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.

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.

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.

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

Objective To investigate the relationship between serum Th1/Th2 cytokine expres-sion levels and the degree of bone destruction in patients with skeletal tuberculosis.Methods A to-tal of 75 patients with skeletal tuberculosis admitted from August 2022 to August 2024 were enrolled as the study subjects.Based on varied degree of bone destruction assessed by imaging,the patients were divided into mild bone destruction group(n=51)and severe bone destruction group(n=24).Clinical data of the two groups were compared.Fasting venous blood samples were collected from the patients in the early morning.The levels of Th1 cytokines[tumor necrosis factor-α(TNF-α),interferon-γ(IFN-γ),interleukin-2(IL-2)]and Th2 cytokines[transforming growth factor-β(TGF-β),inter-leukin-4(IL-4),interleukin-10(IL-10)]in the serum were measured using the enzyme-linkedim-munosorbent assay(ELISA).Meanwhile,imaging data such as plain X-rays and magnetic reso-nance imaging(MRI)were collected to assess the degree of bone destruction.Spearman correlation analysis was used to evaluate the correlation between serum Th1/Th2 cytokine expression and the degree of bone destruction.Multivariate Logistic regression analysis was conducted to identify the in-fluencing factors of the degree of bone destruction in patients with skeletal tuberculosis.The receiver operating characteristic(ROC)curve was employed to analyze the diagnostic efficacy of serum Th1/Th2 cytokines for the degree of bone destruction in patients with skeletal tuberculosis.Results There were no significant differences in age,gender,body mass index(BMI),and the proportions of patients with a history of hypertension,diabetes mellitus,or hyperlipidemia between the two groups(P＞0.05).Compared with the mild bone destruction group,the severe bone de-struction group showed significantly elevated serum levels of Th1 cytokines(TNF-α,IFN-γ,IL-2)and significantly decreased serum levels of Th2 cytokines(TGF-β,IL-10)(P＜0.05).Spearman correlation analysis revealed that serum TNF-α(r=0.363,P=0.001),IFN-γ(r=0.383,P＜0.001),and IL-2(r=0.347,P=0.002)levels were positively correlated with the degree of bone destruction,while serum TGF-β(r=-0.380,P＜0.001)and IL-10(r=-0.365,P=0.001)levels were negatively correlated with the degree of bone destruction.Logistic regression analysis in-dicated that TNF-α,IFN-γ,TGF-β,and IL-10 were independent influencing factors for the degree of bone destruction in patients with skeletal tuberculosis(P＜0.05).ROC curve analysis demon-strated that the areas under the curve(AUCs)for TNF-α,IFN-γ,TGF-β,and IL-10 in predicting the degree of bone destruction in patients with skeletal tuberculosis were 0.725,0.737,0.735,and 0.726,respectively.The corresponding sensitivities were 62.50％,75.00％,62.50％,and 83.33％,and the specificities were 84.31％,62.75％,86.27％,and 62.75％,respectively.The combined AUC of these indicators was 0.887,which was superior to that of individual predic-tions.Conclusion In patients with skeletal tuberculosis,as the degree of bone destruction increa-ses,the serum levels of TNF-α,IFN-γ,and IL-2 rise,the serum levels of TGF-β and IL-10 gradu-ally decrease.TNF-α,IFN-γ,TGF-β,and IL-10 are independent influencing factors for the degree of bone destruction.Moreover,the combined application of Th1/Th2 cytokines has a certain predic-tive value for the degree of bone destruction.

Acute respiratory distress syndrome (ARDS) is the leading cause of respiratory failure with high morbidity and mortality. Pulmonary surfactant (PS)-based complementary therapies have exhibited potential for ARDS healing and applied as an adjunctive therapy strategy. Coacervate (Coac) has the characteristics of softness, deformability and excellent molecular enrichment properties, and has attracted extensive attention in the biomedical field. Here PS and coacervate were combined for the potential ARDS treatment. The Coac, fabricated from polyallylamine hydrochloride (PAH) and adenosine triphosphate (ATP) by simple mixing, exhibited soft droplet property and high enrichment for dexamethasone sodium phosphate (DSP). To avoid the fusion effect of membraneless coacervate and endow it with biological functions of PS, liposomes with PS-biomimetic lipid components (PS-lipo) were further introduced to construct PS-biomimetic membranized coacervate (DSP@PS-Coac). The DSP@PS-Coac demonstrated high lung targeting effect and significant penetration efficiency after intravenous injection. Furthermore, PS-lipo replenished the endogenous PS pool and facilitated the distribution of DSP in inflammatory cells in the lung. In the ARDS mouse model, PS-Coac and DSP exerted synergetic anti-inflammatory functions, via reducing the recruitment of inflammatory neutrophils and modulating macrophages into anti-inflammatory phenotype. The overall results confirmed that DSP@PS-Coac may provide a promising delivery option for the treatment of ARDS.