One of the impediments to the treatment of brain tumors (e.g., gliomas) has been the degree to which they expand, infiltrate surrounding tissue, and migrate widely into normal brain, usually rendering them “elusive” to effective resection, irradiation, chemotherapy, or gene therapy. We demonstrate that neural stem cells (NSCs), when implanted into experimental intracranial gliomas in vivo in adult rodents, distribute themselves quickly and extensively throughout the tumor bed and migrate uniquely in juxtaposition to widely expanding and aggressively advancing tumor cells, while continuing to stably express a foreign gene. The NSCs “surround” the invading tumor border while “chasing down” infiltrating tumor cells. When implanted intracranially at distant sites from the tumor (e.g., into normal tissue, into the contralateral hemisphere, or into the cerebral ventricles), the donor cells migrate through normal tissue targeting the tumor cells (including human glioblastomas). When implanted outside the CNS intravascularly, NSCs will target an intracranial tumor. NSCs can deliver a therapeutically relevant molecule—cytosine deaminase—such that quantifiable reduction in tumor burden results. These data suggest the adjunctive use of inherently migratory NSCs as a delivery vehicle for targeting therapeutic genes and vectors to refractory, migratory, invasive brain tumors. More broadly, they suggest that NSC migration can be extensive, even in the adult brain and along nonstereotypical routes, if pathology (as modeled here by tumor) is present.
Abstract-Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells -without prior segmentation -based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pre-trained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively re-sampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (AUC) (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross-validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.
Mueller matrices can be used as a powerful tool to probe qualitatively the microstructures of biological tissues. Certain transformation processes can provide new sets of parameters which are functions of the Mueller matrix elements but represent more explicitly the characteristic features of the sample. In this paper, we take the backscattering Mueller matrices of a group of tissues with distinctive structural properties. Using both experiments and Monte Carlo simulations, we demonstrate qualitatively the characteristic features of Mueller matrices corresponding to different structural and optical properties. We also calculate two sets of transformed polarization parameters using the Mueller matrix transformation (MMT) and Mueller matrix polar decomposition (MMPD) techniques. We demonstrate that the new parameters can separate the effects due to sample orientation and present quantitatively certain characteristic features of these tissues. Finally, we apply the transformed polarization parameters to the unstained human cervix cancerous tissues. Preliminary results show that the transformed polarization parameters can provide characteristic information to distinguish the cancerous and healthy tissues.
Polarization measurements allow one to enhance the imaging contrast of superficial tissues and obtain new polarization sensitive parameters for better descriptions of the micro- and macro- structural and optical properties of complex tissues. Since the majority of cancers originate in the epithelial layer, probing the morphological and pathological changes in the superficial tissues using an expended parameter set with improved contrast will assist in early clinical detection of cancers. We carry out Mueller matrix imaging on different cancerous tissues to look for cancer specific features. Using proper scattering models and Monte Carlo simulations, we examine the relationship between the microstructures of the samples, which are represented by the parameters of the scattering model and the characteristic features of the Mueller matrix. This study gives new clues on the contrast mechanisms of polarization sensitive measurements for different cancers and may provide new diagnostic techniques for clinical applications.
Chalcogenide nanostructures and nanocomposites have been the focus of semiconductor nanomaterial research due to their remarkable optoelectronic and photocatalytic properties and potential application in photodegrading enviromental pollutions. However, currently available synthesizing methods tend to be costly and ineffi cient. In this paper, we propose a facile two-step solution-phase method to synthesize well-defi ned monodisperse ZnS-CdS nanocomposites. The morphology and size of ZnS nanoparticles can be easily controlled by adjusting the amount of the source of sulfur. After surface modifi cation with tiny CdS nanoparticles through natural electrostatic attrac- tion, uniform ZnS-CdS nanocomposites are obtained, which has been further confi rmed by transmission electron microscopy (TEM) and energy dispersive spectrometry (EDS). The photocatalytic activities of various ZnS samples and ZnS-CdS nanocomposites have been investigated by degrading Rhodamine B under UV-light. Compared with pure ZnS nanoparticles and ZnS powders, the as-obtainedZnS-CdS nanocomposites exhibit excellent photocatalytic performances due to the effective charge separation and increased specifi c surface area by the attachment of CdS. Moreover, resulting from the effective passivation of surface electronic states, the photoluminescence intensity of the ZnS-CdS nanocomposites is also signifi cantly improved relative to plain ZnS.
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