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In this work, we proposed a facile chemical etching/pyrolysis strategy to prepare multi-heteroatoms doped yolk-shell porous carbon by using common ZIF-8 as the precursor and phytic acid as the etching agent. The as-prepared porous carbon was composed by N-doped carbon frame core (N-CF) and N, P-codoped carbon frame shell (N, P-CF) with unique hierarchically porous structure. Besides, the yolk-shell hierarchically porous carbon (N-CF@N, P-CF) exhibited large specific surface and pore volume, promoted electrochemical active area and electron transfer kinetics. Compared with pure ZIF-8 derived N-CF, the as-synthesized N-CF@N, P-CF displayed enhanced electrochemical activity toward the oxidation of various target analytes, including organic pollutants (hydroquinone and catechol), active pharmaceutical molecule (acetaminophen), and small biological molecules (dopamine and uric acid). As a result, a versatile electrochemical sensing platform was successfully developed for the sensing of hydroquinone, catechol, acetaminophen, dopamine and uric acid with high sensitivity and selectivity. Besides, the detection limits were evaluated as 15.4 nM, 18.8 nM, 16.2 nM, 22.2 nM and 24.5 nM, respectively, which were competitive with many other electrode materials. This work helps to prosper the type of carbon-based nanostructure, and provides an advanced electrode material with great application potential for electrochemical sensors design.

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 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.

ObjectiveThe meniscus tear is a common problem in sports trauma. The imaging diagnosis mainly depends on the MRI. To improve the diagnostic accuracy and efficiency, a deep learning model was employed in this study and the identification efficiency has been evaluated.MethodsThe standard knee MRI images of 924 individual patients were used to complete the training, validation, and testing process. The Mask R-CNN was considered as the deep learning network structure, and the ResNet50 was considered as the backbone network. The deep learning model was trained and validated with a dataset containing 504 and 220 patients, respectively. The accuracy testing was performed on a dataset of 200 patients and reviewed by an experienced radiologist and a sports medicine physician.ResultsAfter training and validation, the deep learning model effectively recognized the healthy and injured meniscus. The overall average precision of the bounding box and pixel mask was more than 88% when the IoU threshold value was 0.75. The detailed average precision of three types of menisci (healthy, torn, and degenerated) was ranged from 68% to 80%. The overall sensitivity of the bounding box and pixel mask was more than 74% at the IoU threshold from 0.50 to 0.95. The diagnosis accuracy for the healthy, torn, and degenerated meniscus was 87.50%, 86.96%, and 84.78%, respectively.ConclusionThe Mask R-CNN recognized effectively and predicted the meniscus injury, especially for the tears that occurred at different parts of the meniscus. The recognition accuracy was admirable. The diagnostic accuracy can be further improved with the increase of the training sample size. Therefore, this tool has great potential in the application for the diagnosis of meniscus injury.The translational potential of this articleDeep learning model has unique effect in reducing doctors’ workload and improving diagnosis accuracy. It can identify and classify injured and healthy meniscus more accurately after training and learning datasets. The torn and degenerated meniscus can also be distinguished by this model. This technology could serve as an effective tool for clinical MRI-assisted diagnostics in meniscus injury.

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.

Compared with extensively-used chemically-exfoliated graphene, physically-exfoliated graphene-based heterogeneous structures are rarely used for electrochemical analysis. In this work, physically-exfoliated graphene nanosheets (GN) are used as the substrate material to couple high-activity FeOOH nanoparticles. Multiple characterization technology confirm that a novel hierarchical hybrid composed of physically-exfoliated graphene nanosheets and FeOOH nanoparticles (GN@FeOOH) is easily obtained by a facile solution growth strategy. Plenty of fiber-like FeOOH nanoparticles with size about 20 nm are uniformly anchored on the surface of graphene with robust adhesion. Relative to pristine GN, the electrochemical active area and electron transfer kinetics are greatly boosted for the introduction of high-activity of FeOOH nanoparticles. Benefiting from the remarkably enhanced electrochemical activity, the obtained GN@FeOOH hybrids exhibit excellent electrochemical performance toward the reduction of H2O2. As a result, a novel and rapid electrochemical sensing platform for the detection of H2O2 at low applied potential (-0.25 V vs. SCE) is fabricated with wide linear range (0.25 μM - 1.2 mM) and high sensitivity (265.7 μA mM−1 cm-2), and the limit of detection is as low as 0.08 μM, which is comparable with many other noble metal-based electrochemical sensors and exceeds many non-noble metal-based electrochemical sensors.