BackgroundAnxiety disorder is a common mental disorder， with its prevalence showing a continuous upward trend， significantly affecting the quality of life and social function of patients. Due to the lack of objective and reliable biomarkers in clinical practice， the early identification and treatment of anxiety disorder have been somewhat limited. Plasma proteins have the potential to serve as biomarkers for mental diseases， however， the causal relationship between them and anxiety disorder remains unclear. ObjectiveTo identify the plasma proteins that have a causal relationship with anxiety disorders， and to elucidate the associated biological pathways， in order to provide references for the search for biomarkers of anxiety disorders and the exploration of potential therapeutic targets. MethodsBased on the protein quantitative trait locus （pQTL） data of 4 907 plasma proteins covering 35 559 Icelandic individuals from the deCODE database， and the genome-wide association studies （GWAS） data of 50 486 patients with anxiety disorders and 330 460 healthy controls， the inverse-variance weighted （IVW） method was used as the main analysis method， supplemented by MR-Egger method， weighted median method， simple model method， and weighted model method for bidirectional Mendelian randomization analysis. Enrichment analysis of gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathways was conducted for the related proteins. Sensitivity analysis was performed using Cochran's Q test， MR-Egger intercept test， MR-PRESSO test， and leave-one-out analysis to evaluate the robustness of the results. ResultsA total of 10 plasma proteins were identified as significantly associated with anxiety disorders. Among these， SPATA9 （OR=0.856， 95% CI： 0.784–0.934， P<0.01） and PDE5A （OR=0.911， 95% CI： 0.864–0.961， P<0.01） were identified as protective factors， while CRYGD （OR=1.209， 95% CI： 1.095–1.334， P<0.01）， BTN3A3 （OR=1.045， 95% CI： 1.018–1.073， P<0.01）， SERPINB13 （OR=1.102， 95% CI： 1.040–1.168， P<0.01）， ERBB4 （OR=1.283， 95% CI： 1.109–1.484， P<0.01）， LSAMP （OR=1.096， 95% CI： 1.037–1.158， P<0.01）， ICOSLG （OR=1.283， 95% CI： 1.104–1.490， P<0.01）， DNAJB11 （OR=1.172， 95% CI： 1.076–1.277， P<0.01）， and TREML1 （OR=1.115， 95% CI： 1.054–1.179， P<0.01） were identified as risk factors. The sensitivity analysis showed that the results were robust， with no heterogeneity （Cochran's Q test P>0.05） or pleiotropy （MR-Egger intercept test P>0.05）. Enrichment analysis indicated that these plasma proteins were enriched in biological processes such as T-cell signal transduction， lymphocyte proliferation， cell membrane structure and synaptic function， as well as the intestinal immune network that produces IgA and the ErbB signaling pathway. ConclusionThis study identified 10 plasma proteins associated with anxiety disorders. The functions of these plasma proteins involve multiple biological processes such as neural development and immune regulation.

Objective To understand the current status of blood pressure variability and dyslipidemia in young and middle-aged patients with ischemic stroke, and to explore their relationship with prognosis. Methods A total of 312 young and middle-aged patients with ischemic stroke who met the inclusion criteria in Beijing Pinggu District Hospital from January 2022 to January 2025 were selected. The prognosis status [modified Rankin scale (mRS)], blood pressure variability, and blood lipids [total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C)] were analyzed. The correlation of blood pressure variability and blood lipids levels with prognosis was explored by logistic regression analysis. Results There were 206 patients with good prognosis and 106 patients with poor prognosis. The number of patients who received diversified health education in the good prognosis group was more than that in the poor prognosis group (P<0.05). The systolic blood pressure successive variation (SV) and average real variability (ARV), and diastolic blood pressure SV and ARV were lower in the good prognosis group than those in the poor prognosis group (P<0.05). Furthermore, compared with the poor prognosis group, the good prognosis group had lower levels of TC, TG and LDL-C, while the number of patients receiving diversified health education and the level of HDL-C were higher (P<0.05). After logistic regression analysis, it was found that the levels of TC, TG, LDL-C, systolic blood pressure SV and ARV, and diastolic blood pressure SV and ARV were risk factors for poor prognosis. Conversely, receiving diversified health education and HDL-C level were protective factors for poor prognosis (P<0.05). Conclusion High blood pressure fluctuation, dyslipidemia, and lack of health education will increase the risk of poor prognosis in young and middle-aged stroke patients.

