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.

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.

Objective: To evaluate the effects of a multidisciplinary weight management intervention, so as to provide a reference for the formulation of overweight and obesity intervention measures. Methods: From April to September 2025, overweight and obese residents aged 18-60 years who participated in a weight loss competition at the Health Management Center of Beilun People's Hospital in Ningbo City were selected as study subjects. They were divided into a control group and an intervention group. The control group received conventional weight management, while the intervention group received the multidisciplinary integrated weight management in addition to the conventional weight management, for a total intervention period of 8 weeks. Weight, body mass index (BMI), waist circumference, hip circumference, waist-to-hip ratio, fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and blood pressure were collected before and after the intervention through physical examinations and laboratory tests. The generalized estimating equations (GEE) method was employed to analyze the differences in indicators between the two groups before and after the intervention. Results: The control group comprised 241 participants, including 161 females (66.80%), with a mean age of (35.66±7.80) years. The intervention group consisted of 127 participants, including 86 females (67.72%), with a mean age of (36.80±7.05) years. No statistically significant differences were observed between the two groups at baseline in terms of age, gender, weight, BMI, or waist-to-hip ratio (all P>0.05). Results from the GEE analysis indicated significant interactions between group and time for weight, BMI, waist circumference, and hip circumference (all P<0.05) with greater reductions in these parameters observed in the intervention group compared to the control group before and after the intervention. Similarly, significant interactions between group and time were observed for FBG, TG, TC, and LDL-C (all P<0.05), with the intervention group demonstrating larger decreases in these markers compared to the control group. However, no statistically significant interactions between group and time were observed for waist-to-hip ratio, HDL-C, systolic blood pressure, and diastolic blood pressure (all P>0.05). Following the intervention, a weight loss exceeding 10% was achieved by 13 participants (5.39%) in the control group and 62 participants (48.82%) in the intervention group. The proportion of individuals with a weight loss exceeding 10% was significantly higher in the intervention group compared to the control group (P<0.05). Conclusion Compared to conventional weight management, multidisciplinary integrated weight management demonstrated greater efficacy in improving weight-related indicators and blood glucose, blood lipids, and enhancing weight loss outcomes among overweight and obese residents.

Objective:To review the literature on surrogate decision making in stroke patients, and to clarify the application status of proxy decision making in stroke patients.Methods:Using the scope review guide as the methodological framework, studies on the application of surrogate decision in stroke patients, PubMed, Medline, CINAHL, Web of Science, Cochrane Library, CNKI, Wanfang, China Biology Medicine databases were searched from the database inception to January 12, 2024.Results:A total of 20 articles were included, including 12 cross-sectional studies, 6 qualitative studies, 1 cohort study, and 1 case report. At present, surrogate decision was mainly used in brain surgery, tube placement and comfort care. The agent had a complicated mood when making a decision and self-doubt after making a decision. Agency decision making was susceptible to demographic factors, disease-related factors, cultural factors, emotional and psychological factors and other factors. Although the outcomes of most proxy decisions were in line with patient preferences, there was a difference in the estimates of prognosis between agents and medical staff.Conclusions:Proxy decision making is widely used in the field of stroke, but the psychological burden of agents is heavy. Developing advance care planning for stroke patients and developing decision aids may help agents make the best decision.

This paper reported a case of juvenile trabecular ossifying fibroma（JTOF） originating from the uncinate process. The main clinical manifestation was nasal obstruction and epiphora. Contrast-enhanced sinus CT revealed an irregular heterogeneous soft tissue mass centered in the right uncinate process, with involvement of the right anterior ethmoid sinus, maxillary sinus ostium, frontal process of the maxilla, and partial nasolacrimal duct. The solid components of the tumor demonstrated enhancement on contrast imaging. The patient underwent endoscopic resection of the right sinonasal tumor under general anesthesia. Postoperative pathological examination confirmed the diagnosis of JTOF. No tumor recurrence was observed during the 3-month follow-up period.
Humans ; Ethmoid Bone/pathology* ; Fibroma, Ossifying