ObjectiveThis study aimed to elucidate the mechanistic role of Staphylococcus aureus in the pathogenesis of atopic dermatitis (AD), a chronic inflammatory skin disorder characterized by pruritus and barrier dysfunction. A key focus was screening traditional Chinese medicine (TCM) active components with dual antibacterial and antipruritic efficacy, followed by systematic evaluation of their in vitro antibacterial activity. Additionally, a novel drug delivery system was constructed to enable localized efficient drug delivery, inhibiting S. aureus proliferation and alleviating its induced pruritus, thereby providing new strategies for targeted AD therapy. MethodsMale C57BL/6J mice aged 6-8 weeks (body weight 18-22 g) were used to establish an AD model via repeated oxazolone sensitization. On day 14, microbial samples were collected from the lesional area (1 cm²) using sterile cotton swabs, followed by vortex mixing, serial dilution, and plating on 5% sheep blood agar plates (incubated at 37°C for 24 h). Single colonies with complete transparent β-hemolytic zones were isolated and identified as vancomycin-intermediate S. aureus (VISA) via 16S rRNA sequencing. An S. aureus mono-infection animal model was then established by applying gauze saturated with bacterial suspension (McFarland turbidity 0.1) to the nape and back skin of mice. The pruritic phenotype and inflammatory cell infiltration induced by S. aureus were evaluated using comprehensive approaches including behavioral assays (e.g., scratching frequency recording), hematoxylin-eosin (HE) staining, and toluidine blue staining. The in vitro antibacterial efficacy of the TCM monomer pseudolaric acid B (PAB) and double network hydrogel (DN) was separately assessed by disk diffusion assay, while the minimum inhibitory concentration (MIC) of PAB was determined via broth dilution method. Further validation of the pharmacodynamic characteristics of the composite system (PAB@DN, composed of PAB and DN) was conducted through behavioral assays, HE staining, and dermatitis scoring, with its drug release profile evaluated by mass spectrometry analysis. Based on scratching behavioral analysis and dermatitis scoring, the optimal ratio and concentration of PAB@DN were optimized. ResultsThe S. aureus load in AD lesional tissues was significantly higher than in normal skin ((5.3±0.33)×10⁶ CFU vs. (3.6±0.26)×10⁷ CFU, P<0.001). In the S. aureus mono-infection group, mice exhibited a 6.7-fold increase in scratching frequency compared to the control group. HE staining revealed marked epidermal thickening ((10.4±2.39) μm vs. (85.6±1.95) μm, P<0.000 1), and toluidine blue staining showed a 23-fold increase in mast cell degranulation. Pseudolaric acid B exhibited a significant concentration-dependent inhibitory effect onS. aureus growth, with its in vitro antibacterial effect being 57% that of the antibiotic cefepime (inhibition zone diameter: PAB (1.885±0.036) cm vs. cefepime (3.636±0.005) cm, P<0.000 1) and a minimum inhibitory concentration (MIC) of 1 g/L. The carrier double network hydrogel (DN) itself lacked direct antibacterial activity (no significant difference in inhibition zone diameter compared to the control) but effectively ameliorated the dry symptoms of AD-like lesions. The PAB@DN composite system demonstrated a synergistic effect compared to individual components, resulting in a 50% reduction in scratching behavior, an 86% decrease in dermatitis score, and a 60% reduction in epidermal thickening. It also reduced the S. aureus load in mouse skin by approximately 34%, with the optimal effective formulation being PAB at 1 g/L loaded onto DN. ConclusionS. aureus colonization plays a critical driving role in the onset and progression of AD. Using an S. aureus infection model, this study confirmed that the pseudolaric acidB-hydrogel composite delivery system (PAB@DN) can effectively alleviate S. aureus-induced pruritus and skin damage, providing experimental evidence for microbiota-targeted therapy of AD.

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:To evaluate the epidemiological characteristics and influencing factors of out-of-hospital sudden cardiac death (SCD) in Hangzhou from 2016 to 2019.Methods:SCD events recorded by Hangzhou Emergency Center from January 1, 2016 to December 31, 2019 were reviewed. Demographic and mortality data were recorded, and the distribution patterns of SCD events in terms of date, time, and population with different characteristics were observed. Time series analysis method and a distributed lag nonlinear model based on quasi-Poisson distribution were used to explore the possible nonlinear association between ambient temperature and SCD incidence.Results:A total of 4 744 out-of-hospital sudden death events were recorded by Hangzhou Emergency Center from January 1, 2016 to December 31, 2019. After excluding non-SCD events and observed events with missing items, 3 743 SCD events were finally included in the study. The survey results showed that the incidence of out-of-hospital SCD in Hangzhou was 96.5 cases per 100 000 person-years. Most of the people who experienced SCD were aged ≥60 years. The incidence in males (2 462 cases, 66%) was significantly higher than that in females (1 281 cases, 34%), and the proportion of events occurring during the day (2 737 cases, 73%) was significantly higher than that at night (1 006 cases, 27%), mainly occurring between 7: 00 and 9: 00. High temperature was associated with an increased risk of SCD. When the average daily temperature was higher than 25.5 ℃, the risk of SCD increased with the further increase of average daily temperature.Conclusions:SCD events mainly occur in the elderly population aged ≥60 years, with a significantly higher incidence in males than in females, and more frequently during the day than at night, mainly between 7: 00 and 9: 00 in the morning. High temperature is closely related to the risk of SCD. It is particularly important to carry out targeted SCD screening and prevention for different populations and implement appropriate prevention strategies for high-risk groups of SCD in high-temperature weather.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

Objective:To evaluate the epidemiological characteristics and influencing factors of out-of-hospital sudden cardiac death (SCD) in Hangzhou from 2016 to 2019.Methods:SCD events recorded by Hangzhou Emergency Center from January 1, 2016 to December 31, 2019 were reviewed. Demographic and mortality data were recorded, and the distribution patterns of SCD events in terms of date, time, and population with different characteristics were observed. Time series analysis method and a distributed lag nonlinear model based on quasi-Poisson distribution were used to explore the possible nonlinear association between ambient temperature and SCD incidence.Results:A total of 4 744 out-of-hospital sudden death events were recorded by Hangzhou Emergency Center from January 1, 2016 to December 31, 2019. After excluding non-SCD events and observed events with missing items, 3 743 SCD events were finally included in the study. The survey results showed that the incidence of out-of-hospital SCD in Hangzhou was 96.5 cases per 100 000 person-years. Most of the people who experienced SCD were aged ≥60 years. The incidence in males (2 462 cases, 66%) was significantly higher than that in females (1 281 cases, 34%), and the proportion of events occurring during the day (2 737 cases, 73%) was significantly higher than that at night (1 006 cases, 27%), mainly occurring between 7: 00 and 9: 00. High temperature was associated with an increased risk of SCD. When the average daily temperature was higher than 25.5 ℃, the risk of SCD increased with the further increase of average daily temperature.Conclusions:SCD events mainly occur in the elderly population aged ≥60 years, with a significantly higher incidence in males than in females, and more frequently during the day than at night, mainly between 7: 00 and 9: 00 in the morning. High temperature is closely related to the risk of SCD. It is particularly important to carry out targeted SCD screening and prevention for different populations and implement appropriate prevention strategies for high-risk groups of SCD in high-temperature weather.

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.