Wilson disease （WD） is a hereditary disorder of copper metabolism characterized by abnormal copper accumulation in tissues， including the liver and brain， which leads to severe hepatic and neurological damage. This disease is often accompanied by lipid metabolism abnormalities， and the exploration of related mechanisms has attracted increasing attention. This article introduces the clinical features of lipid metabolism disorders in WD patients， summarizes the research advances in the serum levels of lipids and hepatic steatosis， analyzes the potential mechanisms of the interaction between copper and lipid metabolism， and highlights the significance of lipid-related molecules in disease diagnosis and clinical evaluation. In clinical practice， the monitoring and assessment of lipid metabolism parameters should be taken seriously in patients with WD， in order to promote comprehensive disease management and improve the prognosis 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 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.

In recent years, China has been facing the dual challenges of declining fertility rates and births, with male reproductive health issues, especially the decline in semen quality, identified as a pivotal contributor to this phenomenon. Meanwhile, accumulating evidence indicates that air pollutants, an increasingly severe environmental problem, can damage semen quality not only directly through their biological toxicity but also indirectly by disrupting the composition of microbial communities in the gut and semen, thereby dysregulating immune function, endocrine homeostasis, and oxidative stress responses. The gut microbiota and semen microbiota, as important components of the human microecosystem, play crucial roles in maintaining reproductive health. This article comprehensively reviewed the research progress on the potential effects of air pollutants (particulate matter and gaseous pollutants), gut microbiota, and semen microbiota on semen quality. Specifically, it elucidated the mechanisms of interaction between these factors and explored how they affect male fertility.

Cancer multidrug resistance (MDR) impairs the therapeutic efficacy of various chemotherapeutics. Novel approaches, particularly the development of MDR reversal agents, are critically needed to address this challenge. This study demonstrates that tenacissoside I (TI), a compound isolated from Marsdenia tenacissima (Roxb.) Wight et Arn, traditionally used in clinical practice as an ethnic medicine for cancer treatment, exhibits significant MDR reversal effects in ABCB1-mediated MDR cancer cells. TI reversed the resistance of SW620/AD300 and KBV200 cells to doxorubicin (DOX) and paclitaxel (PAC) by downregulating ABCB1 expression and reducing ABCB1 drug transport function. Mechanistically, protein arginine methyltransferase 1 (PRMT1), whose expression correlates with poor prognosis and shows positive association with both ABCB1 and EGFR expressions in tumor tissues, was differentially expressed in TI-treated SW620/AD300 cells. SW620/AD300 and KBV200 cells exhibited elevated levels of EGFR asymmetric dimethylarginine (aDMA) and enhanced PRMT1-EGFR interaction compared to their parental cells. Moreover, TI-induced PRMT1 downregulation impaired PRMT1-mediated aDMA of EGFR, PRMT1-EGFR interaction, and EGFR downstream signaling in SW620/AD300 and KBV200 cells. These effects were significantly reversed by PRMT1 overexpression. Additionally, TI demonstrated resistance reversal to PAC in xenograft models without detectable toxicities. This study establishes TI's MDR reversal effect in ABCB1-mediated MDR human cancer cells through inhibition of PRMT1-mediated aDMA of EGFR, suggesting TI's potential as an MDR modulator for improving chemotherapy outcomes.
Humans ; Protein-Arginine N-Methyltransferases/antagonists & inhibitors* ; Drug Resistance, Neoplasm/drug effects* ; ErbB Receptors/genetics* ; Animals ; Cell Line, Tumor ; Drug Resistance, Multiple/drug effects* ; Methylation/drug effects* ; Saponins/administration & dosage* ; Mice ; Mice, Nude ; Mice, Inbred BALB C ; ATP Binding Cassette Transporter, Subfamily B/genetics* ; Doxorubicin/pharmacology* ; Paclitaxel/pharmacology* ; Female ; Repressor Proteins

Objective To address the bottlenecks in the application of wastewater biological treatment technology under space conditions,an experimental system for the biological treatment of space wastewater was constructed and its biochemical performance examined.The findings of this study will provide technical support for the biological treatment of space wastewater.Methods Based on the Membrane Aerated Biofilm Reactor(MABR)process,a biological treatment experimental system for space wastewater was constructed and conducted the continuous flow test for 77 days to investigate the performance of PVDF and PP membrane modules in the treatment of simulated air condensate.Results The results demonstrated that both membrane modules exhibited an average TOC removal rate of 90%,indicative of their effective organic matter removal capacity.In the air supply mode,the ammonia oxidation capacity was observed to be comparatively lower,whereas in the oxygen source without bubbling mode,the nitrogen oxidation rate and total nitrogen removal rate could be attained above 90%,indicating a notable degree of simultaneous nitrification and denitrification.The results demonstrated that the mode of gas supply had a significant impact on the nitrogen conversion performance.The abundance of nitrogen-converting bacteria in PP membrane module is higher than that in PVDF membrane module,indicating a better nitrogen-converting performance in PP membrane module.Conclusion The constructed wastewater biological treatment system is optimally suited for the treatment of air condensate,thereby offering a novel technical approach for space wastewater treatment.

