Objective:To investigate the detection rate of nonalcoholic fatty liver disease(NAFLD)and its influencing factors among residents in western Chongqing of China during physical examination.Methods:Based on the clinical diagnostic criteria and ultra-sound examination results of NAFLD,the individuals who underwent physical examination in a grade A tertiary hospital in Western Chongqing from January 1,2020 to November 30,2023 were enrolled as subjects.The methods such as the t-test,the chi-square test,and the multivariate logistic regression analysis were used to clarify the detection rate of NAFLD and its influencing factors among the individuals undergoing physical examination.Results:The detection rate of NAFLD was 23.64%(12 872/54 067)among the individu-als undergoing physical examination.The detection rate of NAFLD in male individuals was significantly higher than that in female indi-viduals(39.22%vs.15.91%,χ2=2 197.112,P<0.001).The individuals in the age group of 50-59 years had the highest NAFLD detec-tion rate of 33.18%,and before the age of 60 years,the detection rate of NAFLD increased with age,while after the age of 60 years,the detection rate of NAFLD decreased with age(χ2=367.554,P<0.001),indicating the detection of NAFLD in younger populations in western Chongqing.The individuals with a body mass index in-dicative of overweight and obesity had a significantly higher detec-tion rate of NAFLD than those with a body mass index indicative of emaciation and normal weight(39.39%/71.40%vs.0.23%/9.68%,χ2=7 644.383,P<0.001).The multivariate logistic regression analy-sis showed that sex,age,body mass index,fasting blood glucose(FBG),systolic blood pressure(SBP),diastolic blood pressure(DBP),triglyceride(TG),uric acid(UA),high-density lipoprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)were risk factors for the detection rate of NAFLD in individuals undergoing physical examination(P<0.05),while total cholesterol(TC)was not a risk factor for the detection rate of NAFLD.Conclusion:The detection rate of NAFLD is 23.64%among the individuals aged 18 years or above who undergo physical examination in Western Chongqing,and there is a relatively high incidence rate of NAFLD in the age group of 50-59 years.Male individuals and overweight or obese individuals are at a high risk of NAFLD.FBG,SBP,DBP,TG,UA,HDL-C,and LDL-C are risk factors for NAFLD,while TC is not a risk factor for NAFLD.

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.

Increasing evidence shows that the early lesions of Parkinson's disease (PD) originate from gut, and correction of microbiota dysbiosis is a promising therapy for PD. FLZ is a neuroprotective agent on PD, which has been validated capable of alleviating microbiota dysbiosis in PD mice. However, the detailed mechanisms still need elucidated. Through metabolomics and 16S rRNA analysis, we identified glycoursodeoxycholic acid (GUDCA) was the most affected differential microbial metabolite by FLZ treatment, which was specially and negatively regulated by Clostridium innocuum, a differential microbiota with the strongest correlation to GUDCA production, through inhibiting bile salt hydrolase (BSH) enzyme. The protection of GUDCA on colon and brain were also clarified in PD models, showing that it could activate Nrf2 pathway, further validating that FLZ protected dopaminergic neurons through promoting GUDCA production. Our study uncovered that FLZ improved PD through microbiota-gut-brain axis, and also gave insights into modulation of microbial metabolites may serve as an important strategy for treating PD.

Although enteric glial cell (EGC) abnormal activation is reported to be involved in the pathogenesis of Parkinson's disease (PD), and inhibition of EGC gliosis alleviated gut and dopaminergic neuronal dysfunction was verified in our previous study, the potential role of gut microbiota on EGC function in PD still need to be addressed. In the present study, fecal microbiota transplantation revealed that EGC function was regulated by gut microbiota. By employing 16S rRNA and metabolomic analysis, we identified that 3-indolepropionic acid (IPA) was the most affected differential microbial metabolite that regulated EGC gliosis. The protective effects of IPA on PD were validated in rotenone-stimulated EGCs and rotenone (30 mg/kg i.g. for 4 weeks)-induced PD mice, as indicated by decreased inflammation, improved intestinal and brain barrier as well as dopaminergic neuronal function. Mechanistic study showed that IPA targeted pregnane X receptor (PXR) in EGCs, and inhibition of IL-13Rα1 involved cytokine-cytokine receptor interaction pathway, leading to inactivation of downstream JAK1-STAT6 pathway. Our data not only provided evidence that EGC gliosis was critical in spreading intestinal damage to brain, but also highlighted the potential role of microbial metabolite IPA in alleviating PD pathological damages through gut-brain axis.

[This corrects the article DOI: 10.1016/j.apsb.2025.02.029.].

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.

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 investigate the reciprocal causal relationships between periodontitis and hepatobiliary diseases through Mendelian randomization (MR) analyses, to provide evidence for joint prevention and clinical decision-making in patients with concurrent periodontitis and hepatobiliary diseases. Methods: Single nucleotide polymorphisms (SNPs) were extracted from the largest genome-wide association study on periodontitis (17 353 cases, 28 210 controls) and hepatobiliary diseases within the European ancestry and used as instrumental variables (IVs). The strength of the associations was examined by calculating the F-statistic. The SNPs significantly associated with the outcome were removed by scanning on Phenoscanner platform. Bidirectional causal associations between periodontitis and hepatobiliary diseases were estimated using inverse variance weighted (IVW), MR-Egger, and Weighted Median methods. The robustness of the findings was further verified through additional sensitive MR approaches, including Cochran’s Q statistic （IVW）, Rucker’s Q statistic （MR-Egger）, MR-PRESSO and Leave-one-out analysis. Further MR analyses, utilizing other available genome-wide association studies (GWAS) on hepatobiliary diseases, were conducted to validate the results. Results: The IVW method found that periodontitis had a causal impact on acalculous cholecystitis (odds ratio = 1.277, 95% CI 1.097-1.485, P=0.002), implying an increased risk of acalculous cholecystitis associated with periodontitis, while the MR-Egger regression and Weighted Median failed to observe significant causal effects of periodontitis on acalculous cholecystitis. However, no bidirectional causal associations between periodontitis and nonalcoholic fatty liver disease, cirrhosis or liver cancer were observed using IVW, MR-Egger regression and Weighted Median. The bidirectional causal relationships were deemed unlikely to be influenced by horizontal pleiotropy. Further, the validation analysis based on alternative GWAS data suggested parallel results. Conclusions The MR analyses suggest that periodontitis may elevate the risk of acalculous cholecystitis. Further investigations, including clinical studies and mechanistic explorations, are warranted to validate these findings. However, the MR analyses do not support bidirectional causal associations between periodontitis and nonalcoholic fatty liver disease, cirrhosis or liver cancer.

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.