Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury.

BACKGROUND: Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated. PURPOSE: To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI). MATERIAL AND METHODS: A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores. RESULTS: Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs. CONCLUSIONS: The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.

Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model.

Objective: Meniscus tear is a common problem in sports trauma, and its imaging diagnosis mainly relies on MRI. To improve the diagnostic accuracy and efficiency, a deep learning model was employed in this study and the identification efficiency was evaluated. Methods: Standard knee MRI images from 924 individual patients were used to complete the training, validation and testing processes. Mask regional convolutional neural network (R-CNN) was used to build the deep learning network structure, and ResNet50 was adopted to develop the backbone network. The deep learning model was trained and validated with a dataset containing 504 and 220 patients, respectively. Internal testing was performed based on a dataset of 200 patients, and 180 patients from 8 hospitals were regarded as an external dataset for model validation. Additionally, 40 patients who were diagnosed by the arthroscopic surgery were enrolled as the final test dataset. Results: After training and validation, the deep learning model effectively recognized healthy and injured menisci. Average precision for the three types of menisci (healthy, torn and degenerated menisci) ranged from 68% to 80%. Diagnostic accuracy for healthy, torn and degenerated menisci was 87.50%, 86.96%, and 84.78%, respectively. Validation results from external dataset demonstrated that the accuracy of diagnosing torn and intact meniscus tear through 3.0T MRI images was higher than 80%, while the accuracy verified by arthroscopic surgery was 87.50%. Conclusion: Mask R-CNN effectively identified and diagnosed meniscal injuries, especially for tears that occurred in different parts of the meniscus. The recognition ability was admirable, and the diagnostic accuracy could be further improved with increased training sample size. Therefore, this deep learning model showed great potential in diagnosing meniscus injuries. Translational potential of this article: Deep learning model exerted unique effect in terms of reducing doctors' workload and improving diagnostic accuracy. Injured and healthy menisci could be more accurately identified and classified based on training and learning datasets. This model could also distinguish torn from degenerated menisci, making it an effective tool for MRI-assisted diagnosis of meniscus injuries in clinical practice.

In-situ synthesis of ultrasmall Au nanoparticles on bimetallic metal-organic framework with enhanced electrochemical activity for estrone sensing.

In this work, ultrasmall Au nanoparticles decorated bimetallic metal-organic framework (US Au NPs@AuZn-MOF) hybrids were facilely prepared by a sequential ion exchange and in-situ chemical reduction strategy. Numerous of Au nanoparticles with size less than 5 nm was homogeneously dispersed on the surface of the whole bimetallic AuZn-MOF polyhedrons. The integration of ultrasmall Au nanoparticles greatly enhanced the electron transfer capacity and electrochemical active surface area of the metal-organic framework host. Compared with the pristine Zn-MOF, bimetallic AuZn-MOF, the as-synthesized US Au NPs@AuZn-MOF hybrids exhibited remarkably promoted electrochemical activity toward the oxidation and sensing of endocrine-disrupting chemical (EDC) estrone. As a result, a highly sensitive electrochemical sensing platform was developed for the detection of estrone in the range of 0.05 µM-5 µM with limit of detection of 12.3 nM (S/N = 3) and sensitivity of 101.3 µA-1 µM-1 cm-2. Considering the structural diversity of MOFs and superior property of ultrasmall Au nanoparticles, the strategy proposed here may open a new avenue for the design and synthesis of other high-activity nanomaterials for electrochemical sensing or other challenging fields.