AIM: To investigate the application value of pre-treatment tear inflammatory factor levels in predicting therapeutic efficacy for dry eye patients.METHODS:Prospective controlled observational study. A total of 120 patients with dry eye(240 eyes)admitted to our hospital from November 2022 to March 2024 were included. Before dry eye treatment, the levels of inflammatory factors, including interlukin-4(IL-4), IL-6, IL-8, IL-10, IL-12, IL-13, IL-15, IL-18, IL-1β, interferon-γ(IFN-γ), tumor necrosis factor-α(TNF-α), granulocyte-colony stimulating factor(G-CSF), granulocyte-macrophage colony-stimulating factor(GM-CSF), monocyte chemoattractant protein-1(MCP-1)in the tear fluid were detected by ELISA. According to the treatment protocol in the Chinese Expert Consensus on the Treatment of Dry Eye(2020), the patients were given treatments, and the related factors affecting the treatment outcomes of dry eye patients were analyzed.RESULTS:After continuous treatment for 4 wk, all the patients completed follow-up, and they were divided into the markedly effective group(60 patients, 120 eyes)and the ineffective group(60 patients, 120 eyes)based on their therapeutic effects. The markedly effective group had significantly lower pre-treatment levels of IL-6, IL-10, IL-18, IL-1β, and TNF-α than the poor efficacy group(all P<0.05). IL-6(OR=0.994), IL-18(OR=0.998), IL-1β(OR=0.933), and TNF-α(OR=0.998)were independently associated with treatment efficacy(all P<0.05). The nomogram model yielded a C-index of 0.971(95% CI: 0.950-0.993), with calibration curves closely aligned to the ideal curve. The model demonstrated significant predictive value for early therapeutic efficacy(sensitivity=96.67%, specificity=71.67%, cutoff=208, AUC=0.866, 95% CI=0.794-0.952, P<0.001).CONCLUSION:The nomogram model constructed based on the levels of inflammatory factors in dry eye patients before treatment can well predict the treatment effect of patients.

AIM:To evaluate the efficacy and safety of trypsin irrigation in the treatment of lacrimal duct obstruction after Nd:YAG laser dacryoplasty.METHODS:This retrospective cohort study included 160 patients(160 eyes)with lacrimal duct obstruction who underwent Nd:YAG laser dacryocystoplasty at our institution. Based on the postoperative irrigation protocol, patients were allocated into two groups: the experimental group, which received lacrimal irrigation with a 25% trypsin solution once weekly for 4 wk postoperatively, and the control group, which received irrigation with normal saline on the same schedule. Patient data was obtained by reviewing electronic medical records, including baseline characteristics, surgical records, postoperative irrigation protocols, and follow-up outcomes at 1, 2, 4, and 6 mo post-surgery.RESULTS:The baseline characteristics were comparable between the two groups. At 1 mo postoperatively, the success rate in the experimental group was 90.0%, significantly higher than 71.3% in the control group(P<0.05). Preoperatively, the tear meniscus height in obstructed eyes was higher than in healthy eyes in all patients(all P<0.01). At 1 mo postoperatively, the tear meniscus height in obstructed eyes decreased significantly compared to preoperative levels(all P<0.01), and was lower in the experimental group than in the control group(P<0.01). The recurrence rates of obstruction at 2, 4, and 6 mo were 1.4%, 6.9%, and 5.6% in the experimental group, compared to 5.3%, 17.5%, and 12.3% in the control group, respectively. The total recurrence rate was significantly lower in the experimental group(13.9%)than in the control group(35.1%; P<0.05). No severe complications occurred in either group. Patient satisfaction with the treatment outcome was significantly higher in the experimental group than in the control group(P<0.01).CONCLUSION:Lacrimal irrigation with trypsin following Nd:YAG laser dacryocystoplasty demonstrates superior efficacy and a lower recurrence rate in the treatment of lacrimal duct obstruction. Trypsin shows promising application prospects for improving surgical success rate and reducing recurrence rate after laser treatment for lacrimal duct obstruction.

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 This study aimed to investigate the association between long-term radon exposure and changes in peripheral blood DNA methylation levels, as well as alterations in the expression of methylation-related regulatory genes (DNMT1, DNMT3A, and MBD2) among underground non-uranium metal miners. Methods Five non-uranium metal mines with varying levels of radon exposure were enrolled in this study. From each mine, 30 underground miners and 30 aboveground workers were randomly recruited. Basic demographic information was collected via a standardized questionnaire, and peripheral venous blood samples were obtained from all participants. An enzyme-linked immunosorbent assay (ELISA) colorimetric method was used to detect peripheral blood DNA methylation levels. Real-time quantitative PCR was used to measure mRNA expression levels of DNMT1, DNMT3A, and MBD2. These indicators were compared between underground and aboveground groups and among underground miners with different radon exposure levels. Multiple linear regression analysis was conducted to examine the associations between cumulative radon dose and two outcomes: peripheral blood DNA methylation levels, and the expression levels of methylation-related regulatory genes in underground miners. Results The average radon concentration in the underground workplaces of the five mines was 4,088 Bq/m³ (range: 116-18,188 Bq/m³), corresponding to an estimated annual effective dose of approximately 25.67 mSv for miners. The average radon concentration in aboveground workplaces was 58 Bq/m³ (range: 28-1796 Bq/m³), equivalent to an annual effective dose of approximately 0.51 mSv for aboveground workers. The average cumulative radon dose of underground miners was approximately 26.56 times that of aboveground workers (Z=−12.469, P<0.001). Compared with the aboveground workers, the peripheral blood DNA methylation level of underground miners was significantly decreased, approximately 0.77 times that of the aboveground group (Z=−2.313，P<0.05), accompanied by decreased expression of DNA methyltransferase genes DNMT1 and DNMT3A, which were 0.83 and 0.68 times those of the aboveground group, respectively (Z=−3.002、−4.190，P<0.01). After adjusting for age, working years, body mass index, smoking status, and alcohol consumption, multiple linear regression analysis revealed two key findings: cumulative radon dose was significantly and inversely correlated with peripheral blood DNA methylation levels (β=−0.752, P<0.001); and cumulative radon dose was significantly negatively associated with the expression of DNMT1 and DNMT3A (β=−0.319, −0.369, P<0.01). Conclusion The reduced DNA methylation levels and downregulated expression of DNA methyltransferase genes in underground miners are closely associated with long-term radon exposure.

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 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