Objective To investigate the causal relationship between circadian rhythm disruption and inflammatory bowel disease(IBD)and the mediating effect of gut microbiota based on Mendelian randomization(MR).Methods Summary statistics of Genome-wide Association Study(GWAS)for circadian rhythm disruption(n=205 527)and IBD(n=214 053)were obtained from IEU OpenGWAS database.Summary statistics of GWAS for the gut microbiota were obtained from the MiBioGen database(n=18 340).Two-sample MR analysis was used to estimate the genetic correlation and causality between circadian rhythm disruption and IBD,and the mediating effect of the gut microbiota was analyzed by two-step MR analysis.Results There was a suggestive causal relationship between circadian rhythm disruption and IBD(odds ratio=1.255,P＜0.05).Rikenellaceae id.967 played a mediating role in the causal chain between them(the mediating effect was-0.028 740).Sensitivity analysis confirmed that the results were not interfered by level pleiotropy and heterogeneity.Conclusion There is a genetic correlation between circadian rhythm disruption and IBD,and gut microbiota may play a mediating role between them.

Objective To construct a discharge preparation plan for patients with head and neck cancer undergo-ing tracheotomy and explore its application effect to provide a reference for improving the at-home self-care ability of tracheostomy patients with head and neck cancer.Methods The discharge preparation plan for patients with head and neck cancer undergoing tracheotomy was constructed through literature analysis and expert consultation.A total of 160 patients with tracheotomy for head and neck cancer who were hospitalized in the Department of Oto-laryngology and Head and Neck Surgery of a tertiary A general hospital in Jinan City,Shandong Province from May 2023 to February 2025 were selected by convenience sampling method as the study subjects,and were divided into an experimental group and a control group by block randomization method,with 80 cases in each group.The ex-perimental group received a discharge preparation plan for patients undergoing tracheotomy for head and neck can-cer,while the control group received routine nursing and health education.Data from admission to 1 month after discharge were collected.The levels of mindfulness awareness,self-care ability,and discharge readiness were compared between 2 groups of patients before and after intervention.Results The constructed discharge preparation plan in-cludes 5 stages(pre-contemplation,contemplation,preparation,action,and maintenance).There were no sample dropouts in either group of patients.The experimental group had higher levels of mindfulness awareness,self-care ability and discharge readiness scores than the control group,and the differences were statistically significant(P＜0.001).Conclusion The discharge preparation plan for patients with head and neck cancer undergoing tra-cheotomy is scientifically reasonable,safe,and feasible,which can effectively improve patients' discharge preparation and mindfulness level,and enhance their self-care ability.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

OBJECTIVE To analyze the results of speech-evoked auditory brainstem response(s-ABR)tests in patients with presbycusis and explore the mechanisms of speech coding in these patients.METHODS Thirty patients with presbycusis(presbycusis group),30 elderly individuals with normal hearing(elderly normal group),and 30 young adults with normal hearing(young control group)were recruited.The s-ABR was elicited using a 40 ms duration complex speech stimulus/da/,and the characteristics of s-ABR were analyzed in each group.RESULTS The latencies of waves V and A in the presbycusis group were significantly prolonged compared to both the elderly normal group and the young control group(P<0.05).However,there was no statistically significant difference in the latencies of waves between the elderly normal group and the young control group(P>0.05).The amplitude of wave A and the slope of the V-A complex wave in the presbycusis group were significantly lower than those in the young control group(P<0.05),while no statistically significant differences were observed in the amplitudes of other waves.CONCLUSION The characteristics of s-ABR in patients with presbycusis suggest that these patients have poor synchronization in response to stimulus timing and deficiencies in coding high-frequency and rapidly changing auditory information,which may be one of the mechanisms underlying the decline in speech abilities in patients with presbycusis.

Objective To construct a discharge preparation plan for patients with head and neck cancer undergo-ing tracheotomy and explore its application effect to provide a reference for improving the at-home self-care ability of tracheostomy patients with head and neck cancer.Methods The discharge preparation plan for patients with head and neck cancer undergoing tracheotomy was constructed through literature analysis and expert consultation.A total of 160 patients with tracheotomy for head and neck cancer who were hospitalized in the Department of Oto-laryngology and Head and Neck Surgery of a tertiary A general hospital in Jinan City,Shandong Province from May 2023 to February 2025 were selected by convenience sampling method as the study subjects,and were divided into an experimental group and a control group by block randomization method,with 80 cases in each group.The ex-perimental group received a discharge preparation plan for patients undergoing tracheotomy for head and neck can-cer,while the control group received routine nursing and health education.Data from admission to 1 month after discharge were collected.The levels of mindfulness awareness,self-care ability,and discharge readiness were compared between 2 groups of patients before and after intervention.Results The constructed discharge preparation plan in-cludes 5 stages(pre-contemplation,contemplation,preparation,action,and maintenance).There were no sample dropouts in either group of patients.The experimental group had higher levels of mindfulness awareness,self-care ability and discharge readiness scores than the control group,and the differences were statistically significant(P＜0.001).Conclusion The discharge preparation plan for patients with head and neck cancer undergoing tra-cheotomy is scientifically reasonable,safe,and feasible,which can effectively improve patients' discharge preparation and mindfulness level,and enhance their self-care ability.

