Preparation and biomedical applications of self-healing hydrogels assembled from hosts of cyclodextrins and cucurbit[n]urils with various guests were reviewed.
Poly(vinyl alcohol) (PVA) is a cytocompatible synthetic polymer and has been commonly used to prepare hydrogels. Bile acids and β-cyclodextrin are both natural compounds and they form stable host-guest inclusion complexes. They are attached covalently onto a low molecular weight PVA separately. Self-healing hydrogels can be easily formed by mixing the aqueous solutions of these PVA based polymers. The mechanical properties of the hydrogels can be tuned by varying the molar fractions of bile acid units on PVA. The dynamic inclusion complexation of the host-guest pair of the hydrogel allows the self-healing rapidly under ambient atmosphere and their mechanical properties could recover their original values in 1 min after incision. These PVA based polymers exhibited the good cytocompatibility and high hemocompatibility as shown by their biological evaluations. The use of natural compounds for host-guest interaction make such gels especially convenient to use as biomaterials, an advantage over conventional hydrogels prepared through freeze-thaw method.
Corneal transplantation is the widely accepted treatment to restore sight for corneal blindness. To date, because of the global donor cornea shortage, there is a need for alternatives to human donor corneas. Biocompatible collagen is an excellent candidate material for corneal repair in the view of biomimetics. Herein a class of polyrotaxane multiple aldehyde (PRA) crosslinkers based on the host−guest supramolecules of α-cyclodextrins and poly(ethylene glycol) is prepared to cross-link with collagen to fabricate materials for corneal repair. Aldehyde groups from rotaxanes and α-cyclodextrin units can synergistically improve the mechanical and optical properties of PRA cross-linked collagen membranes (Col-PRAs). Compared with counterparts cross-linked by traditional a cross-linker of 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide and N-hydroxy-succinimide, Col-PRAs have better mechanical properties, especially suture resistance as well as optical properties. In vivo lamellar keratoplasty results indicate that Col-PRAs not only can bear tight suturing on a rabbit cornea but also are prone to the remodeling of the epithelium and stroma of the cornea due to the outstanding cell adhesion and proliferation. These novel Col-PRAs exhibit great potential for use in the corneal regeneration.
Development of label‐free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cross‐polarized diffraction image (p‐DI) pairs on multiple pixel scales to accurately classify cells in five types. A total of 6185 cells were measured by a polarization diffraction imaging flow cytometry (p‐DIFC) method followed by cell classification with DINet on p‐DI data. The averaged value and SD of classification accuracy were found to be 98.9% ± 1.00% on test data sets for 5‐fold training and test. The invariance of DINet to image translation, rotation, and blurring has been verified with an expanded p‐DI data set. To study feature‐based classification by DINet, two sets of correctly and incorrectly classified cells were selected and compared for each of two prostate cell types. It has been found that the signature features of large dissimilarities between p‐DI data of correctly and incorrectly classified cell sets increase markedly from convolutional layers 1 and 2 to layers 3 and 4. These results clearly demonstrate the importance of high‐order correlations extracted at the deep layers for accurate cell classification.
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