Mechanically skin-like and water-resistant self-healing bioelastomer for high-tension wound healing

The biomedical application of self-healing materials in wet or(under)water environments is quite challenging because the insulation and dissociation effects of water molecules significantly reduce the reconstruction of material–interface interactions.Rapid closure with uniform tension of high-tension wounds is often difficult,leading to further deterioration and scarring.Herein,a new type of thermosetting water-resistant self-healing bioelastomer(WRSHE)was designed by synergistically incorporating a stable polyglycerol sebacate(PGS)covalent crosslinking network and triple hybrid dynamic networks consisting of reversible disulfide metathesis(SS),and dimethylglyoxime urethane(Dou)and hydrogen bonds.And a resveratrol-loaded WRSHE(Res@WRSHE)was developed by a swelling,absorption,and crosslinked network locking strategy.WRSHEs exhibited skin-like mechanical properties in terms of nonlinear modulus behavior,biomimetic softness,high stretchability,and good elasticity,and they also achieved ultrafast and highly efficient self-healing in various liquid environments.For wound-healing applications of high-tension full-thickness skin defects,the convenient surface assembly by self-healing of WRSHEs provides uniform contraction stress to facilitate tight closure.Moreover,Res@WRSHEs gradually release resveratrol,which helps inflammatory response reduction,promotes blood vessel regeneration,and accelerates wound repair.

Flexible antibacterial degradable bioelastomer nanocomposites for ultrasensitive human–machine interaction sensing enabled by machine learning

Flexible wearables have attracted extensive interests for personal human motion sensing,intelligent disease diagnosis,and multifunctional electronic skins.How-ever,the reported flexible sensors,mostly exhibited narrow detection range,low sensitivity,limited degradability to aggravate environmental pollution from vast electronic wastes,and poor antibacterial performance to hardly improve skin dis-comfort and skin inflammation from bacterial growth under long-term wearing.Herein,bioinspired from human skin featuring highly sensitive tactile sensation with spinous microstructures for amplifying sensing sensitivity between epidermis and dermis,a wearable antibacterial degradable electronics is prepared from degrad-able elastomeric substrate with MXene-coated spinous microstructures templated from lotus leaf assembled with the interdigitated electrode.The degradable elas-tomer is facilely obtained with tunable modulus to match the modulus of human skin with improved hydrophilicity for rapid degradation.The as-obtained sensor displays ultra-low detection limit(0.2 Pa),higher sensitivity(up to 540.2 kPa^(-1)),outstand-ing cycling stability(>23,000 cycles),a wide detection range,robust degradability,and excellent antibacterial capability.Facilitated by machine learning,the collected sensing signals from the integrated sensors on volunteer's fingers to the related American Sign Language are effectively recognized with an accuracy up to 99%,showing excellent potential in wireless human movement sensing and smart machine learning-enabled human-machine interaction.

Preparation, structure and properties of fluorescent nano- CdTe/poly （1, 4-butanediol-citrate） bioelastomer nanocomposite in-situ dispersion technique

Hydrophilic photoluminescent CdTe/poly （1, 4-butanediol-citrate） （PBC） bioelastomer nanocomposite was successfully synthesized by a two-step method and characterized by X-ray diffraction （XRD）, Fourier trans- form infrared （FT-IR） spectroscopy, Uv-vis spectroscopy, photoluminescence （PL） spectroscopy and scanning elec- tron microscope （SEM）. The differential scanning calori- metry analysis shows that the bioelastomer nanocom- posites with different mass fractions of CdTe have low glass-transition temperature, which indicates that they possess elastic property in the range from room tempera- ture to the expected applied temperature （37℃）. The measurements of the hydrophilicity, in vitro degradation and PL show that the nanocomposite has good hydro- philicity, degradation and high fluorescence properties.

Computer-aided recognition and assessment of a porous bioelastomer in ultrasound images for regenerative medicine applications

It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images.To solve this problem,this paper proposes a joint algorithm for bioelastomer contour detection and a texture feature extraction method for monitoring the degradation performance of bioelastomers.First,the mean-shift clustering method is utilized to obtain the clustering feature information of bioelastomers and native tissue from manually segmented images,and this information is used as the initial information in the image binarization algorithm for image partitioning.Second,Otsu's thresholding method and mathematical morphology are applied in the process of image binarization.Finally,the Canny edge detector is employed to extract the complete bioelastomers contour from the binary image.To verify the robustness of the proposed joint algorithm,the results using the proposed joint algorithm,where mean-shift clustering is replaced with k-means clustering are also obtained.The proposed joint algorithm based on mean-shift clustering outperforms the joint algorithm based on k-means clustering,as well as algorithms that directly apply the Canny,Sobel and Laplacian methods.Texture feature extraction is based on the computer-aided recognition of bioelastomers.The region of interest(ROI)is set in the scaffold region,and the first-order statistical features and second-order statistical features of the greyscale values of the ROI are extracted and analysed.The proposed joint algorithm can not only extract ideal bioelastomers contours from ultrasound images but also provide valuable feedback on the degradation behaviour of bioelastomers at implant sites.