ObjectiveTo explore the mechanism by which Ruyan Neixiao cream （RUC） induces ferroptosis in breast precancerous lesion （BPL） cells， and to enrich the theoretical foundation for its use in the treatment of BPL. MethodsThe inhibition of cell proliferation by 1%， 2%， and 4% concentrations of Ruyanneixiao Cream transdermal solution （RUT） was assessed using cell counting kit-8 （CCK-8） and a colony formation assay. Reactive oxygen species （ROS） were measured using the DCFH-DA probe， and the levels of ferrous ions （Fe²⁺）， glutathione （GSH）， and malondialdehyde （MDA） were determined using appropriate kits. Lipid peroxidation was detected with the C11-BODIPY^581/591 fluorescent probe. The expression of nuclear factor E₂-related factor 2 （Nrf2）， solute carrier family 7 member 11 （SLC7A11）， and glutathione peroxidase 4 （GPX4） proteins was analyzed by Western blot. The BPL rat model was constructed using 2，2′-bis（hydroxymethyl）butyric acid （DMBA） combined with estrogen and progesterone， and the rats were treated with RUC for external application. After the 12^th cycle， the rats were euthanized， and histopathological changes in breast tissue were observed by hematoxylin-eosin （HE） staining. Fe²⁺ and MDA levels in breast tissue were measured using corresponding kits. The expression of Nrf2， SLC7A11， and GPX4 proteins in BPL rat breast tissue was detected by immunohistochemistry （IHC） and Western blot. ResultsCompared with the matrix group， the cell viability of MCF-10AT cells in the 1%， 2%， and 4% RUT groups was significantly reduced （P<0.05） in a concentration-dependent manner， with the 24-hour half inhibitory concentration （IC₅₀） being 2.23%. Compared with the 4% RUT group， cell viability in the RUT + Fer-1 group was significantly increased （P<0.05）. Compared with the matrix group， the colony formation rates of MCF-10AT cells in the 1%， 2%， and 4% RUT groups were significantly decreased （P<0.05）. Compared with the 4% RUT group， the cell colony formation rate of the RUT + Fer-1 group was significantly increased （P<0.05）. Compared with the matrix group， the levels of ROS and Fe²⁺ in the 1%， 2%， and 4% RUT groups were significantly increased （P<0.05）， while GSH levels were significantly decreased （P<0.05）， and MDA and lipid peroxidation levels were significantly increased （P<0.05）. Compared with the 4% RUT group， ROS and Fe²⁺ levels in the RUT + Fer-1 group were significantly reduced （P<0.05）， while GSH levels were significantly increased （P<0.05）， and MDA and lipid peroxidation levels were significantly reduced （P<0.05）. Compared with the matrix group， the protein expression levels of Nrf2， SLC7A11， and GPX4 in the 1%， 2%， and 4% RUT groups were significantly decreased （P<0.05）. Compared with the 4% RUT group， the protein expression levels of Nrf2， SLC7A11， and GPX4 in the RUT + Fer-1 group were significantly increased （P<0.05）. In the in vivo experiment， compared with the matrix group， the breast tissue histopathological status of the BPL rats in the RUC group was effectively improved， with less dilatation of the mammary ducts and more orderly duct arrangement. No pathological morphology indicative of invasive cancer was observed. Compared with the matrix group， Fe²⁺ and MDA levels in the mammary tissue of the RUC group were significantly increased （P<0.05）. Compared with the matrix group， the protein expression levels of Nrf2， SLC7A11， and GPX4 in the mammary tissue of the RUC group were significantly reduced （P<0.05）. ConclusionRUC may induce ferroptosis in BPL cells by inhibiting the Nrf2/SLC7A11/GPX4 signaling pathway， increasing Fe²⁺ accumulation， and promoting lipid peroxidation.

