From common biomass materials to high-performance tissue engineering scaffold: Biomimetic preparation, properties characterization, in vitro and in vivo evaluations

Converting common biomass materials to high-performance biomedical products could not only reduce the environmental pressure associated with the large-scale use of synthetic materials,but also increase the economic value.Chitosan as a very promising candidate has drawn considerable attention owing to its abundant sources and remarkable bioactivities.However,pure chitosan materials usually exhibit insufficient mechanical properties and excessive swelling ratio,which seriously affected their in vivo stability and integrity when applied as tissue engineering scaf-folds.Thus,simultaneously improving the mechanical strength and biological compatibility of pure chitosan(CS)scaffolds becomes very important.Here,inspired by the fiber-reinforced con-struction of natural extracellular matrix and the porous structure of cancellous bone,we built silk microfibers/chitosan composite scaffolds via ice-templating technique.This biomimetic strategy achieved 500%of mechanical improvement to pure chitosan,and meanwhile still maintaining high porosity(>87%).In addition,the increased roughness of chitosan pore walls by embedded silk microfibers significantly promoted cell adhesion and proliferation.More importantly,after subcutaneous implantation in mice for four weeks,the composite scaffold showed greater struc-tural integrity,as well as better collagenation,angiogenesis,and osteogenesis abilities,suggesting its great potential in biomedicine.

A review of existing strategies for designing long-acting parenteral formulations: Focus on underlying mechanisms, and future perspectives

The need for long-term treatments of chronic diseases has motivated the widespread development of long-acting parenteral formulations(LAPFs)with the aim of improving drug pharmacokinetics and therapeutic efficacy.LAPFs have been proven to extend the half-life of therapeutics,as well as to improve patient adherence;consequently,this enhances the outcome of therapy positively.Over past decades,considerable progress has been made in designing effective LAPFs in both preclinical and clinical settings.Here we review the latest advances of LAPFs in preclinical and clinical stages,focusing on the strategies and underlying mechanisms for achieving long acting.Existing strategies are classified into manipulation of in vivo clearance and manipulation of drug release from delivery systems,respectively.And the current challenges and prospects of each strategy are discussed.In addition,we also briefly discuss the design principles of LAPFs and provide future perspectives of the rational design of more effective LAPFs for their further clinical translation.

A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

Feature Selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data.Most optimization algorithms for FS problems are not balanced in search.A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm(NL-BGWOA)is proposed to solve the problem in this paper.In the proposed method,a new position updating strategy combining the position changes of whales and grasshoppers population is expressed,which optimizes the diversity of searching in the target domain.Ten distinct high-dimensional UCI datasets,the multi-modal Parkinson's speech datasets,and the COVID-19 symptom dataset are used to validate the proposed method.It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets,which shows a high accuracy rate of up to 0.9895.Furthermore,the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem,including accuracy,size of feature subsets,and fitness with best values of 0.913,5.7,and 0.0873,respectively.The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.