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Abstract Surface charge of biological and medical nanocarriers has been demonstrated to play an important role in cellular uptake. Owing to the unique physicochemical properties, charge-reversal delivery strategy has rapidly developed as a promising approach for drug delivery application, especially for cancer treatment. Charge-reversal nanocarriers are neutral/negatively charged at physiological conditions while could be triggered to positively charged by specific stimuli (i.e., pH, redox, ROS, enzyme, light or temperature) to achieve the prolonged blood circulation and enhanced tumor cellular uptake, thus to potentiate the antitumor effects of delivered therapeutic agents. In this review, we comprehensively summarized the recent advances of charge-reversal nanocarriers, including: (i) the effect of surface charge on cellular uptake; (ii) charge-conversion mechanisms responding to several specific stimuli; (iii) relation between the chemical structure and charge reversal activity; and (iv) polymeric materials that are commonly applied in the charge-reversal delivery systems. Graphical Abstract

Abstract Background Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path. Results We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field. Conclusions We leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate).