Xiaoquan Yang

Bio: Xiaoquan Yang is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Photothermal therapy & Medicine. The author has an hindex of 28, co-authored 106 publications receiving 2084 citations.

Metal Ion/Tannic Acid Assembly as a Versatile Photothermal Platform in Engineering Multimodal Nanotheranostics for Advanced Applications.

Abstract: This study reports a family of photothermal materials, metal ion/tannic acid assemblies (MITAs). MITAs from FeIII, VIII, and RuIII afford excellent photothermal efficiency (η ≈ 40%). Sharply differing from the currently existing photothermal agents, MITAs are highlighted by merits including green synthesis, facile incorporation of diagnostic metal ions, and particularly topology-independent adhesion. Owing to the adhesion nature of MITAs, various kinds of MITA-based nanoengineerings are readily available via the self-adhesion of MITAs onto diverse templates, enabling MITAs well suited as a photothermal platform for versatile combination with other therapy approaches and imaging techniques. As a proof of concept, polymeric/inorganic nanoparticle/nanovesicle-supported FeIII-tannic acid (FeIIITA) is fabricated. The photothermal effect is shown to be unaffected by the template origin and type and FeIIITA thickness on the templates. We validate the potency of nanovesicle-supported FeIIITA (PNV@FeIIITA) for tum...

Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

Abstract: Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning We utilized stacked denoise autoencoder (SDAE) method to pretrain the network, which is robust to noise In the top layer of the network, logistic regression (LR) approach is utilized to perform supervised fine-tuning and classification Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU) as activation function in SDAE to extract high level and sparse features Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR) can achieve higher accuracies than the popular support vector machine (SVM) classifier

Precise Cerebral Vascular Atlas in Stereotaxic Coordinates of Whole Mouse Brain.

Abstract: Understanding amazingly complex brain functions and pathologies requires a complete cerebral vascular atlas in stereotaxic coordinates. Making a precise atlas for cerebral arteries and veins has been a century-old objective in neuroscience and neuropathology. Using micro-optical sectioning tomography (MOST) with a modified Nissl staining method, we acquired five mouse brain data sets containing arteries, veins, and microvessels. Based on the brain-wide vascular spatial structures and brain regions indicated by cytoarchitecture in one and the same mouse brain, we reconstructed and annotated the vascular system atlas of both arteries and veins of the whole mouse brain for the first time. The distributing patterns of the vascular system within the brain regions were acquired and our results show that the patterns of individual vessels are different from each other. Reconstruction and statistical analysis of the microvascular network, including derivation of quantitative vascular densities, indicate significant differences mainly in vessels with diameters less than 8 μm and large than 20 μm across different brain regions. Our precise cerebral vascular atlas provides an important resource and approach for quantitative studies of brain functions and diseases.

Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification

Abstract: Graph construction, which is at the heart of graph-based semisupervised learning (SSL), is investigated by using manifold learning (ML) approaches Since each ML method can be demonstrated to correspond to a specific graph, we build the relation between ML and SSL via the graph, where ML methods are employed for graph construction Moreover, sparsity is important for the efficiency of SSL algorithms, and therefore, local ML (LML)-method-based sparse graphs are utilized The LML-based graphs are able to capture the local geometric properties of hyperspectral data and, thus, are beneficial for classification of data with complex geometry and multiple submanifolds In experiments with Hyperion and AVIRIS hyperspectral data, graphs constructed by two LML methods, namely, locally linear embedding and local tangent space alignment (LTSA), performed better than several popular graph construction methods, and the highest accuracies were obtained by using graphs provided by LTSA

Red blood cell membrane-enveloped O2 self-supplementing biomimetic nanoparticles for tumor imaging-guided enhanced sonodynamic therapy.

