ObjectiveTo investigate the effects of lectin-like oxidized low-density lipoprotein receptor-1 （LOX-1） gene G501C on brain structure and cognitive function in patients with white matter hyperintensities （WMH）. MethodsA total of 118 patients with WMH were enrolled. All participants underwent T1-weighted and T2-fluid-attenuated inversion recovery （T2-FLAIR） MRI to assess gray matter structure and WMH burden， and completed the mini-mental state examination （MMSE） and Montreal cognitive assessment （MoCA）. Partial correlation and mediation analyses were performed to explore the impact of LOX-1 polymorphism on cognitive function. ResultsParticipants were divided into GG+GC group （n = 35） and CC group （n = 83）. The GG+GC group showed lower MMSE and MoCA scores （MMSE： P=0.003； MOCA： P=0.015）， as well as greater WMH burden （all P0.001）， compared with the CC group. Voxel-based morphometry （VBM） analysis revealed reduced left thalamic volume in the GG+GC group， which was correlated with cognitive scores （all P0.05）. Subregional thalamic analysis further showed volume reductions in the lateral， ventral， medial， and pulvinar thalamic regions （all P0.05） in the GG+GC group， all positively associated with cognitive performance （all P0.05）. Mediation analyses indicated that volumes of the medial and pulvinar thalamic regions mediated the association between genotype and cognitive function （MMSE， MoCA）， and that WMH volume mediated the effect on MoCA scores. ConclusionThe LOX-1 G501C polymorphism may indirectly affect cognitive function by influencing specific thalamic subregional volumes and white matter damage， suggesting a potential mediating role of thalamic structures between genetic background and cognitive impairment.

Purpose To explore the value of a prediction model based on diffusion weighted imaging(DWI)deep learning features in endometrial cancer microsatellite instability status assessment.Materials and Methods DWI data of 32 microsatellite instability and 55 microsatellite stability endometrial cancer patients were analysed from June 2020 to April 2023 in Xinxiang Central Hospital,retrospectively.Apparent diffusion coefficient(ADC)values of the primary lesions were measured,and deep learning features and imaging histological features of the primary lesions were extracted using multilayer convolutional neural networks and PyRadiomics,respectively.The least absolute shrinkage and selection operator and random forest were used for feature screening and model building,respectively.The area under the receiver operating characteristic curve(AUC)and net reclassification improvement were used to evaluate model performance.Bootstrap based on 1 000 resamples was used for internal validation of the model.Results For the deep learning model,a total of 6 features were included,the 7th,57th,77th,82nd,97th and 108th features,with an AUC of 0.905(95％CI 0.823-0.957);for the radiomics model,a total of 6 features were included,1 neighborhood grey level difference matrix,4 grey level region size matrices and 1 grey level tour length matrix feature,with an AUC was 0.844(95％CI 0.751-0.913);for ADC values,the microsatellite instability group had smaller ADC values than the microsatellite stability group(t=-4.123,P＜0.001),with an AUC of 0.810(95％CI 0.712-0.886).Compared with the radiomics model and ADC values,the deep learning model showed improved risk prediction,with net reclassification improvements of 0.856 and 0.486(P＜0.01,P=0.024),respectively.In Bootstrap-based internal validation,the deep learning model also demonstrated higher performance than the radiomics model,with AUCs of 0.897(95％CI0.889-0.905)and 0.829(95％CI0.812-0.839),respectively.Conclusion A prediction model based on deep learning features of DWI images can provide a better assessment of microsatellite instability status in endometrial cancer patients than radiomics model and ADC values.

Objective:To investigate the application of combined deep learning and radiomic features derived from enhanced arterial phase CT imaging with clinical data to differentiate between T2 and T3 staging in patients with esophageal cancer.Methods:A retrospective study was conducted using clinical and CT data from 388 patients with pathologically confirmed esophageal cancer treated at The First Affiliated Hospital of Wannan Medical College between May 2015 and April 2024.The dataset was randomly divided into a training set(271 cases)and validation set(117 cases)in a 7:3 ratio.Radiomic and deep learning features were extracted from enhanced arterial phase CT images.The least absolute shrinkage and selection operator algorithm was employed for feature reduction and selection,leading to the development of radiomic(Radscore)and deep learning(Deepscore)scores.Univariate and multivariate Logistic regression analyses were conducted to identify independent risk factors,and clinical,radiomic,deep learning,and combined models were constructed.A nomogram was gener-ated for the combined model.The diagnostic performance of the models was evaluated using the area under the receiver operating charac-teristic curve(AUC)and compared using the DeLong test.Clinical net benefit was assessed through decision curve analysis,and model calib-ration was evaluated using calibration curves.Results:Nine radiomicand 12 deep learning features were selected after dimensionality reduc-tion.Multivariate Logistic regression identified tumor length,boundary,Radscore,and Deepscore as independent risk factors for distinguish-ing between T2 and T3 staging.In the training set,the AUC of the combined model was 0.867,which was significantly higher than that of the clinical(0.774,P<0.001),radiomic(0.795,P<0.001),and deep learning(0.821,P=0.001)models.In the validation set,the AUC of the com-bined model was 0.810,which was significantly higher than that of the clinical(0.653,P=0.002),radiomic(0.719,P=0.033),and deep learn-ing(0.750,P=0.009)models.The decision curve analysis indicated that the combined model provided the highest clinical benefit in both datasets.The calibration curves demonstrated a good fit for both datasets(P=0.084,0.053).Conclusion:The integration of deep learning and radiomic features obtained from enhanced arterial phase CT images with clinical data offers a reliable method for accurately distinguishing between preoperative T2 and T3 staging in esophageal cancer,thereby supporting clinical decision-making for treatment planning.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.

Patient experience is regarded as an important indicator of healthcare quality,and in response to the actual needs of patients,the patient experience has been enhanced through continuous innovation of healthcare models and the development of Internet healthcare services.Focusing on the implementation of patient experience enhancement action requirements,from the construction of hospital intelligent service platform,efficiency health care to reshape the new path of medical care,to create a continuity of medical service system,to build a multidisciplinary linkage"one-stop"acute and critical care medical treatment system and other aspects of public hospitals to improve the experience of the path to improve the level of medical services.It will provide a reference for comprehensively improving the level of medical services.