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: 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 retrospectively analyze the clinical efficacy of anterior cruciate ligament (ACL) reconstruction combined with anterolateral complex repair and ACL reconstruction combined with ALL reconstruction in the treatment of anterior cruciate ligament injuries with high-grade pivot shift. METHODS: From January 2018 to June 2022, 49 patients combined ACL and ALL injuries with high-grade pivot shift were retrospectively studied from three hospitals, 29 of them underwent ACL reconstruction with anterolateral complex repair (repair group), including 23 males and 6 females with an average age of (27.5±4.8) years old, ranged from 20 to 37 years old;the injured sides were 13 on the left and 16 on the right, and 11 patients were suffered with meniscus injury. The other 20 patients underwent ACL and ALL reconstruction (reconstruction group) including 17 males and 3 females with the mean age of (27.1±4.5) years old, ranged from 20 to 38 years old;the injured sides were 8 on the left and 12 on the right, and 6 patients were suffered with meniscus injury. Knee stability (pivot shift test, KT-2000), range of motion, knee function (Lysholm scoring scale, Cincinnati sports activity scale (CSAS) scoring scale, and Tegner activity level score between two groups were compared. RESULTS: A total of 49 patients were followed up, the repair group receiving 13 to 20(15.3±1.8) months and the reconstruction group receiving 12 to 21(16.0±2.2) months. There was no statistically significant difference in the preoperative pivot shift test grading distribution between two groups (P>0.05). At the last postoperative follow-up, there were 24 patients with grade 0 and 5 patients with grade 1 in the repair group, and there were 18 patients with grade 0 and 2 patients with grade 1 in the reconstruction group, there is no significant difference in the distribution of axial shift test grading between two groups(P>0.05). The preoperative KT-2000 tibial displacement of two groups were (9.39±0.77) mm (repair group) and (9.14±0.78) mm (reconstruction group) respectively, with no statistically significant difference (P>0.05). At the final postoperative follow-up, there were 24 patients with KT-2000 tibial displacement <3 mm and 5 patients with 3 to 5 mm in the repair group, while 18 patients with <3 mm and 2 patients with 3 to 5 mm in the reconstruction group, KT-2000 tibial displacement distribution of two groups was no significant difference (P>0.05), but the KT-2000 tibial displacement in the reconstruction group (1.30±0.86) mm was significantly smaller than that in the repair group (1.99±1.11) mm (P<0.05). The final postoperative follow-up range of motion of the contralateral side knee between two groups was no significant difference (P>0.05). The range of motion of the suffering knee in the repair group was less than that in the reconstruction group (P<0.05). There was no significant difference in preoperative Lysholm and CSAS scores between two groups (P>0.05). At the final postoperative follow-up, both groups showed significant improvement in Lysholm and CSAS scores, while the Lysholm and CSAS scores of the reconstruction group were better than those of the repair group, and the difference was statistically significant (P<0.05). Significant differences was found in Tegner scores between two groups, which 16 patients in the repair group returned to their pre-injury activity level, and 17 patients in the reconstruction group returned to their pre-injury level (P<0.05). CONCLUSION Compared to anterolateral complex repair, combined ACL and ALL reconstruction in the treatment of ACL injuries with high-grade pivot shift results in better knee joint function and stability. This is advantageous in reducing the risk of ACL reconstruction failure.
Humans ; Male ; Female ; Adult ; Anterior Cruciate Ligament Reconstruction/methods* ; Anterior Cruciate Ligament Injuries/surgery* ; Young Adult ; Retrospective Studies ; Anterior Cruciate Ligament/surgery* ; Range of Motion, Articular

Accurate receptor/ligand binding free energy calculations can greatly accelerate drug discovery by identifying highly potent ligands. By simulating the change from one compound structure to another, the relative binding free energy (RBFE) change can be calculated based on the theoretically rigorous free energy perturbation (FEP) method. However, existing FEP-RBFE approaches may face convergence challenges due to difficulties in simulating non-physical intermediate states, which can lead to increased computational costs to obtain the converged results. To fundamentally overcome these issues and accelerate drug discovery, a new combined-structure RBFE (CS-FEP) calculation strategy was proposed, which solved the existing issues by constructing a new alchemical pathway, smoothed the alchemical transformation, increased the phase-space overlap between adjacent states, and thus significantly increased the convergence and accelerated the relative binding free energy calculations. This method was extensively tested in a practical drug discovery effort by targeting phosphodiesterase-1 (PDE1). Starting from a PDE1 inhibitor (compound 9, IC₅₀ = 16.8 μmol/L), the CS-FEP guided hit-to-lead optimizations resulted in a promising lead (11b and its mesylate salt formulation 11b-Mesylate, IC₅₀ = 7.0 nmol/L), with ∼2400-fold improved inhibitory activity. Further experimental studies revealed that the lead showed reasonable metabolic stability and significant anti-fibrotic effects in vivo.

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.