OBJECTIVE To explore the improvement effect and mechanism of salidroside on radiation-induced parotid gland injury in rats. METHODS Rats were randomly assigned into normal group， radiation group， salidroside low-dose （salidroside-L， 50 mg/kg） group， salidroside high-dose （salidroside-H， 100 mg/kg） group， and salidroside-H+inhibitor （100 mg/kg salidroside+0.1 µmol/kg H-89） group， with 10 rats in each group. Except for the normal group， rats in the other groups were subjected to radiation exposure to establish a model of radiation-induced parotid gland injury. Rats in each group were intraperitoneally injected with the corresponding drug or normal saline， once a day， for 40 consecutive days. After the last administration， the levels of reactive oxygen species （ROS）， cyclic adenosine monophosphate （cAMP）， superoxide dismutase （SOD）， and amylase in serum were detected； the pathological changes of parotid gland tissue were observed； the apoptosis rate of parotid gland tissue cells， the expression levels of B-cell lymphoma-2 （Bcl-2） and its associated X protein （Bax）， mRNA expression levels of interleukin-6 （IL- 6） and tumor necrosis factor-α （TNF-α）， the protein expression levels of type Ⅲ collagen （Col Ⅲ）， vasoactive intestinal peptide （VIP）， and the phosphorylation level of protein kinase A （PKA） in parotid gland tissue were determined. RESULTS Compared with normal group， the levels of ROS， amylase， apoptosis rate， Bax expression level， mRNA expression levels of IL-6 and TNF- α， and protein expression level of Col Ⅲ in the radiation group were significantly increased， while the levels of cAMP， SOD， Bcl-2 expression level， VIP protein expression level and PKA phosphorylation level were significantly decreased （P＜0.05）. Compared with radiation group， the above indicators in the salidroside-L group and salidroside-H group were significantly improved （P＜0.05）， and the improvement in the salidroside-H group was more significant （P＜0.05）； inhibitor H-89 significantly reversed the changes in the above indicators of the salidroside-H group （P＜0.05）. CONCLUSIONS Salidroside can effectively alleviate radiation-induced parotid gland injury in rats， and its mechanism may be related to the activation of the VIP-cAMP pathway.

BACKGROUND: In China, lung cancer remains the cancer with the highest incidence and mortality rate. Among early-stage lung adenocarcinomas (LUAD), the micropapillary (MPP) component is prevalent and typically exhibits high aggressiveness, significantly correlating with early metastasis, lymphatic infiltration, and reduced five-year survival rates. Therefore, the study is to explore the similarities and differences between MPP and non-micropapillary (non-MPP) components in malignant pulmonary nodules characterized by GGOs in early-stage LUAD, identify unique mutational features of the MPP component and analyze the relationship between the ZNF469 gene, a member of the zinc-finger protein family, and the prognosis of early-stage LUAD, as well as its correlation with immune infiltration. METHODS: A total of 31 malignant pulmonary nodules of LUAD were collected and dissected into paired MPP and non-MPP components using microdissection. Whole-exome sequencing (WES) was performed on the components of early-stage malignant pulmonary nodules. Mutational signatures analysis was conducted using R packages such as maftools, Nonnegative Matrix Factorization (NMF), and Sigminer to unveil the genomic mutational characteristics unique to MPP components in invasive LUAD compared to other tumor tissues. Furthermore, we explored the expression of the ZNF469 gene in LUAD using The Cancer Genome Atlas (TCGA) database to investigate its potential association with the prognosis. We also investigated gene interaction networks and signaling pathways related to ZNF469 in LUAD using the GeneMANIA database and conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Lastly, we analyzed the correlation between ZNF469 gene expression and levels of immune cell infiltration in LUAD using the TIMER and TISIDB databases. RESULTS: MPP components exhibited a higher number of genomic variations, particularly the 13th COSMIC (Catalogue of Somatic Mutations in Cancer) mutational signature characterized by the activity of the cytidine deaminase APOBEC family, which was unique to MPP components compared to non-MPP components in tumor tissues. This suggests the potential involvement of APOBEC in the progression of MPP components in early-stage LUAD. Additionally, MPP samples with high similarity to APOBEC signature displayed a higher tumor mutational burden (TMB), indicating that these patients may be more likely to benefit from immunotherapy. The expression of ZNF469 was significantly upregulated in LUAD compared to normal tissue, and was associated with poor prognosis in LUAD patients (P<0.05). Gene interaction network analysis and GO/KEGG enrichment analysis revealed that COL6A1, COL1A1, COL1A2, TGFB2, MMP2, COL8A2 and C2CD4C interacted with ZNF469 and were mainly involved in encoding collagen proteins and participating in the constitution of extracellular matrix. ZNF469 expression was positively correlated with immune cell infiltration in LUAD (P<0.05). CONCLUSIONS The study has unveiled distinctive mutational signatures in the MPP components of early-stage invasive LUAD in the Asian population. Furthermore, we have identified that the elevated expression of mutated ZNF469 impacts the prognosis and immune infiltration in LUAD, suggesting its potential as a diagnostic and prognostic biomarker in LUAD.
Humans ; Lung Neoplasms/genetics* ; Adenocarcinoma of Lung/genetics* ; China ; Prognosis ; Transcription Factors

