Objective:To investigate the clinical efficacy of posterior midline incision combined with anteromedial approach in the treatment of complex olecranon fracture-dislocation.Methods:A retrospective analysis was performed on 26 patients (15 males and 11 females) with olecranon fracture-dislocation who were admitted from January 2020 to January 2024, including 5 cases of anterior transolecranon fracture-dislocation (2 cases of upper ulnar-radial joint dislocation), 21 cases of posterior transolecranon fracture-dislocation (5 cases of them were accompanied by upper ulnar-radial joint dislocation). Among them, there were 13 cases of traffic accidents, 7 cases of falling from heights, and 6 cases of walking falls. The average age is 45.1±15.3 years old (21-84 years old).Results:The operation time was 151.2±41.9 minutes, average tourniquet time was 93.7±22.6 minutes, and the intraoperative blood loss was 76.2±20.2 ml. The average follow-up was 16(12, 23) months, and the VAS score decreased significantly and the MEPS score increased significantly over time. At the last follow-up, the VAS score was 2(1, 2), and the MEPS score was 86.5±10.3, with 16 cases excellent, 7 cases good, and 3 cases medium, with an excellent rate of 89%. The range of motion of flexion-extension and pronation-supination were 119.3°±13.5°and 138.6°±15.2° respectively. Complications included 16 cases of ectopic ossification, of which 4 patients with significant effects on elbow function underwent surgical release 3-6 months after surgery. 1 case of ulnar nerve injury symptoms improved after emergency ulnar nerve release, and 1 case of elbow subluxation due to inaccurate coronoid process reduction and fixation. There were no serious complications such as vascular injury, internal fixation failure, fracture nonunion, and incision infection.Conclusion:The posterior midline incision combined with anteromedial approach can effectively treat complex olecranon fracture-dislocation and meet the requirements of early postoperative elbow rehabilitation.

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.

Objective:To investigate the clinical efficacy of posterior midline incision combined with anteromedial approach in the treatment of complex olecranon fracture-dislocation.Methods:A retrospective analysis was performed on 26 patients (15 males and 11 females) with olecranon fracture-dislocation who were admitted from January 2020 to January 2024, including 5 cases of anterior transolecranon fracture-dislocation (2 cases of upper ulnar-radial joint dislocation), 21 cases of posterior transolecranon fracture-dislocation (5 cases of them were accompanied by upper ulnar-radial joint dislocation). Among them, there were 13 cases of traffic accidents, 7 cases of falling from heights, and 6 cases of walking falls. The average age is 45.1±15.3 years old (21-84 years old).Results:The operation time was 151.2±41.9 minutes, average tourniquet time was 93.7±22.6 minutes, and the intraoperative blood loss was 76.2±20.2 ml. The average follow-up was 16(12, 23) months, and the VAS score decreased significantly and the MEPS score increased significantly over time. At the last follow-up, the VAS score was 2(1, 2), and the MEPS score was 86.5±10.3, with 16 cases excellent, 7 cases good, and 3 cases medium, with an excellent rate of 89%. The range of motion of flexion-extension and pronation-supination were 119.3°±13.5°and 138.6°±15.2° respectively. Complications included 16 cases of ectopic ossification, of which 4 patients with significant effects on elbow function underwent surgical release 3-6 months after surgery. 1 case of ulnar nerve injury symptoms improved after emergency ulnar nerve release, and 1 case of elbow subluxation due to inaccurate coronoid process reduction and fixation. There were no serious complications such as vascular injury, internal fixation failure, fracture nonunion, and incision infection.Conclusion:The posterior midline incision combined with anteromedial approach can effectively treat complex olecranon fracture-dislocation and meet the requirements of early postoperative elbow rehabilitation.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Objective:To analyze the association of apolipoprotein E(ApoE),soluble semaphoring 4D(sSema4D),and cyclophilin A(CypA)levels with unfavorable outcome in patients with chronic heart failure(CHF).Methods:We retrospectively enrolled 108 patients with CHF admitted in the Fifth Affiliated Hospital of Zhengzhou Universi-ty between March 2019 and March 2022.According to the outcome during 6-month follow-up,patients were di-vided into favorable outcome group(n=71)and unfavorable outcome group(n=37).ApoE,sSema4D and CypA levels were compared between the two groups,and multivariate Logistic regression was employed to identify the risk factors of unfavorable outcome within 6-month follow-up in CHF patients.Results:Compared to patients in the favorable outcome group,those in the unfavorable outcome group had significant higher proportion of NYHA classⅣ(59.46％vs.25.35％),serum creatinine[(80.74±3.89)μmol/L vs.(71.36±3.63)μmol/L],ApoE[(69.87±4.25)mg/L vs.(47.36±3.17)mg/L],sSema4D[(916.62±7.32)ng/L vs.(426.42±6.25)ng/L]and CypA[(6.74±1.32)ng/L vs.(4.38±0.72)ng/L](P＜0.001 all).Multivariate Logistic regression indicated that NY-HA class Ⅳ(OR=2.782,95％CI 1.166～6.636),serum creatinine(OR=29.893,95％CI 6.782～131.768),ApoE(OR=12.046,95％CI 4.694～30.913),sSema4D(OR=8.390,95％CI 2.260～31.146)and CypA(OR=6.486,95％CI 1.780～23.635)were independent risk factors for unfavorable outcome within 6-month follow-up in CHF patients(P＜0.05 or＜0.01).Conclusion:The levels of ApoE,sSema4D and CypA were closely associated with short-term unfavorable outcome in patients with chronic heart failure.