ObjectiveTo explore the mechanism by which Ruyan Neixiao cream （RUC） induces ferroptosis in breast precancerous lesion （BPL） cells， and to enrich the theoretical foundation for its use in the treatment of BPL. MethodsThe inhibition of cell proliferation by 1%， 2%， and 4% concentrations of Ruyanneixiao Cream transdermal solution （RUT） was assessed using cell counting kit-8 （CCK-8） and a colony formation assay. Reactive oxygen species （ROS） were measured using the DCFH-DA probe， and the levels of ferrous ions （Fe²⁺）， glutathione （GSH）， and malondialdehyde （MDA） were determined using appropriate kits. Lipid peroxidation was detected with the C11-BODIPY^581/591 fluorescent probe. The expression of nuclear factor E₂-related factor 2 （Nrf2）， solute carrier family 7 member 11 （SLC7A11）， and glutathione peroxidase 4 （GPX4） proteins was analyzed by Western blot. The BPL rat model was constructed using 2，2′-bis（hydroxymethyl）butyric acid （DMBA） combined with estrogen and progesterone， and the rats were treated with RUC for external application. After the 12^th cycle， the rats were euthanized， and histopathological changes in breast tissue were observed by hematoxylin-eosin （HE） staining. Fe²⁺ and MDA levels in breast tissue were measured using corresponding kits. The expression of Nrf2， SLC7A11， and GPX4 proteins in BPL rat breast tissue was detected by immunohistochemistry （IHC） and Western blot. ResultsCompared with the matrix group， the cell viability of MCF-10AT cells in the 1%， 2%， and 4% RUT groups was significantly reduced （P<0.05） in a concentration-dependent manner， with the 24-hour half inhibitory concentration （IC₅₀） being 2.23%. Compared with the 4% RUT group， cell viability in the RUT + Fer-1 group was significantly increased （P<0.05）. Compared with the matrix group， the colony formation rates of MCF-10AT cells in the 1%， 2%， and 4% RUT groups were significantly decreased （P<0.05）. Compared with the 4% RUT group， the cell colony formation rate of the RUT + Fer-1 group was significantly increased （P<0.05）. Compared with the matrix group， the levels of ROS and Fe²⁺ in the 1%， 2%， and 4% RUT groups were significantly increased （P<0.05）， while GSH levels were significantly decreased （P<0.05）， and MDA and lipid peroxidation levels were significantly increased （P<0.05）. Compared with the 4% RUT group， ROS and Fe²⁺ levels in the RUT + Fer-1 group were significantly reduced （P<0.05）， while GSH levels were significantly increased （P<0.05）， and MDA and lipid peroxidation levels were significantly reduced （P<0.05）. Compared with the matrix group， the protein expression levels of Nrf2， SLC7A11， and GPX4 in the 1%， 2%， and 4% RUT groups were significantly decreased （P<0.05）. Compared with the 4% RUT group， the protein expression levels of Nrf2， SLC7A11， and GPX4 in the RUT + Fer-1 group were significantly increased （P<0.05）. In the in vivo experiment， compared with the matrix group， the breast tissue histopathological status of the BPL rats in the RUC group was effectively improved， with less dilatation of the mammary ducts and more orderly duct arrangement. No pathological morphology indicative of invasive cancer was observed. Compared with the matrix group， Fe²⁺ and MDA levels in the mammary tissue of the RUC group were significantly increased （P<0.05）. Compared with the matrix group， the protein expression levels of Nrf2， SLC7A11， and GPX4 in the mammary tissue of the RUC group were significantly reduced （P<0.05）. ConclusionRUC may induce ferroptosis in BPL cells by inhibiting the Nrf2/SLC7A11/GPX4 signaling pathway， increasing Fe²⁺ accumulation， and promoting lipid peroxidation.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.