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 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.

This paper proposed to understand the pathogenesis and provide syndrome differentiated treatment for anxiety and depression-related dry eyes from the perspective of the liver and the lung， in order to provide ideas for treatment of this disease with traditional Chinese medicine. It is believed that the occurrence and development of anxiety and depression-related dry eyes is related to the ethereal qi and blood damage and blocked circulation of qi and blood. The liver and the lung are the main located zang-fu （脏腑） organs of the disease， and the qi movement， sweat pores， meridians and collaterals abnormalities of the liver and the lung are the pathological basis. The basic pathogenesis is disharmony of the liver and the lung， loss nourishment of eyes， and loss calm of the mind. In clinical practice， the root treatment is to restore the functions of the liver governing ascent and the lung governing descent， and to open up the sweat pores， meridians and collaterals， while the branch treatment is to promote the production of body fluids， nourish yin and calm the mind. Both the root and the branch causes are treated to restore the physiological functions， and Danzhi Xiaoyao Powder （丹栀逍遥散） combined with Shengmai Powder （生脉散） with modification is often used as the basic prescription.

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 effect of deep muscle relaxation by rocuronium on oxygenation of normal frequency jet ventilation during rigid bronchoscopy procedures.Methods From December 2021 to February 2023,60 patients with central airway diseases underwent rigid bronchoscopy under general anesthesia,they were randomly divided into deep muscle relaxation group(group D,n = 30)and moderate muscle relaxation group(group M,n = 30).After induction of general anesthesia,the patients were inserted rigid bronchoscopy for jet ventilation,muscle relaxant was rocuronium in induction and maintenance.Train of four(TOF)stimulation was used to measure the depth of muscle relaxation in group M,and the TOF count was maintained at 1 or 2;In Group D,the depth of muscle relaxation was measured by post tetanic count(PTC),and the PTC was maintained at 1 or 2.After operation,Sugammadex antagonized residual muscle relaxation.Results There was no significant difference in operation time,recovery time and extubation time between the two groups(P＞0.05).The total operation time,operation pause time and anesthesia time in group D were shorter than those in group M,the dosage of muscle relaxant in group D was more than that in group M,the incidence of hypoxemia during surgery in group D was less than that in group M,the operators'satisfaction in group D was better than that in group M,and the arterial partial pressure of oxygen(PaO2)in group D was higher than that in group M at 15 min(T1)and 30 min(T2)after jet ventilation,the number of patients with postoperative sore throat in group D was less than that in group M,the differences were statistically significant(P＜0.05).Conclusion The application of deep muscle relaxation by rocuronium in rigid bronchoscopy procedures can improve the oxygenation effect of normal frequency jet ventilation,reduce the operation pause time and anesthesia time,improve the satisfaction of operators,antagonizing residual muscle relaxation with Sugammadex can relieve the worry of delayed recovery from deep muscle relaxation.

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:Childhood traumatic experiences greatly influenced the brain network activities of patients with depression,and there is an urgent need to explore the temporal dynamics for these changes.This study aims to investigate the abnormalities of resting-state electroencephalogram(EEG)microstates in eye-open state of depressed adolescents and to explore the correlations between their EEG microstates and the childhood traumatic experience. Methods:Using resting-state EEG microstate analysis,we explored the temporal dynamics of brain activity in patients with adolescent depression.This study selected 66 adolescents with depression as a patient group,and 27 healthy adolescents as a healthy control group.A modified k-means clustering algorithm was used to classify the 64-channel resting-state EEG data into different microstates.Independent sample t-tests were used to compare the microstate parameters between the 2 groups and further assciations between these parameters and childhood traumatic experience in patients were explored via using Spearman correlation. Results:In this study,significant differences were observed in the occurrence and transition probabilities of EEG microstates between the healthy control and the patient group.Notably,there was a statistically significant difference(P＜0.05)in the occurrence of microstate A across 2 groups,exhibiting a negative correlation with the emotional abuse component within the childhood trauma scores(Spearman's rho=-0.31,P=0.013).Furthermore,patient-specific,non-random transitions from microstate B to A(Spearman's rho=-0.30,P=0.015)and C to A(Spearman's rho=-0.31,P=0.013)were inversely associated with the scores of emotional abuse factors from childhood trauma in the patient group,showing statistically significant differences when comparing to the healthy controls(P＜0.05).Upon stratification into quartiles based on the emotional abuse factor scores,the occurrence of microstate A,as well as the transition rates from microstates B to A and C to A,retained statistical significance following adjustment for multiple comparisons(all P＜0.05). Conclusion:The abnormal temporal dynamics in brain networks of adolescents with depression are linked to childhood emotional abuse.Those who have suffered severe emotional abuse may show greater impairments in the brain's visual and central executive networks.EEG microstate analysis could be a potential tool for detecting adolescent depression with severe childhood trauma.

