Geraniin, a polyphenol derived from the fruit peel of Nephelium lappaceum L., has been shown to possess anti-inflammatory and antioxidant properties in the cardiovascular system. The present study explored whether geraniin could protect against an isoproterenol (ISO)-induced cardiac hypertrophy model. Mice in the ISO group received an intraperitoneal injection of ISO (5 mg/kg) once daily for 9 days, and the administration group were injected with ISO after 5 days of treatment with geraniin or spironolactone. Potential therapeutic effects and related mechanisms analysed by anatomical coefficients, histopathology, blood biochemical indices, reverse transcription-PCR and immunoblotting. Geraniin decreased the cardiac pathologic remodeling and myocardial fibrosis induced by ISO, as evidenced by the modifications to anatomical coefficients, as well as the reduction in collagen I/III á1mRNA and protein expression and cross-sectional area in hypertrophic cardiac tissue. In addition, geraniin treatment reduced ISO-induced increase in the mRNA and protein expression levels of interleukin (IL)-6, IL-1β and tumor necrosis factor-α, whereas ISO-induced IL-10 showed the opposite behaviour in hypertrophic cardiac tissue.Further analysis showed that geraniin partially reversed the ISO-induced increase in malondialdehyde and nitric oxide, and the ISO-induced decrease in glutathione, superoxide dismutase and glutathione. Furthermore, it suppressed the ISO-induced cellular apoptosis of hypertrophic cardiac tissue, as evidenced by the decrease in Bcell lymphoma-2 (Bcl-2)-associated X/caspase-3/caspase-9 expression, increase in Bcl-2 expression, and decrease in TdT-mediated dUTP nick-end labeling-positive cells.These findings suggest that geraniin can attenuate ISO-induced cardiac hypertrophy by inhibiting inflammation, oxidative stress and cellular apoptosis.

Geraniin, a polyphenol derived from the fruit peel of Nephelium lappaceum L., has been shown to possess anti-inflammatory and antioxidant properties in the cardiovascular system. The present study explored whether geraniin could protect against an isoproterenol (ISO)-induced cardiac hypertrophy model. Mice in the ISO group received an intraperitoneal injection of ISO (5 mg/kg) once daily for 9 days, and the administration group were injected with ISO after 5 days of treatment with geraniin or spironolactone. Potential therapeutic effects and related mechanisms analysed by anatomical coefficients, histopathology, blood biochemical indices, reverse transcription-PCR and immunoblotting. Geraniin decreased the cardiac pathologic remodeling and myocardial fibrosis induced by ISO, as evidenced by the modifications to anatomical coefficients, as well as the reduction in collagen I/III á1mRNA and protein expression and cross-sectional area in hypertrophic cardiac tissue. In addition, geraniin treatment reduced ISO-induced increase in the mRNA and protein expression levels of interleukin (IL)-6, IL-1β and tumor necrosis factor-α, whereas ISO-induced IL-10 showed the opposite behaviour in hypertrophic cardiac tissue.Further analysis showed that geraniin partially reversed the ISO-induced increase in malondialdehyde and nitric oxide, and the ISO-induced decrease in glutathione, superoxide dismutase and glutathione. Furthermore, it suppressed the ISO-induced cellular apoptosis of hypertrophic cardiac tissue, as evidenced by the decrease in Bcell lymphoma-2 (Bcl-2)-associated X/caspase-3/caspase-9 expression, increase in Bcl-2 expression, and decrease in TdT-mediated dUTP nick-end labeling-positive cells.These findings suggest that geraniin can attenuate ISO-induced cardiac hypertrophy by inhibiting inflammation, oxidative stress and cellular apoptosis.

Geraniin, a polyphenol derived from the fruit peel of Nephelium lappaceum L., has been shown to possess anti-inflammatory and antioxidant properties in the cardiovascular system. The present study explored whether geraniin could protect against an isoproterenol (ISO)-induced cardiac hypertrophy model. Mice in the ISO group received an intraperitoneal injection of ISO (5 mg/kg) once daily for 9 days, and the administration group were injected with ISO after 5 days of treatment with geraniin or spironolactone. Potential therapeutic effects and related mechanisms analysed by anatomical coefficients, histopathology, blood biochemical indices, reverse transcription-PCR and immunoblotting. Geraniin decreased the cardiac pathologic remodeling and myocardial fibrosis induced by ISO, as evidenced by the modifications to anatomical coefficients, as well as the reduction in collagen I/III á1mRNA and protein expression and cross-sectional area in hypertrophic cardiac tissue. In addition, geraniin treatment reduced ISO-induced increase in the mRNA and protein expression levels of interleukin (IL)-6, IL-1β and tumor necrosis factor-α, whereas ISO-induced IL-10 showed the opposite behaviour in hypertrophic cardiac tissue.Further analysis showed that geraniin partially reversed the ISO-induced increase in malondialdehyde and nitric oxide, and the ISO-induced decrease in glutathione, superoxide dismutase and glutathione. Furthermore, it suppressed the ISO-induced cellular apoptosis of hypertrophic cardiac tissue, as evidenced by the decrease in Bcell lymphoma-2 (Bcl-2)-associated X/caspase-3/caspase-9 expression, increase in Bcl-2 expression, and decrease in TdT-mediated dUTP nick-end labeling-positive cells.These findings suggest that geraniin can attenuate ISO-induced cardiac hypertrophy by inhibiting inflammation, oxidative stress and cellular apoptosis.

