OpenSim is an open source, free motion simulation and gait analysis software, which can be used to dynamically simulate and analyze the complex motion of the human body, and is widely used in human biomechanical research. Since OpenSim can analyze multi-dimensional motion data such as muscle strength, joint torque, and muscle synergistic activation during human movement, it can be used to study the biomechanical mechanism of musculoskeletal imbalance diseases and various treatment methods in TCM orthopedics, and has a broad application prospect in the field of TCM orthopedics. By the analysis of the basic characteristics, elements, analysis process, and application prospects of OpenSim, it is concluded that OpenSim musculoskeletal model has a large application space in the field of traditional Chinese medicine orthopedic, which is helpful to explain the pathogenesis and mechanism of diseases, and promote the precision diagnosis and treatment of orthopedics diseases;the application of OpenSim musculoskeletal model can solve the problem that the previous research paid attention to the bone malalignment and not enough attention to the tendon, and provide a new method for the research of orthopedic diseases. At present, there are still problems in the promotion and application of OpenSim, such as large equipment requirements and high operation threshold. Therefore, multidisciplinary cooperation, clinical research, and data sharing are the basic research strategies in this field.
Humans ; Biomechanical Phenomena ; Orthopedics ; Traumatology ; Software ; Medicine, Chinese Traditional ; Musculoskeletal System ; Models, Biological

OBJECTIVE: To investigate the inhibitory effect of Tanreqing Injection (TRQ) on the activation of nucleotide-binding oligomerization domain-like receptor pyrin domain containing 3 (NLRP3) inflammasome in macrophages infected with influenza A virus and the underlying mechanism based on mitophagy pathway. METHODS: The inflammatory model of murine macrophage J774A.1 induced by influenza A virus [strain A/Puerto Rico/8/1934 (H1N1), PR8] was constructed and treated by TRQ, while the mitochondria-targeted antioxidant Mito-TEMPO and autophagy specific inhibitor 3-methyladenine (3-MA) were used as controls to intensively study the anti-inflammatory mechanism of TRQ based on mitophagy-mitochondrial reactive oxygen species (mtROS)-NLRP3 inflammasome pathway. The levels of NLRP3, Caspase-1 p20, microtubule-associated protein 1 light chain 3 II (LC3II) and P62 proteins were measured by Western blot. The release of interleukin-1β (IL-1β) was tested by enzyme linked immunosorbent assay, the mtROS level was detected by flow cytometry, and the immunofluorescence and co-localization of LC3 and mitochondria were observed under confocal laser scanning microscopy. RESULTS: Similar to the effect of Mito-TEMPO and contrary to the results of 3-MA treatment, TRQ could significantly reduce the expressions of NLRP3, Caspase-1 p20, and autophagy adaptor P62, promote the expression of autophagy marker LC3II, enhance the mitochondrial fluorescence intensity, and inhibit the release of mtROS and IL-1β (all P<0.01). Moreover, LC3 was co-localized with mitochondria, confirming the type of mitophagy. CONCLUSION TRQ could reduce the level of mtROS by promoting mitophagy in macrophages infected with influenza A virus, thus inhibiting the activation of NLRP3 inflammasome and the release of IL-1β, and attenuating the inflammatory response.
Mitophagy/drug effects* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Animals ; Macrophages/virology* ; Inflammasomes/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Mitochondria/metabolism* ; Reactive Oxygen Species/metabolism* ; Influenza A virus/physiology* ; Interleukin-1beta/metabolism* ; Cell Line ; Injections

Immunotherapy has emerged as a revolutionary approach to treat immune-related diseases. Dendritic cells (DCs) play a pivotal role in orchestrating immune responses, making them an attractive target for immunotherapeutic interventions. Modulation of gene expression in DCs using genome editing techniques, such as the CRISPR-Cas system, is important for regulating DC functions. However, the precise delivery of CRISPR-based therapies to DCs has posed a significant challenge. While lipid nanoparticles (LNPs) have been extensively studied for gene editing in tumor cells, their potential application in DCs has remained relatively unexplored. This study investigates the important role of cholesterol in regulating the efficiency of BAMEA-O16B lipid-assisted nanoparticles (BLANs) as carriers of CRISPR/Cas9 for gene editing in DCs. Remarkably, BLANs with low cholesterol density exhibit exceptional mRNA uptake, improved endosomal escape, and efficient single-guide RNA release capabilities. Administration of BLAN_mCas9/gPD-L1 results in substantial PD-L1 gene knockout in conventional dendritic cells (cDCs), accompanied by heightened cDC1 activation, T cell stimulation, and significant suppression of tumor growth. The study underscores the pivotal role of cholesterol density within LNPs, revealing potent influence on gene editing efficacy within DCs. This strategy holds immense promise for the field of cancer immunotherapy, offering a novel avenue for treating immune-related diseases.

