To summarize Professor DING Ying's clinical experience in treating children's IgA nephropathy from the perspective of "wind-induced water turbidity". It is believed that the core pathogenesis of IgA nephropathy in children is the wind stimμlating water to become turbidity， and the basic treatment principles are to eliminate wind and settle viscera， and to remove turbidity and drain water. For those with the syndrome of wind-heat invading the lungs and injury to blood collaterals， modified Yinqiao Powder （银翘散） combined with Xiaoji Decoction （小蓟饮子） could be used； for those with dampness-heat in Sanjiao， heavy dampness and light heat pattern， modified Sanren Decoction （三仁汤） combined with Bazheng Powder （八正散） could be used； for those with lung-spleen qi deficiency and kidney essence depletion pattern， modified Buzhong Yiqi Decoction （补中益气汤） combined with Wuzi Yanzong Pill （五子衍宗丸） could be used； for those with deficiency of both qi and yin， kidney deficiency with stasis pattern， self-prescribed Yishen Huazhuo Formula （益肾化浊方） could be used. Meanwhile on the basis of pattern identification and treatment， rattan-type herbs could be combined in use in order to unblock the meridians and collaterals.

ObjectiveStarting from the etiology， pathogenesis， and treatment strategies of endometriosis and adenomyosis， to integrate and sort out the academic characteristics of contemporary renowned experts and schools in the field of traditional Chinese medicine gynecology. MethodsAccording to the systematic review of text and opinion （SrTO） process developed by the Joanna Briggs Institute （JBI） in Australia， this paper determined literature screening criteria by searching China National Knowledge Infrastructure （CNKI）， VIP， Wanfang， and China Biomedical Literature Database. Information was extracted after literature screening， and quality evaluation was conducted using the JBI Narrative， Text， and Opinion Systematic Review Strict Evaluation Checklist. The JBI Narrative， Opinion， Text Evaluation， and Review Tool Summary Table was used for information synthesis， and data analysis and display were conducted in the form of text and charts. ResultsThe 146 articles related to 39 renowned experts and 19 articles related to 10 schools of thought were included. Research has found that contemporary experts and schools in traditional Chinese medicine gynecology consider blood stasis as the core pathogenesis in understanding the etiology and pathogenesis of two diseases and related infertility. Their viewpoints varied from multiple aspects such as clinical symptom characteristics， meridian circulation location， pathological product evolution， disease duration， emotional psychology， lifestyle habits， preference for food and drink， innate endowment， and acquired injury. In terms of treatment， it was advocated to divide the stage， treat according to different types， adapt to the times， integrate nature and humans， and combine multiple methods to treat comprehensively when necessary. It was also recommended to skillfully use insects， make good use of classic formulas and small prescriptions， pay attention to protecting the spleen and stomach and regulating emotions， and make good use of self-formulated empirical formulas for internal or external use. Besides， individualized long-term management of patients was also advocated. ConclusionThis study applies the SrTO process to systematically summarize the academic ideas of contemporary renowned experts and schools in traditional Chinese medicine gynecology regarding the causes， mechanisms， diagnosis， and treatments of endometriosis， providing a scientific and standardized reference for future theoretical exploration.

ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

ObjectiveTo investigate the independent risk factors for poor prognosis in patients with acute pancreatitis （AP） by analyzing inflammatory factors， lung ultrasound （LUS） scores， and CT scores， to establish a nomogram prediction model， and to provide a basis for early clinical intervention. MethodsA total of 409 patients with AP who were admitted to Changshu Hospital Affiliated to Soochow University from January 2021 to October 2023 were enrolled as subjects， and they were divided into modeling group with 288 patients and validation group with 121 patients using the simple random sampling method at a ratio of 7∶3. According to the prognosis， each group was further divided into poor prognosis group and good prognosis group. The levels of C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， interleukin-10 （IL-10）， and tumor necrosis factor-α （TNF-α） were measured for both groups within 72 hours after admission， and LUS scores， modified CT severity index （MCTSI）， and extrapancreatic inflammation on computed tomography （EPIC） scores were assessed within 48‍ ‍—‍ ‍72 hours after admission. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A LASSO regression analysis was used to screen for the variables that were included in the multivariate logistic regression model to identify the independent risk factors for the poor prognosis of AP， and then a nomogram prediction model was established. The receiver operating characteristic （ROC） curve and the calibration curve were used to assess the discriminatory ability and goodness of fit of the nomogram model， and a decision curve analysis was used to assess the clinical applicability of the model. ResultsAmong the 288 patients with AP in the modeling group， there were 33 （11.46%） in the poor prognosis group and 255 （88.54%） in the good prognosis group； among the 121 patients with AP in the validation group， there were 13 （10.74%） in the poor prognosis group and 108 （89.26%） in the good prognosis group. Compared with the good prognosis group， the poor prognosis group had significantly higher levels of CRP （Z=3.607， P<0.05）， IL-6 （Z=4.189， P<0.05）， and TNF-α （t=2.584， P<0.05）， and significantly higher scores of LUS （t=8.075， P<0.05）， MCTSI （t=5.929， P<0.05）， and EPIC （t=8.626， P<0.05）. The multivariate logistic regression analysis showed that CRP （odds ratio ［OR］=3.592， 95% confidence interval ［CI］： 1.272‍ ‍—‍ ‍10.138， P<0.05）， IL-6 （OR=4.225， 95%CI： 1.468‍ ‍—‍ ‍12.156， P<0.05）， TNF-α （OR=3.540， 95%CI： 1.205‍ ‍—‍ ‍10.401， P<0.05）， LUS （OR=7.094， 95%CI： 2.398‍ ‍—‍ ‍20.986， P<0.05）， MCTSI （OR=7.612， 95%CI： 2.832‍ ‍—‍ ‍20.462， P<0.05）， and EPIC （OR=11.915， 95%CI： 4.007‍ ‍—‍ ‍35.432， P<0.05） were independent risk factor for poor prognosis in patients with AP. A nomogram prediction model was established based on the above 6 indicators， which had an area under the ROC curve of 0.924 （95%CI： 0.883‍ ‍—‍ ‍0.964）， and the Youden index for the optimal cut-off value was 0.670， with a sensitivity of 0.909 and a specificity of 0.761. The calibration curve showed good consistency between the predicted and observed results in both the modeling group and the validation group. The decision curve analysis showed that the predictive model had certain clinical effectiveness. ConclusionThe nomogram model for predicting the risk of poor prognosis in AP patients based on CRP， IL-6， TNF-α， LUS score， MCTSI score， and EPIC score has relatively good predictive performance and can provide important strategic guidance for developing early intensified treatment regimens for AP patients in clinical practice.

