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.

ObjectiveTo investigate the effect of Zishen Huoxue Formula （ZSXHF） on molecular-targeted therapy-associated proteinuria in patients with primary liver cancer （PLC）， to assess the efficacy of ZSXHF in the treatment of molecular-targeted therapy-associated proteinuria， and to provide a basis for clinical medication. MethodsA retrospective cohort study was conducted among the PLC patients with molecular-targeted therapy-associated proteinuria who were diagnosed and treated in The Department of Hepatology of Chinese PLA General Hospital， from January 1， 2022 to July 1， 2025. With ZSXHF treatment as the exposure factor， the patients with a cumulative treatment duration of ≥9 weeks were enrolled as traditional Chinese medicine （TCM） group， while those without TCM treatment were enrolled as control group. Propensity score matching was performed for the two groups at a ratio of 1∶1 based on sex， age， 24-hour urinary protein， blood urea nitrogen， and serum creatinine. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between groups. Univariate and multivariate Logistic regression analyses were used to investigate the influencing factors for promoting the improvement of targeted-therapy-associated proteinuria. ResultsA total of 137 PLC patients with targeted-therapy-associated proteinuria were enrolled， with 34 patients in the TCM group and 103 in the control group. After follow-up for 6 months， the TCM group had a significant improvement in urinary protein grade compared with the control group （χ²=9.261， P=0.016）. There were 25 patients in each group after propensity score matching， and after follow-up for 6 months， there were significant differences between the two groups in urinary protein grade （χ²=15.689， P<0.001） and 24-hour urinary protein （Z=-3.075， P=0.002）. After cumulative treatment with ZSXHF for ≥9 weeks， the TCM group had a significantly greater change in 24-hour urinary protein from baseline compared with the control group （t=-2.514， P=0.016）， while there were no significant differences in the changes in liver and renal function after ZSXHF intervention between the two groups （all P>0.05）. The multivariate Logistic regression analysis showed that ZSXHF treatment （odds ratio=2.901， 95% confidence interval： 1.135 — 7.417， P=0.026） was an independent influencing factor for improvement in molecular-targeted therapy-associated proteinuria. ConclusionZSHXF can effectively alleviate molecular-targeted therapy-associated proteinuria in PLC patients with a favorable safety profile， which provides a new reference for TCM prevention and treatment of molecular-targeted therapy-associated adverse reactions in PLC 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.

Normal heart function depends on complex regulation by the brain, and abnormalities in the brain‒heart axis affect various diseases, such as myocardial infarction and anxiety disorders. However, systematic tracking of the brain regions associated with the input and output of the heart is lacking. In this study, we injected retrograde transsynaptic pseudorabies virus (PRV) and anterograde transsynaptic herpes simplex virus (HSV) into the left ventricular wall of mice to identify the whole-brain regions associated with the input to and output from the heart. We successfully detected PRV and HSV expression in at least 170 brain subregions in both male and female mice. Sex differences were discovered mainly in the hypothalamus and medulla, with male mice exhibiting greater correlation and hierarchical clustering than female mice, indicating reduced similarity and increased modularity of virus expression patterns in male mice. Further graph theory and multiple linear regression analysis of different injection timelines revealed that hub regions of PRV had highly similar clusters, with different brain levels, suggesting a top-down, hierarchically transmitted neural control pattern of the heart. Hub regions of HSV had scattered clusters, with brain regions gathered in the cortex and brainstem, suggesting a bottom-up, leapfrog, multipoint neural sensing pattern of the heart. Both patterns contain many hub brain regions that have been previously overlooked in brain‒heart axis studies. These results provide brain targets for future research and will lead to deeper insight into the brain mechanisms involved in specific heart conditions.
Animals ; Male ; Female ; Heart/physiology* ; Mice ; Herpesvirus 1, Suid ; Brain/physiology* ; Mice, Inbred C57BL ; Brain Mapping ; Efferent Pathways/physiology* ; Afferent Pathways/physiology* ; Simplexvirus ; Sex Characteristics

