Objective: This study aims to evaluate the clinical benefits of the integrated optical and magnetic surgical navigation system in assisting transforaminal endoscopic lumbar discectomy (TELD) for the treatment of lumbar disc herniation (LDH). Methods: A retrospective analysis was conducted on patients who underwent TELD for LDH at Beijing Chaoyang Hospital, Capital Medical University from November 2022 to December 2023. Patients treated with the integrated optical and magnetic surgical navigation system were defined as the navigation-guided TELD (Ng-TELD) group (30 cases), while those treated with the conventional x-ray fluoroscopy method were defined as the control group (31 cases). Record and compare baseline characteristics, surgical parameters, efficacy indicators, and adverse events between the 2 patient groups. Results: The average follow-up duration for the 61 patients was 11.8 months. Postoperatively, both groups exhibited significant relief from back and leg pain, which continued to improve over time. At the final follow-up, patients’ lumbar function and quality of life had significantly improved compared to preoperative levels (p < 0.05). The Ng-TELD group had significantly shorter total operation time (58.43 ± 12.37 minutes vs. 83.23 ± 25.90 minutes), catheter placement time (5.83 ± 1.09 minutes vs. 15.94 ± 3.00 minutes), decompression time (47.17 ± 11.98 minutes vs. 67.29 ± 24.23 minutes), and fewer intraoperative fluoroscopies (3.20 ± 1.45 vs. 16.58 ± 4.25) compared to the control group (p < 0.05). There were no significant differences between the groups in terms of efficacy evaluation indicators and hospital stay. At the final follow-up, the excellent and good rate of surgical outcomes assessed by the MacNab criteria was 98.4%, and the overall adverse event rate was 8.2%, with no statistically significant differences between the groups (p > 0.05). Conclusion This study demonstrates that the integrated optical and magnetic surgical navigation system can reduce the complexity of TELD, shorten operation time, and minimize radiation exposure for the surgeon, highlighting its promising clinical potential.

With the latest development of information and communication technology, mobile applications for health care have attracted much attention to help cancer patients better manage their diseases. Digital therapeutics is an evidence-based intervention driven by high-quality software programs that prevent, manage, or treat medical disorders or diseases. This paper summarizes the origin, concept and characteristics of digital therapeutics and the application form, effect, challenge and opportunity of symptom management in cancer patients, in order to provide reference for the scientific and efficient application of Digital therapeutics to cancer patients.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

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 evaluate the safety and effectiveness of fuzuloparib for the treatment of ovarian epithelial cancer patients in the real world setting.Methods:A retrospective analysis was conducted on the baseline data of 4 620 ovarian cancer patients who had received fuzuloparib monotherapy or combination therapy. Another 224 ovarian cancer patients who were willing to receive fuzuloparib monotherapy or combination therapy were prospectively enrolled, and their baseline characteristics, drug effectiveness, and safety data were analyzed.Results:(1) Among the 4 620 patients in the retrospective cohort, the median age of patients was 60 years; tumor types: 89.8% (4 149/4 620) had ovarian cancer. Among patients with clearly documented information, the vast majority had a histological type of serous carcinoma (82.9%, 3 770/4 546) and International Federation of Gynecology and Obstetrics (FIGO) staging of Ⅲ-Ⅳ (90.9%, 1 537/1 691). (2) Among the 224 patients in the prospective cohort, the median age of patients was 57 years; tumor types: 83.9% (188/224) had ovarian cancer. Among patients with clearly documented records, the predominant pathologic type was serous carcinoma (91.9%, 193/210), and FIGO stage was Ⅲ-Ⅳ in 79.9% (139/174). (3) Among the 224 prospective patients: 84 patients received first-line fluzoparib maintenance therapy, 92 patients received fluzoparib maintenance therapy after platinum-sensitive recurrence, 23 patients received direct fluzoparib treatment after platinum-sensitive recurrence, 19 patients received direct fluzoparib treatment after platinum-resistant recurrence. The median follow-up durations were 8.5, 8.7, 7.9, and 6.7 months, respectively. The median durations of fluzoparib treatment were 6.7, 4.8, 3.1, and 1.9 months, respectively. The median progression-free survival (PFS) times were not reached during follow-up, 12.6 months, not reached during follow-up, and 4.8 months, respectively. The 1-year PFS rates were 84.1%, 55.0%, 69.8%, and 45.5%, respectively. The remaining 6 patients received other fluzoparib regimens. (4) Among the 224 patients in the prospective dataset, 205 had safety data recorded. Of these, 127 patients (62.0%, 127/205) experienced treatment-related adverse events, with common events including anemia (24.4%, 50/205), thrombocytopenia (21.0%, 43/205), and leukopenia (19.5%, 40/205). Among the 205 patients, 43 (21.0%, 43/205) experienced grade 3 or higher treatment-related adverse events, with common events including anemia (8.3%, 17/205) and thrombocytopenia (8.3%, 17/205).Conclusions:The effectiveness of fuzuloparib in clinical application is generally consistent with other drugs in the same class, with good safety. This study provids new clinical evidence for the treatment of ovarian cancer with fuzuloparib.

