OBJECTIVE To investigate the mechanism by which Ginkgo flavone aglycone （GA） reduces the cardiotoxicity of doxorubicin （DOX） based on transcriptomics and proteomics. METHODS Thirty-six mice were randomly assigned to control group （CON group， tail vein injection of equal volume of physiological saline every other day+daily intragastric administration of an equal volume of physiological saline）， DOX group （tail vein injection of 3 mg/kg DOX every other day）， and GDOX group （daily intragastric administration of 100 mg/kg GA+tail vein injection of 3 mg/kg DOX every other day）， with 12 mice in each group. The administration of drugs/physiological saline was continued for 15 days. Mouse heart tissues were collected for RNA-Seq transcriptomic sequencing and 4D-Label-free quantitative proteomic analysis to screen differentially expressed genes and proteins， which were then subjected to Kyoto encyclopedia of genes and genomes （KEGG） enrichment analysis. The expression levels of Apelin peptide （Apelin）， phosphatidylinositol 3-kinase （PI3K）， and protein kinase B （Akt） mRNA and protein in mouse heart tissues， as well as the phosphorylation levels of PI3K and Akt proteins， were verified. H9c2 cardiomyocytes were divided into control group （CON group）， DOX group （2 μmol/L）， and GDOX group （2 μg/mL GA+2 μmol/L DOX） to determine cell viability and the levels of key glycolytic substances in the cells. RESULTS Six common pathways were identified from transcriptomics and proteomics， including the Apelin signaling pathway， the PI3K-Akt signaling pathway， and insulin resistance. Among them， the Apelin and PI3K-Akt signaling pathways were the most enriched in terms of gene numbers. Target validation experiments showed that compared to the CON group， the relative expression of Apelin， PI3K and Akt mRNA and protein levels， as well as the phosphorylation levels of PI3K and Akt proteins， were significantly decreased in the DOX group （P＜0.05 or P＜0.01）. The relative expression of Apelin， PI3K and Akt mRNA and the phosphorylation levels of PI3K and Akt proteins were significantly increased in the GDOX group as compared with the DOX group （P＜0.05 or P＜0.01）. Cellular experiments indicated that compared to the CON group， cell viability in the DOX group was significantly decreased （P＜0.05）， the relative uptake of glucose and the relative production of pyruvate and lactate were significantly increased （P＜0.05）， and the relative production of ATP was significantly reduced （P＜0.05）. Compared to the DOX group， cell viability in the GDOX group was significantly increased （P＜ 0.05）， and the relative production of pyruvate and lactate was significantly reduced （P＜0.05）. CONCLUSIONS GA may alleviate DOX-induced cardiotoxicity by upregulating the mRNA and protein expression of Apelin， PI3K， and Akt in heart tissues， and regulating glycolytic processes.

