OBJECTIVES: To evaluate the effectiveness of single-balloon and double-balloon enteroscopy in diagnosing pediatric small bowel diseases and assess the diagnostic efficacy of computed tomography enterography (CTE) for small bowel diseases using enteroscopy as the reference standard. METHODS: Clinical data from 576 children who underwent enteroscopy at Hunan Children's Hospital between January 2017 and December 2023 were retrospectively collected. The children were categorized based on enteroscopy type into the single-balloon enteroscopy (SBE) group (n=457) and double-balloon enteroscopy (DBE) group (n=119), and the clinical data were compared between the two groups. The sensitivity and specificity of CTE for diagnosing small bowel diseases were evaluated using enteroscopy results as the standard. RESULTS: Among the 576 children, small bowel lesions were detected by enteroscopy in 274 children (47.6%).There was no significant difference in lesion detection rates or complication rates between the SBE and DBE groups (P>0.05), but the DBE group had deeper insertion, longer procedure time, and higher complete small bowel examination rate (P<0.05). The complication rate during enteroscopy was 4.3% (25/576), with 18 cases (3.1%) of mild complications and 7 cases (1.2%) of severe complications, which improved with symptomatic treatment, surgical, or endoscopic intervention. Among the 412 children who underwent CTE, the sensitivity and specificity for diagnosing small bowel diseases were 44.4% and 71.3%, respectively. CONCLUSIONS SBE and DBE have similar diagnostic efficacy for pediatric small bowel diseases, but DBE is preferred for suspected deep small bowel lesions and comprehensive small bowel examination. Enteroscopy in children demonstrates relatively good overall safety. CTE demonstrates relatively low sensitivity but comparatively high specificity for diagnosing small bowel diseases.
Retrospective Studies ; Treatment Outcome ; Double-Balloon Enteroscopy/statistics & numerical data* ; Single-Balloon Enteroscopy/statistics & numerical data* ; Humans ; Male ; Female ; Child ; Operative Time ; Tomography, X-Ray Computed/statistics & numerical data* ; Sensitivity and Specificity ; Intestine, Small/surgery* ; Intestinal Diseases/surgery*

Objective:This study explored the designs of physicians'compensation incentives that were compatible with the reform of Diagnosis-related groups(DRG)payment,so as to provide a reference for optimizing policies related to medical insurance payment reform.Methods:We designed seven different physicians'compensation schemes that converted DRG payment incentives into salary incentives,using economic experiments.The total of 210 medical students and 65 doctors were recruited as subjects.We tested the quantity of medical services for patients that participants provided and the corresponding patient health benefits under different incentive schemes.Results:The two designs of feedback of DRG payment to physicians and linking DRG payment surplus to physicians'performance wages both could transmit the incentive of the payment methods to the service providers.On this basis,a quality-based pay-for-performance payment was introduced,and the deviation between the quantity of services provided by subjects and the optimal quantity of services decreased,and the loss ratio of patient health benefits also decreased.Conclusion:When transmitting DRG payment incentives to medical service providers,the physician compensation design combined with quality-based pay-for-performance payments is more conducive to improving patient health benefits.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

AIM: To analyze the efficacy and safety of safflor yellow injection combined with anti-vascular endothelial growth factor(VEGF)drug in the treatment of non-ischemic central retinal vein occlusion(CRVO).METHODS: A total of 91 patients(91 eyes)with non-ischemic CRVO complicated with macular edema who were treated in the Affiliated Eye Hospital of Nanchang University from April 2017 to December 2021 were selected. They were randomly divided into observation group, with 47 cases(47 eyes)treated with safflor yellow injection combined with intravitreal injections of ranibizumab, and control group with 44 cases(44 eyes)who were treated with intravitreal injections of ranibizumab. Followed-up for 11mo, the best corrected visual acuity(BCVA)and macular central retinal thickness(CRT)of the two groups were observed and the cases of complete absorption of retinal hemorrhage, the times of anti-VEGF drug injections, the cases of ischemic CRVO, and the occurrence of systemic or ocular complications were recorded.RESULTS: At 1, 2, 3, 5, 7, 9 and 11mo after treatment, the BCVA and CRT in both groups were significantly improved compared with those before treatment, and BCVA and CRT in the observation group were superior to the control group at 3, 5, 7, 9 and 11mo after treatment(all P<0.05). At 5, 7, 9 and 11mo after treatment, the complete absorption rate of retinal hemorrhage in the observation group was higher than that in the control group(P<0.05). During the follow-up period, the anti-VEGF drug injection in the observation group was significantly less than that in the control group(4.83±1.05 vs. 5.75±1.01, P<0.05), and the incidence of ischemic CRVO was significantly lower than that in the control group(21% vs. 86%, P<0.05), and there were no treatment-related systemic and ocular complications in both groups.CONCLUSION: Safflor yellow injection combined with anti-VEGF drugs is a safe and effective method for the treatment of non-ischemic CRVO, which can significantly improve vision and reduce CRT. It can increase the complete absorption rate of retinal hemorrhage, reduce the times of anti-VEGF drug injections and the incidence of ischemic CRVO compared with monotherapy of anti-VEGF drug.

