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: Lynch syndrome (LS) is a hereditary cancer predisposition syndrome with a significantly increased risk of colorectal and endometrial cancers. Current standard practice involves universal screening for LS in patients with newly diagnosed colorectal or endometrial cancer using a multi-step screening protocol (MSP). However, MSP may not always accurately identify LS cases. To address this limitation, we compared the diagnostic performance of immediate germline sequencing (IGS) with MSP in a high-risk group. Methods: A total of 31 Taiwanese women with synchronous or metachronous endometrial and colorectal malignancies underwent MSP which included immunohistochemical staining of DNA mismatch repair (MMR) proteins, MLH1 promoter hypermethylation analysis, and germline sequencing to identify pathogenic variants. All patients who were excluded during MSP received germline sequencing for MMR genes to simulate IGS for the detection of LS. Results: Our findings indicate that IGS surpassed MSP in terms of diagnostic yield (29.0% vs.19.4%, respectively) and sensitivity (90% vs. 60%, respectively). Specifically, IGS successfully identified nine LS cases, which is 50% more than the number detected through MSP.Additionally, germline methylation analysis revealed one more LS case with constitutional MLH1 promoter hypermethylation, bringing the total LS cases to ten (32.3%). Intriguingly, we observed no significant differences in clinical characteristics or overall survival between patients with and without LS in our cohort. Conclusion Our study suggests that IGS may potentially offer a more effective approach compared to MSP in identifying LS among high-risk patients. This advantage is evident when patients have been pre-selected utilizing specific clinical criteria.

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: Lynch syndrome (LS) is a hereditary cancer predisposition syndrome with a significantly increased risk of colorectal and endometrial cancers. Current standard practice involves universal screening for LS in patients with newly diagnosed colorectal or endometrial cancer using a multi-step screening protocol (MSP). However, MSP may not always accurately identify LS cases. To address this limitation, we compared the diagnostic performance of immediate germline sequencing (IGS) with MSP in a high-risk group. Methods: A total of 31 Taiwanese women with synchronous or metachronous endometrial and colorectal malignancies underwent MSP which included immunohistochemical staining of DNA mismatch repair (MMR) proteins, MLH1 promoter hypermethylation analysis, and germline sequencing to identify pathogenic variants. All patients who were excluded during MSP received germline sequencing for MMR genes to simulate IGS for the detection of LS. Results: Our findings indicate that IGS surpassed MSP in terms of diagnostic yield (29.0% vs.19.4%, respectively) and sensitivity (90% vs. 60%, respectively). Specifically, IGS successfully identified nine LS cases, which is 50% more than the number detected through MSP.Additionally, germline methylation analysis revealed one more LS case with constitutional MLH1 promoter hypermethylation, bringing the total LS cases to ten (32.3%). Intriguingly, we observed no significant differences in clinical characteristics or overall survival between patients with and without LS in our cohort. Conclusion Our study suggests that IGS may potentially offer a more effective approach compared to MSP in identifying LS among high-risk patients. This advantage is evident when patients have been pre-selected utilizing specific clinical criteria.

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: Lynch syndrome (LS) is a hereditary cancer predisposition syndrome with a significantly increased risk of colorectal and endometrial cancers. Current standard practice involves universal screening for LS in patients with newly diagnosed colorectal or endometrial cancer using a multi-step screening protocol (MSP). However, MSP may not always accurately identify LS cases. To address this limitation, we compared the diagnostic performance of immediate germline sequencing (IGS) with MSP in a high-risk group. Methods: A total of 31 Taiwanese women with synchronous or metachronous endometrial and colorectal malignancies underwent MSP which included immunohistochemical staining of DNA mismatch repair (MMR) proteins, MLH1 promoter hypermethylation analysis, and germline sequencing to identify pathogenic variants. All patients who were excluded during MSP received germline sequencing for MMR genes to simulate IGS for the detection of LS. Results: Our findings indicate that IGS surpassed MSP in terms of diagnostic yield (29.0% vs.19.4%, respectively) and sensitivity (90% vs. 60%, respectively). Specifically, IGS successfully identified nine LS cases, which is 50% more than the number detected through MSP.Additionally, germline methylation analysis revealed one more LS case with constitutional MLH1 promoter hypermethylation, bringing the total LS cases to ten (32.3%). Intriguingly, we observed no significant differences in clinical characteristics or overall survival between patients with and without LS in our cohort. Conclusion Our study suggests that IGS may potentially offer a more effective approach compared to MSP in identifying LS among high-risk patients. This advantage is evident when patients have been pre-selected utilizing specific clinical criteria.

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.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

The effect of Tujia medicine Berberidis Radix on endogenous metabolites in the serum and feces of mice with ulcerative colitis(UC) induced by dextran sulfate sodium(DSS) was analyzed by metabolomics technology to explore the metabolic pathway and underlying mechanism of Berberidis Radix in the intervention of UC. The UC model was induced in mice by DSS. Body weight, disease activity index(DAI), and colon length were recorded. The levels of tumor necrosis factor-α(TNF-α) and interleukin-10(IL-10) in colon tissues were determined by ELISA. The levels of endogenous metabolites in the serum and feces were detected by ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS). Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were employed to characterize and screen differential metabolites. The potential metabolic pathways were analyzed by MetaboAnalyst 5.0. The results showed that Berberidis Radix could significantly improve the symptoms of UC mice and increase the level of the anti-inflammatory factor IL-10. A total of 56 and 43 differential metabolites were identified in the serum and feces, respectively, belonging to lipids, amino acids, fatty acids, etc. After the intervention by Berberidis Radix, the metabolic disorder gradually recovered. The involved metabolic pathways included biosynthesis of phenylalanine, tyrosine, and tryptophan, linoleic acid metabolism, phenylalanine metabolism, and glycerophospholipid metabolism. Berberidis Radix can alleviate the symptoms of mice with DSS-induced UC, and the mechanism may be closely related to the re-gulation of lipid metabolism, amino acid metabolism, and energy metabolism.
Mice ; Animals ; Colitis, Ulcerative/drug therapy* ; Interleukin-10 ; Metabolomics/methods* ; Chromatography, High Pressure Liquid