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.

Humans ; PPAR gamma/metabolism* ; Multiple Sclerosis ; Transcription Factors/metabolism* ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha

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.

Further evidence is needed to explore the impact of high-altitude environments on the neurologic function of neonates.Non-invasive techniques such as cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography can provide data on cerebral oxygenation and brain electrical activity.This study will conduct multiple cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography monitoring sessions at various time points within the first 3 days postpartum for healthy full-term neonates at different altitudes.The obtained data on cerebral oxygenation and brain electrical activity will be compared between different altitudes,and corresponding reference ranges will be established.The study involves 6 participating centers in the Chinese High Altitude Neonatal Medicine Alliance,with altitude gradients divided into 4 categories:800 m,1 900 m,2 400 m,and 3 500 m,with an anticipated sample size of 170 neonates per altitude gradient.This multicenter prospective cohort study aims to provide evidence supporting the impact of high-altitude environments on early brain function and metabolism in neonates.[Chinese Journal of Contemporary Pediatrics,2024,26(4):403-409]

Objective To study the composition,abundance,and functional profiles of the intestinal microbiota in infants and young children with Kawasaki disease(KD)during the acute phase,and to explore the potential role of intestinal microbiota in the pathogenesis of KD.Methods Six children aged 0-3 years with acute KD admitted to the Department of Cardiology,Children's Hospital Affiliated to Capital Institute of Pediatrics from July to October 2021 were prospectively included as the KD group.Six age-and sex-matched healthy children who underwent physical examinations at the hospital during the same period were selected as the healthy control group.Metagenomics sequencing was used to detect and compare the differences in the microflora structure and functional profiles of fecal samples between the two groups.Results There were significant differences in the structural composition and diversity of intestinal microbiota between the two groups(P<0.05).Compared with the healthy control group,the abundance of Listeria_monocytogenes(family Listeriaceae and genus Listeria),Bifidobacterium_rousetti,Enterococcus_avium,and Enterococcus_hirae was significantly higher in the intestinal microbiota in the KD group(|LDA|>2.0,P<0.05).The steroid degradation and apoptosis pathways were significantly upregulated in the KD group compared with the healthy control group,while the Bacterial_secretion_system,Sulfur_metabolism,Butanoate_metabolism,Benzoate_degradation,β-alanine metabolism,and α-linolenic acid pathways were significantly downregulated(|LDA|>2,P<0.05).Conclusions There are significant differences in the structure and diversity of intestinal microbiota between children aged 0-3 years with acute KD and healthy children,suggesting that disturbances in intestinal microbiota occur during the acute phase of KD.In particular,Listeria_monocytogenes,Enterococcus_avium,and Enterococcus_hirae may be involved in the pathogenesis of KD through steroid degradation and apoptosis pathways.