Objective:To evaluate the application value of interactive learning in enhancing the diagnosis of breast cancer by residents in the department of radiology through training based on the interpretation of breast magnetic resonance imaging (MRI) features by the breast imaging reporting and data system (BI-RADS).Methods:A total of 23 trainees completed BI-RADS standardized reports of 250 cases. These cases were divided into a pre-training group (Group 1) and post-training groups (initial training, Groups 2-4; advanced training, Groups 5-6), forming a total of six groups. The efficacy of interactive learning through course lectures and case-based practice in enhancing their ability in breast cancer diagnosis was analyzed. All trainees generated reports based on the BI-RADS scoring criteria. Interpretation agreement rates, evaluation time, and confidence levels were recorded. SPSS 25.0 was used for independent samples t test, chi-square test, and rank-sum test. Results:During the initial stage of training, the agreement rate of 150 cases reached 80.00%, which was recommended as the endpoint for completion of the initial learning phase. A significant difference existed between Group 4 and Group 1 ( P=0.012) in agreement rate. Statistically significant differences were noted in evaluation time for Groups 5 and 6 before and after advanced training ( P=0.001 and 0.007, respectively). A significant difference in confidence level was observed for Group 5 ( P=0.005). Conclusions:Interactive training based on BI-RADS standardized reporting can improve the diagnosis of breast diseases by residents in the department of radiology, particularly for enhancing the quality of reports for mass-like enhancement breast diseases.

Objective:To investigate the transcriptomic signature of refractory nasal polyps (NP) after endoscopic sinus surgery.Methods:Tissue samples were collected from 36 patients with NP who underwent endoscopic sinus surgery at the Seventh Affiliated Hospital of Sun Yat-sen University and the First Affiliated Hospital of Sun Yat-sen University from January 2020 to December 2021. Raw sequencing data of normal nasal mucosa samples were downloaded from publicly available GEO database (Accession Number: GSE136825). Differential expression genes (DEGs) and Gene Ontology (GO) enrichment analysis were conducted to analyze the differences between refractory NP and normal controls, as well as among refractory, controlled, and partially controlled NP. Hierarchical clustering method was employed to analyze the inflammatory endotypes of NP. Weighted Gene Co-expression Network Analysis (WGCNA) and STRING database were used in combination with Cytoscape software to identify the characteristic transcriptional expression profiles of refractory NP. R software (version 4.3.1) was used for statistical analysis.Results:Refractory NP patients had significantly higher asthma comorbidity rates than controlled/partially controlled groups ( P<0.05). The numbers and percentages of peripheral blood and tissue eosinophilic granulocytes were significantly higher in the refractory subgroup than in the other two subgroups ( P<0.05). Compared to normal mucosa, controlled and partially controlled NP groups, 27 genes were consistently upregulated in refractory NP. Hierarchical cluster analysis showed that the refractory NP exhibited a mixed endotype dominated by type 2 inflammation with co-existing type 1 features. Differential genes were enriched in extracellular matrix organization, leukocyte activation, cytokine receptor activation, cystatin-mediated protease inhibition, granule exocytosis, and olfactory nerve development regulation. Further WGCNA analysis and protein-protein interaction network identified 33 hub genes represented by ITGAM, NCF1, NCF2, CD1C, PTAFR, CLEC10A, SIRPA, TREM2, ALOX5AP, PTGDR2 (officially PTGDR), F13A1, DUOX2, NOS2, CTSG, and SALL1.Conclusion:This study reveals the distinctive transcriptional signature of refractory NP through transcriptomic methods, providing novel research avenues and therapeutic targets for the treatment of refractory NP after surgery.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Objective:To evaluate the application value of interactive learning in enhancing the diagnosis of breast cancer by residents in the department of radiology through training based on the interpretation of breast magnetic resonance imaging (MRI) features by the breast imaging reporting and data system (BI-RADS).Methods:A total of 23 trainees completed BI-RADS standardized reports of 250 cases. These cases were divided into a pre-training group (Group 1) and post-training groups (initial training, Groups 2-4; advanced training, Groups 5-6), forming a total of six groups. The efficacy of interactive learning through course lectures and case-based practice in enhancing their ability in breast cancer diagnosis was analyzed. All trainees generated reports based on the BI-RADS scoring criteria. Interpretation agreement rates, evaluation time, and confidence levels were recorded. SPSS 25.0 was used for independent samples t test, chi-square test, and rank-sum test. Results:During the initial stage of training, the agreement rate of 150 cases reached 80.00%, which was recommended as the endpoint for completion of the initial learning phase. A significant difference existed between Group 4 and Group 1 ( P=0.012) in agreement rate. Statistically significant differences were noted in evaluation time for Groups 5 and 6 before and after advanced training ( P=0.001 and 0.007, respectively). A significant difference in confidence level was observed for Group 5 ( P=0.005). Conclusions:Interactive training based on BI-RADS standardized reporting can improve the diagnosis of breast diseases by residents in the department of radiology, particularly for enhancing the quality of reports for mass-like enhancement breast diseases.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.