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:To investigate the clinical outcome of biplanar botulinum toxin type A injection in alleviating platysmal bands.Methods:From November 2022 to May 2023, the clinical data of patients with platysmal bands treated by botulinum toxin type A injection in Department of Face and Neck Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, and Department of Plastic Surgery, Chengdu Badachu Cosmetic Hospital were retrospectively analyzed. The platysmal bands were marked, while patients were grinning, before injection. Using a 13 mm 30 G needle, 20 U/ml botulinum toxin was injected into the muscle layer along the bands from the clavicle direction. The dose was 1 U at a single point every 1.5 cm. Using a 3 mm 30 G needle, 10 U/ml botulinum toxin was injected into the deep surface of dermis along the bands with a single point dose of 0.5 U. Effects were evaluated by overall subjective satisfaction of patients, which were categorized into 4 grades: very satisfied, satisfied, dissatisfied, very dissatisfied. In addition, accessment by a third-party physician with global aesthetic improvement scale (GAIS) (1-5 points, the lower the score, the more significant the improvement is) and Geister platysmal band scale (0-4 points, the higher the score, the more severe the platysmal band is). Normal distribution data was represented by Mean±SD.Results:A total of 19 patients were included, including 3 males and 16 females, with the average age of 36.1 years. After a mean follow-up of 1.3 months (1-5 months), the overall subjective satisfaction was 100%(19/19). The GAIS score of third-party physicians was 1.12±0.33. 100%(19/19) of patients received a rating over moderate improvement(significant improvement in 17 cases and moderate improvement in 2 cases). The Geister platysmal band score decreased from preoperative 3.65 ± 0.33 to postoperative 0.76 ± 0.44. No serious complications were found except 5 cases of local congestion and 2 cases of injection pain, which were relieved in 1 week and 3 hours respectively. 2 cases felt mild neck weakness, but neck activity was not affected. The adverse symptoms all completely resolved spontaneously within 4 weeks. All patients have no mouth deviation, difficulty speaking, facial paralysis, allergies, or other noticeable complications.Conclusion:The injection of botulinum toxin type A in dual-plane of platysmal intramuscular layer and deep intradermal layer can effectively alleviate platysmal bands and achieve neck rejuvenation.

Objective:To investigate the clinical outcome of biplanar botulinum toxin type A injection in alleviating platysmal bands.Methods:From November 2022 to May 2023, the clinical data of patients with platysmal bands treated by botulinum toxin type A injection in Department of Face and Neck Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, and Department of Plastic Surgery, Chengdu Badachu Cosmetic Hospital were retrospectively analyzed. The platysmal bands were marked, while patients were grinning, before injection. Using a 13 mm 30 G needle, 20 U/ml botulinum toxin was injected into the muscle layer along the bands from the clavicle direction. The dose was 1 U at a single point every 1.5 cm. Using a 3 mm 30 G needle, 10 U/ml botulinum toxin was injected into the deep surface of dermis along the bands with a single point dose of 0.5 U. Effects were evaluated by overall subjective satisfaction of patients, which were categorized into 4 grades: very satisfied, satisfied, dissatisfied, very dissatisfied. In addition, accessment by a third-party physician with global aesthetic improvement scale (GAIS) (1-5 points, the lower the score, the more significant the improvement is) and Geister platysmal band scale (0-4 points, the higher the score, the more severe the platysmal band is). Normal distribution data was represented by Mean±SD.Results:A total of 19 patients were included, including 3 males and 16 females, with the average age of 36.1 years. After a mean follow-up of 1.3 months (1-5 months), the overall subjective satisfaction was 100%(19/19). The GAIS score of third-party physicians was 1.12±0.33. 100%(19/19) of patients received a rating over moderate improvement(significant improvement in 17 cases and moderate improvement in 2 cases). The Geister platysmal band score decreased from preoperative 3.65 ± 0.33 to postoperative 0.76 ± 0.44. No serious complications were found except 5 cases of local congestion and 2 cases of injection pain, which were relieved in 1 week and 3 hours respectively. 2 cases felt mild neck weakness, but neck activity was not affected. The adverse symptoms all completely resolved spontaneously within 4 weeks. All patients have no mouth deviation, difficulty speaking, facial paralysis, allergies, or other noticeable complications.Conclusion:The injection of botulinum toxin type A in dual-plane of platysmal intramuscular layer and deep intradermal layer can effectively alleviate platysmal bands and achieve neck rejuvenation.

