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.

The exploration and pilot studies of applying blockchain to drug supply chain show great potential in promoting information sharing, collaboration competence among the actors, regulatory efficiency, and etc. In the future, with the help of blockchain, the optimization of the entire supply chain for orphan drugs is expected to be realized. However, there is no such exploration in China at present. This paper systematically sorts out the whole process of supply chain for orphan drugs and the existing problems of the chain. The article concludes that at present, blockchain is mainly used in the " circulation" and " use" of the drug supply chain. It helps to improve the traceability of drugs, to cope with the problem of counterfeit drugs, to enable actors of the drug supply chain to form a collaborative network in optimizing resource allocation, and to improve the operation and supervision efficiency of the supply chain. In the future, the application faces challenges such as high costs in system conversion, lack of personnel awareness, and incomplete supporting systems. Based on the three dimensions of technology, practice, and research, this paper also looks into the future and suggests for the future use of blockchain in the supply chain of orphan drugs by constructing a practice model, the so called DI-GIVE (Digital, Intelligence, Government′s supervision, Innovation, Views of variety, Evaluation-based) hoping to innovate the supply chain of orphan drugs and to ensure the drug use for the patients with rare diseases in China.

Objective To investigate prognostic value of serum levels of cathepsin B(CTSB)and NADPH oxidase 4(NOX4)in patients with sepsis associated acute kidney injury(S-AKI).Methods A total of 306 pa-tients with sepsis treated to the hospital from June 2023 to June 2024 were selected,including 192 patients with S-AKI(S-AKI group)and 114 patients without S-AKI(non-S-AKI group).According to the prognosis of S-AKI patients,they were divided into poor prognosis group(n=127)and non-poor prognosis group(n=65).The differences of CTSB and NOX4 in different groups were compared.Pearson analysis was conducted to analyze the correlation between CTSB,NOX4 and clinical indicators.The influencing factors of poor prognosis in S-AKI patients was analyzed according to multivariate Logistic regression.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of CTSB and NOX4 on the poor prognosis of S-AKI pa-tients.Results Th e serum levels of CTSB,NOX4 and serum creatinine(SCr)in S-AKI group were signifi-cantly higher than those in non-S-AKI group(P＜0.05),while the mean arterial pressure and oxygenation in-dex were lower than those in non-S-AKI group(P＜0.05).Serum levels of CTSB and NOX4 in patients with sepsis were positively correlated with SCr(P＜0.05).Compared to non-poor prognosis group,the age,SCr,CTSB and NOX4 of patients in the poor prognosis group were higher,while the mean arterial pressure and ox-ygenation index were lower(P＜0.05).The increased mean arterial pressure and oxygenation index were pro-tective factors for poor prognosis in S-AKI patients(P＜0.05),and the increased age,SCr,CTSB and NOX4 levels were risk factors for poor prognosis in S-AKI patients(P＜0.05).ROC curve showed that the area un-der the curve(AUC)of CTSB combined with NOX4 was significantly higher than AUC of single detection of CTSB and NOX4(Z=4.066,P＜0.001,Z=3.801,P＜0.001).Conclusion The serum levels of CTSB and NOX4 in S-AKI patients are elevated,and can indirectly reflect kidney function.CTSB combined with NOX4 can significantly improve the prediction efficiency of poor prognosis in S-AKI patients,which has potential ap-plication value for clinical treatment and nursing.

The peripheral pulmonary lesions are located far from the central airway and close to the pleura, so it is a challenge for clinical diagnosis of their nature through biopsy. Therefore, the Ion robot-assisted bronchoscopy system which has started its commercialization in China is proposed to diagnose and treat peripheral pulmonary lesions. The Ion system can be used for navigation, registration, biopsy and treatment. In this paper, the structural principle of the Ion system is expounded, and its technical advantages such as shape perception, slender catheter and flexible operation are summarized. It represents the latest development direction of the diagnosis and treatment of peripheral pulmonary lesions. Then, the clinical application and development status of the Ion system are analyzed and discussed in detail. Finally, the development trend of the robot-assisted bronchoscopy system is prospected, which provides new ideas for realizing the "integrated and one-stop" diagnosis and treatment services for peripheral pulmonary lesions based on this system.
Bronchoscopy/instrumentation* ; Humans ; Robotics ; Robotic Surgical Procedures