Objective To investigate the genetic effects of 15 peripheral blood inflammatory factors on the risk of aortic aneurysm and aortic dissection.Methods According to the relevance,independence,and ex-clusion assumptions of Mendelian randomization,instrumental variables predicting C-reactive protein and 14 interleukins(ILs)were extracted from human genome-wide association study data.The associations between these 15 peripheral inflammatory factors and the risk of aortic aneurysm and aortic dissection were investiga-ted using two-sample Mendelian randomization methods,including inverse-variance weighting,MR-PRESSO,and MR-Egger methods.Multivariable Mendelian randomization was used to assess the interactions of these inflammatory factors in influencing disease risk.Cochran's Q test and the MR-Egger intercept were used to e-valuate the heterogeneity and horizontal pleiotropy of the instrumental variables.Results The results of the inverse-variance weighted method showed that increased IL-16 levels were nominally associated with a reduced risk of aortic dissection(OR=0.837,95%CI:0.726 to 0.964,P=0.014).Increased IL-16 and IL-31 levels were also nominally associated with a reduced risk of aortic aneurysm(OR=0.949,95%CI:0.901 to 0.999,P=0.048;OR=0.934,95%CI:0.879 to 0.993,P=0.029).Increased IL-17F levels were nominally associat-ed with an increased risk of aortic aneurysm(OR=1.128,95%CI:1.007 to 1.264,P=0.038).No other in-flammatory factors were found to be associated with the risk of these two diseases using the inverse-variance weighted method.The MR-PRESSO and MR-Egger methods supported the results of the inverse-variance weighted method.Cochran's Q test and MR-Egger intercept test did not detect significant heterogeneity or horizontal pleiotropy.Conclusion IL-16,IL-31,and IL-17F may be involved at the genetic level in the inflam-matory mechanisms underlying the occurrence and development of aortic aneurysms and/or aortic dissections.Among them,IL-16 and IL-31 may play protective roles in aortic aneurysms and/or aortic dissections,whereas IL-17F may have a pathogenic effect on aortic aneurysms.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Aerobic glycolysis(AEG),as the main pathway of energy metabolism of various malignant tumor cells,is involved in the whole process of the occurrence and development of lung cancer,and plays a key role in inducing tumor proliferation,invasion and metastasis.Traditional Chinese medicine monomers and compounds can regulate the expression of related signaling pathways and key proteases and genes by interfering with AEG pathway,promote the apoptosis of lung cancer cells,inhibit the AEG process and the proliferation and growth of lung cancer cells,and thus play an anti-tumor role.Based on this,this paper summarized the biological function of AEG,the mechanism of regulating lung cancer and the intervention mechanism of traditional Chinese medicine,in order to provide new ideas and scientific basis for the development of clinical drugs for lung cancer.

Objective To investigate the infection status and changing trend of hepatitis C virus(HCV)infection in hospitalized patients in medical institutions,and provide reference for formulating HCV infection prevention and control strategies.Methods HCV infection surveillance results from cross-sectional survey data reported to China Healthcare-associated Infection(HAI)Surveillance System in 2020 were summarized and analyzed,HCV positive was serum anti-HCV positive or HCV RNA positive,survey result was compared with the survey results from 2003.Results In 2020,1 071 368 inpatients in 1 573 hospitals were surveyed,738 535 of whom underwent HCV test,4 014 patients were infected with HCV,with a detection rate of 68.93％and a HCV positive rate of 0.54％.The positive rate of HCV in male and female patients were 0.60％and 0.48％,respectively,with a statistically sig-nificant difference(x2=47.18,P＜0.001).The HCV positive rate in the 50-＜60 age group was the highest(0.76％),followed by the 40-＜50 age group(0.71％).Difference among all age groups was statistically signifi-cant(x2=696.74,P＜0.001).In 2003,91 113 inpatients were surveyed.35 145 of whom underwent HCV test,resulting in a detection rate of 38.57％;775 patients were infected with HCV,with a positive rate of 2.21％.In 2020,HCV positive rates in hospitals of different scales were 0.46％-0.63％,with the highest in hospital with bed numbers ranging 600-899.Patients'HCV positive rates in hospitals of different scales was statistically signifi-cant(X2=35.34,P＜0.001).In 2020,12 provinces/municipalities had over 10 000 patients underwent HCV-rela-ted test,and HCV positive rates ranged 0.19％-0.81％,with the highest rate from Hainan Province.HCV posi-tive rates in different departments were 0.06％-0.82％,with the lowest positive rate in the department of pedia-trics and the highest in the department of internal medicine.In 2003 and 2020,HCV positive rates in the depart-ment of infectious diseases were the highest,being 7.95％and 3.48％,respectively.Followed by departments of orthopedics(7.72％),gastroenterology(3.77％),nephrology(3.57％)and general intensive care unit(ICU,3.10％)in 2003,as well as departments of gastroenterology(1.35％),nephrology(1.18％),endocrinology(0.91％),and general intensive care unit(ICU,0.79％)in 2020.Conclusion Compared with 2003,HCV positive rate decreased significantly in 2020.HCV infected patients were mainly from the department of infectious diseases,followed by departments of gastroenterology,nephrology and general ICU.HCV infection positive rate varies with gender,age,and region.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

In the study of age estimation in living individuals,a lot of data needs to be analyzed by mathematical statistics,and reasonable medical statistical methods play an important role in data design and analysis.The selection of accurate and appropriate statistical methods is one of the key factors af-fecting the quality of research results.This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics,difference analysis,consistency test and multivariate statistical analysis,as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals,and summarizes the relevance and application prospects between medical statistical methods and machine learning methods.This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.