Real-time identification of multiple nanoclusters with a protein nanopore in single-cluster level

It is important and challenging to analyze nanocluster structure with atomic precision.Herein,α-hemolysin nanopore was used to identify nanoclusters at the single molecule level by providing two-dimensional(2D)dwell time–current blockage spectra and translocation event frequency which sensitively depended on their structures.Nanoclusters such as Anderson,Keggin,Dawson,and a few lacunary Dawson polyoxometalates with very similar structures,even with only a two-atom difference,could be discriminated.This nanopore device could simultaneously measure multiple nanoclusters in a mixture qualitatively and quantitatively.Furthermore,molecular dynamics(MD)simulations provided microscopic understandings of the nanocluster translocation dynamics and yielded 2D dwell time–current blockage spectra in close agreement with experiments.The nanopore platform provides a novel powerful tool for nanocluster characterization.

Evolution of Monkeypox C9L RNA G-Quadruplex Elevates the Translation of Its Only Kelch-like Protein by Accelerating G-Quadruplex Unfolding Kinetics

Genomic surveillance of monkeypox virus(MPXV)is essential to explore the reason of its unusual outbreak.Current phylogenomic analysis of the MPXV genome mainly focuses on the effect of amino acid mutations.Herein,we explore the evolutionary variation of RNA G-quadruplex(RG4)of MPXV and find that the genome evolution of MPXV can also produce new effects through changes in the RG4 structure.This RG4 is located in MPXV’s only Kelch-like C9L gene,which encodes for an antagonist of the innate immune response.The evolution of this virus increases the unfolding kinetic constant of C9L RG4 and promotes the C9 protein level in living cells.Importantly,all reported MPXV genomes in 2022 carry the C9L-RG4-5 pattern with the highest unfolding kinetic constant.Additionally,the RG4 ligand,RGB-1,can impede the unfolding of C9L-RG4-5 and thereby reduce the C9 protein level.These findings carve out a new path to comprehensively understanding MPXV virology.

pH/GSH dual responsive nanosystem for nitric oxide generation enhanced type I photodynamic therapy

Tumor hypoxia diminishes the effectiveness of traditional type II photodynamic therapy(PDT)due to oxygen consumption.Type I PDT,which can operate independently of oxygen,is a viable option for treating hypoxic tumors.In this study,we have designed and synthesized JSK@PEG-IR820 NPs that are responsive to the tumor microenvironment(TME)to enhance type I PDT through glutathione(GSH)depletion.Our approach aims to expand the sources of therapeutic benefits by promoting the generation of superoxide radicals(O_(2)^(-).)while minimizing their consumption.The diisopropyl group within PEG-IR820 serves a dual purpose:it functions as a pH sensor for the disassembly of the NPs to release JSK and enhances intermolecular electron transfer to IR820,facilitating efficient O_(2)^(-).generation.Simultaneously,the release of JSK leads to GSH depletion,resulting in the generation of nitric oxide(NO).This,in turn,contributes to the formation of highly cytotoxic peroxynitrite(ONOO^(-).),thereby enhancing the therapeutic efficacy of these NPs.NIR-II fluorescence imaging guided therapy has achieved successful tumor eradication with the assistance of laser therapy.

Indole-3-acetic acid,a potential therapeutic target in Alzheimer's disease

Alzheimer's disease(AD)is a chronic and progressive neurodegenerative disease that is characterized by the presence of amyloid-β(Ap)plaques,hyperphosphorylated microtubule-associated tau protein(p-tau)neurofibrillary tangles and neuronal degeneration.According to recent data,AD is the most common global cause of dementia in the elderly.Although billions of dollars have been invested in research,there is still no effective drug for the treatment of AD;consequently,this disease imposes a huge burden on individuals,families,and healthcare systems[1,2].

Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis:Convergence and Challenges

Single-molecule bioelectronic sensing,a groundbreaking domain in biological research,has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes.The advent of emergent technologies,such as nanogapped electrodes and nanopores,has greatly enhanced this field,providing exceptional sensitivity,resolution,and integration capabilities.However,challenges persist,such as complex data sets with high noise levels and stochastic molecular dynamics.Artificial intelligence(AI)has stepped in to address these issues with its powerful data processing capabilities.AI algorithms effectively extract meaningful features,detect subtle changes,improve signal-to-noise ratios,and uncover hidden patterns in massive data.This review explores the synergy between AI and single-molecule bioelectronic sensing,focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability.We also discuss current limitations and future directions for integrating AI,highlighting its potential to advance biological research and technological innovation.