ObjectiveTo investigate the distribution of traditional Chinese medicine （TCM） syndromes in intrahepatic cholestasis of pregnancy （ICP） and its association with perinatal outcomes， and to provide a basis for precise treatment based on TCM syndrome differentiation. MethodsA cross-sectional study was conducted among 275 patients with ICP who were admitted to The Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from April 2023 to April 2025. A hierarchical cluster analysis was used to summarize TCM syndromes. The Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups， and the chi-square test was used for comparison of categorical data between groups. A multivariate Logistic regression analysis was used to identify the clinical features significantly associated with TCM syndrome. ResultsThe cluster analysis identified three core TCM syndromes among the 275 patients with ICP， i.e.， liver-gallbladder damp-heat syndrome （45.8%）， syndrome of blood deficiency generating wind （30.9%）， and liver depression and spleen deficiency syndrome （23.3%）. There was a significant difference in the distribution of TCM syndromes between different groups stratified by maternal age at delivery， parity， history of ICP recurrence， gestational weeks at disease onset， total bile acid （TBA）， alanine aminotransferase （ALT）， and comorbidity with gestational diabetes mellitus （GDM） （all P<0.05）. The multivariate Logistic regression analysis showed that<34 gestational weeks at disease onset was significantly associated with all three syndromes （damp-heat： odds ratio ［OR］=3.769， P<0.001； blood deficiency： OR=4.031， P<0.001； liver stagnation： OR=3.552， P<0.001）. Liver-gallbladder damp-heat syndrome was associated with maternal age ≥35 years at disease onset （OR=2.048， P=0.014）， parity ≥2 times （OR=1.921， P=0.034）， history of ICP recurrence （OR=2.404， P=0.030）， ALT ≥200 U/L （OR=2.051， P=0.018）， comorbidity with GDM （OR=1.944， P=0.029）， and TBA ≥40 μmol/L （OR=2.542， P=0.024）. The syndrome of blood deficiency generating wind syndrome was associated with maternal age ≥35 years （OR=2.939， P=0.003）， parity ≥2 time （OR=3.222， P=0.003）， history of ICP recurrence （OR=3.809， P=0.010）， ALT ≥200 U/L （OR=2.889， P=0.006）， comorbidity with GDM （OR=3.711， P=0.001）， and comorbidity with hypertensive disorders of pregnancy （OR=4.472， P=0.011）. Liver depression and spleen deficiency syndrome was associated with TBA ≥40 μmol/L （OR=2.995， P=0.044）. The analysis of perinatal outcomes showed that there were significant differences in mode of delivery， gestational weeks at the time of delivery， postpartum blood loss， and neonatal birth weight between the three groups with different TCM syndromes （all P<0.05）. ConclusionLiver-gallbladder damp-heat syndrome， syndrome of blood deficiency generating wind， and liver depression and spleen deficiency syndrome are the main TCM syndrome types in ICP， and the distribution of TCM syndromes is closely associated with clinical factors and perinatal outcomes， which provides a basis for precise TCM syndrome differentiation and individualized treatment.

Endometriosis (EMS) is a chronic systemic disease prevalent in women of reproductive age. The signs and symptoms of this disease are not specific, and the severity of the disease varies, leading to clinical heterogeneity, which increases the difficulty of diagnosis. It seriously affects the quality of life of patients and also occupies a large amount of social health resources. Artificial intelligence (AI) refers to the ability of a computer technology to enable machines to simulate human intelligence, including perception, learning, reasoning, decision making and language understanding. AI serves as a powerful data-mining tool capable of processing large amounts of medical data and diagnosing, managing, and treating patients according to their specific conditions. Especially in the diagnostic process of EMS, the use of AI can improve the detection rate and shorten the diagnostic time while increasing the diagnostic sensitivity and specificity. In this paper, we provide an overview of the application of medical big data and AI to the diagnosis and treatment of EMS in order to provide new aids to further improve diagnostic efficiency and therapeutic efficacy.

