Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

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: We evaluated the therapeutic effects on overactive bladder (OAB) symptoms and sexual function of behavioral therapy with or without mirabegron in sexually active male patients with OAB. Mirabegron, a selective β3 adrenoceptor agonist for the treatment of OAB, has been shown to induce corpus cavernosum relaxation. Methods: In this 4-site, randomized controlled trial, 150 sexually active men with OAB were enrolled between June 2020 and May 2022. Participants were randomly allocated (1:2) into 2 treatment groups: (1) behavioral therapy alone (n = 50) and (2) a combination of mirabegron 50 mg daily and behavioral therapy (n = 100). The evaluation was based on the overactive bladder symptoms score (OABSS), the International Index of Erectile Function, the ejaculatory domain short form, the International Prostate Symptom Score, patient perception of bladder condition, quality of life, and urodynamic parameters. The therapeutic outcomes were assessed at baseline, 4 weeks, and 12 weeks. Results: There were 65 patients (65%) in the combination subgroup and 36 patients in the behavioral therapy who completed all 12 weeks of treatment. Both groups had a statistically significant improvement in OABSS after 12 weeks of treatment. The combination therapy group achieved a statistically significant improvement in all 4 subscores of OABSS, however, the urinary frequency (P = 0.120) and urinary incontinence (P = 0.234) subscores in the behavioral therapy only group did not show a significant change. Additionally, the combination group had a significant improvement in functional bladder capacity, which was not seen in the behavioral therapy group. However, both groups did not have a significant change in erectile or ejaculatory function. Conclusions Behavioral therapy combined with mirabegron had more significant impact on the improvement of OAB than behavior therapy alone. However, both groups did not have significant changes in erectile or ejaculatory function.

Background: Shared decision making using patient decision aids (PtDAs) was established over a decade ago, but few studies have evaluated its efficacy in Asian countries. We therefore evaluated the application of PtDAs in a decision conflict between two muscle relaxant reversal agents, neostigmine and sugammadex, and sequentially analyzed the regional differences and operating room turnover rates. Methods: This multicenter, outcome-assessor-blind, randomized controlled trial included 3,132 surgical patients from two medical centers admitted between March 2020 and August 2020. The patients were randomly divided into the classical and PtDA groups for pre-anesthesia consultations. Their clinicodemographic characteristics were analyzed to identify variables influencing the choice of reversal agent. On the day of the pre-anesthesia consultation, the patients completed the four SURE scale (sure of myself, understand information, risk-benefit ratio, encouragement) screening items. The operating turnover rates were also evaluated using anesthesia records. Results: Compared with the classical group, the PtDA group felt more confident about receiving sufficient medical information (P < 0.001), felt better informed about the advantages and disadvantages of the medications (P < 0.001), exhibited a superior understanding of the benefits and risks of their options (P < 0.001), and felt surer about their choice (P < 0.001). Moreover, the PtDA group had a significantly greater tendency to choose sugammadex over neostigmine (P < 0.001). Conclusions PtDA interventions in pre-anesthesia consultations provided surgical patients with clear knowledge and better support. PtDAs should be made available in other medical fields to enhance shared clinical decision-making.