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:To investigate the effect of laboratory indicators on hair loss in patients with female pattern hair loss (FPHL).Methods:Patients with FPHL who visited the Outpatient Clinic of the Department of Medical Aesthetics in Hangzhou First People’s Hospital from November 2022 to November 2023 were selected as the study group, and healthy women who matched the age of the study group in the physical examination center during the same period were selected as the control group. The general information of the patient was recorded, and was also tested by trichoscopy to rule out other patterns of alopecia. Representative indicators including testosterone, dehydroepiandrosterone sulfate(DHEA-S), thyroid-stimulating hormone, 25-hydroxyvitamin D, and serum ferritin were selected from laboratory tests for further analysis. Otherwise, the proportion of deficiency in vitamin D(<20 ng/ml) was calculated based on 25-hydroxyvitamin D levels (number of deficiency cases/total number of cases in each group×100%). Count data were presented as samples (percentages), and chi-square test was used for comparison between groups. Normally distributed continuous data were presented with Mean±SD, independent samples t-test was used for comparison between groups, M( Q1, Q3) was used for non-normally distributed continuous data, and Wilcoxon rank-sum test was used for comparison between groups. Multivariate logistic regression was used to analyze the influencing factors of FPHL. P<0.05 was statistically significant. Results:A total of 37 patients were selected in both groups. The mean age was (28.8±1.3) years in the study group and (29.6±0.9) years in the control group ( t=0.49, P=0.625). The body mass index was (22.8±0.4) kg/m 2 in the study group, and (23.5±0.3) kg/m 2 in the control group ( t=1.26, P=0.211). The testosterone level was 0.58 (0.49, 0.79) nmol/L in the study group, and 0.54 (0.50, 0.78) nmol/L in the control group( Z=1.42, P=0.157). The level of DHEA-S was 6.21 (5.18, 9.60) μmol/L in the study group, and 6.20 (5.20, 9.34) μmol/L in the control group ( Z=2.75, P=0.006). The level of thyroid-stimulating hormone was 2.56 (1.55, 3.66) mU/L in the study group and 1.49 (1.05, 2.65) mU/L in the control group ( Z=2.51, P=0.012). The level of 25-hydroxyvitamin D was 15.44 (11.80, 21.20) ng/ml in the study group, and the level of 25-hydroxyvitamin D was 20.32 (12.07, 21.20) ng/ml in the control group ( Z=2.30, P=0.021), and the proportion of 25-hydroxyvitamin D deficiency in the study group was 64.9% (24/37), which was higher than that in the control group [40.5% (15/37)] ( χ2=4.39, P=0.036). The serum ferritin level was 64.44 (39.47, 133.45) μg/L in the study group and 67.75 (52.63, 143.83) μg/L in the control group ( Z=0.70, P=0.484). The results of multivariate logistic regression analysis showed that the risk of FPHL was increased by the high level of DHEA-S and thyroid-stimulating hormone, and the low level of 25-hydroxyvitamin D (all P<0.05). Conclusion:Abnormal level of DHEA-S, thyroid-stimulating hormone, and 25-hydroxyvitamin D may be risk factors for FPHL.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective:To investigate the effect of laboratory indicators on hair loss in patients with female pattern hair loss (FPHL).Methods:Patients with FPHL who visited the Outpatient Clinic of the Department of Medical Aesthetics in Hangzhou First People’s Hospital from November 2022 to November 2023 were selected as the study group, and healthy women who matched the age of the study group in the physical examination center during the same period were selected as the control group. The general information of the patient was recorded, and was also tested by trichoscopy to rule out other patterns of alopecia. Representative indicators including testosterone, dehydroepiandrosterone sulfate(DHEA-S), thyroid-stimulating hormone, 25-hydroxyvitamin D, and serum ferritin were selected from laboratory tests for further analysis. Otherwise, the proportion of deficiency in vitamin D(<20 ng/ml) was calculated based on 25-hydroxyvitamin D levels (number of deficiency cases/total number of cases in each group×100%). Count data were presented as samples (percentages), and chi-square test was used for comparison between groups. Normally distributed continuous data were presented with Mean±SD, independent samples t-test was used for comparison between groups, M( Q1, Q3) was used for non-normally distributed continuous data, and Wilcoxon rank-sum test was used for comparison between groups. Multivariate logistic regression was used to analyze the influencing factors of FPHL. P<0.05 was statistically significant. Results:A total of 37 patients were selected in both groups. The mean age was (28.8±1.3) years in the study group and (29.6±0.9) years in the control group ( t=0.49, P=0.625). The body mass index was (22.8±0.4) kg/m 2 in the study group, and (23.5±0.3) kg/m 2 in the control group ( t=1.26, P=0.211). The testosterone level was 0.58 (0.49, 0.79) nmol/L in the study group, and 0.54 (0.50, 0.78) nmol/L in the control group( Z=1.42, P=0.157). The level of DHEA-S was 6.21 (5.18, 9.60) μmol/L in the study group, and 6.20 (5.20, 9.34) μmol/L in the control group ( Z=2.75, P=0.006). The level of thyroid-stimulating hormone was 2.56 (1.55, 3.66) mU/L in the study group and 1.49 (1.05, 2.65) mU/L in the control group ( Z=2.51, P=0.012). The level of 25-hydroxyvitamin D was 15.44 (11.80, 21.20) ng/ml in the study group, and the level of 25-hydroxyvitamin D was 20.32 (12.07, 21.20) ng/ml in the control group ( Z=2.30, P=0.021), and the proportion of 25-hydroxyvitamin D deficiency in the study group was 64.9% (24/37), which was higher than that in the control group [40.5% (15/37)] ( χ2=4.39, P=0.036). The serum ferritin level was 64.44 (39.47, 133.45) μg/L in the study group and 67.75 (52.63, 143.83) μg/L in the control group ( Z=0.70, P=0.484). The results of multivariate logistic regression analysis showed that the risk of FPHL was increased by the high level of DHEA-S and thyroid-stimulating hormone, and the low level of 25-hydroxyvitamin D (all P<0.05). Conclusion:Abnormal level of DHEA-S, thyroid-stimulating hormone, and 25-hydroxyvitamin D may be risk factors for FPHL.

Liver size is regulated by circadian clock and exhibits a diurnal rhythm. Pregnane X receptor (PXR) and peroxisome proliferator-activated receptor α (PPARα), members of nuclear receptor superfamily, are important regulators of liver size. We previously demonstrated that mPXR agonist pregnenolone 16α-carbonitrile (PCN) and mPPARα agonist pirinixic acid (WY-14643) promoted liver enlargement and liver regeneration after partial hepatectomy. However, whether PXR or PPARα activation-induced liver enlargement exhibits diurnal rhythm with normal liver diurnal oscillations remains unclear. The aim of this study was to investigate the diurnal rhythm of PXR or PPARα activation-induced liver enlargement. Male C57BL/6 mice were intraperitoneally injected with corn oil, PCN or WY-14643 for three days, and liver samples were weighed and collected at various time points for analysis. The animal experiment protocol was reviewed and approved by Institutional Animal Care and Use Committee of Sun Yat-sen University (approval No. SYSU-IACUC-2023-001613, SYSU-IACUC-2023-001783). The results showed that PXR or PPARα activation at various time points significantly induced liver enlargement, and liver size maintained normal diurnal oscillations during PXR or PPARα activation-induced hepatomegaly, without significant effects on the expression of core clock genes. This study reveals PXR or PPARα activation-induced liver enlargement exhibits diurnal rhythm with normal liver diurnal oscillations, and provides novel data for nuclear receptor-induced liver enlargement and liver diurnal rhythm.

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.