Objective:To investigate the correlation between imaging features of mixed ground-glass nodules(mGGNs)under dual lung enhanced CT and pathological subtypes of lung adenocarcinoma.Methods:Retrospective analysis of clinical data of 102 isolated mGGN lung adenocarcinoma patients admitted to Dalian Central Hospital from October 2016 to October 2018.The patients were divided into adenocarcinoma in situ(AIS)group,minimally invasive adenocarcinoma(MIA)group and invasive adenocarcinoma(IAC)group according to postoperative pathological examination results.Measure the maximum diameter lesions,the maximum diameter of solid components and the proportion of solid components to evaluate imaging features of three groups.The relationship between pathological subtypes of lung adenocarcinoma and baseline features,imaging features,mGGN lesions,maximum diameter of solid components and proportion of solid components were analyzed.The diagnostic value of the maximum diameter of lesions,the maximum diameter of solid components,and the proportion of solid components in IAC were analyzed by receiver operating characteristic(ROC)curves.Results:102 patients were divided into AIS group(n=20),MIA group(n=29)and IAC group(n=53)based on postoperative pathological diagnosis.There was a statistically significant difference in age among the three groups(P＜0.05).Tumor distribution locations:35 cases in the upper lobe of the right lung,10 cases in the middle lobe of the right lung,and 15 cases in the lower lobe of the right lung;23 cases in the upper lobe of the left lung and 19 cases in the lower lobe of the left lung,the tumor distribution locations in the upper lobe of the right lung was relatively high in various pathological subtypes.The lesions in AIS and MIA groups were mostly circular or elliptical in shape,whiile the lesions in the IAC group was mostly irregular in shape.There was a statistically significant difference in morphological comparisons among the three groups(P＜0.05).There was a statistically significant difference in burr sign between MIA group and IAC group(P＜0.05).There was a statistically significant difference in pleural indentation sign and bronchial inflation sign between MIA group and IAC group(P＜0.05).There was a statistically significant difference in the maximum diameter of lesions and the maximum diameter of solid components among the three groups(P＜0.05).The proportion of solid components in IAC group was higher than that in AIS and MIA groups,and the difference was statistically significant(P＜0.05).The ROC curve shows that,the area under curve(AUC)for diagnosing IAC based on the maximum diameter of the lesion,the maximum diameter of the solid component,and the proportion of the solid component were 0.840,0.966 and 0.816,respectively.The AUC of diagnosing IAC with the maximum diameter of the solid component was greater than the AUC of the maximum diameter of the lesion and the proportion of solid components(P＜0.05).Conclusion:Dual lung enhanced CT can evaluate the imaging features of mGGN,and it can distinguish the pathological subtypes of lung adenocarcinoma,when the maximum diameter of the lesion is ≥ 16.5 mm,the maximum diameter of the solid component is ≥5.5 mm,or the proportion of solid component is ≥47.00％,it can effectively diagnose IAC,the maximum diameter of solid components has the best diagnostic efficiency for IAC.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

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 dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]