Objective To introduce an innovative technique, the "balance-shaped sternal elevation device" and its application in the subxiphoid uniportal video-assisted thoracoscopic surgery (VATS) for anterior mediastinal masses resection. Methods Patients who underwent single-port thoracoscopic assisted anterior mediastinal tumor resection through the xiphoid process at the Department of Thoracic Surgery, West China Hospital, Sichuan University from May to June 2024 were included, and their clinical data were analyzed. Results A total of 7 patients were included, with 3 males and 4 females, aged 28-72 years. The diameter of the tumor was 1.9-17.0 cm. The operation time was 62-308 min, intraoperative blood loss was 5-100 mL, postoperative chest drainage tube retention time was 0-9 days, pain score on the 7th day after surgery was 0-2 points, and postoperative hospital stay was 3-12 days. All patients underwent successful and complete resection of the masses and thymus, with favorable postoperative recovery. Conclusion The "balance-shaped sternal elevation device" effectively expands the retrosternal space, providing surgeons with satisfactory surgical views and operating space. This technique significantly enhances the efficacy and safety of minimally invasive surgery for anterior mediastinal masses, reduces trauma and postoperative pain, and accelerates patient recovery, demonstrating important clinical significance and application value.

Objective To introduce the modeling method of pendulum-like modified rat abdominal heart heterotopic transplantation model and evaluate the quality of the model. Methods An operator without transplantation experience performed 15 consecutive models, recorded the time of each step, changes in body weight and modified Stanford scores, and calculated the surgical success rate, postoperative 1-week survival rate and technical success rate. Ultrasound examinations was performed in 1 week postoperatively. Results The times for donor heart acquisition, donor heart processing, recipient preparation and transplantation anastomosis were (14.3±1.4) min, (3.5±0.6) min, (13.6±2.1) min and (38.3±5.2) min respectively. The surgical success rate was 87% (13/15), and the survival rate 1 week after operative was 100% (13/13). The improved Stanford score indicated a technical success rate of 92% (12/13), and the postoperative 1-week ultrasound examination showed that grafts with Stanford scores ≥3 had detectable pulsation and blood flow signals. Conclusions The pendulum-like modified rat abdominal heart heterotopic transplantation improved model further optimizes the operational steps with a high success rate and stable quality, may be chosen as a modeling option for basic research in heart transplantation in the future.

Objective To compare the efficacy and safety of video-assisted thoracoscopic surgery (VATS) thymectomy for the treatment of thymoma through subxiphoid uniportal approach using double sternum retractors, and subxiphoid and subcostal arch approach. Methods We retrospectively analyzed the clinical data of the patients diagnosed with thymoma who underwent VATS thymectomy from June 2023 to June 2024 in West China Hospital. Patients were categorized based on the surgical approach into two groups: a subxiphoid uniportal VATS thymectomy (SUVT) group and a subxiphoid and subcostal arch VATS thymectomy (SASAT) group. Comparisons were made between the two groups regarding surgical duration, intraoperative blood loss, postoperative drainage, thymoma size and location, and postoperative pain assessed using the visual analogue scale (VAS). Results The SUVT group consisted of 20 patients, including 11 males and 9 females, with an average age of (51.5±14.3) years. The SASAT group comprised 40 patients, including 26 males and 14 females, with an average age of (50.0±13.0) years. Compared to the SASAT group, the SUVT group had significantly larger thymomas [ (5.9±2.7) cm vs. (4.2±2.1) cm, P=0.010] and a higher proportion of neoplasms located in the superior mediastinum (30.0% vs. 2.5%, P=0.007). Additionally, the VAS pain scores on postoperative days 3, 7, and 30 were significantly lower in the SUVT group compared to the SASAT group (P<0.05). There were no statistical differences between the two groups in demographic characteristics, operative time, intraoperative blood loss, duration and volume of postoperative drainage, length of postoperative hospital stay, or the VAS pain score on the first postoperative day. Conclusion SUVT using double sternum retractors significantly reduces postoperative pain and provides superior efficacy in the resection of larger thymomas or those situated in the superior mediastinum.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