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 develop fluorescent nanoprobes with aggregation-induced emission characteristics and to systematically evaluate their optical properties, biosafety, anti-tumor activity, and imaging capability, thereby assessing their potential for early precision diagnosis and treatment of oral cancer in mice.Methods:Control probes (PEG@TPD) were prepared by encapsulating ( E)-4-(2-(4′-(1-phenyl-2,2-bis(4-methoxyphenyl)vinyl)biphenyl-4-yl)vinyl)-4-(dicyanomethylene)-4 H-chromene (TPD) using 1,2-distearoyl- SN-glycerol-3-phosphoethanolamine- N-polyethylene glycol 2000-maleimide as the carrier. Fluorescent nanoprobes (GE11-PEG@TPD) were subsequently fabricated by surface modification with the targeting GE11 peptide. The morphology and particle size of the nanoprobes were characterized by transmission electron microscopy and dynamic light scattering. The optical properties of the nanoprobes were analyzed using ultraviolet-visible spectrophotometry and fluorescence spectrophotometry. Mouse squamous carcinoma SCC-7 cells were randomly divided into six groups by the random number table method. The PBS, PEG@TPD, and GE11-PEG@TPD groups were not treated with light, while the PBS+L, PEG@TPD+L, and GE11-PEG@TPD+L groups were exposed to white light (25 W/cm 2, 10 min) at a nanoprobe concentration of 20 μg/ml (based on TPD concentration). Cell survival rate was assessed by the cell counting kit-8 assay. Cellular uptake, intracellular reactive oxygen species levels, and cytotoxicity were evaluated using laser scanning confocal microscopy. The apoptosis rate was evaluated by cell apoptosis assay. Twelve 6-week-old female C3H/HeN mice were randomly divided into two groups: PEG@TPD-1 group and GE11-PEG@TPD-1 group, with 6 mice in each group. Subcutaneous oral cancer models were established by injecting SCC-7 cell suspensions into the dorsal region of mice in two groups. Each mouse was intravenously administered 200 μl of PEG@TPD or GE11-PEG@TPD solution (1 mg/ml, based on TPD concentration). Tumor boundaries and scope were visualized using a small animal in vivo imaging system. At the optimal imaging time point, three mice from each group were euthanized, and major organs and tumor tissues were collected to measure probe accumulation. Statistical comparisons between two groups were performed using independent samples t-tests, while one-way or two-way analysis of variance was applied for multiple group comparisons. Results:Both PEG@TPD and GE11-PEG@TPD exhibited a relatively regular sphere, with average particle sizes of (92.76±8.80 and 117.50±6.40) nm, respectively. PEG@TPD showed two obvious absorption peaks at 352 and 444 nm. GE11 peptide showed a polypeptide characteristic absorption peak at 280 nm, GE11-PEG@TPD showed three characteristic absorption peaks at 280, 352 and 444 nm. Under dark conditions, cell survival rate remained above 80% even at a concentration of 160 μg/ml. After light irradiation, cell survival rate in the PEG@TPD+L group at 20 and 40 μg/ml [(68.2±5.2)% and (48.6±7.1)%] were higher than those in the GE11-PEG@TPD+L group [(55.0±2.8)% and (30.0±9.2)%], with statistically significant differences ( P<0.05, 0.01). At incubation time points of 2, 4, and 6 h, the relative fluorescence intensity of the GE11-PEG@TPD group (119.4±10.2, 192.9±14.2, and 234.1±4.8) were higher than those of the PEG@TPD group (98.6±7.5, 163.8±3.1, 204.6±11.2), with statistically significant differences (all P<0.05). The relative fluorescence intensity of the PEG@TPD+L and GE11-PEG@TPD+L group (68.5±4.7 and 86.8±10.0) were higher than those in the PBS, PEG@TPD, GE11-PEG@TPD, and PBS+L groups (6.1±8.0, 7.6±1.8, 4.7±4.2 and 21.1±7.6), with statistically significant differences (all P<0.01). And the difference between the GE11-PEG@TPD+L and PEG@TPD+L groups was also statistically significant ( P<0.05). Viable cell proportions in the PBS, PEG@TPD, GE11-PEG@TPD, and PBS+L groups all exceeded 95.0%, while those in the PEG@TPD+L and GE11-PEG@TPD+L groups decreased to (11.1±3.7)% and (4.3±1.1)%, respectively, with a statistically significant difference between them ( P<0.05). The apoptotic cell proportions in the PEG@TPD+L and GE11-PEG@TPD+L groups [(40.5±4.3)% and (55.3±7.4)%] were higher than those in the PBS, PEG@TPD, GE11-PEG@TPD, and PBS+L groups [(27.3±2.0)%, (28.2±1.9)%, (28.6±1.2)%, and (29.7±3.0)%], with statistically significant differences ( P<0.05, 0.01). Moreover, the difference between the GE11-PEG@TPD+L and the PEG@TPD+L groups was also statistically significant ( P<0.01). The mean fluorescence intensities of the GE11-PEG@TPD-1 group at 1, 3, 5, 8, and 24 h, as well as in ex vivo tumor tissues[(5.2±0.8, 5.9±0.7, 6.6±1.0, 7.9±0.6, 7.8±0.7 and 20.6±3.5)×10 6 p/s/cm 2/sr] were all higher than those in the PEG@TPD-1 group [(3.2±0.7, 4.2±0.7, 4.6±0.9, 5.1±0.9, 4.7±0.9 and 14.2±1.8)×10 6 p/s/cm 2/sr], with statistically significant differences ( P<0.05, 0.01). Conclusions:The fluorescent nanoprobes exhibit uniform particle size, high photostability, and good biocompatibility. They demonstrate significant tumor-killing effects at the cellular level and possess tumor-targeting capability in vivo, showing promising application potential for the early precision diagnosis and treatment of oral cancer.

Post-acute pancreatitis diabetes mellitus (PPDM-A) is a common type of exocrine pancreatic diabetes. It is very important to clarify the risk factors of PPDM-A and effectively screen for diabetes in patients with acute pancreatitis to prevent the occurrence and development of PPDM-A. This article elaborates on the three aspects of PPDM-A, including high-risk population, screening timing and methods, as well as PPDM-A screening in children and adolescents, aiming to provide a basis for early detection of PPDM-A and timely targeted treatment for patients.

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.