Over the past decade, numerous studies have attempted to enhance the effectiveness of radiotherapy (external beam radiotherapy and internal radioisotope therapy) for cancer treatment. However, the low radiation absorption coefficient and radiation resistance of tumors remain major critical challenges for radiotherapy in the clinic. With the development of nanomedicine, nanomaterials in combination with radiotherapy offer the possibility to improve the efficiency of radiotherapy in tumors. Nanomaterials act not only as radiosensitizers to enhance radiation energy, but also as nanocarriers to deliver therapeutic units in combating radiation resistance. In this review, we discuss opportunities for a synergistic cancer therapy by combining radiotherapy based on nanomaterials designed for chemotherapy, photodynamic therapy, photothermal therapy, gas therapy, genetic therapy, and immunotherapy. We highlight how nanomaterials can be utilized to amplify antitumor radiation responses and describe cooperative enhancement interactions among these synergistic therapies. Moreover, the potential challenges and future prospects of radio-based nanomedicine to maximize their synergistic efficiency for cancer treatment are identified.
Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results. 相似文献
在模具加工刀路编制时,很多用户会将C reo设计模型导入M aster C A M、C im atron等软件中进行加工刀路编制,往往引起很多数据转换的问题。文中采用C reo软件的一个加工模块N C A ssem bly进行无缝衔接,完成刀路编制。并结合一个实例进行了工艺分析,进而实现粗加工和精加工。 相似文献
Cerebral microbleeds (CMBs) are small hemosiderin deposits indicative of prior cerebral microscopic hemorrhage and previously thought to be clinically silent. Recent population‐based cross‐sectional studies and prospective longitudinal cohort studies have revealed association between CMB and cognitive dysfunction. In the general population, CMBs are associated with age, hypertension, and cerebral amyloid angiopathy. In the chronic kidney disease (CKD) population, diminished estimated glomerular filtration rate has been found to be an independent risk factor for CMB, raising the possibility that a uremic milieu may predispose to microbleeds. In the end‐stage renal disease (ESRD) population on hemodialysis, the incidence of microbleeds is significantly higher compared with a control group without history of CKD or stroke. We present an ESRD patient on chronic hemodialysis with a history of gradual cognitive decline and progressive CMBs. Through this case and literature review, we illustrate the need to develop detection and prediction models to treat this frequent development in ESRD patients. 相似文献