Objective To examine the safety, efficacy and durability of totally endoscopic minimally invasive (TEMI) mitral valve repair in Barlow’s disease (BD). Methods A retrospective study was performed on patients who underwent mitral valve repair for BD from January 2010 to June 2021 in the Guangdong Provincial People’s Hospital. The patients were divided into a MS group and a TEMI group according to the surgery approaches. A comparison of the clinical data between the two groups was conducted. Results A total of 196 patients were enrolled, including 133 males and 63 females aged (43.8±14.9) years. There were 103 patients in the MS group and 93 patients in the TEMI group. No hospital death was observed. There was a higher percentage of artificial chordae implantation in the TEMI group compared to the MS group (P=0.020), but there was no statistical difference between the two groups in the other repair techniques (P>0.05). Although the total operation time between the two groups was not statistically different (P=0.265), the TEMI group had longer cardiopulmonary bypass time (P<0.001) and aortic clamp time (P<0.001), and shorter mechanical ventilation time (P<0.001) and postoperative hospitalization time (P<0.001). No statistical difference between the two groups in the adverse perioperative complications (P>0.05). The follow-up rate was 94.2% (180/191) with a mean time of 0.2-12.4 (4.0±2.4) years. Two patients in the MS group died with non-cardiac reasons during the follow-up period. The 3-year, 5-year and 10-year overall survival rates of all patients were 100.0%, 99.2%, 99.2%, respectively. Compared with the MS group, there was no statistical difference in the survival rate, recurrence rate of mitral regurgitation, reoperation rate of mitral valve or adverse cardiovascular and cerebrovascular events in the TEMI group (P＞0.05). Conclusion TEMI approach is a safe, feasible and effective approach for BD with a satisfying long-term efficacy.

Objective To research the procedure for creating an animal model of mitral regurgitation by implanting a device through the apical artificial chordae tendineae, and to assess the stability and dependability of the device. Methods Twelve large white swines were employed in the experiments. Through a tiny hole in the apex of the heart, the artificial chordae tendineae of the mitral valve was inserted under the guidance of transcardiac ultrasonography. Before, immediately after, and one and three months after surgery, cardiac ultrasonography signs were noted. Results All models were successfully established. During the operation and the follow-up, no swines died. Immediately after surgery, the mitral valve experienced moderate regurgitation. Compared with preoperation, there was a variable increase in the amount of regurgitation and the values of heart diameters at a 3-month follow-up (P<0.05). Conclusion In off-pump, the technique of pulling the mitral valve leaflets with chordae tendineae implanted transapically under ultrasound guidance can stably and consistently create an animal model of mitral regurgitation.

OBJECTIVE To predict the amount of colorants used in the preparation of placebo of Chinese medicine by construc-ting particle swarm optimization-back propagation neural network PSO-BPNN compound granules,and to provide a new idea for the simulation of placebo color of Chinese medicine compound granules.METHODS The BP neural network was used to establish the model of sample color parameters L,a?,b?and pigment mass fraction.The global search ability of particle swarm optimization algo-rithm was used to optimize the weight and bias of BP neural network to prevent the local minimum value of the model.The linear reduc-tion weight coefficient method and the introduction of mutation operator were used to improve the global optimization ability of particle swarm optimization algorithm.The color comprehensive evaluation index(ΔE)was used as the objective evaluation standard to verify the test results.RESULTS The training results show that the fitting accuracy of the improved PSO-BP neural network was up to 98.31%.The prediction results show that the prediction error of the improved PSO-BP neural network was the smallest,and the mean absolute percentage error(MAPE),root mean square error(RMSE)and mean color difference(ΔE)were 0.411 5,2.164 6 and 2.56,respectively.The verification samples of three kinds of particles were prepared for verification.The ΔE of the verification sample and the model drug were 1.73,2.63 and 4.11,respectively.The color difference between the two groups and the model drug was small by visual evaluation.CONCLUSION The BP neural network based on the improved particle swarm optimization algorithm can simulate the prediction of the amount of colorants used in the preparation of Chinese medicine compound granules,and can be used as a recommended optimization model for placebo color matching research.

The quality control during the manufacturing process of traditional Chinese medicine TCM)is paramount to ensuring the quality of the final product.However,the sheer diversity of TCM ingredients and the intricacies of the production process pose sig-nificant challenges to achieving precise quality control.The review introduced an intelligent control concept centered on functional sub-stances in TCM quality,which comprehensively explained the scientific essence of TCM quality,and provided a theoretical foundation for determining"what to control"and"how to control"within the complex system of TCM components.Furthermore,through the ad-vanced informatization,digitization,and intelligent technologies,our team has conducted extensive research and construction practices on the intelligent transformation and digital upgrade of traditional Chinese medicine TCM)manufacturing processes.Ultimately,the establishment of a quality control system tailored specifically for TCM manufacturing with functional substances serving as the primary quality target,which presented an opportunity to solve the difficult problem of process quality control in TCM manufacturing.