Geraniin, a polyphenol derived from the fruit peel of Nephelium lappaceum L., has been shown to possess anti-inflammatory and antioxidant properties in the cardiovascular system. The present study explored whether geraniin could protect against an isoproterenol (ISO)-induced cardiac hypertrophy model. Mice in the ISO group received an intraperitoneal injection of ISO (5 mg/kg) once daily for 9 days, and the administration group were injected with ISO after 5 days of treatment with geraniin or spironolactone. Potential therapeutic effects and related mechanisms analysed by anatomical coefficients, histopathology, blood biochemical indices, reverse transcription-PCR and immunoblotting. Geraniin decreased the cardiac pathologic remodeling and myocardial fibrosis induced by ISO, as evidenced by the modifications to anatomical coefficients, as well as the reduction in collagen I/III á1mRNA and protein expression and cross-sectional area in hypertrophic cardiac tissue. In addition, geraniin treatment reduced ISO-induced increase in the mRNA and protein expression levels of interleukin (IL)-6, IL-1β and tumor necrosis factor-α, whereas ISO-induced IL-10 showed the opposite behaviour in hypertrophic cardiac tissue.Further analysis showed that geraniin partially reversed the ISO-induced increase in malondialdehyde and nitric oxide, and the ISO-induced decrease in glutathione, superoxide dismutase and glutathione. Furthermore, it suppressed the ISO-induced cellular apoptosis of hypertrophic cardiac tissue, as evidenced by the decrease in Bcell lymphoma-2 (Bcl-2)-associated X/caspase-3/caspase-9 expression, increase in Bcl-2 expression, and decrease in TdT-mediated dUTP nick-end labeling-positive cells.These findings suggest that geraniin can attenuate ISO-induced cardiac hypertrophy by inhibiting inflammation, oxidative stress and cellular apoptosis.

Geraniin, a polyphenol derived from the fruit peel of Nephelium lappaceum L., has been shown to possess anti-inflammatory and antioxidant properties in the cardiovascular system. The present study explored whether geraniin could protect against an isoproterenol (ISO)-induced cardiac hypertrophy model. Mice in the ISO group received an intraperitoneal injection of ISO (5 mg/kg) once daily for 9 days, and the administration group were injected with ISO after 5 days of treatment with geraniin or spironolactone. Potential therapeutic effects and related mechanisms analysed by anatomical coefficients, histopathology, blood biochemical indices, reverse transcription-PCR and immunoblotting. Geraniin decreased the cardiac pathologic remodeling and myocardial fibrosis induced by ISO, as evidenced by the modifications to anatomical coefficients, as well as the reduction in collagen I/III á1mRNA and protein expression and cross-sectional area in hypertrophic cardiac tissue. In addition, geraniin treatment reduced ISO-induced increase in the mRNA and protein expression levels of interleukin (IL)-6, IL-1β and tumor necrosis factor-α, whereas ISO-induced IL-10 showed the opposite behaviour in hypertrophic cardiac tissue.Further analysis showed that geraniin partially reversed the ISO-induced increase in malondialdehyde and nitric oxide, and the ISO-induced decrease in glutathione, superoxide dismutase and glutathione. Furthermore, it suppressed the ISO-induced cellular apoptosis of hypertrophic cardiac tissue, as evidenced by the decrease in Bcell lymphoma-2 (Bcl-2)-associated X/caspase-3/caspase-9 expression, increase in Bcl-2 expression, and decrease in TdT-mediated dUTP nick-end labeling-positive cells.These findings suggest that geraniin can attenuate ISO-induced cardiac hypertrophy by inhibiting inflammation, oxidative stress and cellular apoptosis.

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.