Dynamic changes and clinic significance of serum high mobility group box 1 in patients with total hip arthroplasty.

BACKGROUND: Deeper understanding on the risk factors and seeking potential predicted biomarkers for prognosis of total hip arthroplasty (THA) patients are of great significance. Limited researches focused the correlation between high mobility group box protein-1 (HMGB1) and the prognosis of THA patients. OBJECTIVE: The objective of this study was to investigate the role of HMGB1 and inflammatory factors in patients underwent total hip arthroplasty (THA). METHODS: The present prospective study enrolled 208 THA patients who went to our hospital during January 2020 to January 2022. Serum levels of HMGB1, C-reactive protein (CRP), interleukin-1b (IL-1ß), and IL-6 were detected at the admission, 1 day, 3 days, 7 days, 30 days, and 90 days after surgery. The levels of Harris score, Fugl-Meyer, 36-item short-form health survey (SF-36), and Pittsburgh sleep quality index (PSQI) were detected on 90 days after surgery in two groups. Receiver operating characteristic curve (ROC) was performed for analyzing the diagnostic value of HMGB1 and logistic regression model was used for identifying the risk factor for poor prognosis of THA patients. RESULTS: Serum levels of HMGB1 and inflammatory factors increased after surgery compared with the baselines. A positive correlation was found between HMGB1 and CRP on 1 day after surgery, and positive correlations were found among HMGB1, IL-1ß, and IL-6 on 3 day after surgery. Besides, low HMGB1 reduced the incidence of post-operative complications and improved prognosis of THA patients. CONCLUSION: Serum HMGB1 was correlated with inflammatory factors and the prognosis of THA patients.

OBJETIVO: Profundizar la comprensión de los factores de riesgo y buscar predecir biomarcadores potenciales para el pronóstico de pacientes con reemplazo total de cadera es de gran importancia. Los estudios limitados se han centrado en la correlación entre la nhigh mobility group box 1 protein (HMGB1) y el pronóstico en pacientes con artroplastia total de cadera. Investigar el papel de la HMGB1 sérica y los factores inflamatorios en pacientes sometidos a artroplastia total de cadera. MÉTODO: Estudio prospectivo que incluyó 208 pacientes con artroplastia total de cadera que acudieron a nuestro hospital. Los niveles de puntuación de Harris, Fugl-Meyer, encuesta de salud de formato corto de 36 ítems (SF-36) e índice de calidad del sueño de Pittsburgh (PSQI) se determinaron 90 días después de la cirugía en dos grupos. Se realizó la curva característica operativa del receptor (ROC) para analizar el valor diagnóstico de HMGB1 y se utilizó un modelo de regresión logística para identificar el factor de riesgo para mal pronóstico de los pacientes con artroplastia total de cadera. RESULTADOS: Las concentraciones séricas de HMGB1 y los factores inflamatorios aumentaron después de la cirugía en comparación con los valores iniciales. Se encontró una correlación positiva entre la HMGB1 y la proteína C reactiva 1 día después de la cirugía, y correlaciones positivas entre la HMGB1 y las interleucinas 1b y 6 a los 3 días de la cirugía. CONCLUSIONES: La HMGB1 sérica se correlacionó con los factores inflamatorios y con el pronóstico de los pacientes con artroplastia total de cadera.

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning.

Compared with traditional imaging, the light field contains more comprehensive image information and higher image quality. However, the available data for light field reconstruction are limited, and the repeated calculation of data seriously affects the accuracy and the real-time performance of multiperspective light field reconstruction. To solve the problems, this paper proposes a multiperspective light field reconstruction method based on transfer reinforcement learning. Firstly, the similarity measurement model is established. According to the similarity threshold of the source domain and the target domain, the reinforcement learning model or the feature transfer learning model is autonomously selected. Secondly, the reinforcement learning model is established. The model uses multiagent (i.e., multiperspective) Q-learning to learn the feature set that is most similar to the target domain and the source domain and feeds it back to the source domain. This model increases the capacity of the source-domain samples and improves the accuracy of light field reconstruction. Finally, the feature transfer learning model is established. The model uses PCA to obtain the maximum embedding space of source-domain and target-domain features and maps similar features to a new space for label data migration. This model solves the problems of multiperspective data redundancy and repeated calculations and improves the real-time performance of maneuvering target recognition. Extensive experiments on PASCAL VOC datasets demonstrate the effectiveness of the proposed algorithm against the existing algorithms.

Evaluation of a combined posterior lateral and anteromedial approach in the treatment of terrible triad of the elbow: A retrospective study.

BACKGROUND: This retrospective study aims to investigate the efficacy and safety of a combined posterior lateral and anteromedial approach in the treatment of terrible triad of the elbow (TTE). METHODS: TTE patients who received a combination of posterior lateral and anteromedial approach or other conservative treatments were included in the present study. The postoperative functions of the elbow and the severity of traumatic arthritis were assessed using the Mayo Elbow Performance Score (MEPS) and visual analog scale (VAS). Extension-flexion of elbow joint and rotation of forearm were also measured. RESULTS: A combined posterior lateral and anteromedial approach or other conservative treatments showed significant improvements in the activity of the elbow, MEPS, VAS, the excellent rate, and x-ray results. The postoperative healing time and complication rate of patients who received a combined posterior lateral and anteromedial approach significantly decreased compared to those who received other conservative treatments. CONCLUSIONS: Patients with TTE who received a combined posterior lateral and anteromedial treatment had an increased fracture healing rate, showed improved recovery of elbow functions and had fewer complications.

A Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest.

The functional region of interest (fROI) approach has increasingly become a favored methodology in functional magnetic resonance imaging (fMRI) because it can circumvent inter-subject anatomical and functional variability, and thus increase the sensitivity and functional resolution of fMRI analyses. The standard fROI method requires human experts to meticulously examine and identify subject-specific fROIs within activation clusters. This process is time-consuming and heavily dependent on experts' knowledge. Several algorithmic approaches have been proposed for identifying subject-specific fROIs; however, these approaches cannot easily incorporate prior knowledge of inter-subject variability. In the present study, we improved the multi-atlas labeling approach for defining subject-specific fROIs. In particular, we used a classifier-based atlas-encoding scheme and an atlas selection procedure to account for the large spatial variability across subjects. Using a functional atlas database for face recognition, we showed that with these two features, our approach efficiently circumvented inter-subject anatomical and functional variability and thus improved labeling accuracy. Moreover, in comparison with a single-atlas approach, our multi-atlas labeling approach showed better performance in identifying subject-specific fROIs.

Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI.

Representing brain morphology as a network has the advantage that the regional morphology of 'isolated' structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics.