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Orthopedic implants containing biodegradable magnesium have been used for fracture repair with considerable efficacy; however, the underlying mechanisms by which these implants improve fracture healing remain elusive. Here we show the formation of abundant new bone at peripheral cortical sites after intramedullary implantation of a pin containing ultrapure magnesium into the intact distal femur in rats. This response was accompanied by substantial increases of neuronal calcitonin gene-related polypeptide-α (CGRP) in both the peripheral cortex of the femur and the ipsilateral dorsal root ganglia (DRG). Surgical removal of the periosteum, capsaicin denervation of sensory nerves or knockdown in vivo of the CGRP-receptor-encoding genes Calcrl or Ramp1 substantially reversed the magnesium-induced osteogenesis that we observed in this model. Overexpression of these genes, however, enhanced magnesium-induced osteogenesis. We further found that an elevation of extracellular magnesium induces magnesium transporter 1 (MAGT1)-dependent and transient receptor potential cation channel, subfamily M, member 7 (TRPM7)-dependent magnesium entry, as well as an increase in intracellular adenosine triphosphate (ATP) and the accumulation of terminal synaptic vesicles in isolated rat DRG neurons. In isolated rat periosteum-derived stem cells, CGRP induces CALCRL-and RAMP1-dependent activation of cAMP-responsive element binding protein 1 (CREB1) and SP7 (also known as osterix), and thus enhances osteogenic differentiation of these stem cells. Furthermore, we have developed an innovative, magnesium-containing intramedullary nail that facilitates femur fracture repair in rats with ovariectomy-induced osteoporosis. Taken together, these findings reveal a previously undefined role of magnesium in promoting CGRP-mediated osteogenic differentiation, which suggests the therapeutic potential of this ion in orthopedics.

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Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification. In this study, a novel deep learning scheme based on restricted Boltzmann machine (RBM) is proposed. Specifically, frequency domain representations of EEG signals obtained via fast Fourier transform (FFT) and wavelet package decomposition (WPD) are obtained to train three RBMs. These RBMs are then stacked up with an extra output layer to form a four-layer neural network, which is named the frequential deep belief network (FDBN). The output layer employs the softmax regression to accomplish the classification task. Also, the conjugate gradient method and backpropagation are used to fine tune the FDBN. Extensive and systematic experiments have been performed on public benchmark datasets, and the results show that the performance improvement of FDBN over other selected state-of-the-art methods is statistically significant. Also, several findings that may be of significant interest to the BCI community are presented in this article.

Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular non-intrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is hard to collect fall data and instead simulated falls are recorded to construct the training dataset, which is restricted to limited quantity. To address these problems, a three-dimensional convolutional neural network (3D CNN) based method for fall detection is developed which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution. 2D CNN could only encode spatial information, and the employed 3D convolution could extract motion feature from temporal sequence, which is important for fall detection. To further locate the region of interest in each frame, a LSTM (Long Short-Term Memory) based spatial visual attention scheme is incorporated. Sports dataset Sports-1M with no fall examples is employed to train the 3D CNN, which is then combined with LSTM to train a classifier with fall dataset. Experiments have verified the proposed scheme on fall detection benchmark with high accuracy as 100%. Superior performance has also been obtained on other activity databases.

Highlights d Injury-elevated Lipin1 and DGAT in retinal ganglion cells suppress regeneration d Neuronal lipin1 and DGATs increase triglyceride and decrease phospholipids d Redirecting triacylglyceride to phospholipid synthesis promotes axon regeneration

SUMMARY Craniofacial abnormalities often involve sutures, the growth centers of the skull. To characterize the organization and processes governing their development, we profile the murine frontal suture, a model for sutural growth and fusion, at the tissue- and single-cell level on embryonic days (E)16.5 and E18.5. For the wild-type suture, bulk RNA sequencing (RNA-seq) analysis identifies mesenchyme-, osteogenic front-, and stage-enriched genes and biological processes, as well as alternative splicing events modifying the extracellular matrix. Single-cell RNA-seq analysis distinguishes multiple subpopulations, of which five define a mesenchymeosteoblast differentiation trajectory and show variation along the anteroposterior axis. Similar analyses of in vivo mouse models of impaired frontal suturogenesis in Saethre-Chotzen and Apert syndromes, Twist1 +/− and Fgfr2 +/S252W , demonstrate distinct transcriptional changes involving angiogenesis and ribogenesis, respectively. Co-expression network analysis reveals gene expression modules from which we validate key driver genes regulating osteoblast differentiation. Our study provides a global approach to gain insights into suturogenesis.

Glycyrrhizic acid (GA), a triterpene isolated from the roots and rhizomes of licorice, named Glycyrrhiza glabra, is the principal bioactive ingredient of anti-viral, anti-inflammatory and hepatoprotective effects. GA has been used in the clinical treatment of hepatitis, bronchitis, gastric ulcer, AIDS (acquired immunodeficiency syndrome), certain cancers and skin diseases. It has a direct effect on anti-HBV (hepatitis B virus) via affecting the HBsAg (hepatitis B surface antigen) to extracellular secretion, improving liver dysfunction in patients with chronic hepatitis B, and ultimately improving the immune status of HBV. GA can significantly inhibit the proliferation of HIV, showing an immune activation. The clinical application of GA on the prevention and treatments of various diseases may derive from its numerous pharmacological properties. This review provides the summary of the antiviral effects of GA on research progress and mechanism in recent years.