Ultra-Strong Protein-Based Hydrogels via Promoting Intermolecular Entanglement of the Amorphous Region.

Proteins are classified as biopolymers which share similar structural features with semi-crystalline polymers. Although their unique biocompatibility facilitates the universal applications of protein-based hydrogels in the biomedical field, the mechanical performances of protein-based hydrogels fall short of practical requirements. Conventional strategies for enhancing mechanical properties focus on forming regularly folded secondary structures as analogs of crystalline regions. This concept is based on proteins as the analogy of semi-crystalline polymers, in which crystalline regions profoundly contribute to the mechanical performances. Even though the contribution of the amorphous region is equally weighted for semi-crystalline polymers, their capacity to improve the mechanical performances of protein-based structures is still undervalued. Herein, the potential of promoting the mechanical performances is explored by controlling the state of amorphous regions in protein-based hydrogels. A fibril protein is chosen, regenerated silk fibroin (RSF), as a model molecule for its similar viscoelasticity with a semi-crystalline polymer. The amorphous regions in the RSF hydrogels are transformed from extended to entangled states through a double-crosslinking method. The formation of entanglement integrates new physically crosslinked points for remarkable improvement in mechanical performances. A robust hydrogel is not only developed but also intended to provide new insights into the structural-property relationship of protein-based hydrogels.

Electron-injection-engineering induced dual-phase MoO2.8F0.2/MoO2.4F0.6 heterostructure for magnesium storage.

Rechargeable magnesium batteries (RMBs) have received increased attention due to their high volumetric capacity and safety. Nevertheless, the sluggish diffusion kinetics of highly polarized Mg2+ in host lattices severely hinders the development of RMBs. Herein, we report an electron injection strategy for modulating the Mo 4d-orbital splitting manner and first fabricate a dual-phase MoO2.8F0.2/MoO2.4F0.6 heterostructure to accelerate Mg2+ diffusion. The electron injection strategy triggers weak Jahn-Teller distortion in MoO6 octahedra and reorganization of the Mo 4d-orbital, leading to a partial phase transition from orthorhombic phase MoO2.8F0.2 to cubic phase MoO2.4F0.6. As a result, the designed heterostructure generates a built-in electric field, simultaneously improving its electronic conductivity and ionic diffusivity by at least one order of magnitude compared to MoO2.8F0.2 and MoO2.4F0.6. Importantly, the assembled MoO2.8F0.2/MoO2.4F0.6//Mg full cell exhibits a remarkable reversible capacity of 172.5 mAh g-1 at 0.1 A g-1, pushing forward the orbital-scale manipulation for high-performance RMBs.

Identifying the critical state of cancers by single-sample Markov flow entropy.

Background: The progression of complex diseases sometimes undergoes a drastic critical transition, at which the biological system abruptly shifts from a relatively healthy state (before-transition stage) to a disease state (after-transition stage). Searching for such a critical transition or critical state is crucial to provide timely and effective scientific treatment to patients. However, in most conditions where only a small sample size of clinical data is available, resulting in failure when detecting the critical states of complex diseases, particularly only single-sample data. Methods: In this study, different from traditional methods that require multiple samples at each time, a model-free computational method, single-sample Markov flow entropy (sMFE), provides a solution to the identification problem of critical states/pre-disease states of complex diseases, solely based on a single-sample. Our proposed method was employed to characterize the dynamic changes of complex diseases from the perspective of network entropy. Results: The proposed approach was verified by unmistakably identifying the critical state just before the occurrence of disease deterioration for four tumor datasets from The Cancer Genome Atlas (TCGA) database. In addition, two new prognostic biomarkers, optimistic sMFE (O-sMFE) and pessimistic sMFE (P-sMFE) biomarkers, were identified by our method and enable the prognosis evaluation of tumors. Conclusions: The proposed method has shown its capability to accurately detect pre-disease states of four cancers and provide two novel prognostic biomarkers, O-sMFE and P-sMFE biomarkers, to facilitate the personalized prognosis of patients. This is a remarkable achievement that could have a major impact on the diagnosis and treatment of complex diseases.