With the increasing demand for dental aesthetic outcomes, techniques for composite resin restoration intended for anterior teeth have been widely applied due to their minimally invasive and superior esthetic performance. Despite promising short-term outcomes, the long-term prognosis of anterior resin restorations remains challenging. Frequently reported complications include restoration fractures and decoloration. Material selection, operative procedures, and patient-related factors can affect the long-term outcomes of restorations. This review aims to systematically analyze the long-term clinical performance of resin restorations in anterior teeth. The key factors influencing treatment efficacy are also investigated. The findings are expected to provide a basis for optimizing clinical strategies in procedures for anterior composite resin restoration.
Humans ; Composite Resins ; Dental Restoration, Permanent/methods* ; Prognosis ; Esthetics, Dental ; Treatment Outcome ; Time Factors

Objective To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.Methods We proposed a two-stage carotid plaque detection method based on deep convolutional neural network(SM-YOLO).A series of algorithms such as median filtering,histogram equalization,and Gamma transformation were used to preprocess the dataset to improve image quality.In the first stage of the model construction,a candidate plaque set was built based on the YOLOX_l target detection network,using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes.In the second stage,the Histogram of Oriented Gradient(HOG)features and Local Binary Pattern(LBP)features were extracted and fused,and a Support Vector Machine(SVM)classifier was used to screen the candidate plaque set to obtain the final detection results.This model was compared quantitatively and visually with several target detection models(YOLOX_l,SSD,EfficientDet,YOLOV5_l,Faster R-CNN).Results SM-YOLO achieved a recall of 89.44%,an accuracy of 90.96%,a F1-Score of 90.19%,and an AP of 92.70%on the test set,outperforming other models in all performance indicators and visual effects.The constructed model had a much shorter detection time than the Faster R-CNN model(only one third of that of the latter),thus meeting the requirements of real-time detection.Conclusion The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.

Objective To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.Methods We proposed a two-stage carotid plaque detection method based on deep convolutional neural network(SM-YOLO).A series of algorithms such as median filtering,histogram equalization,and Gamma transformation were used to preprocess the dataset to improve image quality.In the first stage of the model construction,a candidate plaque set was built based on the YOLOX_l target detection network,using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes.In the second stage,the Histogram of Oriented Gradient(HOG)features and Local Binary Pattern(LBP)features were extracted and fused,and a Support Vector Machine(SVM)classifier was used to screen the candidate plaque set to obtain the final detection results.This model was compared quantitatively and visually with several target detection models(YOLOX_l,SSD,EfficientDet,YOLOV5_l,Faster R-CNN).Results SM-YOLO achieved a recall of 89.44%,an accuracy of 90.96%,a F1-Score of 90.19%,and an AP of 92.70%on the test set,outperforming other models in all performance indicators and visual effects.The constructed model had a much shorter detection time than the Faster R-CNN model(only one third of that of the latter),thus meeting the requirements of real-time detection.Conclusion The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.

Bone organoids can simulate the complex structure and function of the bone tissues, which makes them a frontier technology in organoid researches. Bone organoids show a tremendous potential of applications in bone disease modeling, bone injury repair, and medicine screening. Although advancements have been made so far in constructing bone organoids with functional structures like mineralization, bone marrow, trabecular bone, callus, woven bone, etc, the researches in this field are confronted with numerous challenges such as lack of standardized construction strategies and unified evaluation criteria, which limits their further promotion and application. To standardize researches in bone organoids, the Orthopedic Expert Committee of Geriatric Branch of Chinese Association of Gerontology and Geriatrics, the Youth Osteoporosis Group of Orthopedic Branch of Chinese Medical Association, the Osteoporosis Group of Orthopedic Surgeon Branch of Chinese Medical Doctor Association, and the Osteoporosis Committee of Shanghai Association of Integrated Traditional Chinese and Western Medicine organized related experts to formulate Expert consensus on the construction, evaluation, and application of bone organoids ( version 2024) based on an evidence-based approach. A total of 17 recommendations were put forth, aiming to standardize researches and clinical applications of bone organoids and enhance their value in scientific research and clinical practice.