resistant group. Among the differentially expressed proteins, MCP-1 showed the highest specificity (1.00) for predicting antihistamine resistance, followed by CCL11 (0.97) . Correlation analysis revealed a significant positive correlation between MCP-1 and CCL11, and a significant negative correlation between IL-15RA and ST1A1. ROC curve analysis showed that MCP-1 and CCL11 had area under the curve values of 0.603 and 0.630, respectively, in predicting antihistamine resistance. Conclusion:MCP-1 and CCL11 may be potential biomarkers for predicting antihistamine resistance in CSU patients.

Objective:To evaluate the clinical value of an obstetric intelligent assistant in predicting postpartum hemorrhage (PPH) after vaginal delivery.Methods:This retrospective cohort study included 4 832 women who delivered vaginally at ≥26 weeks of gestation at the Third Affiliated Hospital, Guangzhou Medical University between May 2023 and April 2025. Participants were categorized into PPH (382 cases, blood loss ≥500 ml within 24 h after delivery) and non-PPH groups (4 450 cases). Using traditional statistical methods combined with machine learning approaches, including support vector machines and extreme gradient boosting, supplemented with deep learning techniques, we developed a novel artificial neural network model—the obstetric intelligent assistant. This model provides a refined classification of PPH occurrence and estimated blood loss volume. The obstetric intelligent assistant integrates 70 antenatal and intrapartum risk factors through hospital information system interfacing to generate visualized risk probability outputs. Predictive performance was compared between the obstetric intelligent assistant and four conventional prediction tools (Chinese Labor Room Traffic Light System; Association of Women's Health, Obstetric and Neonatal Nurses; American College of Obstetrics and Gynecology Safe Motherhood Initiative; and California Maternal Quality Care Collaborative prediction tools) using receiver operating characteristic curve.Results:(1) For antenatal prediction, the obstetric intelligent assistant achieved an area under the curve of 0.826 (95% CI: 0.774-0.838), with sensitivity of 0.794 and specificity of 0.712, while the four conventional prediction tools showed area under the curve ranging from 0.569 to 0.586. (2) For intrapartum prediction, the obstetric intelligent assistant achieved an area under the curve of 0.786 (95% CI: 0.751-0.820), with sensitivity of 0.837 and specificity of 0.762, whereas the conventional tools showed area under the curve between 0.600 and 0.613. Conclusion:The obstetric intelligent assistant demonstrates superior performance in predicting PPH compared to conventional prediction tools.