Parkinson's disease （PD） is a complex neurodegenerative disease involving multiple systems and neurotransmitters. Due to the high clinical heterogeneity of PD，it is urgent to establish a comprehensive and long-term traditional Chinese medicine （TCM） management model. In this paper，the conceptual framework of full-cycle management of PD is preliminarily constructed：based on the evolution of the pathophysiological mechanisms of protein deposition and neurotransmitter disorder in PD，the three-stage syndrome characteristics of the prodromal stage （predominant healthy Qi with subtle pathogenic factors），the early clinical stage （declining healthy Qi with growing pathogenic factors） and the middle and late stages （overwhelming pathogenic factors with deficient healthy Qi） are longitudinally described. Through the syndrome differentiation of visceral manifestations，the etiology and pathogenesis of PD motor and non-motor symptoms were comprehensively analyzed，while the matching treatment methods and prescriptions were inferred，and the modular scheme of the combining main symptoms，accompanying symptoms and secondary symptoms was proposed. The conceptual gap of TCM regarding motor complications （'variable syndrome'） and PD-related hyperpyrexia syndrome （'critical syndrome'） was explained. This framework reflects the characteristics of combination of disease and syndrome and overall constant motion，and provides new theories and research ideas for individualized and whole-process management of PD in TCM.

Parkinson's disease （PD） is a complex neurodegenerative disease involving multiple systems and neurotransmitters. Due to the high clinical heterogeneity of PD，it is urgent to establish a comprehensive and long-term traditional Chinese medicine （TCM） management model. In this paper，the conceptual framework of full-cycle management of PD is preliminarily constructed：based on the evolution of the pathophysiological mechanisms of protein deposition and neurotransmitter disorder in PD，the three-stage syndrome characteristics of the prodromal stage （predominant healthy Qi with subtle pathogenic factors），the early clinical stage （declining healthy Qi with growing pathogenic factors） and the middle and late stages （overwhelming pathogenic factors with deficient healthy Qi） are longitudinally described. Through the syndrome differentiation of visceral manifestations，the etiology and pathogenesis of PD motor and non-motor symptoms were comprehensively analyzed，while the matching treatment methods and prescriptions were inferred，and the modular scheme of the combining main symptoms，accompanying symptoms and secondary symptoms was proposed. The conceptual gap of TCM regarding motor complications （'variable syndrome'） and PD-related hyperpyrexia syndrome （'critical syndrome'） was explained. This framework reflects the characteristics of combination of disease and syndrome and overall constant motion，and provides new theories and research ideas for individualized and whole-process management of PD in TCM.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To investigate the role of JAK2/STAT3 signaling pathway mediated Treg/Th17 imbalance in children with cow's milk protein allergy and the effect of dietary avoidance on the imbalance.Methods A total of 103 children with cow's milk protein allergy in the hospital were enrolled and divided in-to the IgE-mediated group(n=38)and the non-IgE-mediated group(n=65).All patients underwent a 3-month strict dietary avoidance intervention.A total of 100 healthy children who underwent health checkups at the hospital's physical examination center during the same period,with no history of food allergy or other im-mune-related diseases,were selected as the healthy control group.The percentages of Treg and Th17 cells were measured by flow cytometry.The mRNA expression levels of JAK2,STAT3,SOCS1,and SOCS3 were determined by qPCR.Differences between groups,changes before and after dietary avoidance,and correlations between these indicators and clinical symptoms were analyzed using Pearson's test.Results At baseline,both the IgE-mediated group and the non-IgE-mediated group had significantly lower Treg percentages and SOCS1/SOCS3 mRNA levels,and higher Th17 percentages and JAK2/STAT3 mRNA levels compared to the healthy control group(P＜0.05).After dietary avoidance,Treg percentages and SOCS1/SOCS3 mRNA levels in-creased,while Th17 percentages and JAK2/STAT3 mRNA levels decreased in the IgE-mediated group and the non-IgE-mediated group(P＜0.05).The increase of Treg percentage and the decrease of Th17 percentage in the non-IgE-mediated group were greater than in the IgE-mediated group,whereas the increases of SOCS1 and SOCS3 mRNA levels were smaller in the non-IgE-mediated group(P＜0.05).Logistic regression identified JAK2,STAT3,SOCS1,and SOCS3 mRNA levels as factors influencing Treg and Th17 percentages(P＜0.05).Pearson correlation analysis revealed that allergic symptom scores and serum milk-specific IgE levels were positively correlated with JAK2/STAT3 mRNA levels and Th17 percentage,and negatively correlated with SOCS1/SOCS3 mRNA levels,Treg percentage,and the Treg/Th17 ratio(P＜0.05).Conclusion Dieta-ry avoidance can modulate the activity of JAK2/STAT3 signaling pathway,with a more pronounced improve-ment in non-IgE-mediated cow's milk protein allergy.