The concept,connotation and extension,goals and tasks of the discipline of Chinese medicinal materials resource chem-istry have been proposed and developed for 20 years.Looking back at the 20-year construction and development process,continuous exploration and innovative practice have been carried out around the scientific production and effective utilization of traditional Chinese medicinal materials.The theoretical connotation has been further enriched,the research mode has been further improved,and the tech-nical system has been further expanded.A series of research results have been formed and promoted for application,serving the high-quality development of the traditional Chinese medicinal materials industry,and contributing to the improvement of quality,efficiency,and green development of the entire industry chain of Chinese medicinal resources.However,with the rapid growth of Chinese medici-nal materials industry and the continuous expansion and extension of the industry chain,the waste and by-products generated in the production process of Chinese medicinal agriculture and industry are increasing day by day,causing resource waste and environmental pollution,which has become a new major problem facing the development of the industry.This article focuses on the establishment and case analysis of a model for the full industry chain recycling and low-carbon green development of Chinese medicinal materials,as well as the creation of an ecological industry demonstration park for the recycling of Chinese medicinal materials.It showcases the phased a-chievements made in recent years,aiming to provide demonstration and reference for the low-carbon and green transformation of the Chinese medicinal materials industry from a linear economy model to a circular economy model.It provides reference for improving the efficiency of Chinese medicinal materials utilization and creating new quality productivity,and helps promote low-carbon and green de-velopment in the field of Chinese medicinal materials industry.

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 develop a novel transcatheter tricuspid valve replacement device and test its performance. Methods The transcatheter tricuspid valve stent consisted of double-layer self-expanding nitinol stent, biotissue-derived bovine pericardial leaflets, and PTFE woven. The delivery system, mainly consisting of a handle control unit and a delivery sheath, was sent to the correct position via right atrium or jugular vein. The sheath had a visualization feature, and the handle control unit could realize the functions of stable release and partial recovery of the interventional valve. In addition, this study performed animal survival experiments on the basis of in vitro experiments. A large-white pig was used as the experimental animal. Cardiopulmonary bypass was established through median thoracotomy, then the right atrium was opened, and the interventional valve was released under direct vision without cardiac arrest. Approximately 1 month after interventional valve implantation, the maneuverability and stability of the interventional tricuspid device were evaluated by autopsy. Results Through the animal experiment, the interventional valve was successfully released, and the anchoring was satisfactory. Postoperative transthoracic echocardiography showed that the interventional valve opened and closed well, the flow rate of tricuspid valve was 0.6 m/s, and there was no obvious tricuspid regurgitation. One month after the operation, we dissected the large-white pig and found the interventional valve was not deformed or displaced, the leaflets were well aligned, and there was thrombus attachment in the groove between the inner and outer layers of the interventional valve. Conclusion Animal experiment shows that the novel device can stably and firmly attach to the tricuspid annulus, with good anchoring effect, and effectively reduce paravalvular leakage.

Objective To investigate the relationship between two-lung ventilation (TLV) with single-lumen endotracheal tube (SLT), one-lung ventilation (OLV) with double-lumen endotracheal tube (DLT) and postoperative pulmonary complications (PPCs) after total thoracoscopic cardiac surgery. Methods The clinical data of patients who underwent totally thoracoscopic cardiac surgeries in the Guangdong Provincial People’s Hospital from October 2019 to October 2021 were retrospectively analyzed. The patients were divided into 2 group according to the type of endotracheal tube, including a SLT group and a DLT group. Baseline data, surgical variables and PPCs were compared. The influencing factors of PPCs in the two groups were analyzed by binary logistic regression analysis. Results Finally 349 patients were enrolled, including 180 males and 169 females with an average age of (50.0±14.8) years. There were 219 patients in the SLT group and 130 patients in the DLT group. There was no statistical difference in baseline data, surgical variables or PPCs between the two groups (P＞0.05). Binary logistic regression analysis showed that PPCs were related to body mass index in the SLT group (OR=0.778, 95%CI 0.637 to 0.951, P=0.014) and preoperative smoking history in the DLT group (OR=0.058, 95%CI 0.004 to 0.903, P=0.042). Conclusion For the patients who undergo totally thoracoscopic cardiac surgery, TLV with SLT and OLV with DLT show no significant association with PPCs. At the same time, PPCs are associated with body mass index in the SLT group, while associated with preoperative smoking history in the DLT group.

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 Establish the fingerprint of the group and optimize the extraction process of the Yunpi Huatan Tongqiao decoction.Methods The macroscopic characterization and similarity analysis of the extract fingerprint were carried out by applying the total statistical moment method.With baicalin,wogonin,rosmarinic acid,liquiritin,glycyrrhizic acid and paste yield as key quality attributes,and extraction time,water addition and extraction times as key process parameters,the optimal extraction process was selected by AHP-independent weight method,and the process was verified.Results Establishing a total statistical moment similarity evaluation method,and the average statistical moment similarity of the total amount of fingerprints of different extraction methods was 0.8807.The ANOVA results of the process evaluation model function are displayed that P<0.0001,R2=0.9904,indicating that the model was statistically significant.The best extraction process was determined as follows:adding 10-fold volume of water in the whole prescription,extracting for 1.5 h and extracting twice.Conclusion The total statistical moment analysis method that conforms to the fingerprint characteristics of the fingerprint of the extract was established,which was stable and reliable,which provided a reference basis for the quality control of the whole process of the subsequent process research.