Objective To explore the trajectory and influencing factors of joint awareness after total knee arthroplasty.Methods With the method for convenience sampling,patients who met the standards for total knee arthroplasty from July 2021 to March 2022 were selected.General information,severity of osteoarthritis,anxiety and depression,and self-efficacy were investigated before surgery(T0).Joint awareness was investigated at 1 month(T1),3 months(T2),and 6 months(T3)after surgery,respectively.Latent growth curve model was used to describe the overall trend of joint awareness;latent class growth model was used to analyze latent subgroups;logistic regression analysis was used to determine the impact of related variables on the trajectory of joint awareness.Results The scores of joint awareness were(32.70±5.80),(47.67±4.67)and(61.53±7.81)respectively at T1 to T3,and joint awareness showed an increasing trend(P＜0.001).The trajectory of joint awareness was divided into 2 potential subgroups:the rapid growth group(61.83％)and the slow growth group(38.17％).Age,BMI,other chronic diseases or not,years of osteoarthritis,severity of osteoarthritis,anxiety,depression,and self-efficacy affected the trend of joint awareness(all P＜0.05).Conclusion Joint awareness showed a linear growth trend with the postoperative time,and there were 2 potential subgroups in the trajectory.Medical workers could develop corresponding interventions based on the influencing factors to improve patients'postoperative awareness of artificial joints.

The study presents a method for the personalized biomechanical modeling of the human head and validates the generated model.Based on the TUST 50th percentile head biomechanical model,the method utilizes head CT data of the target model,and employs three-dimensional point cloud registration and free-form deformation techniques to rapidly develop a personalized head finite element model with detailed brain tissue structures.By reconstructing classic cadaver tests,it is found that the personalized head biomechanical model created by the proposed method shows a good consistency with the results of cadaver tests in kinematic and biomechanical responses.Furthermore,no significant differences are observed when compared with the head biomechanical model developed using reverse engineering method,thus verifying the effectiveness of the developed model.Consequently,the proposed method can be used to quickly construct personalized head biomechanical models with detailed anatomical structures,providing a fundamental computational analysis tool for researches in injury biomechanics,clinical medicine,and forensic identification.

Objective To investigate the injury mechanisms of three-year-old child occupants by reconstructing a real traffic accident.Methods A traffic accident case from the CIREN database was reconstructed using a vehicle finite element model and a three-year-old child occupant injury bionic model(TUST IBMs 3YO-O).The Δv,mass of the vehicle,and deformation energy were comprehensively analyzed to calculate the collision velocity of the vehicle.This accident was simulated to present injuries to a child occupant,and the injury mechanisms were analyzed in depth.Results The TUST IBMs 3YO-O fully reconstructed the injuries of the child occupant in this case.The kinematic and biomechanical responses of the children's heads differed.The biomechanical response of the internal tissues and organs in the chest cavity showed no injury,however,the result ant chest acceleration at 3 ms reached 54 g,which exceeded the threshold.Conclusions In the future,it will be necessary to adopt biomechanical parameters for occupant safety evaluations.The application of human biomechanical models with high biofidelity to reconstruct occupant injuries in traffic accidents can not only be used to observe the kinematic responses of the occupant in the accident and analyze the injury mechanisms in depth,but also to provide references for virtual testing,as well as for the research and development of child occupant protection devices and the formulation of safety regulations.

Background:Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium.OLGA/OLGIM staging systems are scoring systems for gastric mucosal atrophy and intestinal metaplasia based on gastroscopic pathology.Serum pepsinogen(PG)and PG Ⅰ/PG Ⅱ ratio(PGR)can reflect the gastric mucosal function and status.Studying the relationship between PG and OLGA/OLGIM stages and the changing trends is of great value for early screening of gastric cancer.Aims:To explore the relationship between serum PG and OLGA/OLGIM stages,and its application value in screening of early gastric cancer.Methods:A total of 188 healthy individuals underwent gastroscopy from June 2021 to June 2023 at Shanghai Health and Medical Center were selected.The serum PG level was measured,and the status of Helicobacter pylori(Hp)infection was evaluated.OLGA/OLGIM stages were assessed according to the findings of gastroscopic pathology,and correlated with the serum PG level.After two years of follow-up,changes of PG and PGR in subjects with different baseline OLGA/OLGIM stages were analyzed.Results:There were significant differences in PG Ⅰand PGR among subjects with various baseline OLGA/OLGIM stages(P＜0.05).OLGA/OLGIM stages were correlated with Hp infection,family history of malignant gastrointestinal tumors and spicy diet(P＜0.05).The incidences of gastric mucosal atrophy in PG Ⅰ ≤70 μg/L group and PGR≤3.0 group were significantly higher than those in PG Ⅰ＞70 μg/L group and PGR＞3.0 group,respectively(P＜0.05).The follow-up results showed that for baseline OLGA/OLGIM stage 0,PG Ⅰ showed a decreasing trend over time,while no significant change of PG Ⅰ was found in other OLGA/OLGIM stages;PG Ⅱ showed a decreasing trend while PGR showed an increasing trend over time in all stages.But all these trends were not statistically significant(P＞0.05).Conclusions:The combined detection of PG and Hp infection has important value in evaluating the severity of chronic atrophic gastritis and the risk of gastric cancer.It can be applied as an early screening project for gastric cancer in individuals undergoing physical examination.