Objective The occurrence of chickenpox in rapidly developing areas poses substantial seasonal risk to children.However,certain factors influencing local chickenpox outbreaks have not been studied.Here,we examined the relationship between spatial clustering,heterogeneity of chickenpox outbreaks,and socioeconomic factors in Southern China. Methods We assessed chickenpox outbreak data from Southern China between 2006 and 2021,comprising both relatively fast-growing parts and slower sub-regions,and provides a representative sample of many developing regions.We analyzed the spatial clustering attributes associated with chickenpox outbreaks using Moran's I and local indicators of spatial association and quantified their socioeconomic determinants using Geodetector q statistics. Results There were significant spatial heterogeneity in the risk of chickenpox outbreaks,with strong correlations between chickenpox risk and various factors,particularly demographics and living environment.Furthermore,interactive effects among specific are factors,such as population density and per capita residential building area,percentage of households with toilets,percentage of rental housing,exhibited q statistics of 0.28,0.25,and 0.24,respectively. Conclusion This study provides valuable insights into the spatial dynamics of chickenpox outbreaks in rapidly developing regions,revealing the socioeconomic factors affecting disease transmission.These implications extend the formulation of effective public health strategies and interventions to prevent and control chickenpox outbreaks in similar global contexts.

Cognitive impairment is a kind of senile disease that leads to the decline of personality and behavior ability of the elderly,which seriously affects the quality of daily life of patients.The prevalence rate of the disease increases year by year with the acceleration of the aging process of the society,and its incidence is affected by many risk factors.At this stage,the curative effect for middle and advanced patients is poor.So early identification and intervention to delay the progression of cognitive impairment have become the focus of relevant research.Blood pressure variability can lead to damage of target organs such as heart,brain tissue and kidney,which is closely related to cognitive impairment.In order to expand a new perspective of early intervention in cognitive impairment,this paper reviews the effects of blood pressure variability on different cognitive impairment and its possible pathogenic mechanism.

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.

Purpose: Polyacrylamide hydrogel (PAHG), which had been used widely for breast augmentation, has been banned for more than 15 years. Patients who had been injected PAHG for breast augmentation need evacuation surgery to remove as much as possible. To provide a series of diagnosis and treatment process MRI and intraoperative color Doppler ultrasound are combined for maximal removal of PAHG. Methods: The patients who received evacuation surgery in Peking University Third Hospital from 2010 to 2022 after PAHG injection for breast augmentation were included in this research. MR scanning was performed preoperatively and postoperatively in some of these patients and color Doppler ultrasound was applied to help evacuate PAHG intraoperatively. The mean clearance rate of PAHG was calculated according to the MRI outcomes. Results: Two hundred and 4 patients had received evacuation surgery after PAHG injection for breast augmentation with an average age of 42.8 years and an average body mass index of 21.2 kg/m 2 . The average PAHG retention time was 13.5 years. Among them, 52 patients underwent pre- and postoperative MRI scanning. The mean three-dimensional (3D) volume of PAHG was 684.8 mL (range, 350.0–1,123.9 mL), and the average residual 3D volume of PAHG was 53.7 mL (range, 12.4–98.3 mL). The mean clearance rate was 92.1%. Conclusion MRI and intraoperative color Doppler ultrasound can provide effective and precise location information of PAHG for evacuation surgery, which is a reliable method to ensure the maximal removal of PAHG.