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.

Biomaterials provide a reliable and safe treatment for the repair of ear defects,especially microtia.With the continuous promotion of interdisciplinary research,the types and properties of materials used in auricle reconstruction have been greatly expanded and improved.In this paper,the latest research progress of polymer materials and tissue engineering scaffolds in the field of auricle reconstruction was reviewed,and the physical and chemical properties and clinical application effects of these scaffolds in vitro and in vivo were discussed.However,despite significant progress in ear stent research,there are still some challenges and limitations in the application of current polymer materials,such as the hydrophobicity of high-density polyethylene and the uncontrolled differen-tiation of stem cells in tissue engineering.Therefore,this paper discusses the research and clinical application of new modified materials,especially emphasizing the importance of surface modification technology and innovative synthesis strategies,so that the properties of modified materials are infinitely close to the characteristics of human ear cartilage.In addition,this paper also summarizes the control methods of stem cell differentiation in ear carti-lage tissue engineering.The application performance and biocompatibility of different ear scaffolds were reviewed in order to provide guidance for auricle reconstruction.

Objective To study the role and influence of basic Alkaline ceramidase 1 on mucosal barrier and immune regulation in ulcerative colitis(UC).Methods Acer1 knockout mice(Acer1 KO)were constructed,and the UC model was induced by continuous drinking water of sodium dextran sulfate(DSS)for 7 days,and the severity of symptoms of UC disease in C57 and Acer1 KO mice was compared.The expression levels of intestinal mucosal barriers(ZO-1,Occludin,Claudin,JAMA)were detected by RT-PCR and immunohistochemistry.Systemic immune response levels(IL-1b、IL-6、IL-23、IL-17、IL-10,and TNF-a)were assessed by ELISA and intestinal inflammatory infiltration levels(TNF-a、IL-1b、IL-6、IL-17、IL-21,etc.)were assessed by RT-PCR.Results There was no significant difference between C57 and Acer1 KO mice in free drinking water.In the DSS induced UC model,compared with C57 mice,the survival rate of Acer1 KO mice decreased,the weight decreased significantly,the mechanical barrier and mucus barrier protein expression levels decreased significantly,the intes-tinal epithelial barrier was seriously damaged,the inflammatory response was strong,and the cytokine infiltration was obvious,with statistical differences(P<0.05).Conclusion Acer1 can inhibit inflammatory infiltration by maintaining the integrity of intestinal mucosal barrier,preventing endotoxin,and delaying the progression of UC.

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.

Objective To study the role and influence of basic Alkaline ceramidase 1 on mucosal barrier and immune regulation in ulcerative colitis(UC).Methods Acer1 knockout mice(Acer1 KO)were constructed,and the UC model was induced by continuous drinking water of sodium dextran sulfate(DSS)for 7 days,and the severity of symptoms of UC disease in C57 and Acer1 KO mice was compared.The expression levels of intestinal mucosal barriers(ZO-1,Occludin,Claudin,JAMA)were detected by RT-PCR and immunohistochemistry.Systemic immune response levels(IL-1b、IL-6、IL-23、IL-17、IL-10,and TNF-a)were assessed by ELISA and intestinal inflammatory infiltration levels(TNF-a、IL-1b、IL-6、IL-17、IL-21,etc.)were assessed by RT-PCR.Results There was no significant difference between C57 and Acer1 KO mice in free drinking water.In the DSS induced UC model,compared with C57 mice,the survival rate of Acer1 KO mice decreased,the weight decreased significantly,the mechanical barrier and mucus barrier protein expression levels decreased significantly,the intes-tinal epithelial barrier was seriously damaged,the inflammatory response was strong,and the cytokine infiltration was obvious,with statistical differences(P<0.05).Conclusion Acer1 can inhibit inflammatory infiltration by maintaining the integrity of intestinal mucosal barrier,preventing endotoxin,and delaying the progression of UC.

Objective:To implement an overview of systematic reviews on the effects of brain-computer interface training on upper limb functional rehabilitation in stroke patients.Methods:The systematic review/Meta-analysis of the effect of brain-computer interface training on upper limb functional rehabilitation in stroke patients was electronically retrieved in PubMed, Web of Science, Cochrane Library, Embase, CINAHL, Embase, Joanna Briggs Institute Evidence-Based Health Care Center Database, China National Knowledge Infrastructure, Wanfang Data, VIP and China Biology Medicine disc. The search period was from database establishment to November 2024. Two researchers independently performed literature screening and data extraction and evaluated the quality of methodology, reporting, and evidence.Results:A total of 14 systematic reviews/Meta-analyses were included. The results showed that brain-computer interface training helped to improve upper limb motor function, muscle strength and activities of daily living in stroke patients, but the rehabilitative effect on muscle spasm needed to be further confirmed.Conclusions:Brain-computer interface training helps to improve upper limb motor function, muscle strength and activities of daily living in stroke patients. However, the methodological quality and reporting quality of the current studies are poor, and there is still a need for high-quality studies with rigorous design and standardized process to provide reference for clinical practice.