ObjectiveTo investigate the potential mechanisms and pathways through which Gandouling （GDL） exerts its effects in the treatment of liver fibrosis in Wilson disease. MethodsSixty male SD rats were randomly divided into six groups： the normal group， the model group， the GDL low-， medium-， and high-dose groups （0.24， 0.48， 0.96 g·kg^-1）， and the penicillamine group （90 mg·kg^-1）， with 10 rats in each group. A copper-loaded Wilson disease rat model was established by gavage administration of 300 mg·kg^-1 copper sulfate pentahydrate to all groups except the normal group. Hematoxylin-eosin （HE） staining and Masson staining were used to observe the pathomorphological changes in the liver. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the levels of hyaluronic acid （HA）， laminin （LN）， procollagen type-Ⅲ peptide （PC-Ⅲ）， and collagen type-Ⅳ （C-Ⅳ）. Transmission electron microscopy was used to examine the ultrastructure of liver tissues. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the expression levels of liver tissues and serum exosomal long noncoding RNA H19 （LncRNA H19）， phosphatidylinositol 3-kinase （PI3K）， protein kinase B （Akt）， and mammalian target of rapamycin （mTOR）. Western blot analysis was performed to assess the expression levels of PI3K， Akt， mTOR， and their phosphorylated forms， as well as autophagy-related proteins Beclin1 and microtubule-associated protein 1 light chain 3B （LC3-Ⅱ/LC3-Ⅰ） in liver tissues. Beclin1 and LC3-Ⅱ fluorescence signal intensity was observed by immunofluorescence. ResultsCompared with the normal group， the model group exhibited inflammatory cell infiltration in hepatocytes， unclear nuclear boundaries with cell cleavage and necrosis， and collagen fiber deposition around confluent areas. The levels of HA， LN， PC-Ⅲ， and C-Ⅳ were significantly elevated （P<0.01）. Transmission electron microscopy revealed an increased number of autophagic vesicles， with autophagic lysosomes exhibiting a single-layer membrane structure following degradation of most envelopes. Expression levels of Beclin1 and LC3-Ⅱ/LC3-Ⅰ were significantly increased （P<0.01）， and fluorescence signals of Beclin1 and LC3-Ⅱ were markedly enhanced. The protein expression levels of PI3K， Akt， mTOR， p-PI3K， p-Akt， and p-mTOR were reduced （P<0.01）， while LncRNA H19 expression was increased （P<0.01）， and mRNA expression levels of PI3K， Akt， and mTOR were decreased （P<0.01）. After treatment with GDL， the degree of liver fibrosis was significantly improved， with decreased levels of HA， LN， PC-Ⅲ， and C-Ⅳ. The number of autophagic vesicles was significantly reduced， and expression levels of Beclin1 and LC3-Ⅱ/LC3-Ⅰ proteins were lower （P<0.01）. The fluorescence signals of Beclin1 and LC3-Ⅱ weakened dose-dependently. The protein levels of PI3K， Akt， mTOR， p-PI3K， p-Akt， and p-mTOR were elevated （P<0.01）， while the expression level of LncRNA H19 was reduced （P<0.01）. Furthermore， the mRNA expression levels of PI3K， Akt， and mTOR increased （P<0.05， P<0.01）. ConclusionGDL may alleviate liver fibrosis and reduce liver injury by regulating the PI3K/Akt/mTOR autophagy signaling pathway via LncRNA H19.

