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To achieve an accurate diagnosis and efficient tumor treatment, developing a facile and powerful strategy to build multifunctional nanotheranostics is highly desirable. Benefiting from the distinct characteristics of black phosphorus quantum dots (BPQDs), herein, a versatile nanoprobe (H-MnO2/DOX/BPQDs) is constructed for dual-modality cancer imaging and synergistic chemo-phototherapy. The hollow mesoporous MnO2 (H-MnO2) nanoparticles are sequentially decorated with a cationic polymer poly (allylamine hydrochloride) (PAH) and an anionic polymer poly (acrylic acid) (PAA). The obtained H-MnO2-PAH-PAA is covalently grafted with BPQDs-PEG-NH2 via a carbodiimide cross-linking reaction and then loaded with anti-cancer drug DOX to form final nanoprobe H-MnO2/DOX/BPQDs. Under the tumor microenvironment, H-MnO2/DOX/BPQDs is degraded to release encapsulated functional molecules DOX and BPQDs. DOX acts as the chemotherapy and fluorescence imaging agent, and BPQDs endows the nanoprobe with photodynamic therapy (PDT) and photothermal therapy (PTT) abilities under dual laser irradiation of 630 and 808 nm. H-MnO2 offers contrasts for magnetic resonance imaging (MRI) and facilitates conversion of endogenous H2O2 to oxygen, thereby relieving tumor hypoxia and enhancing PDT efficacy. All in vitro and in vivo results demonstrate that the designed nanoprobe displays dual-modality MRI/FL imaging and synergistic chemotherapy/PDT/PTT, which ultimately enhances the accuracy of cancer diagnosis and therapeutic performance.  相似文献   
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目的 针对当前视频情感判别方法大多仅依赖面部表情、而忽略了面部视频中潜藏的生理信号所包含的情感信息,本文提出一种基于面部表情和血容量脉冲(BVP)生理信号的双模态视频情感识别方法。方法 首先对视频进行预处理获取面部视频;然后对面部视频分别提取LBP-TOP和HOG-TOP两种时空表情特征,并利用视频颜色放大技术获取BVP生理信号,进而提取生理信号情感特征;接着将两种特征分别送入BP分类器训练分类模型;最后利用模糊积分进行决策层融合,得出情感识别结果。结果 在实验室自建面部视频情感库上进行实验,表情单模态和生理信号单模态的平均识别率分别为80%和63.75%,而融合后的情感识别结果为83.33%,高于融合前单一模态的情感识别精度,说明了本文融合双模态进行情感识别的有效性。结论 本文提出的双模态时空特征融合的情感识别方法更能充分地利用视频中的情感信息,有效增强了视频情感的分类性能,与类似的视频情感识别算法对比实验验证了本文方法的优越性。另外,基于模糊积分的决策层融合算法有效地降低了不可靠决策信息对融合的干扰,最终获得更优的识别精度。  相似文献   
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针对传统ECT/ERT成像系统中连接数据采集部分和成像终端的连线复杂、抗干扰能力差等问题,设计了一种基于无线模块nRF24L01进行数据传输的ECT/ERT双模态成像系统;介绍了基于无线传输的ECT/ERT双模态成像系统硬件结构,采用一种简化的基于跳频的无线传输通信协议,该法可以在多台同频干扰机发送的环境下较好地避免采集数据受干扰,并实现每秒50帧数据传输;实验结果表明,采用无线跳频技术可以解决工业现场的干扰,使测量数据准确快速地传输到成像终端。  相似文献   
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Significant attenuation and overheating,caused by the absorption of the excitation band (980 nm) in water,are the major obstacles in the in vivo application of lanthanide-doped upconversion nanopartides (UCNPs).Therefore,appropriately-structured Nd3+-doped UCNPs with 808 nm excitation could be a promising alternative.Herein,we developed core-shell-shell structured Nd3+-sensitized UCNPs as imaging agents,and decorated them onto the surface of polydopamine (PDA) to construct a novel multifunctional core/satellite nanotheranostic (PDA@UCNPs) for in vivo imaging guidance photothermal therapy using single 808 nm laser irradiation.The core-shell-shell structured design enabled outstanding upconversion luminescence properties and strong X-ray attenuation,thereby making the nanocomposites potential candidates for excellent upconversion luminescence/computed tomography dual modal imaging.In addition,the PDA core not only provides high photothermal conversion efficiency and outstanding antitumor effect,but also endows the platform with robust biocompatibility owing to its natural features.Therefore,this multifunctional nanocomposite could be a promising theranostic in future oncotherapy,with high therapeutic effectiveness but low side effects.This study would stimulate interest in designing bioapplication-compatible multifunctional nanocomposites,especially for cancer diagnosis and treatment in vivo.  相似文献   
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Traditional Chinese medicine (TCM) influences the Chinese and global medical systems, with its quality essential to its effectiveness. The origin of TCM material impacts the quality of the same TCM materials. However, the existing origin classification methods of the same TCM materials from different places mainly have two disadvantages: slow processing speed and extensive experience. To address these issues, a fast and real-time technology, laser-induced breakdown spectroscopy (LIBS), is introduced into our solution. We propose a TCM classification system that combines one-dimensional LIBS spectra with two-dimensional images. This dual-modality fusion approach represents a significant advancement in multi-view data analysis for TCM classification. As a case study, we focus on wolfberry and construct a new dataset comprising 10,800 pairs of LIBS spectrum and image data to fill the gap. To achieve superior multiple feature fusion, a two-stage fusion network (TFNet) in a coarse-to-fine way is proposed. In the first coarse fusion, the Depth Attention Fusion (DAF) module is applied to extract the key features of stacked spectrum and image. In the second fine fusion, the Line to Area (LTA) module entirely focuses on and highlights the critical spectral line features. Experimental accuracy is over 0.99 with less computation and parameters, indicating the high efficiency and accuracy of the proposed TFNet. Therefore, the classification system achieves exceptional accuracy and efficiency due to its simple sample preparation, real-time data collection and the high-accuracy lightweight network.  相似文献   
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