[Objective] : To explore the clinical characteristics and methods for syndrome differentiation prediction, as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ischemic stroke (AIS). [Methods] : This study employed a retrospective case-control design to analyze patients with AIS who received inpatient treatment at the Neurology Department of The First Hospital of Hunan University of Chinese Medicine from January 1, 2013 to December 31, 2022. AIS patients meeting the diagnostic criteria for Qi deficiency and blood stasis syndrome were stratified into case group, while those without Qi deficiency and blood stasis syndrome were stratified into control group. The demographic characteristics (age and gender), clinical parameters [time from onset to admission, National Institutes of Health Stroke Scale (NIHSS) score, and blood pressure], past medical history, traditional Chinese medicine (TCM) diagnostic characteristics (tongue and pulse), neurological symptoms and signs, imaging findings [magnetic resonance imaging-diffusion weighted imaging (MRI-DWI)], and biochemical indicators of the two groups were collected and compared. The indicators with statistical difference (P < 0.05) in univariate analysis were included in multivariate logistic regression analysis to evaluate their predictive value for the diagnosis of Qi deficiency and blood stasis syndrome, and the predictive model was constructed by receiver operating characteristic (ROC) curve analysis. [Results] : The study included 1 035 AIS patients, with 404 cases in case group and 631 cases in control group. Compared with control group, patients in case group were significantly older, had extended onset-to-admission time, lower diastolic blood pressure, and lower NIHSS scores (P < 0.05). Case group showed lower incidence of hypertension history (P < 0.05). Regarding tongue and pulse characteristics, pale and dark tongue colors, white tongue coating, fine pulse, astringent pulse, and sinking pulse were more common in case group. Imaging examinations demonstrated higher proportions of centrum semiovale infarction, cerebral atrophy, and vertebral artery stenosis in case group (P < 0.05). Among biochemical indicators, case group showed higher proportions of elevated fasting blood glucose and glycated hemoglobin (HbA1c), while lower proportions of elevated white blood cell count, reduced hemoglobin, and reduced high-density lipoprotein cholesterol (HDL-C) (P < 0.05). Multivariate logistic regression analysis identified significant predictors for Qi deficiency and blood stasis syndrome including: fine pulse [odds ratio (OR) = 4.38], astringent pulse (OR = 3.67), superficial sensory abnormalities (OR = 1.86), centrum semiovale infarction (OR = 1.57), cerebral atrophy (OR = 1.55), vertebral artery stenosis (OR = 1.62), and elevated HbA1c (OR = 3.52). The ROC curve analysis of the comprehensive prediction model yielded an area under the curve (AUC) of 0.878 [95% confidence interval (CI) = 0.855 – 0.900]. [Conclusion] This study finds out that Qi deficiency and blood stasis syndrome represents one of the primary types of AIS. Fine pulse, astringent pulse, superficial sensory abnormalities, centrum semiovale infarction, cerebral atrophy, vertebral artery stenosis, elevated blood glucose, elevated HbA1c, pale and dark tongue colors, and white tongue coating are key objective diagnostic indicators for the syndrome differentiation of AIS with Qi deficiency and blood stasis syndrome. Based on these indicators, a syndrome differentiation prediction model has been developed, offering a more objective basis for clinical diagnosis, and help to rapidly identify this syndrome in clinical practice and reduce misdiagnosis and missed diagnosis.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.