The present study employed UPLC-MS/MS to analyze and identify compounds in the raw powder of Tongluo Shenggu capsules. An HPLC method for the determination of vitexin content was established. The analysis of this drug was performed on a 30 ℃ thermostatic Acquity UPLC® BEH C18 (2.1 mm×100 mm,1.7 μm) column, with the mobile phase comprising 0.2% formic acid-methanol flowing at 0.3 mL /min in a gradient elution manner. Mass spectrometry was detected by ESI sources in both positive and negative ion modes for qualitative identification of chemical constituents. 12 flavonoid and 3 stilbenes compounds in the raw powder of Tongluo Shenggu capsules were successfully identified. Additionally, an HPLC method for the determination of vitexin content was established using a XBridge C18 column (4.6 mm × 250 mm, 5 µm) with a mobile phase of 0.05% glacial acetic acid in methanol for gradient elution, at a column temperature of 30 °C, a flow rate of 1.0 mL/min, and an injection volume of 20 μL. The method demonstrated good linearity in the concentration range of 10 µg/mL to 40 µg/mL (R=1.000) with an average recovery rate of 96.7%. The establishment of these methods provides a scientific basis for the quality control and development of the raw powder of Tongluo Shenggu capsules.

Objective:To explore the relationship between the dark triad, fear of missing out and smartphone addiction, as well as the mediating role of fear of missing out between the dark triad and smartphone addiction.Methods:A total of 408 college students were assessed using dirty dozen, fear of missing out scale, and mobile phone addiction index. SPSS 24.0 software was used for common method bias tests, descriptive statistical analysis, and correlation analysis. Mplus 8.3 software was employed to construct a structural equation model, and the Bootstrap method was used for mediation effect analysis.Results:The dark triad (36.14±9.06) was positively correlated with fear of missing out (22.55±6.60) and smartphone addiction (46.77±13.62) ( r=0.48, 0.45, both P<0.01). Fear of missing out was also positively correlated with smartphone addiction ( r=0.54, P<0.01). Fear of missing out played a partial mediating role in the relationship between the dark triad and smartphone addiction, with a total effect value of 0.495 and a mediating effect value of 0.217. Conclusion:Fear of missing out partially mediates the relationship between the dark triad and smartphone addiction. The dark triad not only directly predicts smartphone addiction but also indirectly influences smartphone addiction through fear of missing out.

Objective:To examine the association among childhood emotional neglect, thwarted belongingness, perceived burdensomeness, and depression in junior high school students.Methods:From October to December 2024, a total of 314 junior high school students were recruited in Dongying City, Shandong Province. The Chinese versions of the childhood trauma questionnaire-short form (CTQ-SF), the center for epidemiologic studies depression scale (CES-D), and the interpersonal needs questionnaire (INQ) were administered for investigation. SPSS 27.0 software was used for common-method bias test, descriptive analysis, difference test, and correlation analysis. Mplus 8.3 software was employed to build a structural equation model, and the Bootstrap method was adopted for mediation analysis.Results:The scores of depression, childhood emotional neglect, thwarted belongingness, and perceived burdensomeness in junior high school students were 14.00 (9.00, 24.00), 9.00 (5.00, 12.00), 13.00 (8.00, 18.00) and 8.00 (6.00, 19.25), respectively. All variables were positively correlated with one another ( r=0.33-0.51, P<0.01). The total effect of childhood emotional neglect on depression was 0.493 (95% CI=0.396-0.591). The direct effect was 0.126 (95% CI=0.013-0.240), accounting for 25.56%(0.126/0.493) of the total effect. The total indirect effect was 0.367 (95% CI=0.287-0.447), accounting for 74.44%(0.367/0.493) of the total effect. Specifically, the pathway " childhood emotional neglect → thwarted belongingness→ depression" yielded an effect of 0.109, and the pathway " childhood emotional neglect → perceived burdensomeness → depression" yielded an effect of 0.258. Conclusion:Thwarted belongingness and perceived burdensomeness play parallel mediating effects between childhood emotional neglect and depression in junior high school students.