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: Danshensu, a phenylpropanoid compound, is derived from the dry root and rhizome of Danshen (Salvia miltiorrhiza), a traditional Chinese medicinal herb. Evidence suggests that danshensu protects isolated rat hearts against ischemia/reperfusion injury by activating the protein kinase B (Akt)/extracellular signal-regulated kinase (ERK) pathway or by inhibiting autophagy and apoptosis through the activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, danshensu promotes the postischemic regeneration of brain cells by upregulating the expression of brain-derived neurotrophic factor (BDNF) in the peri-infarct region. However, basic and clinical studies are needed to investigate the antidepressant effects danshensu and determine whether brain mTOR signaling and BDNF activation mediate these effects. The aforementioned need prompted us to conduct the present study. Methods: Using a C57BL/6 mouse model, we investigated the antidepressant-like effects of danshensu and the mechanisms that mediate these effects. To elucidate the mechanisms, we analyzed the roles of Akt/ERK–mTOR signaling and BDNF activation in mediating the antidepressant-like effects of danshensu. Results: Danshensu exerted its antidepressant-like effects by activating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) of Akt/ERK–mTOR signaling and promoting BDNF release. Treatment with danshensu increased the level of glutamate receptor 1 phosphorylation at the protein kinase A site. Conclusion Our study may be the first to demonstrate that the antidepressant effects of danshensu are dependent on the activation of the AMPAR–mTOR signaling pathway, are correlated with the elevation of BDNF level, and facilitate the insertion of AMPAR into the postsynaptic membrane. This study also pioneers in unveiling the potential of danshensu against depressive disorders.

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: Danshensu, a phenylpropanoid compound, is derived from the dry root and rhizome of Danshen (Salvia miltiorrhiza), a traditional Chinese medicinal herb. Evidence suggests that danshensu protects isolated rat hearts against ischemia/reperfusion injury by activating the protein kinase B (Akt)/extracellular signal-regulated kinase (ERK) pathway or by inhibiting autophagy and apoptosis through the activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, danshensu promotes the postischemic regeneration of brain cells by upregulating the expression of brain-derived neurotrophic factor (BDNF) in the peri-infarct region. However, basic and clinical studies are needed to investigate the antidepressant effects danshensu and determine whether brain mTOR signaling and BDNF activation mediate these effects. The aforementioned need prompted us to conduct the present study. Methods: Using a C57BL/6 mouse model, we investigated the antidepressant-like effects of danshensu and the mechanisms that mediate these effects. To elucidate the mechanisms, we analyzed the roles of Akt/ERK–mTOR signaling and BDNF activation in mediating the antidepressant-like effects of danshensu. Results: Danshensu exerted its antidepressant-like effects by activating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) of Akt/ERK–mTOR signaling and promoting BDNF release. Treatment with danshensu increased the level of glutamate receptor 1 phosphorylation at the protein kinase A site. Conclusion Our study may be the first to demonstrate that the antidepressant effects of danshensu are dependent on the activation of the AMPAR–mTOR signaling pathway, are correlated with the elevation of BDNF level, and facilitate the insertion of AMPAR into the postsynaptic membrane. This study also pioneers in unveiling the potential of danshensu against depressive disorders.