Objective To investigate the ameliorative effects and mechanisms of six types of tea(green tea,cyan tea,red tea,white tea,black tea and yellow tea)on metabolic disorders in obesity mice induced by high-fat diet(HFD).Methods Four-week-old male C57BL/6J mice were randomly divided into 8 groups with 7 mice per group.An HFD-induced obese mouse model was established,and the mice in control group maintained on standard diet followed by intragastric administration of different teas for 5 weeks.The body weight,liver weight ratio,fasting blood glucose,and lipid profile of the mice were measured to assess glucose and lipid metabolism.Serum inflammatory factors including IL-6,tumor necrosis factor-alpha(TNF-α)and oxidative stress markers[malondialdehyde(MDA)and superoxide dismutase(SOD)were measured.Additionally,liver histopathology and the expression of key glycolipid metabolism-related genes,adenosine monophosphate-activated protein kinase(AMPK)and carnitine palmitoyltransferase 1(CPT-1),were analyzed to explore underlying mechanisms.Results Cyan tea significantly suppressed weight gain,demonstrating superior weight control.White tea markedly reduced fasting blood glucose levels and decreased the area under the curve of oral glucose tolerance test(OGTT)and insulin tolerance test(ITT),indicating synergistic improvements in glucose metabolism and insulin sensitivity.Yellow tea exhibited exceptional anti-inflammatory and antioxidant effects,reducing hepatic IL-6 and MDA while enhancing SOD activity.Green tea activated the lipid oxidation pathway by upregulating AMPK/CPT-1 expression.All kinds of tea significantly attenuated hepatic lipid droplet accumulation.Conclusion All six types of tea alleviated metabolic disorders by reducing hepatic fat content in obesity mice.However,different types of tea exert their unique effects on improving metabolic disorders through differential mechanisms such as glucose metabolism regulation,lipid oxidation,and anti-inflammatory and antioxidant actions.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Endometriosis (EMS) is a chronic systemic disease prevalent in women of reproductive age. The signs and symptoms of this disease are not specific, and the severity of the disease varies, leading to clinical heterogeneity, which increases the difficulty of diagnosis. It seriously affects the quality of life of patients and also occupies a large amount of social health resources. Artificial intelligence (AI) refers to the ability of a computer technology to enable machines to simulate human intelligence, including perception, learning, reasoning, decision making and language understanding. AI serves as a powerful data-mining tool capable of processing large amounts of medical data and diagnosing, managing, and treating patients according to their specific conditions. Especially in the diagnostic process of EMS, the use of AI can improve the detection rate and shorten the diagnostic time while increasing the diagnostic sensitivity and specificity. In this paper, we provide an overview of the application of medical big data and AI to the diagnosis and treatment of EMS in order to provide new aids to further improve diagnostic efficiency and therapeutic efficacy.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Objective To investigate the ameliorative effects and mechanisms of six types of tea(green tea,cyan tea,red tea,white tea,black tea and yellow tea)on metabolic disorders in obesity mice induced by high-fat diet(HFD).Methods Four-week-old male C57BL/6J mice were randomly divided into 8 groups with 7 mice per group.An HFD-induced obese mouse model was established,and the mice in control group maintained on standard diet followed by intragastric administration of different teas for 5 weeks.The body weight,liver weight ratio,fasting blood glucose,and lipid profile of the mice were measured to assess glucose and lipid metabolism.Serum inflammatory factors including IL-6,tumor necrosis factor-alpha(TNF-α)and oxidative stress markers[malondialdehyde(MDA)and superoxide dismutase(SOD)were measured.Additionally,liver histopathology and the expression of key glycolipid metabolism-related genes,adenosine monophosphate-activated protein kinase(AMPK)and carnitine palmitoyltransferase 1(CPT-1),were analyzed to explore underlying mechanisms.Results Cyan tea significantly suppressed weight gain,demonstrating superior weight control.White tea markedly reduced fasting blood glucose levels and decreased the area under the curve of oral glucose tolerance test(OGTT)and insulin tolerance test(ITT),indicating synergistic improvements in glucose metabolism and insulin sensitivity.Yellow tea exhibited exceptional anti-inflammatory and antioxidant effects,reducing hepatic IL-6 and MDA while enhancing SOD activity.Green tea activated the lipid oxidation pathway by upregulating AMPK/CPT-1 expression.All kinds of tea significantly attenuated hepatic lipid droplet accumulation.Conclusion All six types of tea alleviated metabolic disorders by reducing hepatic fat content in obesity mice.However,different types of tea exert their unique effects on improving metabolic disorders through differential mechanisms such as glucose metabolism regulation,lipid oxidation,and anti-inflammatory and antioxidant actions.