Objective To investigate the expressions of H3.3G34W,p63 and SATB2 in giant cell tumor of bone(GCTB)and the effect and value of their combined application in the diagnosis of GCTB.Methods We collected the samples and medical records of 54 cases of GCTB and 83 cases of non-giant cell tumor of bone(14 cases of aneurysmal bone cyst,16 cases of chondroblastoma and 53 cases of non-ossifying fibroma)diagnosed between 2020 and 2022 in the Department of Pathology of Honghui Hospital Affiliated to Xi'an Jiaotong University.The expressions of H3.3G34W,p63 and SATB2 were detected by EliVision immunohistochemical method.X2 test was used to determine whether there are significant differences in the positive rates of H3.3G34W,p63 and SATB2 among all the groups.The combined diagnostic model including H3.3G34W,p63 and SATB2 was established by Logistic regression analysis,and the diagnostic value of the model was evaluated by ROC curve analysis.Results The positive rates of H3.3G34W,p63 and SATB2 in GCTB group were 81.5％,90.7％and 92.6％,respectively;the positive rates in NGCTB group were 2.4％,28.9％and 62.7％.Compared with NGCTB group,the age of GCTB group was significantly older[(41.222±14.849)vs.(16.566±9.439),P＜0.001],and the prevalence was higher in women than in men(51.9％vs.48.1％,P＜0.001).In addition,compared with the NGCTB group,the positive rates of H3.3G34W(81.5％vs.2.4％,P＜0.001),p63(90.7％vs.28.9％,P＜0.001)and SATB2(92.6％vs.62.7％,P＜0.001)were significantly higher in the GCTB group.Univariate regression analysis built a univariate prediction modeland ROC curve analysis showed that age(AUC=92.9％,P＜0.001),sex(AUC=64.5％,P=0.004),H3.3G34W positive rate(AUC=89.5％,P＜0.001),p63 positive rate(AUC=80.9％,P＜0.001)and SATB2 positive rate(AUC=65.0％,P=0.003)were independent predictors of diagnosis of giant cell tumor of bone.Multivariate regression analysis(Logistic)constructed a hybrid prediction model.ROC curve analysis suggested that the hybrid model showed better prediction value than the single factor model(AUC=98.4％,P＜0.001).Conclusion H3.3G34W,p63 and SATB2 are effective molecular markers for the diagnosis of GCTB,and their combined application can improve the prediction efficiency of the diagnosis of GCTB.

Objective To construct the theoretical framework of rehabilitation big data based on International Classification of Func-tioning,Disability and Health(ICF). Methods Drawing upon international rehabilitation policy documents,such as the World Health Organization's Rehabili-tation in health systems;Rehabilitation in health systems:guide for action;Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME);Template for Rehabilita-tion Information Collection(TRIC):a tool accompanying the Systematic Assessment of Rehabilitation Situation(STARS);and Framework and Standards for Country Health Information Systems;this study examined the com-position and function of rehabilitation big data.The content structure of the rehabilitation big data domain was an-alyzed using the World Health Organization Family of International Classifications(WHO-FICs).Furthermore,the generation patterns of rehabilitation big data was constructed drawing on the Health Metrics Network and big data hierarchical classification. Results Within the six primary elements of the health service system,the information system element was particularly significant,encompassing a substantial branch known as rehabilitation big data.There were three components of rehabilitation big data:health condition,health-related factors and health services.The content framework for this data was derived from the WHO-FICs framework,which covered three dimensions:health and function,dis-ease and function,and disease,function and intervention.A comprehensive model for generating and applying re-habilitation big data in rehabilitation services was developed in line with the requirements for constructing big da-ta architectures.The sources of this data included population censuses,social registration information,population surveys,resources,services and personal records.The result chain of rehabilitation big data encompassed five major processes:input,process,output,outcome and impact.The processing and utilization of this data involved collection,storage,management,analysis and application. Conclusion A theoretical framework for rehabilitation big data has been constructed based on the ICF theory.