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: Danshensu, a phenylpropanoid compound, is derived from the dry root and rhizome of Danshen (Salvia miltiorrhiza), a traditional Chinese medicinal herb. Evidence suggests that danshensu protects isolated rat hearts against ischemia/reperfusion injury by activating the protein kinase B (Akt)/extracellular signal-regulated kinase (ERK) pathway or by inhibiting autophagy and apoptosis through the activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, danshensu promotes the postischemic regeneration of brain cells by upregulating the expression of brain-derived neurotrophic factor (BDNF) in the peri-infarct region. However, basic and clinical studies are needed to investigate the antidepressant effects danshensu and determine whether brain mTOR signaling and BDNF activation mediate these effects. The aforementioned need prompted us to conduct the present study. Methods: Using a C57BL/6 mouse model, we investigated the antidepressant-like effects of danshensu and the mechanisms that mediate these effects. To elucidate the mechanisms, we analyzed the roles of Akt/ERK–mTOR signaling and BDNF activation in mediating the antidepressant-like effects of danshensu. Results: Danshensu exerted its antidepressant-like effects by activating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) of Akt/ERK–mTOR signaling and promoting BDNF release. Treatment with danshensu increased the level of glutamate receptor 1 phosphorylation at the protein kinase A site. Conclusion Our study may be the first to demonstrate that the antidepressant effects of danshensu are dependent on the activation of the AMPAR–mTOR signaling pathway, are correlated with the elevation of BDNF level, and facilitate the insertion of AMPAR into the postsynaptic membrane. This study also pioneers in unveiling the potential of danshensu against depressive disorders.

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: Danshensu, a phenylpropanoid compound, is derived from the dry root and rhizome of Danshen (Salvia miltiorrhiza), a traditional Chinese medicinal herb. Evidence suggests that danshensu protects isolated rat hearts against ischemia/reperfusion injury by activating the protein kinase B (Akt)/extracellular signal-regulated kinase (ERK) pathway or by inhibiting autophagy and apoptosis through the activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, danshensu promotes the postischemic regeneration of brain cells by upregulating the expression of brain-derived neurotrophic factor (BDNF) in the peri-infarct region. However, basic and clinical studies are needed to investigate the antidepressant effects danshensu and determine whether brain mTOR signaling and BDNF activation mediate these effects. The aforementioned need prompted us to conduct the present study. Methods: Using a C57BL/6 mouse model, we investigated the antidepressant-like effects of danshensu and the mechanisms that mediate these effects. To elucidate the mechanisms, we analyzed the roles of Akt/ERK–mTOR signaling and BDNF activation in mediating the antidepressant-like effects of danshensu. Results: Danshensu exerted its antidepressant-like effects by activating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) of Akt/ERK–mTOR signaling and promoting BDNF release. Treatment with danshensu increased the level of glutamate receptor 1 phosphorylation at the protein kinase A site. Conclusion Our study may be the first to demonstrate that the antidepressant effects of danshensu are dependent on the activation of the AMPAR–mTOR signaling pathway, are correlated with the elevation of BDNF level, and facilitate the insertion of AMPAR into the postsynaptic membrane. This study also pioneers in unveiling the potential of danshensu against depressive disorders.

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: Danshensu, a phenylpropanoid compound, is derived from the dry root and rhizome of Danshen (Salvia miltiorrhiza), a traditional Chinese medicinal herb. Evidence suggests that danshensu protects isolated rat hearts against ischemia/reperfusion injury by activating the protein kinase B (Akt)/extracellular signal-regulated kinase (ERK) pathway or by inhibiting autophagy and apoptosis through the activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, danshensu promotes the postischemic regeneration of brain cells by upregulating the expression of brain-derived neurotrophic factor (BDNF) in the peri-infarct region. However, basic and clinical studies are needed to investigate the antidepressant effects danshensu and determine whether brain mTOR signaling and BDNF activation mediate these effects. The aforementioned need prompted us to conduct the present study. Methods: Using a C57BL/6 mouse model, we investigated the antidepressant-like effects of danshensu and the mechanisms that mediate these effects. To elucidate the mechanisms, we analyzed the roles of Akt/ERK–mTOR signaling and BDNF activation in mediating the antidepressant-like effects of danshensu. Results: Danshensu exerted its antidepressant-like effects by activating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) of Akt/ERK–mTOR signaling and promoting BDNF release. Treatment with danshensu increased the level of glutamate receptor 1 phosphorylation at the protein kinase A site. Conclusion Our study may be the first to demonstrate that the antidepressant effects of danshensu are dependent on the activation of the AMPAR–mTOR signaling pathway, are correlated with the elevation of BDNF level, and facilitate the insertion of AMPAR into the postsynaptic membrane. This study also pioneers in unveiling the potential of danshensu against depressive disorders.