Abstract: Non-invasive sonodynamic therapy (SDT) was developed because of its advantages of high penetration depth and low side effects; however, tumor hypoxia greatly restricts its therapeutic effect. In this study, we aimed to develop ideal O2 self-supplementing nanoparticles for imaging-guided enhanced sonodynamic therapy of tumors with the adept coalescence of biology with nanotechnology. Methods: Based on the natural enzyme system of red blood cells (RBC), biomimetic nanoparticles (QD@P)Rs were fabricated by encapsulating Ag2S quantum dots (QD) in RBC vesicle membranes. The anti-tumor drug PEITC was employed to increase the intracellular H2O2 concentration in tumor cells. Results: In vitro and in vivo experiments demonstrated excellent biocompatibility and prolonged blood circulation of (QD@P)Rs. Following oral administration of PEITC in mice to improve the H2O2 concentration, the enzyme in the nanoprobe catalyzed endogenous H2O2 to increase O2 content and effectively alleviate tumor hypoxia. Triggered by ultrasound under the guidance of fluorescence imaging, (QD@P)Rs generated reactive oxygen species (ROS) to induce tumor cell death, and the increased content of O2 significantly enhanced the effect of SDT. Conclusion: Ag2S QDs were used, for the first time, as a sonosensitizer in the SDT field. In this study, we integrated the advantages of the natural enzyme system and SDT to develop a novel approach for effective non-invasive treatment of cancer.

Photothermal therapy and photoacoustic imaging via nanotheranostics in fighting cancer

Abstract: The nonradiative conversion of light energy into heat (photothermal therapy, PTT) or sound energy (photoacoustic imaging, PAI) has been intensively investigated for the treatment and diagnosis of cancer, respectively. By taking advantage of nanocarriers, both imaging and therapeutic functions together with enhanced tumour accumulation have been thoroughly studied to improve the pre-clinical efficiency of PAI and PTT. In this review, we first summarize the development of inorganic and organic nano photothermal transduction agents (PTAs) and strategies for improving the PTT outcomes, including applying appropriate laser dosage, guiding the treatment via imaging techniques, developing PTAs with absorption in the second NIR window, increasing photothermal conversion efficiency (PCE), and also increasing the accumulation of PTAs in tumours. Second, we introduce the advantages of combining PTT with other therapies in cancer treatment. Third, the emerging applications of PAI in cancer-related research are exemplified. Finally, the perspectives and challenges of PTT and PAI for combating cancer, especially regarding their clinical translation, are discussed. We believe that PTT and PAI having noteworthy features would become promising next-generation non-invasive cancer theranostic techniques and improve our ability to combat cancers.

A practical guide to photoacoustic tomography in the life sciences

Abstract: The life sciences can benefit greatly from imaging technologies that connect microscopic discoveries with macroscopic observations. One technology uniquely positioned to provide such benefits is photoacoustic tomography (PAT), a sensitive modality for imaging optical absorption contrast over a range of spatial scales at high speed. In PAT, endogenous contrast reveals a tissue's anatomical, functional, metabolic, and histologic properties, and exogenous contrast provides molecular and cellular specificity. The spatial scale of PAT covers organelles, cells, tissues, organs, and small animals. Consequently, PAT is complementary to other imaging modalities in contrast mechanism, penetration, spatial resolution, and temporal resolution. We review the fundamentals of PAT and provide practical guidelines for matching PAT systems with research needs. We also summarize the most promising biomedical applications of PAT, discuss related challenges, and envision PAT's potential to lead to further breakthroughs.

Quantum dots: bright and versatile in vitro and in vivo fluorescence imaging biosensors

Abstract: Semiconductor quantum dots (QDs) have become important fluorescent probes for in vitro and in vivo bioimaging research. Their nanoparticle surfaces for versatile bioconjugation, their adaptable photophysical properties for multiplexed detection, and their superior stability for longer investigation times are the main advantages of QDs compared to other fluorescence imaging agents. Here, we review the recent literature dealing with the design and application of QD-bioconjugates for advanced in vitro and in vivo imaging. After a short summary of QD preparation and their most important properties, different QD-based imaging applications will be discussed from the technological and the biological point of view, ranging from super-resolution microscopy and single-particle tracking over in vitro cell and tissue imaging to in vivo investigations. A substantial part of the review will focus on multifunctional applications, in which the QD fluorescence is combined with drug or gene delivery towards theranostic approaches or with complementary technologies for multimodal imaging. We also briefly discuss QD toxicity issues and give a short outlook on future directions of QD-based bioimaging.

Deep learning classifiers for hyperspectral imaging: A review

Abstract: Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .