The disposable sterile urethral catheter was described in terms of the current status of the standards of foreign countries and China and its regulation and registration.The national supervision and sampling inspection and exploratory research of the disposable sterile urethral catheter in 2018,2019 and 2021 were introduced,and the problems found and the causes were analyzed and then the countermeasures were proposed accordingly.References were provided for guiding and standardizing the development of catheter products industry.[Chinese Medical Equipment Journal,2024,45(1):84-88]

The genus jujube(Ziziphus jujuba Mill.)within the Rhamnaceae family encompasses numerous varieties,such as Ziziphus jujuba Mill.var.jujuba,Ziziphus jujuba var.inermis,and var.spinosa,etc.Among these,the jujube fructus has the most abundant cultivated variants across the country,including Ziziphus jujuba cv.Hamidazao and Ziziphus jujuba cv.Huanghetanzao.Jujube plants are rich in variety and are used for both medicinal and food purposes.Polysaccharides,one of the main active ingredients of jujube,are important medicinal components that contribute to its efficacy.Jujube polysaccharides have been found to promote hematopoiesis,exhibit antioxidant and anti-tumor activities,repair liver damage,regulate the immune system,and provide anti-inflammatory effects.By comprehensively summarizing and analyzing the literature on jujube polysaccharides from different varieties and origins,this paper reviews the potential mechanisms of action of jujube polysaccharides in exerting biological activities.It also summarizes the primary structural features,such as relative molecular mass,monosaccharide composition,glycosidic linkage,and the substituent modifications of jujube polysaccharides by sulfation,phosphorylation,carboxymethylation,selenization,and acetylation.This review aims to provide a reference for the research and development of jujube in the fields of innovative polysaccharide drugs and functional foods.

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: . A novel J-shaped anterolateral thigh (ALT) flap reconstruction technique was developed to simultaneously restore swallowing and speech functions in patients following total laryngopharyngectomy. This study aimed to assess the outcomes and surgical complications in patients who underwent J-flap reconstruction over time. Methods: . Patients who underwent J-shaped ALT flap phonatory tube reconstruction were enrolled. Surgical morbidities and outcomes were evaluated every 3 months post-surgery for a period of 12 months or until death. Results: . Of the 36 patients, 13 underwent circumferential pharyngeal wall resection (circumferential defect [CD] group), and 23 underwent partial resection (partial defect [PD] group). After 12 months, 97% of the patients were able to resume oral intake without the need for a nasogastric tube, and 50% achieved fluent speech using the reconstructed phonatory tube. The CD group experienced a higher rate of delayed healing than the PD group (30.8% vs. 0%, p=0.012). Additionally, the PD group showed significantly higher percentages of individuals consuming solid food at both the 3- and 12-month intervals than the CD group (81.0% vs. 23.1% and 78.9% vs. 40%, respectively). Conclusions . This study investigated the progression of speech and swallowing functions over time after reconstruction of the voice tube with a J-flap. Using a J-shaped ALT flap phonatory tube effectively restored both speech and swallowing functions, providing long-term benefits, regardless of whether the defect was circumferential or partial.

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: . A novel J-shaped anterolateral thigh (ALT) flap reconstruction technique was developed to simultaneously restore swallowing and speech functions in patients following total laryngopharyngectomy. This study aimed to assess the outcomes and surgical complications in patients who underwent J-flap reconstruction over time. Methods: . Patients who underwent J-shaped ALT flap phonatory tube reconstruction were enrolled. Surgical morbidities and outcomes were evaluated every 3 months post-surgery for a period of 12 months or until death. Results: . Of the 36 patients, 13 underwent circumferential pharyngeal wall resection (circumferential defect [CD] group), and 23 underwent partial resection (partial defect [PD] group). After 12 months, 97% of the patients were able to resume oral intake without the need for a nasogastric tube, and 50% achieved fluent speech using the reconstructed phonatory tube. The CD group experienced a higher rate of delayed healing than the PD group (30.8% vs. 0%, p=0.012). Additionally, the PD group showed significantly higher percentages of individuals consuming solid food at both the 3- and 12-month intervals than the CD group (81.0% vs. 23.1% and 78.9% vs. 40%, respectively). Conclusions . This study investigated the progression of speech and swallowing functions over time after reconstruction of the voice tube with a J-flap. Using a J-shaped ALT flap phonatory tube effectively restored both speech and swallowing functions, providing long-term benefits, regardless of whether the defect was circumferential or partial.

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: . A novel J-shaped anterolateral thigh (ALT) flap reconstruction technique was developed to simultaneously restore swallowing and speech functions in patients following total laryngopharyngectomy. This study aimed to assess the outcomes and surgical complications in patients who underwent J-flap reconstruction over time. Methods: . Patients who underwent J-shaped ALT flap phonatory tube reconstruction were enrolled. Surgical morbidities and outcomes were evaluated every 3 months post-surgery for a period of 12 months or until death. Results: . Of the 36 patients, 13 underwent circumferential pharyngeal wall resection (circumferential defect [CD] group), and 23 underwent partial resection (partial defect [PD] group). After 12 months, 97% of the patients were able to resume oral intake without the need for a nasogastric tube, and 50% achieved fluent speech using the reconstructed phonatory tube. The CD group experienced a higher rate of delayed healing than the PD group (30.8% vs. 0%, p=0.012). Additionally, the PD group showed significantly higher percentages of individuals consuming solid food at both the 3- and 12-month intervals than the CD group (81.0% vs. 23.1% and 78.9% vs. 40%, respectively). Conclusions . This study investigated the progression of speech and swallowing functions over time after reconstruction of the voice tube with a J-flap. Using a J-shaped ALT flap phonatory tube effectively restored both speech and swallowing functions, providing long-term benefits, regardless of whether the defect was circumferential or partial.

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.