Small diameter expanded polytetrafluoroethylene vascular graft with differentiated inner and outer biomacromolecules for collaborative endothelialization, anti-thrombogenicity and anti-inflammation.

Without differentiated inner and outer biological function, expanded polytetrafluoroethylene (ePTFE) small-diameter (<6 mm) artificial blood vessels would fail in vivo due to foreign body rejection, thrombosis, and hyperplasia. In order to synergistically promote endothelialization, anti-thrombogenicity, and anti-inflammatory function, we modified the inner and outer surface of ePTFE, respectively, by grafting functional biomolecules, such as heparin and epigallocatechin gallate (EGCG), into the inner surface and polyethyleneimine and rapamycin into the outer surface via layer-by-layer self-assembly. Fourier-transform infrared spectroscopy showed the successful incorporation of EGCG, heparin, and rapamycin. The collaborative release profile of heparin and rapamycin lasted for 42 days, respectively. The inner surface promoted human umbilical vein endothelial cells (HUVECs) adhesion and growth and that the outer surface inhibited smooth muscle cells growth and proliferation. The modified ePTFE effectively regulated the differentiation behavior of RAW264.7, inhibited the expression of proinflammatory mediator TNF-α, and up-regulated the expression of anti-inflammatory genes Arg1 and Tgfb-1. The ex vivo circulation results indicated that the occlusions and total thrombus weight of modified ePTFE was much lower than that of the thrombus formed on the ePTFE, presenting good anti-thrombogenic properties. Hence, the straightforward yet efficient synergistic surface functionalization approach presented a potential resolution for the prospective clinical application of small-diameter ePTFE blood vessel grafts.

Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback.

Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score (p < 0.05). The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients' motor function clinically.