Poly (Betulinic Acid) Nanoparticles Loaded with bFGF Improve Functional Recovery After Spinal Cord Injury.

Oxidative stress (OS) is one of the crucial molecular events of secondary spinal cord injury (SCI). Basic fibroblast growth factor (bFGF) is a multipotent cell growth factor with an anti-oxidant effect. However, bFGF has a short half-life in vivo, which limits its therapeutic application. Biodegradable polymers with excellent biocompatibility have been recently applied in SCI. The negative aspect is that polymers cannot provide a significant therapeutic effect. Betulinic acid (BA), a natural anti-inflammatory compound, has been polymerized into poly (betulinic acid) (PBA) to serve as a drug carrier for bFGF. This study explores the therapeutic effects and underlying molecular mechanisms of PBA nanoparticles (NPs) loaded with bFGF (PBA-bFGF NPs) in SCI. Results show that PBA-bFGF NPs produce remarkable biocompatibility in vivo and in vitro. The results also demonstrate that local delivery of PBA-bFGF NPs enhances motor function recovery, inhibits OS, mitigates neuroinflammation, and alleviates neuronal apoptosis following SCI. Furthermore, the results indicate that local delivery of PBA-bFGF NPs activates the nuclear factor erythroid 2-related factor 2 (Nrf-2) signaling pathway following SCI. In summary, results suggest that local delivery of PBA-bFGF NPs delivers potential therapeutic advantages in the treatment and management of SCI.

Over-activation of TRPM2 ion channel accelerates blood-spinal cord barrier destruction in diabetes combined with spinal cord injury rat.

Spinal cord injury (SCI) is a devastating neurological disorder that often results in loss of motor and sensory function. Diabetes facilitates the blood-spinal cord barrier (BSCB) destruction and aggravates SCI recovery. However, the molecular mechanism underlying it is still unclear. Our study has focused on transient receptor potential melastatin 2 (TRPM2) channel and investigated its regulatory role on integrity and function of BSCB in diabetes combined with SCI rat. We have confirmed that diabetes is obviously not conductive to SCI recovery through accelerates BSCB destruction. Endothelial cells (ECs) are the important component of BSCB. It was observed that diabetes significantly worsens mitochondrial dysfunction and triggers excessive apoptosis of ECs in spinal cord from SCI rat. Moreover, diabetes impeded neovascularization in spinal cord from SCI rat with decreases of VEGF and ANG1. TRPM2 acts as a cellular sensor of ROS. Our mechanistic studies showed that diabetes significantly induces elevated ROS level to activate TRPM2 ion channel of ECs. Then, TRPM2 channel mediated the Ca2+ influx and subsequently activated p-CaMKII/eNOS pathway, and which in turn triggered the ROS production. Consequently, over-activation of TRPM2 ion channel results in excessive apoptosis and weaker angiogenesis during SCI recovery. Inhibition of TRPM2 with 2-Aminoethyl diphenylborinate (2-APB) or TRPM2 siRNA will ameliorate the apoptosis of ECs and promote angiogenesis, subsequently enhance BSCB integrity and improve the locomotor function recovery of diabetes combined with SCI rat. In conclusion, TRPM2 channel may be a key target for the treatment of diabetes combined with SCI rat.

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification.

In recent years, the hyperspectral classification algorithm based on deep learning has received widespread attention, but the existing network models have higher model complexity and require more time consumption. In order to further improve the accuracy of hyperspectral image classification and reduce model complexity, this paper proposes an asymmetric coordinate attention spectral-spatial feature fusion network (ACAS2F2N) to capture distinguishing hyperspectral features. Specifically, adaptive asymmetric iterative attention was proposed to obtain the discriminative spectral-spatial features. Different from the common feature fusion method, this feature fusion method can adapt to most skip connection tasks. In addition, there is no manual parameter setting. Coordinate attention is used to obtain accurate coordinate information and channel relationship. The strip pooling module was introduced to increase the network's receptive field and avoid irrelevant information brought by conventional convolution kernels. The proposed algorithm is tested on the mainstream hyperspectral datasets (IP, KSC, and Botswana), experimental results show that the proposed ACAS2F2N can achieve state-of-the-art performance with lower time complexity.

Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval.

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.