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.

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 taste of drugs has an important impact on the compliance of patients, but most of the active drug ingredients have an uncomfortable taste, especially traditional Chinese medicine. Through a variety of pharmaceutical excipients with taste masking properties combined with corresponding technologies can improve the taste of drugs and the characteristics of other dosage forms, so as to improve patient compliance. Here, we mainly summarize the auxiliary materials used for taste masking, explain the mechanism of taste masking from the point of view of excipients and introduces related uses, so as to provide reference for further research on taste masking of pediatric preparations.

Good medicine tastes bitter, but it is often difficult to swallow because the drug is bitter and astringent, so that the compliance of patients with medication is poor. However, the use of taste masking technology can better improve this situation. Appropriate and effective taste masking technology can improve the drug compliance of patients, especially children, it can also improve the curative effect and the clinical value of drugs. Herein, we summarize the latest research progress of taste masking technology, and summarize the traditional taste masking technology from the aspects of action mechanisms and application scopes. Finally, the novel and efficient taste masking technologies were presented.

Before progress was recently made in the application of temporary anchorage devices (TADs) in bio-mechanical design, orthodontists were rarely able to intrude molars to reduce upper posterior dental height (UPDH). However, TADs are now widely used to intrude molars to flatten the occlusal plane or induce counterclockwise rotation of the mandible. Previous studies involving clinical or animal histological evaluation on changes in periodontal conditions after molar intrusion have been reported, however, studies involving human histology are scarce. This case was a Class I malocclusion with a high mandibular plane angle. Upper molar intrusion with TADs was performed to reduce UPDH, which led to counterclockwise rotation of the mandible. After 5 months of upper molar intrusion, shortened clinical crowns were noticed, which caused difficulties in oral hygiene and hindered orthodontic tooth movement. The mid-treatment cone-beam computed tomography revealed redundant bone physically interfering with buccal attachment and osseous resective surgeries were followed. During the surgeries, bilateral mini screws were removed and bulging alveolar bone and gingiva were harvested for biopsy. Histological examination revealed bacterial colonies at the bottom of the sulcus. Infiltration of chronic inflammatory cells underneath the non-keratinized sulcular epithelium was noted, with abundant capillaries being filled with red blood cells. Proximal alveolar bone facing the bottom of the gingival sulcus exhibited active bone remodeling and woven bone formation with plump osteocytes in the lacunae.On the other hand, buccal alveolar bone exhibited lamination, indicating slow bone turnover in the lateral region.

In this study, the compound search was completed through SciFinder and CNKI databases, and the drug-like properties were screened in FAFdrugs4 and SEA Search Server databases. In addition, based on the target sets related to acute myocardial ischemia(AMI) searched in disease target databases such as OMIM database, GeneCards database and DrugBank, a network diagram of chemical component-target-pathway-disease was established via Cytoscape to predict the potential active components of Corydalis Herba, a traditional Tibetan herbal medicine which derived from the aerial parts of Corydalis hendersonii and C. mucronifera against AMI. A protein-protein interaction(PPI) network was constructed through the STRING database and the core targets in the network were predicted. And the enrichment analyses of core targets were completed by DAVID database and R software. Furthermore, a molecular docking method was used to verify the binding of the components with core targets using softwares such as Autodock Vina. The present results showed that there were 60 compounds related to AMI in Corydalis Herba, involving 73 potential targets. The GO functional enrichment analysis obtained 282 biological processes(BP), 49 cell components(CC) and 78 molecular functions(MF). KEGG was enriched into 85 pathways, including alcoholism pathway, endocrine resistance pathway, calcium signaling pathway, cAMP signaling pathway, vascular endothelial growth factor signaling pathway and adrenergic signaling transduction pathway of myocardial cells. The results of network topology analysis showed that the key components of anti-AMI of Corydalis Herba might be tetrahydropalmatine, etrahydrocolumbamine, N-trans-feruloyloctopamine, N-cis-p-coumaroyloctopamine, N-trans-p-coumaroylnoradrenline and N-trans-p-coumaroyloctopamine, and their core targets might be CDH23, SCN4 B and NFASC. The results of molecular docking showed that the key components of Corydalis Herba had stable binding activity with the core targets. This study provides reference for further elucidation of the pharmacological effects of Corydalis Herba against AMI, subsequent clinical application, and development.
Corydalis ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Tibetan Traditional ; Molecular Docking Simulation ; Myocardial Ischemia/drug therapy* ; Vascular Endothelial Growth Factor A