Objective To explore and organize the standards of rehabilitation big data. Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning,Disability and Health(ICF)framework.Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module,Rehabilitation in health systems:guide for action,Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME),and Data quality assurance.Module 1.Framework and metrics,the sources,patterns,clas-sification systems and coding standards were discussed under the ICF theory,and the metadata standards were ex-plored.The application and management of rehabilitation big data standards were discussed according to Nation-al Health Medical Big Data Standards,Security and Service Management Measures(Trial). Results The rehabilitation big data included rehabilitation service data and personal health data,coming from population-based and institution-based data,covering macro,meso and micro levels.The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service,to organize and manage rehabilitation big data.The classification system included object classes,object feature classes,participant role classes,relationship classes,and activity and event classes,each of which was further subdivided into subcatego-ries to cover the entities,features,roles,relationships and activities involved in the rehabilitation process.The metadata standards included three levels:core,general and specialized metadata,ensuring standardized manage-ment,sharing and interoperability of rehabilitation data. Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework,encompass-ing multiple dimensions such as the connotation and extension of rehabilitation big data,data sources,data mod-els,classification systems,coding standards,and metadata standards.The construction of a rehabilitation big data standard system involves standardization efforts in various aspects,including data content,data structure,data coding,and metadata.These standards not only adhere to the norms of data flow,but also take into account the complexity of data composition.This system aligns with health big data standards,ensuring data consistency,ac-curacy,and interoperability,thus providing a foundation for effective exchange and comparison between different data sources.The establishment of a rehabilitation big data standard system not only ensures the standardized pro-cessing of rehabilitation big data,but also lays a solid foundation for effective exchange between rehabilitation big data and other health data,as well as for the widespread application of rehabilitation big data.This provides crucial support for improving the quality and efficiency of rehabilitation services,ensuring that patients receive appropriate care,rehabilitation and support.It holds significant theoretical and practical implications for promot-ing the development of the rehabilitation field.

OBJECTIVES: This work aimed to investigate the molecular mechanism of cyclic tensile stress (CTS) stimulating autophagy in human periodontal ligament cells (hPDLCs). METHODS: hPDLCs were isolated and cultured from normal periodontal tissues. hPDLCs were loaded with tensile stress by force four-point bending extender to simulate the autophagy of hPDLCs induced by orthodontic force du-ring orthodontic tooth movement. XMU-MP-1 was used to inhibit the Hippo signaling pathway to explore the role of the Hippo-YAP signaling pathway in activating hPDLC autophagy by tensile stress. The expression levels of autophagy-related genes (Beclin-1, LC3, and p62) in hPDLCs were detected by real-time quantitative polymerase chain reaction. Western blot was used to detect the expression levels of autophagy-related proteins (Beclin-1, LC3-Ⅱ/LC3-Ⅰ, and p62) and Hippo-YAP pathway proteins (active-YAP and p-YAP) in hPDLCs. Immunofluorescence was used to locate autophagy-related proteins (LC3-Ⅱand p62) and Hippo-YAP pathway proteins (active-YAP) of hPDLCs. RESULTS: CTS-activated autophagy in hPDLCs and expression of autophagy-related proteins initially increased and then decreased; it began to increase at 30 min, peaked at 3 h, and decreased (P<0.05). CTS increased the expression of active-YAP protein and decreased the expression of p-YAP protein (P<0.05). When XMU-MP-1 inhibited the Hippo-YAP signaling pathway (P<0.05), active-YAP protein was promoted to enter the nucleus and autophagy expression was enhanced (P<0.05). CONCLUSIONS The Hippo-YAP signaling pathway is involved in the regulation of autophagy activation in hPDLCs under CTS.
Humans ; Hippo Signaling Pathway ; Periodontal Ligament/metabolism* ; Beclin-1/metabolism* ; Cells, Cultured ; Autophagy

Objective To study influencing factors of renal blunt impact injury by using finite element (FE) method. Methods Based on CT images of the kidney, the kidney FE models for different age groups were constructed. The renal blunt impact test was reconstructed, and the influence of kidney material constitutive parameters, kidney tissue structure, kidney size, impact position and impact velocity on injury severity were analyzed. Results Under the same impact condition, the stress of renal cortex decreased with the kidney mass increasing, and increased with the impact velocity of the hammer increasing. The renal capsule had a certain energy absorption effect, so as to reduce the kidney stress. When the kidney was impacted, the stress of renal cortex under side impact was significantly higher than that under frontal impact. Conclusions Compared with viscoelastic constitutive model, Mooney Rivlin material constitutive model is more suitable for FE evaluation on renal injury severity. The renal injury decreases with the kidney mass increasing. The increase of impact velocity will intensify the renal injury severity. Renal capsule will reduce renal injury to a certain extent, so the existence of renal capsule structure must be considered in FE modeling of the kidney. Compared with frontal and rear impact, the renal injury severity is greater when the kidney is impacted from the lateral side.

Objective To explore the influence of child head injury under different impact angles by applying the finite element model of six-year-old child pedestrian as specified in the European New Car Assessment Programme (Euro NCAP). Methods Based on the finite element model of 6-year-old pedestrian with detailed anatomical structure as specified by the Euro NCAP (TB024), four groups of simulation experiments were set up to explore the mechanism of head injury in children under different impact angles. The initial position for head mass center was on the longitudinal center line of the car. The initial speed of the car was 40 km/h. The car contacted with the model from the direction of the right (0°), the front (90°), the left (180°) and the back (270°). The kinematics differences and head impact responses were compared, and injuries of the facial bone and skull were analyzed. Results Through the analysis of head contact force, acceleration of head mass center, resultant velocity of head mass center with the vehicle, head injury criterion (HIC15), facial bone fracture and skull stress distribution, it was found that the risk of head fracture and brain contusion under back impact and front impact was higher than that under side impact. The risk of head fracture and brain contusion was highest under back impact, while the lowest under side impact. Conclusions Child pedestrian head injury was the largest under back impact. The results have important application values for the assessment and development of car-pedestrian collision protection device.