Exosomes:the next-generation therapeutic platform for ischemic stroke

Current therapeutic strategies for ischemic stroke fall short of the desired objective of neurological functional recovery.Therefore,there is an urgent need to develop new methods for the treatment of this condition.Exosomes are natural cell-derived vesicles that mediate signal transduction between cells under physiological and pathological conditions.They have low immunogenicity,good stability,high delivery efficiency,and the ability to cross the blood–brain barrier.These physiological properties of exosomes have the potential to lead to new breakthroughs in the treatment of ischemic stroke.The rapid development of nanotechnology has advanced the application of engineered exosomes,which can effectively improve targeting ability,enhance therapeutic efficacy,and minimize the dosages needed.Advances in technology have also driven clinical translational research on exosomes.In this review,we describe the therapeutic effects of exosomes and their positive roles in current treatment strategies for ischemic stroke,including their antiinflammation,anti-apoptosis,autophagy-regulation,angiogenesis,neurogenesis,and glial scar formation reduction effects.However,it is worth noting that,despite their significant therapeutic potential,there remains a dearth of standardized characterization methods and efficient isolation techniques capable of producing highly purified exosomes.Future optimization strategies should prioritize the exploration of suitable isolation techniques and the establishment of unified workflows to effectively harness exosomes for diagnostic or therapeutic applications in ischemic stroke.Ultimately,our review aims to summarize our understanding of exosome-based treatment prospects in ischemic stroke and foster innovative ideas for the development of exosome-based therapies.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

Simultaneous Solar-driven Steam and Electricity Generation by Cost-effective,Easy Scale-up MnO 2-based Flexible Membranes

Harvesting solar energy in an effective manner for steam and electricity generation is a promising technique to simultaneously cope with the energy and water crises.However,the construction of efficient and easy scale-up photothermal materials for steam and electricity cogeneration remains challenging.Herein,we report a facile and cost-effective strategy to prepare MnO_(2)-decorated cotton cloth(MCx).The wide adsorption spectrum and excellent photothermal conversion ability of the in situ-formed MnO_(2)nanoparticles make the MCx to be advanced photothermal materials.Consequently,the hybrid device integrated with MCx as the photothermal layer and the thermoelectric(TE)module for electricity power conversion exhibits an extremely high evaporation rate of 2.24 kg m^(−2)h^(−1)under 1 kW m^(−2)irradiation,which is ranked among the most powerful solar evaporators.More importantly,during solar evaporation,the hybrid device produces an open-circuit voltage of 0.3 V and a power output of 1.6 W m^(−2)under 3 Sun irradiation,and outperforms most of the previously reported solar-driven electricity generation devices.Therefore,the integrated device with synergistic solar-thermal utilization opens up a green way toward simultaneous solar vapor and electric power generation in remote and resource-constrained areas.

滇中高原半湿润常绿阔叶林的岛屿化与物种多样性

森林破碎化(或岛屿化)是导致当前生物多样性丧失的重要成因之一。滇中地区地带性的半湿润常绿阔叶林(semi-humid evergreen broad-leaved forests,SEBFs)具有显著的岛屿化特征。当前村寨周边面积大小、形状不一的“岛状”森林为研究破碎化的半湿润常绿阔叶林及其物种多样性特征提供了理想对象。本文通过植被遥感制图和群落样方调查,运用空间统计、物种多样性分析、群落排序及环境解释,探讨了滇中高原半湿润常绿阔叶林的岛屿化特征及其对群落物种多样性的影响。结果表明:(1)滇中高原现存半湿润常绿阔叶林仅占其潜在分布范围的1.5%,面积≥100 ha的林斑有92块,而面积<10 ha的林斑占斑块总数的54.6%和总面积的7.8%;该植被类型面积萎缩和破碎化严重;(2)99%的林斑到村寨的距离<3 km,距离村寨越远,林斑平均面积越大,但样方物种α多样性除均匀度外变化不显著;不同面积等级斑块中样方物种α多样性也仅物种丰富度有显著变化,面积超过100ha的大斑块有更高的物种丰富度;离村寨越近,样方群落物种组成简单化和同质化现象加强;(3)样方群落β多样性主要由物种替换引起。森林岛屿化是导致半湿润常绿阔叶林群落物种组成变化的主要因子,而人类干扰强度对群落物种组成的影响高于局部生境异质性(水分条件)和区域气候梯度(低温限制)的影响。滇中高原半湿润常绿阔叶林以破碎小斑块大量存在于村寨周边,在人为干扰下群落退化明显,亟待引起足够重视,并采取必要措施加强保护。