血管内介入治疗特发性颅内压增高并颅内静脉窦狭窄的现状及进展

特发性颅内压增高(Idiopathic Intracranial Hypertension, IIH),也称为假性脑瘤(Pseudotumor Cerebri, PTC),是一种病因不明的非占位性的颅内压增高性疾病。其特征为颅内压升高的症状与体征,出现头痛(严重致残并降低生活质量)、恶心与呕吐,偶有复视(由于外展神经麻痹所致),常伴有心率减慢,视神经乳头水肿(具有永久性视力丧失的潜在风险)。而颅内静脉窦狭窄(Intracranial Venous Sinus Stenosis, IVSS)是一种少见的脑血管病,其横窦和乙状窦交界处的硬脑膜静脉窦狭窄导致局部静脉高压和通过蛛网膜颗粒的脑脊液引流的二次减少,越来越被认为是IIH病理生理学的重要组成部分,且部分静脉窦狭窄患者通过支架植入后IIH得以改善,这使得血管内介入治疗成为一种可选的治疗方法,但颅内静脉窦与IIH之间的因果关系仍未明确。作者旨在讨论IIH与静脉窦狭窄两者间的关系,及通过相关检查进一步说明两者直接的联系,并比较血管内介入治疗与其他外科手术的优缺点,以及血管内介入治疗在IIH治疗中的未来。

用于低能耗人工视觉系统的具有互补光调制和低功耗的双极突触有机/无机异质结晶体管

光电突触晶体管将光传感和突触功能集成到单个器件中,在视觉信息采集、识别、记忆和处理的神经形态计算具有显著的优势.然而,现有光电突触的权重更新主要是基于光刺激和电刺激分别调节突触的兴奋和抑制.这种方式严重限制了器件的处理速度和应用场景.在这项工作中,我们提出了双极突触有机/无机异质结晶体管(BSOIHT),可以有效地模拟光刺激下的双向(兴奋/抑制)突触行为.此外,通过优化电极接触位置以及电极材料,晶体管的载流子注入得到了显著改善,使得突触事件功耗降至2.4 fJ.此外,采用BSOIHT构建的神经形态视觉系统,有效地促进了图像预处理,将识别准确率从44.93%大幅提高到87.01%.这为构建低能耗的人工视觉系统提供了新的途径.

基于肖特基势垒调控的低能耗高识别精度的有机突触晶体管

为了构建类脑神经形态计算网络,单一的人工突触器件应该表现出极低的能量消耗,达到飞焦耳级别.然而,大多数现有的基于欧姆接触的低能耗突触器件实施方案,要么结构复杂,要么需要特定材料,这些因素都阻碍了人工神经网络的进一步发展.本文报告了一种肖特基势垒调控的有机突触晶体管(SBROST).通过在源电极和半导体之间的接触界面引入肖特基势垒,显著降低了单个突触事件的能耗,与使用欧姆接触的传统有机突触晶体管相比,SBROST的性能得到了改善.SBROST不仅可在低工作电压和电流下运行,还具有可适用于不同有机突触器件的简单结构.此外,SBROST可以实现低能耗下的高识别精度.经过100个周期,基于SBROST的手写人工神经网络表现出卓越的识别精度(93.53%),接近理想精度(95.62%).将肖特基势垒引入突触晶体管的方案为构建类脑神经计算网络提供了新的视角.

High-performance n-type thin-film transistor based on bilayer MXene/semiconductor with enhanced electrons transport

Interfacial engineering at the dielectric/semiconductor interface is highly crucial for fabricating organic field-effect transistors with high performance.In this study,a bilayer MXene/semiconductor configuration is introduced to fabricate a high-performance n-type transistor,where electrical charges are formed and modulated at the SiO/semiconductor interface,and MXene nanosheets serve as the primary electrical charge channel due to their high mobility and long lateral size.The electrical performance is optimized by adjusting the degree of connectivity of the MXene nanosheets.The proposed MXene/poly{[N,N’-bis(2-octyl-dodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5’-(2,2’-bithiophene)}(N2200)transistors show boosted ntype characteristics,including a 100-fold increase in field-effect mobility,a large ON/OFF ratio of 10^(4),and a small subthreshold swing of 0.65 V dec^(-1),all of which are significantly improved compared with single-layer N2200 transistors.The high performance of the two-dimensional MXene nanochannel is due to its electronegativity and high mobility.The electronegativity significantly enhances electron transfer from N2200 to the MXene channel,where they are efficiently transported along the MXene channel.Interestingly,the MXene/p-type semiconductor transistors show suppressed ptype performance because of the highly negative MXene nanosheets.Additionally,the proposed bilayer MXene/n-type semiconductor configuration shows a good configuration generality and improved performance.These findings demonstrate the feasibility of fabricating high-performance ntype transistors using a bilayer MXene/semiconductor combination.

CsPbBr_(3)quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing

Photonic synaptic transistors are promising neuromorphic computing systems that are expected to circumvent the intrinsic limitations of von Neumann-based computation.The design and construction of photonic synaptic transistors with a facile fabrication process and highefficiency information processing ability are highly desired,while it remains a tremendous challenge.Herein,a new approach based on spin coating of a blend of CsPbBr_(3) perovskite quantum dot(QD)and PDVT-10 conjugated polymer is reported for the fabrication of photonic synaptic transistors.The combination of flat surface,outstanding optical absorption,and remarkable charge transporting performance contributes to high-efficiency photon-to-electron conversion for such perovskite-based synapses.High-performance photonic synaptic transistors are thus fabricated with essential synaptic functionalities,including excitatory postsynaptic current(EPSC),paired-pulse facilitation(PPF),and long-term memory.By utilizing the photonic potentiation and electrical depression features,perovskite-based photonic synaptic transistors are also explored for neuromorphic computing simulations,showing high pattern recognition accuracy of up to 89.98%,which is one of the best values reported so far for synaptic transistors used in pattern recognition.This work provides an effective and convenient pathway for fabricating perovskite-based neuromorphic systems with high pattern recognition accuracy.

A light-emitting electrochemical artificial synapse with dual output of photoelectric signals

Despite recent remarkable progress in multiple synaptic devices,searching for artificial synapses with new functions is still an important task in the construction of artificial neural networks.The parallel output functionality of photoelectric signals in artificial synaptic devices is interesting and desirable as on-chip optoelectronic interconnection technology allows the connections between neurons weighted by current and light.In turn,it provides degrees of freedom and reduces circuit lead density in the design of large-scale neural networks.Hence,for the first time,a light-emitting electrochemical artificial synapse(LEEAS)based on poly[2-methoxy-5-(2'-ethylhexyloxy)-1,4-phenylenevinylene]/poly(ethylene oxide)/lithium salt blends with dual output of photoelectric signals was developed in this study.The electrochemical redox reaction enables the device to achieve synaptic plasticity in biology and emulate the memory enhancement process,high-pass filtering characteristic,and classical Pavlov’s conditioned reflex experiment.In addition,the transient luminescence intensity of the LEEAS induced by identical electric spikes exhibits a synaptic-like potentiation behavior.Owing to the combination of electroluminescence(EL)and synaptic memory behavior,an LEEAS array exhibits a unique image display and storage functions that can memorize displayed images.The LEEAS proposed in this work enriches the diversity of artificial synapses,promoting the diversified design and development of next-generation optoelectronic hybrid artificial neural networks.