Objective:To investigate the therapeutic effects of internal fixation via the posterolateral combined posteromedial approach in the treatment of posterior pilon fracture (Klammer type Ⅲ).Methods:A retrospective study was performed to analyze the 69 posterior pilon fractures (Klammer type Ⅲ) which had been treated by internal fixation with hollow screws or a buttress plate at Department of Orthopaedic Trauma, Yantai Shan Hospital from January 2015 to January 2020. There were 36 males and 33 females with an age of (45.3±10.0) years and duration from injury to surgery of (6.0±1.5) d. They were assigned into 2 groups according to different surgical approaches. The observation group (41 cases) was treated through the posterolateral combined posteromedial approach while the control group (28 cases) through the posterolateral approach alone. The therapeutic effects were evaluated by comparing the 2 groups in terms of incision length, intraoperative bleeding, operation time, fracture union time, fracture reduction (evaluated by the Burrwell-Charnley radiological score), the ankle-hindfoot score of American Orthopaedic Foot and Ankle Society (AOFAS) and complications.Results:There was no significant difference in the preoperative general data between the 2 groups, showing comparability ( P>0.05). All the 69 cases were followed up for (16.9±4.0) months, revealing primary healing of all the incisions and no vascular injury or wound infection. The incision length [(11.2±1.8) cm] and operation time [(76.0±6.6) min] in the observation group were significantly shorter than those in the control group [(12.4±1.9) cm and (79.7±6.8) min], the excellent and good rate of reduction in the observation group (97.6%, 40/41) was significantly higher than that in the control group (89.3%, 25/28), and the ankle-hindfoot scores of AOFAS at 1, 3 and 12 months after operation in the observation group [(78.4±5.6), (79.5±2.8) and (86.9±2.1)] were significantly higher than those in the control group [(75.2±5.5), (78.0±3.2) and (85.8±2.3)] (all P<0.05). There was no significant difference in the intraoperative bleeding between the 2 groups ( P>0.05). In the control group, 2 patients developed numbness in the dorsum of foot, which gradually disappeared after 3 months of treatment, but no other complications like persistent pain or flexor contracture within 1 year after operation. Conclusion:In the treatment of Klammer type Ⅲ posterior pilon fracture, the posterolateral combined posteromedial approach can result in satisfactory therapeutic effects, because the surgical approach can fully expose the fracture and facilitate better reduction.

Objective　To investigate the effect of fluid attenuated inversion recovery (FLAIR) on hyperintense vascular sign in patients with unilateral middle cerebral artery chronic occlusion.Changes of HVS and prediction of cerebral infarction were also analyzed.Methods　Patients with unilateral middle cerebral artery chronic occlusion who were hospitalized in Jiangdu People’s Neurology Department of Yangzhou City from July 2016 to August 2021 were enrolled.According to the presence or absence of cerebral infarction,they were divided into non-cerebral infarction group and cerebral infarction group.According to whether the cerebral infarction recurred during the follow-up,the cerebral infarction group was divided into recurrence group and non-recurrence group.Multivariate logistic regression model and ROC curve were used to analyze the risk and predictive value of FVHs and cerebral infarction in patients with unilateral middle cerebral artery chronic occlusion.Results　(1)Univariate analysis showed that the fibrinogen level,HVS signs and FVHs score in cerebral infarction group were significantly higher than those in non-cerebral infarction group (P<0.05).The baseline FVHs score of recurrent cerebral infarction patients were significantly higher than that of non-recurrent cerebral infarction patients (P<0.05).(2)Logistic regression analysis showed that FVHs score had a significant independent positive correlation with the first occurrence of cerebral infarction (OR=2.499; 95%CI 1.481~4.218;P=0.001),and FVHs score was not independently associated with cerebral infarction recurrence (OR=1.356;95%CI 0.922~1.994;P=0.112);(3)ROC curve analysis showed that FVHs score ≥4 had certain predictive value for cerebral infarction in patients with unilateral middle cerebral artery chronic occlusion,with sensitivity of 73.3%,specificity of 66.2%,area under curve (AUC) of 0.669 (95%CI 0.476~0.861;P=0.041).Conclusion　For patients with unilateral middle cerebral artery chronic occlusion,HVS may change in a process from scratch,and once the occurrence of HVS signs suggests that cerebral blood flow is decompensated,which is prone to cerebral infarction.FHVs score has certain predictive value for cerebral infarction.