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*

The implementation of amplicon sequencing and metagenomic sequencing methods in the whole-genome sequen-cing for MPXV testing was compared,to provide a technical reference for sequencing,tracing,and epidemic prevention and control of MPXV.For amplicon sequencing,targeted amplification of the viral whole genome was performed on MPXV DNA,and was followed by next-generation sequencing of the amplification products.For metagenomic sequencing,next-generation sequencing was performed directly on MPXV DNA.After the sequences were obtained,software such as CLC and IGV were used to analyze the effective data percentage,sequencing depth,and whole-genome sequencing coverage under different sequen-cing depths for both sequencing methods,to evaluate sequencing quality.Nextclade was used to analyze virus typing,muta-tions,and deletions.Subsequently,the similarity and completeness of sequences obtained through both sequencing methods were further compared.On the basis of mapping to the refer-ence sequence of strain MPXV-M5312_HM12_Rivers(Gen-Bank number NC_063383.1),the percentage effective data obtained from amplicon sequencing and metagenomic sequen-cing was 99.72％and 7.54％,respectively,with a sequencing depth range of 0× to 334 839 ×,and 44 × to 1 000 ×.On the basis of a sequencing depth of 10 ×,the site coverage of the above was 90.3％and 100％,respectively.IGV was used to validate the whole-genome coverage under different sequencing depths.The depth coverage of whole-genome sites for metagenomic sequencing was uniform,whereas that of the whole-genome sites for amplicon sequencing was uneven and significantly differed.Virus typing and sequence similarity analysis indicated that the viral sequences obtained with the two sequencing methods all belonged to the Ⅱb B.1 lineage of MPXV.Comparison with the reference sequence indicated that metagenomic sequencing identified 73 nucleotide mutation sites,whereas amplicon sequen-cing identified 68 mutation sites.Further analysis demonstrated that seven common mutation sites of Ⅱb B.1 were not detected in the amplicon sequencing,and two false positive private mutation sites were identified.Amplicon or metagenomic sequencing methods thus can be flexibly used in MPXV virus whole-genome sequencing.Amplicon sequencing yields more effective data,whereas metagenomic sequencing provides better uniformity of coverage and sequence accuracy.This study provides a prelimi-nary understanding of the efficacy of each method and may serve as a technical reference for improving the success rate of whole-genome sequencing of MPXV.

Objective:To assess the association between diabetes mellitus and the risk of sudden cardiac death (SCD), and to identify potential contributing factors.Methods:This meta-analysis was an updated version of the original study Diabetes mellitus and the risk of sudden cardiac death： a systematic review and meta-analysis of prospective studies. The original review included all eligible case-control and cohort studies published in PubMed and Embase up to 2017 that investigated the association between diabetes and SCD risk. In this updated study, newly published studies were added, including those available in PubMed, Embase, China National Knowledge Infrastructure (CNKI), and WANFANG MED ONLINE up to December 3, 2023. Search terms included "diabetes""glucose""sudden cardiac death" "cardiac arrest" and their Chinese equivalent. The primary outcome was the risk of SCD, while factors such as country, ethnicity, skin color, follow-up duration, left ventricular ejection fraction (LVEF), baseline comorbidities, and other relevant variables were analyzed as potential influencing factors. Relative risk ( RR) was used as the summary measure. A random-effects model was used when significant heterogeneity was detected, otherwise a fixed-effects model was used. Cochran′s Q test was used for subgroup analysis to assess the influence of factors such as region, baseline diseases, LVEF, and ethnicity (based on skin color) on the outcomes. Results:A total of 32 cohort/case-control studies with a combined sample size of 3 252 954 individuals were included. The meta-analysis showed that the risk of SCD in patients with diabetes was double that of non-diabetics ( RR=2.00, 95% CI: 1.83-2.19, P<0.001). In Asian populations, the risk of SCD in diabetic patients was 1.78 times that of non-diabetic individuals ( RR=1.78, 95% CI: 1.51-2.10), 2.05 times that of in European populations ( RR=2.05, 95% CI: 1.79-2.34), and 2.12 times that of in American populations ( RR=2.12, 95% CI: 1.82-2.47), with no statistically significant heterogeneity between regions ( P=0.287). Among individuals without other baseline comorbidities, the risk of SCD was 2.12 times higher in diabetic patients than in those without diabetes ( RR=2.12, 95% CI: 1.89-2.38). In patients with baseline coronary heart disease, the risk was 1.75 times that of non-diabetics ( RR=1.75, 95% CI: 1.45-2.11). In those with baseline heart failure, the risk was 1.92 times that of non-diabetics ( RR=1.92, 95% CI: 1.51-2.43). In patients with baseline atrial fibrillation, the risk was 4.00 times that of non-diabetic individuals ( RR=4.00, 95% CI: 1.38-11.56). In patients undergoing hemodialysis due to renal failure, the risk was 1.76 times that of non-diabetic individuals ( RR=1.76, 95% CI: 1.25-2.48), with no statistically significant heterogeneity between groups ( P=0.262). In cardiac patients with LVEF>50%, the risk of SCD in diabetic patients was 2.08 times that of non-diabetic individuals ( RR=2.08, 95% CI: 1.57-2.75), and in those with LVEF<50%, the risk was 1.69 times that of non-diabetic individuals ( RR=1.69, 95% CI: 1.30-2.18), with no statistically significant heterogeneity between groups ( P=0.277). In yellow-skinned populations, the risk of SCD in diabetic patients was 1.80 times that of healthy individuals ( RR=1.80, 95% CI: 1.73-1.87), and in white-skinned populations, it was 2.18 times that of healthy individuals ( RR=2.18, 95% CI: 1.88-2.54), with statistically significant heterogeneity between groups ( P=0.014). Conclusions:Diabetes mellitus significantly increased the risk of SCD, and this effect may be more pronounced in white-skinned populations, while region, baseline comorbidities, and LVEF had no further effect.

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.

Effective treatments for neuropathic pain are lacking due to our limited understanding of the mechanisms. The circRNAs are mainly enriched in the central nervous system. However, their function in various physiological and pathological conditions have yet to be determined. Here, we identified circFhit, an exon-intron circRNA expressed in GABAergic neurons, which reduced the inhibitory synaptic transmission in the spinal dorsal horn to mediate spared nerve injury-induced neuropathic pain. Moreover, we found that circFhit decreased the expression of GAD65 and induced hyperexcitation in NK1R⁺ neurons by promoting the expression of its parental gene Fhit in cis. Mechanistically, circFhit was directly bound to the intronic region of Fhit, and formed a circFhit/HNRNPK complex to promote Pol II phosphorylation and H2B monoubiquitination by recruiting CDK9 and RNF40 to the Fhit intron. In summary, we revealed that the exon-intron circFhit contributes to GABAergic neuron-mediated NK1R⁺ neuronal hyperexcitation and neuropathic pain via regulating Fhit in cis.
Rats ; Animals ; Posterior Horn Cells/pathology* ; Spinal Cord Dorsal Horn/metabolism* ; Neuralgia ; Synaptic Transmission

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