As representative soft materials with widespread applications, gels with various functions have been developed. However, traditional gels are vulnerable to stress-induced formation of cracks. The propagation of these cracks may affect the integrity of network structures of gels, resulting in the loss of functionality and limiting the service life of the gels. To address this challenge, self-healing gels that can restore their functionalities and structures after damage have been developed as "smart" soft materials. In this paper, we present an overview of the current strategies for synthesizing self-healing gels based on the concept of constitutional dynamic chemistry, which involves molecular structures capable of establishing dynamic networks based upon physical interactions or chemical reactions. The characterization methods of self-healing gels and the key factors that affect self-healing properties are analyzed. We also illustrate the emerging applications of self-healing gels, with emphasis on their usage in industry (coatings, sealants) and biomedicine (tissue adhesives, agents for drug or cell delivery). We conclude with a perspective on challenges facing the field, along with prospects for future development.

1352 wileyonlinelibrary.com maintain the integrity of network structures and mechanical properties of bulk gels, leading to their long-term use with stable functionality. [9][10][11][12][13] The scientifi c community nowadays focus on two major approaches, based on dynamic covalent bond [14][15][16][17] and noncovalent bond, [18][19][20][21][22][23][24][25][26][27][28] to design self-healing hydrogels. Dynamic covalent bond integrates both the stability of covalent bond and the reversibility of noncovalent bond in one system. [ 29 ] These dynamic covalent bonds can build an intrinsic dynamic equilibrium of bond generation and dissociation in hydrogel networks, endowing self-healing performance to the hydrogels. Despite a few examples of self-healing hydrogels based on the dynamic covalent bonds (e.g., phenylboronate esters, [30][31][32] acylhydrazone bonds, [ 29,33 ] disulfi de bonds, [34][35][36] and Diels-Alder reactions, [ 37,38 ] the diffi culty of manipulating in vivo due to their nonautonomous self-healing characteristics, impedes their applications. For instance, selfhealing hydrogel based on dynamically restructuring of phenylboronic esters needs an acid environment (pH 4.2), [ 30 ] while hydrogel based on dynamic disulfi de bonds usually needs an alkali environment (pH 9), [ 34 ] to trigger the corresponding healing process. Moreover, complicated synthetic procedures and unconfi rmed biocompatibility of these self-healing hydrogels may limit their applications. For instance, the self-healing A novel biocompatible polysaccharide-based self-healing hydrogel, CEC-l-OSA-l-ADH hydrogel ("l" means "linked-by"), is developed by exploiting the dynamic reaction of N -carboxyethyl chitosan (CEC) and adipic acid dihydrazide (ADH) with oxidized sodium alginate (OSA). The self-healing ability, as demonstrated by rheological recovery, macroscopic observation, and beam-shaped strain compression measurement, is attributed to the coexistence of dynamic imine and acylhydrazone bonds in the hydrogel networks. The CEC-l-OSA-l-ADH hydrogel shows excellent self-healing ability under physiological conditions with a high healing effi ciency (up to 95%) without need for any external stimuli. In addition, the CEC-l-OSA-l-ADH hydrogel exhibits good cytocompatibility and cell release as demonstrated by threedimensional cell encapsulation. With these superior properties, the developed hydrogel holds great potential for applications in various biomedical fi elds, e.g., as cell or drug delivery carriers.

A dextran-based self-healing hydrogel is prepared by reversible Diels-Alder reaction under physiological conditions. Cytocompatible fulvene-modified dextran as main polymer chains and dichloromaleic-acid-modified poly(ethylene glycol) as cross-linkers are used. Both macro- and microscopic observation as well as the rheological recovery test confirm the self-healing property of the dextran-l-poly(ethylene glycol) hydrogels ("l" means "linked-by"). In addition, scanning electrochemical microscopy is used to qualitatively and quantitatively in situ track the self-healing process of the hydrogel for the first time. It is found that the longitudinal depth of scratch on hydrogel surface almost completely healed at 37 °C after 7 h. This work represents a facile approach for fabrication of polysaccharide self-healing hydrogel, which can be potentially used in several biomedical fields.

The native extracellular matrix (ECM) generally exhibits dynamic mechanical properties and displays time‐dependent responses to deformation or mechanical loading, in terms of viscoelastic behaviors (e.g., stress relaxation and creep). Viscoelasticity of the ECM plays a critical role in development, homeostasis, and tissue regeneration, and its implication in disease progression has also been recognized recently. Hydrogels with tunable viscoelastic properties hold a great promise to recapitulate such time‐dependent mechanics found in native ECM, which have been recently used to regulate cell behavior and guide cell fate. Here the importance of tissue viscoelasticity is first highlighted, the molecular mechanisms of hydrogel viscoelasticity are summarized, and characterization techniques used at the macroscale and microscale are reviewed. Then, recent advances in developing novel hydrogels with tunable viscoelasticity through varying crosslinking strategies, engineering of viscoelastic cell microenvironment and its substantial effects on cell behavior and fate are described, and the underlying mechanobiology mechanisms are subsequently discussed. Finally, the ongoing challenges and future perspectives on the design and modulation of viscoelastic hydrogels and the mechanobiology mechanisms on cellular responses to viscoelastic cell microenvironment are proposed.

BackgroundWe attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).MethodAll computed tomography (CT) images were acquired for 562 patients from the Nan Fang Hospital (NFH), 89 patients from Zhu Hai Hospital Affiliated with Jinan University (ZHHAJU), and 138 patients from the Sun Yat-sen University Cancer Center (SYUCC). We built a predictive model from the outputs using the transfer learning techniques of a residual convolutional neural network (ResNet50). The prediction accuracy for each patch was revaluated in two independent validation cohorts.ResultsIn the training set (NFH), the deep learning model had an accuracy of 84.3% and areas under curves (AUCs) of 0.97, 0.96, 0.95, and 0.96 for complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), respectively. In the other two validation sets (ZHHAJU and SYUCC), the deep learning model had accuracies of 85.1% and 82.8% for CR, PR, SD, and PD. The ResNet50 model also had high AUCs for predicting the objective response of TACE therapy in patches and patients of three cohorts. Decision curve analysis (DCA) showed that the ResNet50 model had a high net benefit in the two validation cohorts.ConclusionThe deep learning model presented a good performance for predicting the response of TACE therapy and could help clinicians in better screening patients with HCC who can benefit from the interventional treatment.Key Points• Therapy response of TACE can be predicted by a deep learning model based on CT images. • The probability value from a trained or validation deep learning model showed significant correlation with different therapy responses. • Further improvement is necessary before clinical utilization. Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06318-1) contains supplementary material, which is available to authorized users.