Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective:To construct an intelligent APP-assisted process assessment mode for standardized residency training and explore its application effect.Methods:Based on the "XUEYIKU" APP platform developed in Peking University Third Hospital, we built a standardized process of process assessment for standardized residency training assisted by intelligent APP and combining both online and offline components. We analyzed the frequency of APP use, satisfaction with the APP, the problems solved by the APP, and the aspects that need to be improved. SPSS 26.0 was used for t-test and Chi-square test. Results:The satisfaction score of teachers with the APP was (7.66±1.86) points. The satisfaction score of professional base teaching directors/process assessment coordinators was higher than that of clinical teachers [(8.28±1.30) vs. (7.42±1.99), P=0.013]. Most residents were satisfied with the APP (67.44%, 29/43), regarded its role in supporting the entire assessment process as important (55.81%, 24/43), and reviewed teacher feedback (65.12%, 28/43). Some teachers indicated that the APP solved problems such as paperless exam scoring (59.00%, 59/100), standardized exam processes (43.00%, 43/100), score statistics (42.00%, 42/100), and score reporting (42.00%, 42/100). The residents believed that the APP resolved issues such as exam notification (44.19%, 19/43), scheduling (41.86%, 18/43), and result feedback (41.86%, 18/43). Both teachers and residents mentioned the need for further strengthening the stability of the APP system and simplifying operational steps. Conclusions:With APP as a link, through the instant transmission and feedback of data, the intelligent APP-assisted process assessment mode drives the reflection and summarization of professional bases, clinical teachers, and residents, and promotes the post competence of residents.

Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective:To construct an intelligent APP-assisted process assessment mode for standardized residency training and explore its application effect.Methods:Based on the "XUEYIKU" APP platform developed in Peking University Third Hospital, we built a standardized process of process assessment for standardized residency training assisted by intelligent APP and combining both online and offline components. We analyzed the frequency of APP use, satisfaction with the APP, the problems solved by the APP, and the aspects that need to be improved. SPSS 26.0 was used for t-test and Chi-square test. Results:The satisfaction score of teachers with the APP was (7.66±1.86) points. The satisfaction score of professional base teaching directors/process assessment coordinators was higher than that of clinical teachers [(8.28±1.30) vs. (7.42±1.99), P=0.013]. Most residents were satisfied with the APP (67.44%, 29/43), regarded its role in supporting the entire assessment process as important (55.81%, 24/43), and reviewed teacher feedback (65.12%, 28/43). Some teachers indicated that the APP solved problems such as paperless exam scoring (59.00%, 59/100), standardized exam processes (43.00%, 43/100), score statistics (42.00%, 42/100), and score reporting (42.00%, 42/100). The residents believed that the APP resolved issues such as exam notification (44.19%, 19/43), scheduling (41.86%, 18/43), and result feedback (41.86%, 18/43). Both teachers and residents mentioned the need for further strengthening the stability of the APP system and simplifying operational steps. Conclusions:With APP as a link, through the instant transmission and feedback of data, the intelligent APP-assisted process assessment mode drives the reflection and summarization of professional bases, clinical teachers, and residents, and promotes the post competence of residents.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Objective To observe the value of subregional non-contrast CT(NCCT)radiomics features based on habitat imaging technology for predicting hematoma expansion(HE)in patients with spontaneous intracranial hemorrhage(sICH).Methods Data of 228 sICH patients with negative conventional imaging signs were retrospectively analyzed and divided into HE group(n=99)or non HE(NHE)group(n=129)based on the occurrence of HE nor not.also divided into training set(n=182)or test set(n=46)at a ratio of 8:2.Clinical data,NCCT data and laboratory examination results were compared between groups.Logistic regressive analysis was performed to screen the impact factors of HE.ROI of whole hematoma(ROIwhole)was sketched and clustered into 3 sub-regions(ROIsub1,ROIsub2 and ROIsub3,the latter located in the critical area between hematoma and brain tissue)with habitat imaging technology,and radiomics features of ROI were extracted and screened.Then 4 prediction models were constructed based on the above 4 ROI,and the efficacy of each model for predicting HE was analyzed.Results The fasting blood glucose in HE group was higher than that in NHE group(t=2.047,P=0.041),which was not independent impact factor for predicting HE in sICH patients(P=0.070)according to logistic regression analysis.The area under the curve of ROIsub3 radiomics model for predicting sICH HE in training and test set was 0.945 and 0.863,respectively,not significantly different with that of ROIwhole(0.921,0.813),ROIsub1(0.925,0.807)nor ROIsub2(0.909,0.720)(all P＞0.05).Decision curve analysis showed that ROIsub3 radiomics model could bring greater benefits than the other 3 models.Conclusion NCCT radiomics features of the critical area between hematoma and brain tissue based on habitat imaging technology had high value for predicting HE in sICH patients.