Objective·To investigate the effects of liraglutide on liver fibrosis in mice with non-alcoholic fatty liver disease(NAFLD)and the underlying mechanisms.Methods·Twenty 8-week-old C57BL/6J mice were randomly divided into a normal chow diet group(Chow group)and a methionine-choline-deficient(MCD)diet group(MCD group),with 10 mice per group.The MCD diet was used to induce NAFLD.Each group was further divided into two subgroups,resulting in four subgroups:Chow+saline,Chow+liraglutide,MCD+saline,and MCD+liraglutide group.After daily intraperitoneal injection of liraglutide(400 μg/kg)or an equivalent volume of saline for 4 weeks,an intraperitoneal glucose tolerance test(IPGTT)was performed.Serum levels of aspartate transaminase(AST),alanine aminotransferase(ALT),total cholesterol(TC),triglyceride(TAG),high-density lipoprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)were measured.Liver tissues were collected post-euthanasia to assess TAG content.Histopathological changes,lipid deposition,and fibrosis were evaluated via hematoxylin-eosin(HE)staining,Oil Red O staining,and Masson staining.Real-time quantitative PCR(qPCR)and Western blotting were used to analyze the expression of α-smooth muscle actin(α-SMA),fibronectin(FN),collagen type Ⅰ α(COL1A),matrix metalloproteinase 9(MMP9),tissue inhibitor of metalloproteinase 1(TIMP1),transforming growth factor β(TGF-β),SMAD3,and phosphorylated SMAD3(pSMAD3).Results·The IPGTT revealed that liraglutide intervention reduced blood glucose levels at 15,30,and 60 min,with a decreased area under the curve(AUC)(both P<0.05).Biochemical analysis showed that liraglutide lowered AST and ALT levels(both P<0.001),increased TC and HDL-C levels(both P<0.05),but had no significant effect on TAG or LDL-C in MCD mice.HE staining and Oil Red O staining revealed reduced lipid droplets,ballooning degeneration,and inflammatory infiltration in hepatocytes after liraglutide treatment.Masson staining indicated decreased collagen fiber deposition in the liver.qPCR and Western blotting analysis demonstrated upregulated expression of α-SMA,FN,COL1A,TIMP1,TGF-β,and pSMAD3/SMAD3,alongside downregulated MMP9 in MCD mice.Liraglutide reversed these changes,lowering α-SMA,FN,COL1A,TIMP1,TGF-β,and pSMAD3/SMAD3 expression while increasing MMP9 expression.Conclusion·Liraglutide ameliorates liver injury,lipid deposition,and fibrosis in NAFLD mice,through modulation of the TGF-β/SMAD3 pathway and regulating fibrosis-associated protein expression.

Objective This study aimed to adapt the data-driven Palliative Care Outcomes Collaboration(PCOC)model to the local context and evaluate its feasibility and preliminary effectiveness in a palliative care unit in China,with the goal of informing its broader integration into national palliative care practice.Methods Based on international experience,a localized implementation protocol for the PCOC model was developed through expert con-sultations and a pilot study.The protocol incorporated key elements including organizational and managerial sup-port,team training and capacity building,information system integration,supervision and feedback mechanisms,pro-cess optimization,and data-driven decision-making.From June to December 2023,the protocol was piloted in the palliative care unit of a tertiary cancer hospital in Changsha,China.Implementation outcomes were assessed by comparing patients' urgent care response rates,symptom stability rates,and symptom improvement rates between the first 1～3 months and 4～6 months after implementation.Results During the study period,a total of 355 inpatients were enrolled,with the PCOC assessment achieving full coverage(100％)and a completion rate of 97.78％.There was no statistically significant difference in the urgent needs response rate between the first 1～3 months and the 4～6 months after the implementation of the PCOC model(P=0.533).However,compared to the first 1～3 months af-ter implementation,patients in the 4～6 months period showed significantly higher symptom stability rates for pain,psychological/spiritual issues,and family/caregiver problems,as well as a higher improvement rate for pain(P＜0.05).Conclusion The localized PCOC implementation protocol facilitates standardized assessment and symptom manage-ment,and its application can enhance the quality of palliative care.

The integration of artificial intelligence(AI)into medical practice has significantly impacted the field of cerebrovascular disease.AI algorithms are increasingly being employed to enhance the diagnosis and management of cerebrovascular conditions.However,the clinical application and accuracy of these AI tools require further rigorous evaluation.This review probes into the current applications of AI in diagnosis and decision-making in cerebrovascular disease,and explores the potential and challenges associated with their implementation.