Purpose: This study explored the association of the elasticity modulus and shear wave velocity (SWV) of the tibialis anterior muscle, as measured by two-dimensional shear wave elastography (2D-SWE), with the intracompartmental pressure (ICP) determined using the Whitesides method in a New Zealand rabbit model of acute compartment syndrome (ACS). Additionally, it evaluated the viability of 2D-SWE as a noninvasive, quantitative tool for the early detection of ACS. Methods: An ACS model was established through direct external compression by applying pressure bandaging to the lower legs of 15 New Zealand rabbits using neonatal blood pressure cuffs. Another five animals represented a non-modeled control group. To measure the elasticity modulus and SWV of the tibialis anterior muscles, 2D-SWE was employed. Blood oxygen saturation, serum creatine kinase (CK), and myoglobin levels were monitored. Subsequently, the anterior tibial compartment was dissected, and the tibialis anterior was removed for hematoxylin and eosin staining to assess muscle injury. Results: The elasticity modulus and SWV of the tibialis anterior muscle increased with compression duration, as did serum CK and myoglobin levels. ICP was strongly positively correlated with these parameters, particularly mean velocity (r=0.942, P<0.001) and CK (r=0.942, P<0.001). Blood oxygen saturation was negatively correlated with ICP (r=-0.887, P<0.001). Histological analysis indicated progressive muscle cell swelling over time, with damage transitioning from reversible to irreversible and culminating in necrosis. Conclusion In a rabbit ACS model, ICP was strongly positively correlated with muscle elasticity modulus/SWV. Consequently, 2D-SWE may represent a novel tool for assessing early-phase ACS.

Purpose: This study explored the association of the elasticity modulus and shear wave velocity (SWV) of the tibialis anterior muscle, as measured by two-dimensional shear wave elastography (2D-SWE), with the intracompartmental pressure (ICP) determined using the Whitesides method in a New Zealand rabbit model of acute compartment syndrome (ACS). Additionally, it evaluated the viability of 2D-SWE as a noninvasive, quantitative tool for the early detection of ACS. Methods: An ACS model was established through direct external compression by applying pressure bandaging to the lower legs of 15 New Zealand rabbits using neonatal blood pressure cuffs. Another five animals represented a non-modeled control group. To measure the elasticity modulus and SWV of the tibialis anterior muscles, 2D-SWE was employed. Blood oxygen saturation, serum creatine kinase (CK), and myoglobin levels were monitored. Subsequently, the anterior tibial compartment was dissected, and the tibialis anterior was removed for hematoxylin and eosin staining to assess muscle injury. Results: The elasticity modulus and SWV of the tibialis anterior muscle increased with compression duration, as did serum CK and myoglobin levels. ICP was strongly positively correlated with these parameters, particularly mean velocity (r=0.942, P<0.001) and CK (r=0.942, P<0.001). Blood oxygen saturation was negatively correlated with ICP (r=-0.887, P<0.001). Histological analysis indicated progressive muscle cell swelling over time, with damage transitioning from reversible to irreversible and culminating in necrosis. Conclusion In a rabbit ACS model, ICP was strongly positively correlated with muscle elasticity modulus/SWV. Consequently, 2D-SWE may represent a novel tool for assessing early-phase ACS.

Objective To investigate the application value of ultrasound-guided fine needle aspiration (FNA) in the diagnosis of internal mammary nodes (IMN) in breast cancer. Methods A total of 160 breast cancer patients with suspected IMN were selected as the study subjects. All patients underwent IMN puncture biopsy under ultrasound guidance, with 88 cases in core needle biopsy (CNB) group and 72 cases in FNA group. The diagnostic efficacy of FNA and CNB for IMN was analyzed and compared according to pathological diagnosis results. Results The maximum diameter and maximum thickness of IMN in the FNA group were smaller than those in the CNB group (P < 0.05). There was no statistically significant difference in the classification of IMN diagnosed by puncture between the two groups (P>0.05). There were also no statistically significant differences in the unsatisfactory rate of puncture specimens and the positive rate of IMN between the two groups (P>0.05). The consistency test results showed good consistency of the CNB and FNA groups compared with pathological diagnosis (Kappa>0.70); compared with the CNB group, the consistency of the FNA group was higher (Kappa=0.890). There were no statistically significant differences in the sensitivity, specificity, and positive predictive value of IMN diagnosis between the CNB and FNA groups (P>0.05); the negative predictive value of the CNB group was lower than that of the FNA group, and the false-negative rate was higher (P < 0.05). Eight cases (9.09%) in the CNB group developed puncture complications, including 6 cases of mild or asymptomatic pneumothorax and 2 cases of intermuscular hemorrhage, with relatively mild symptoms. No puncture complications occurred in the FNA group. Conclusion Ultrasound-guided FNA has a high efficacy in the qualitative diagnosis of IMN in breast cancer, with low puncture risk and fewer complications, making it more suitable for IMN puncture biopsy in special anatomical locations.