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.

Background: The initiation of sodium-glucose cotransporter-2 inhibitors (SGLT2i) typically leads to a reversible initial dip in estimated glomerular filtration rate (eGFR). The implications of this phenomenon on clinical outcomes are not well-defined. Methods: We searched MEDLINE, Embase, and Cochrane Library from inception to March 23, 2023 to identify randomized controlled trials and cohort studies comparing kidney and cardiovascular outcomes in patients with and without initial eGFR dip after initiating SGLT2i. Pooled estimates were calculated using random-effect meta-analysis. Results: We included seven studies in our analysis, which revealed that an initial eGFR dip following the initiation of SGLT2i was associated with less annual eGFR decline (mean difference, 0.64; 95% confidence interval [CI], 0.437 to 0.843) regardless of baseline eGFR. The risk of major adverse kidney events was similar between the non-dipping and dipping groups but reduced in patients with a ≤10% eGFR dip (hazard ratio [HR], 0.915; 95% CI, 0.865 to 0.967). No significant differences were observed in the composite of hospitalized heart failure and cardiovascular death (HR, 0.824; 95% CI, 0.633 to 1.074), hospitalized heart failure (HR, 1.059; 95% CI, 0.574 to 1.952), or all-cause mortality (HR, 0.83; 95% CI, 0.589 to 1.170). The risk of serious adverse events (AEs), discontinuation of SGLT2i due to AEs, kidney-related AEs, and volume depletion were similar between the two groups. Patients with >10% eGFR dip had increased risk of hyperkalemia compared to the non-dipping group. Conclusion Initial eGFR dip after initiating SGLT2i might be associated with less annual eGFR decline. There were no significant disparities in the risks of adverse cardiovascular outcomes between the dipping and non-dipping groups.

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.