A major problem in the treatment of head and neck cancer today is the resistance of tumors to traditional radiation therapy, which results in 40% local failure, despite aggressive treatment. The main objective of this study was to develop a technique which will overcome tumor radioresistance by increasing the radiation absorbed in the tumor using cetuximab targeted gold nanoparticles (GNPs), in clinically relevant energies and radiation dosage. In addition, we have investigated the biological mechanisms underlying tumor shrinkage and the in vivo toxicity of GNP. The results showed that targeted GNP enhanced the radiation effect and had a significant impact on tumor growth (P < 0.001). The mechanism of radiation enhancement was found to be related to earlier and greater apoptosis (TUNEL assay), angiogenesis inhibition (by CD34 level) and diminished repair mechanism (PCNA staining). Additionally, GNPs have been proven to be safe as no evidence of toxicity has been observed.
Oocyte endowment dwindles away during prepubertal and adult life until menopause occurs, and apoptosis has been identified as a central mechanism responsible for oocyte elimination. A few recent reports suggest that uncontrolled inflammation may adversely affect ovarian reserve. We tested the possible role of the proinflammatory cytokine IL-1 in the age-related exhaustion of ovarian reserve using IL-1α and IL-1β-KO mice. IL-1α-KO mice showed a substantially higher pregnancy rate and litter size compared with WT mice at advanced age. The number of secondary and antral follicles was significantly higher in 2.5-mo-old IL-1α-KO ovaries compared with WT ovaries. Serum anti-Müllerian hormone, a putative marker of ovarian reserve, was markedly higher in IL-1α-KO mice from 2.5 mo onward, along with a greater ovarian response to gonadotropins. IL-1β-KO mice displayed a comparable but more subtle prolongation of ovarian lifespan compared with IL-1α-KO mice. The protein and mRNA of both IL-1α and IL-1β mice were localized within the developing follicles (oocytes and granulosa cells), and their ovarian mRNA levels increased with age. Molecular analysis revealed decreased apoptotic signaling [higher B-cell lymphoma 2 (BCL-2) and lower BCL-2-associated X protein levels], along with a marked attenuation in the expression of genes coding for the proinflammatory cytokines IL-1β, IL-6, and TNF-α in ovaries of IL-1α-KO mice compared with WT mice. Taken together, IL-1 emerges as an important participant in the age-related exhaustion of ovarian reserve in mice, possibly by enhancing the expression of inflammatory genes and promoting apoptotic pathways.
Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and enable clinicians and biologists to differentiate between various cell organelles. However, cell staining is not always allowed in certain medical procedures. In other cases, staining may be time consuming or expensive to implement. Furthermore, staining protocols may be operator-sensitive, and hence lead to varying analytical results by different users, as well as cause artificial imaging artifacts or false heterogeneity. Here, we present a new deep-learning approach, called HoloStain, which converts images of isolated biological cells acquired without staining by holographic microscopy to their virtually stained images. We demonstrate this approach for human sperm cells, as there is a well-established protocol and global standardization for characterizing the morphology of stained human sperm cells for fertility evaluation, but, on the other hand, staining might be cytotoxic and thus is not allowed during human in vitro fertilization (IVF). We use deep convolutional Generative Adversarial Networks (DCGANs) with training that is based on both the quantitative phase images and two gradient phase images, all extracted from the digital holograms of the stain-free cells, with the ground truth of bright-field images of the same cells that subsequently underwent chemical staining. After the training stage, the deep neural network can take images of unseen sperm cells, retrieved from the coinciding holograms acquired without staining, and convert them to their stain-like images. To validate the quality of our virtual staining approach, an experienced embryologist analyzed the unstained cells, the virtually stained cells, and the chemically stained sperm cells several times in a blinded and randomized manner. We obtained a 5-fold recall (sensitivity) improvement in the analysis results, demonstrating the advantage of using virtual staining for sperm cell analysis. With the introduction of simple holographic imaging methods in clinical settings, the proposed method has a great potential to become a common practice in human IVF procedures, as well as to significantly simplify and facilitate other cell analyses and techniques such as imaging flow cytometry.Submitted
Currently, the delicate process of selecting sperm cells to be used for in vitro fertilization (IVF) is still based on the subjective, qualitative analysis of experienced clinicians using non-quantitative optical microscopy techniques. In this work, a method was developed for the automated analysis of sperm cells based on the quantitative phase maps acquired through use of interferometric phase microscopy (IPM). Over 1,400 human sperm cells from 8 donors were imaged using IPM, and an algorithm was designed to digitally isolate sperm cell heads from the quantitative phase maps while taking into consideration both the cell 3D morphology and contents, as well as acquire features describing sperm head morphology. A subset of these features was used to train a support vector machine (SVM) classifier to automatically classify sperm of good and bad morphology. The SVM achieves an area under the receiver operating characteristic curve of 88.59% and an area under the precision-recall curve of 88.67%, as well as precisions of 90% or higher. We believe that our automatic analysis can become the basis for objective and automatic sperm cell selection in IVF. © 2017 International Society for Advancement of Cytometry.
We suggest that the central location of GV within the oocytes, which is associated with an absence of Fyn at the GV and the presence of thick filamentous MTs in the ooplasm, may serve as a predictor of successful maturation and provide new insights for the use of IVM.
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