OBJECTIVES: To evaluate the inhibitory effect of Macrothele raveni crude venom against proliferation of different cancer cells and identify the active components in the venom. METHODS: Different cancer cell lines were treated with different concentrations of Macrothele raveni venom for 48 h, and cell proliferation and the half-maximal inhibitory concentrations (IC₅₀) of the venom were assessed with CCK-8 assay. The apoptosis rate of breast cancer MCF7 cells following the treatment was analyzed with flow cytometry, and the changes in cellular caspase-8 and caspase-9 expressions were detected. The crude venom was separated into protein, peptide, and small-molecule compound fractions using gel filtration chromatography and high-performance liquid chromatography (HPLC). The protein and peptide components were identified using proteomics analysis, and small-molecule compounds were structurally characterized using nuclear magnetic resonance (NMR), mass spectrometry (MS), and HPLC. RESULTS The crude venom exhibited strong concentration-dependent inhibitory effects on proliferation of MCF7 cells and nasopharyngeal carcinoma SUNE1 and HONE1 cells (IC₅₀ of 2.14±0.29, 1.57±0.14, and 2.85±0.15 µg/mL, respectively), with less potent inhibitory effects in gastric cancer HGC27 cells and colorectal cancer SW620 cells (IC₅₀ of 3.02±0.27 and 3.02±0.28 µg/mL, respectively). The crude venom significantly promoted MCF7 cell apoptosis likely via the caspase 8 signaling pathway. The protein fraction from the crude venom showed a weak inhibitory effect in MCF7 cells, whereas the peptide fraction exhibited a much stronger inhibitory effect (IC₅₀ of 6.41±0.31 µg/mL). The peptides in the peptide fraction, with relative molecular mass around 10 000, were homologous to those found in Macrothele gigas venom. The small-molecule fraction consisted mainly of nucleotide metabolites without obvious inhibitory effects in MCF7 cells, but its combination with the peptide fraction showed significantly enhanced inhibitory activity. Conclusion The inhibitory effects of Macrothele raveni venom, which vary significantly across different cancer cell lines, are attributed primarily to its peptide components, which may act synergistically with the nucleotide metabolites.
Humans ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Animals ; Cell Line, Tumor ; MCF-7 Cells ; Caspase 8/metabolism* ; Peptides/pharmacology* ; Caspase 9/metabolism*

OBJECTIVES: To explore the value of ferroptose-related genes in the diagnosis and prediction of ulcerative colitis (UC). METHODS: We used UC dataset from the GEO database to screen for differentially expressed genes (DEGs) in UC. The DEGs related to ferroptositis were screened from the FerrDb database and their functions were analyzed. The hub genes were identified by constructing the protein-protein interaction network (PPI), the differences in immune infiltration levels between UC and the control group were evaluated using CIBERSORT, and the diagnostic values of the hub genes for UC were verified by using the training set. In a mouse model of UC, we examined the expression levels of the hub genes in the colon tissues of the mice using real-time fluorescence quantitative PCR (qPCR). RESULTS: We identified a total of 76 DEGs related to ferroptosis. Functional enrichment analysis showed that these genes were significantly enriched in ferroptosis and hypoxia pathways. The PPI network identified 10 hub genes, and 9 of them were highly expressed in UC. Analysis of immune cell infiltration showed that 27 cell types were significantly increased in UC (P<0.05), and the immune checkpoints-related genes had the strongest correlation with the hub gene PPARG (P<0.05). Verification analysis using the training set showed that P4HB, PPARG and STAT3 had the best predictive value for UC (P<0.05). In the UC mouse model, the expression of PPARG was significantly decreased and the expressions of P4HB and STAT3 were significantly increased in the colon tissues of the mice as compared with the normal mice. CONCLUSIONS Ferroptose-related genes have significant value for diagnosis and prediction of UC.
Colitis, Ulcerative/genetics* ; Animals ; Mice ; Ferroptosis/genetics* ; Humans ; Protein Interaction Maps ; Disease Models, Animal ; Gene Expression Profiling ; STAT3 Transcription Factor/genetics*