Targeting the organelle for radiosensitization in cancer radiotherapy

Radiotherapy is a well-established cytotoxic therapy for local solid cancers, utilizing high-energy ionizing radiation to destroy cancer cells. However, this method has several limitations, including low radiation energy deposition, severe damage to surrounding normal cells, and high tumor resistance to radiation. Among various radiotherapy methods, boron neutron capture therapy (BNCT) has emerged as a principal approach to improve the therapeutic ratio of malignancies and reduce lethality to surrounding normal tissue, but it remains deficient in terms of insufficient boron accumulation as well as short retention time, which limits the curative effect. Recently, a series of radiosensitizers that can selectively accumulate in specific organelles of cancer cells have been developed to precisely target radiotherapy, thereby reducing side effects of normal tissue damage, overcoming radioresistance, and improving radiosensitivity. In this review, we mainly focus on the field of nanomedicine-based cancer radiotherapy and discuss the organelle-targeted radiosensitizers, specifically including nucleus, mitochondria, endoplasmic reticulum and lysosomes. Furthermore, the organelle-targeted boron carriers used in BNCT are particularly presented. Through demonstrating recent developments in organelle-targeted radiosensitization, we hope to provide insight into the design of organelle-targeted radiosensitizers for clinical cancer treatment.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Phylogenetic analysis of Fulgoroidea based on the morphological characters of the forewing base structure(Hemiptera:Auchenorrhyncha)

The forewing base structure of a representative species from 13 families of Fulgoroidea were examined.Results show that these families differ mainly from the characters of various sclerites,the presence or absence of humeral plate,proximal and distal median plate,and the method of connection between each sclerite.The phylogenetic relationships based on forewing structure support the monophyly of Fulgoroidea.Ricaniidae and Flatidae were recovered as sister taxa.Kinnaridae is the most primitive family in Fulgoroidea.

Membrane fusion reverse micelle platforms as potential oral nanocarriers for efficient internalization of free hydrophilic peptides

Orally administered peptides or proteins are garnering increasing preference owing to their superiority in terms of patient compliance and convenience.However,the development of oral protein formulations has stalled due to the low bioavailability of macromolecules that encounter the aggressive gastrointestinal environment and harsh mucus villi barrier.Herein,we propose an ideal reverse micelle/self-emulsifying drug delivery system(RM/SEDDS)nanoplatform that is capable of improving the oral bioavailability of hydrophilic peptides by preventing enzymatic degradation and enhancing mucosal permeability.Upon the passage through the mucus,the self-emulsifying drug delivery system with optimal surface properties effectively penetrates the viscoelastic mucosal barrier,followed by the exposure of the inner reverse micelle amphipathic vectors,which autonomously form continua with the lipidic cell membrane and facilitate the internalization of drugs.This membrane-fusion mechanism inaugurates a new way for hydrophilic peptide delivery in the free form,circumventing the traditional impediments of the cellular internalization of nanocarriers and subsequent poor release of drugs.And more importantly,reverse micelles are not spatially specific to the laden drugs,which enables their delivery for a myriad of peptide clinical drugs.In conclusion,as an exquisitely designed nanoplatform,RM/SEDDS overcomes multiple physiological barriers and opens a new path for drug cellular entry,providing new prospects for the development of oral drug delivery systems.

Erratum to:Membrane fusion reverse micelle platforms as potential oral nanocarriers for efficient internalization of free hydrophilic peptides

Erratum to Nano Research 2023,16(7):9768-9780 https://doi.org/10.1007/s12274-023-5645-7 The complete affiliations of co-author Yiying Lu is Institute of Pharmaceutics,College of Pharmaceutical Sciences,Zhejiang University,Hangzhou 310058,China and Department of Pharmacy,Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310058,China.