A macromolecular cross-linked alginate aerogel with excellent concentrating effect for rapid hemostasis.

Alginate-based materials present promising potential for emergency hemostasis due to their excellent properties, such as procoagulant capability, biocompatibility, low immunogenicity, and cost-effectiveness. However, the inherent deficiencies in water solubility and mechanical strength pose a threat to hemostatic efficiency. Here, we innovatively developed a macromolecular cross-linked alginate aerogel based on norbornene- and thiol-functionalized alginates through a combined thiol-ene cross-linking/freeze-drying process. The resulting aerogel features an interconnected macroporous structure with remarkable water-uptake capacity (approximately 9000 % in weight ratio), contributing to efficient blood absorption, while the enhanced mechanical strength of the aerogel ensures stability and durability during the hemostatic process. Comprehensive hemostasis-relevant assays demonstrated that the aerogel possessed outstanding coagulation capability, which is attributed to the synergistic impacts on concentrating effect, platelet enrichment, and intrinsic coagulation pathway. Upon application to in vivo uncontrolled hemorrhage models of tail amputation and hepatic injury, the aerogel demonstrated significantly superior performance compared to commercial alginate hemostatic agent, yielding reductions in clotting time and blood loss of up to 80 % and 85 %, respectively. Collectively, our work illustrated that the alginate porous aerogel overcomes the deficiencies of alginate materials while exhibiting exceptional performance in hemorrhage, rendering it an appealing candidate for rapid hemostasis.

A bionic multichannel nanofiber conduit carrying Tubastatin A for repairing injured spinal cord.

Spinal cord injury is a kind of nerve injury disease with high disability rate. The bioscaffold, which presents a biomimetic structure, can be used as "bridge" to fill the cavity formed by the liquefaction and necrosis of spinal nerve cells, and connects the two ends of the fracture to promote the effective recovery of nerve function. Tubasatin A (TUBA) is a potent selective histone deacetylase 6 (HDAC6) inhibitor, which can inhibit the overexpression of HDAC6 after spinal cord injury. However, TUBA is limited by high efflux ratios, low brain penetration and uptake in the treatment of spinal cord injury. Therefore, an effective carrier with efficient load rate, sustained drug release profile, and prominent repair effect is urgent to be developed. In this study, we have prepared a bionic multichannel Tubasatin A loaded nanofiber conduit (SC-TUBA(+)) through random electrospinning and post-triple network bond crosslinking for inhibiting HDAC6 as well as promoting axonal regeneration during spinal cord injury treatment. The Tubasatin A-loaded nanofibers were shown to be successfully contained in poly(glycolide-co-ε-caprolactone) (PGCL)/silk fibroin (SF) matrix, and the formed PGCL/SF-TUBA nanofibers exhibited an uniform and smooth morphology and appropriate surface wettability. Importantly, the TUBA loaded nanofibers showed a sustained-release profile, and still maintains activity and promoted the extension of axonal. In addition, the total transection large span model of rat back and immunofluorescent labeling, histological, and neurobehavioral analysis were performed for inducing spinal cord injury at T9-10, evaluating therapeutic efficiency of SC-TUBA(+), and elucidating the mechanism of TUBA release system in vivo. All the results demonstrated the significantly reduced glial scar formation, increased nerve fiber number, inhibited inflammation, reduced demyelination and protected bladder tissue of TUBA-loaded nanofibers for spinal cord injury compared to SC-TUBA, SC and Control groups, indicating their great potential for injured spinal cord healing clinically.

Construction of classifier based on MPCA and QSA and its application on classification of pancreatic diseases.

A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA). In order to verify the effectiveness of the proposed algorithm, the normal SVM method has been chosen as comparing algorithm. The experimental results show that the proposed method can effectively extract the eigenfeatures and improve the classification accuracy of pancreatic images.