Objective:To investigate the effect of 125I particles in alveolar echinococcosis with the animal model (nude mice and Sprague Dawley rats). Methods:Twenty 10 weeks nude mice with body weight ranged from 20 to 24 g were divided into three groups. Sixteen nude mice were divided into experimental group ( n=8), puncture group ( n=4) and model group ( n=4). There was no intervention in the model group and only particle puncture needle was used in the puncture group. 125I particles were implanted in the experimental group. 14 male Sprague Dawley rats without specific pathogen, with body weight 280-320 g, 12 weeks old, were used to construct the model of hepatic alveolar echinococcosis. Then the rats were divided into intervention group ( n=10) and control group ( n=4). In the intervention group, 125I particles were pushed into the lesions. The abdomen was only open and closed in the control group. All the mice were sacrificed 45 days after intervention. The tumor size was measured. The activity of protoscolex and pathological changes of Echinococcus multilocularis in each group were observed. Results:At the timepoint of 22nd, 30th and 40th day of intervention, the largest diameter of tumor in nude mice experimental group was (10.7±5.2) mm, (10.9±5.0) mm, (8.5±4.3) mm, smaller than that in puncture group (24.5±4.4) mm, (25.4±4.1) mm, (31.4±2.8) mm and model group (22.5±7.3) mm, (25.0±5.4) mm, (26.7±6.3) mm, with statistically significant difference ( P<0.05). The number and activity of protoscoleces in experimental group were lower than those in puncture group and model group. Under the light microscope, the structure of echinococcus vesiculae and its body in the experimental group was obviously destroyed, and the cuticle and germinal layer of echinococcus vesiculae in the puncture group and the model group were normal, with multiple intact protoscoleces. The pathological changes of Sprague Dawley rats in the intervention group and the control group were basically the same as those in the nude mice model. Conclusion:The 125I particle radiation effect can kill Echinococcus multilocularis protoscoleces and inhibit the growth of alveolar echinococcosis.

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecip-itation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which inte-grated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations:(1) browse or search general targets of a query TF across datasets;(2) browse TF–target regulations for a query TF in a specific dataset or tissue;(3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploringhuman TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtar-get.

[Abstract ] Objective The orthodontic-surgery is the most effective way to treat the severe skeletal class Ⅲmalocclusion,but the long-term stability is still disputed .The aim of this systematic review is to analyze the long-term stability of hard tissue of seriously skeletal Class Ⅲ malocclusion patients treated with orthodontic combined surgical treatment . Methods Literature were searched through the Cochrane Central Register of Controlled Trials , Cochrane Library, Medline via pubmed (1950-2014), EMBASE (1980-2014) and other foreign databases , and Chinese Biomedical Literature Database , China National Knowledge Infrastructure Database , VIP Database for Chinese Technical Periodicals , digital journal of Wan fang Data and so on .Unpublished conference papers and gray litera-tures were collected manually .The literatures consist of randomized controlled trials ( RCT) , quasi-randomized controlled trials and clini-cal case-control trial (CCT) were selected.Then meta analysis was performed for annexable literatures and qualitative description was performed for diverged documents . Results Five foreign articles and 1 Chinese article suitable for analysis were ultimately studied . All the studies were CCT and a total of 260 patients were involved in the present systematic review .Meta analysis showed that the SNA , SNB, ANB, MP SN and Y-axis did not change significantly during 3 years after operation (P>0.05) and a good skeletal class I facial types were maintained .The comparison results of MP-SN and Y-axis showed that there was no significant statistical difference and the mandibular plane angle maintained the relative stability .Meta analysis was not performed because of the different measurement methods of A, B, Pg and Ramus inclination , so the qualitative description was used .Point A and Ramus inclination remained relatively stable posi-tion, but point B and Pg had some replace compared with post-operative. Conclusion The orthodontic and surgical treatment for skeletal classⅢmalocclusion could keep the hard tissue relative stabil-ity except a little replace of the mandibular .