Objective: To investigate the impact of molecular classification and key oncogenes on the oncologic outcomes in patients with endometrial carcinoma (EC) and atypical endometrial hyperplasia (AEH) receiving fertility-preserving treatment. Methods: Patients with EC and AEH undergoing progestin-based fertility-preserving treatment and receiving molecular classification as well as key oncogenes test at Obstetrics and Gynecology Hospital, Fudan University from January 2021 to March 2023 were reviewed. Hysteroscopic lesion resection and endometrial biopsy were performed before initiating hormone therapy and every 3 months during the treatment to evaluate the efficacy. The risk factors which had impact on the treatment outcomes in EC and AEH patients were further analyzed. Results: Of the 171 patients analyzed, the median age was 32 years, including 86 patients with EC and 85 patients with AEH. The distribution of molecular classification was as follows: 157 cases (91.8%) were classified as having no specific molecular profile (NSMP); 9 cases (5.3%), mismatch repair deficient (MMR-d); 3 cases (1.8%), POLE-mutated; 2 cases (1.2%), p53 abnormal. No difference was found in the cumulative 40-week complete response (CR) rate between the patients having NSMP or MMR-d (61.6% vs 60.0%; P=0.593), while the patients having MMR-d had increased risk than those having NSMP to have recurrence after CR (50.0% vs 14.4%; P=0.005). Multi-variant analysis showed PTEN gene multi-loci mutation (HR=0.413, 95%CI: 0.259-0.658; P<0.001) and PIK3CA gene mutation (HR=0.499, 95%CI: 0.310-0.804; P=0.004) were associated with a lower cumulative 40-week CR rate, and progestin-insensitivity (HR=3.825, 95%CI: 1.570-9.317; P=0.003) and MMR-d (HR=9.014, 95%CI: 1.734-46.873; P=0.009) were independent risk factors of recurrence in EC and AEH patients. Conclusions: No difference in cumulative 40-week CR rate is found in the patients having NSMP or MMR-d who received progestin-based fertility-preserving treatment, where the use of hysteroscopy during the treatment might be the reason, while those having MMR-d have a higher risk of recurrence after CR. Oncogene mutation of PTEN or PIK3CA gene might be associated with a lower response to progestin treatment. The molecular profiles help predict the fertility-preserving treatment outcomes in EC and AEH patients.
Pregnancy ; Female ; Humans ; Adult ; Hyperplasia ; Progestins ; Fertility Preservation ; Endometrial Neoplasms/pathology* ; Endometrial Hyperplasia/surgery* ; Treatment Outcome ; Precancerous Conditions ; Fertility ; Class I Phosphatidylinositol 3-Kinases ; Retrospective Studies

Objective: To study the correlation between ceramic and chronic obstructive pulmonary disease (COPD), and explore its related risk factors. Methods: In January 2021, five representative ceramic enterprises were selected from Chancheng District, Nanhai District, Gaoming District and Sanshui District of Foshan City. The ceramic workers who came to Chancheng Hospital of Foshan First People's Hospital for physical examination from January to October 2021 were selected as the research objects, and 525 people were included. Conduct questionnaire survey and pulmonary function test. Logistic regresion was performed to analyze the influencing facters of COPD among ceramic workers. Results: The subjects were (38.51±1.25) years old, 328 males and 197 females, and the detection rate of COPD was 9.52% (50/525). The incidence of respiratory symptoms such as dyspnea, chronic cough, wheezing and chest tightness, the detection rates of abnormal lung age, abnormal lung function and COPD in males were higher than those in females (P<0.05). The logistic regression analysis showed that male, age, working years, smoking status and family history of COPD were the risk factors for COPD among ceramic workers (P<0.05) . Conclusion: The ceramic workers are the high risk population of COPD. We should do a good job in health education, and do a regular physical examination to find the changes of lung function in time, and prevent the occurrence of COPD as soon as possible.
Female ; Humans ; Male ; Adult ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Ceramics ; Health Education ; Hospitals ; Physical Examination