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:To construct training indexes of post competency of dental hygienist, and to provide objective basis for the establishment of the index system of the transition from dental nurse to dental hygienist in the future.Methods:The relevant literature of post competency of dental hygienist was searched from the databases such as PubMed, Medline, Web of Science, China National Knowledge Internet, Wanfang database. The search time was from the establishment of the database to March 2021. The expert letter inquiry questionnaire was designed through preliminary consultation.Delphi expert consultation was conducted for 20 dental experts from Beijing city, Chongqing city, Jiangsu province, Sichuan province, Jilin province from May to November 2021, and model indexes and weight assignment of post competency of dental hygienist were determined.Results:Three rounds of Delphi expert consultation were conducted, the effective recoveries of the questionnaires were 100%, the expert authority coefficients were 0.81, 0.81, 0.83, respectively, the coefficient of variation of expert consultation was 0.000-0.386, 0.000-0.300 and 0.000-0.250, respectively, the coordination degree of expert opinions in the third round of consultation was 0.679, 0.428 and 0.389 (all P<0.01). The formed training indexes of post competency of dental hygienist included 4 first-level indexes, 20 second-level indexes and 60 third-level indexes. Conclusions:The training index system of post competency of dental hygienist in this study is scientific, reliable, and practical, to provide reference for training and assessment of post competency of dental hygienist and objective basis for establishing the indexes system of dental nurse to dental hygienist in the future.

Lipid-based nanocarrier is a classic drug delivery system with great biocompatibility and biodegradability. It can effectively reduce the toxicity of anti-tumor and anti-infective drugs in clinical practice. However, it has not yet met the clinical demand for enhanced therapeutic efficacy, and the clinical application is still very limited. The complex in vivo delivery process of lipid-based nanomedicine and the reciprocal interactions with body lead to unexpected changes in in vivo performance of nanomedicine and seriously hinder clinical translation. Therefore, the in-depth study of the relationships among intrinsic properties of lipid-based nanomedicine, the in vivo delivery process, and the regulatory mechanisms will not only provide guidance for the rational design of nanocarriers, but also promote the clinical translation and precision medicine of new lipid-based nanomedicine. In this review, we summarize the in vivo delivery process, regulating factors and intervention strategies for the in vivo delivery of lipid-based nanomedicine.

Objectives: This study sought to describe our institutional experience of repeated percutaneous stellate ganglion blockade (R-SGB) as a treatment option for drug-refractory electrical storm in patients with nonischemic cardiomyopathy (NICM). Methods: This prospective observational study included 8 consecutive NICM patients who had drug-refractory electrical storm and underwent R-SGB between June 1, 2021 and January 31, 2022. Lidocaine (5 ml, 1%) was injected in the vicinity of the left stellate ganglion under the guidance of ultrasound, once per day for 7 days. Data including clinical characteristics, immediate and long-term outcomes, and procedure related complications were collected. Results: The mean age was (51.5±13.6) years. All patients were male. 5 patients were diagnosed as dilated cardiomyopathy, 2 patients as arrhythmogenic right ventricular cardiomyopathy and 1 patient as hypertrophic cardiomyopathy. The left ventricular ejection fraction was 37.8%±6.6%. After the treatment of R-SGB, 6 (75%) patients were free of electrical storm. 24 hours Holter monitoring showed significant reduction in ventricular tachycardia (VT) episodes from 43.0 (13.3, 276.3) to 1.0 (0.3, 34.0) on the first day following R-SGB (P<0.05) and 0.5 (0.0, 19.3) after whole R-SGB process (P<0.05). There were no procedure-related major complications. The mean follow-up was (4.8±1.1) months, and the median time of recurrent VT was 2 months. Conclusion: Minimally invasive R-SGB is a safe and effective method to treat electrical storm in patients with NICM.
Humans ; Male ; Adult ; Middle Aged ; Aged ; Female ; Stroke Volume ; Stellate Ganglion/surgery* ; Ventricular Function, Left ; Cardiomyopathies/complications* ; Tachycardia, Ventricular/therapy* ; Treatment Outcome ; Catheter Ablation