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نتیجه جستجو - Immunohistochemistry

تعداد مقالات یافته شده: 5
ردیف عنوان نوع
1 Within-animal comparisons of novelty and cocaine neuronal ensemble overlap in the nucleus accumbens and prefrontal cortex
مقایسه بین حیوانات از تازگی و مجموعه عصبی عصبی کوکائین در هسته جمع شده و قشر جلوی مغز-2020
Novelty seeking is a personality trait associated with an increased vulnerability for substance abuse. In rodents, elevated novelty seeking has been shown to be a predictor for elevated drug self-administration and compulsive use. While previous studies have shown that both novelty and drugs of abuse have actions within similar mesocorticolimbic regions, little is known as to whether the same neural ensembles are engaged by these two stimuli. Using the TetTag mouse model and Fos immunohistochemistry, we measured neurons engaged by novelty and acute cocaine exposure, respectively in the prefrontal cortex (PFC) and nucleus accumbens (NAc). While there was no significant impact of novelty exposure on the size of the EGFP+ ensemble, we found that cocaine engaged significantly more Fos+ neurons in the NAc, while stress increased the size of the Fos+ ensemble in the PFC. Analysis of ensemble reactivation was specific to the emotional valence of the second stimuli. We found that a greater proportion of the EGFP+ ensemble was reactivated in the groups that paired novelty with a positive (cocaine) or neutral (saline) experience in the NAc, while the novelty/stress paired groups exhibited significantly less ensemble overlap in the PFC. However, only in the NAc shell was this increase in ensemble overlap specific to those exposed to both novelty and cocaine. This suggests that the NAc shell, but not the NAc core or PFC, may play an important role in general reward processing by engaging a similar network of neurons.
Keywords: Novelty seeking | Stress | Cocaine | Psychostimulant | TetTag mouse model | Sex differences
مقاله انگلیسی
2 Learning to detect lymphocytes in immunohistochemistry with deep learning
یادگیری لنفوسیت ها در ایمونوهیستوشیمی با یادگیری عمیق-2019
The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3 + and CD8 + cells, which we used to train deep learning algorithms for auto- matic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and com- pare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation ( κ= 0 . 72 ), whereas the average pathologists agreement with reference standard was κ= 0 . 64 . The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org .
Keywords: Deep learning | Immune cell detection | Computational pathology | Immunohistochemistry
مقاله انگلیسی
3 Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network
تقسیم بندی خودکار غشای سلولی برای ارزیابی وضعیت HER2 در تصاویر اسلاید کل با استفاده از یک شبکه یادگیری عمیق اصلاح شده-2019
The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the United States. Normally, human epidermal growth factor receptor 2 (HER2) proteins are responsible for the division and growth of healthy breast cells. HER2 status is currently assessed using immunohistochemistry (IHC) as well as in situ hybridization (ISH) in equivocal cases. Manual HER2 evaluation of IHC stained microscopic images involves an error-prone, tedious, inter-observer variable, and time-consuming routine lab work due to diverse staining, overlapped regions, and non-homogeneous remarkable large slides. To address these issues, digital pathology offers reproducible, automatic, and objective analysis and interpretation of whole slide image (WSI). In this paper, we present a machine learning (ML) framework to segment, classify, and quantify IHC breast cancer images in an effective way. The proposed method consists of two major classifying and segmentation parts. Since HER2 is associated with tumors of an epithelial region and most of the breast tumors originate in epithelial tissue, it is crucial to develop an approach to segment different tissue structures. The proposed technique is comprised of three steps. In the first step, a superpixel-based support vector machine (SVM) feature learning classifier is proposed to classify epithelial and stromal regions from WSI. In the second stage, on classified epithelial regions, a convolutional neural network (CNN) based segmentation method is applied to segment membrane regions. Finally, divided tiles are merged and the overall score of each slide is evaluated. Experimental results for 127 slides are presented and compared with state-of-the-art handcraft and deep learning-based approaches. The experiments demonstrate that the proposed method achieved promising performance on IHC stained data. The presented automated algorithm was shown to outperform other approaches in terms of superpixel based classifying of epithelial regions and segmentation of membrane staining using CNN.
Keywords: CNN | Digital pathology | Whole slide image | Deep learning | HER2 assessment | Membrane segmentation
مقاله انگلیسی
4 Toll-like receptor 3 downregulation is an escape mechanism from apoptosis during hepatocarcinogenesis
Toll-like receptor 3 downregulation is an escape mechanism from apoptosis during hepatocarcinogenesis-2019
Background & Aims: Low levels of toll-like receptor 3 (TLR3) in patients with hepatocellular carcinoma (HCC) are associated with poor prognosis, primarily owing to the loss of inflammatory signaling and subsequent lack of immune cell recruitment to the liver. Herein, we explore the role of TLR3-triggered apoptosis in HCC cells. Methods: Quantitative reverse transcription PCR, western blotting, immunohistochemistry and comparative genomic hybridization were used to analyze human and mouse HCC cell lines, as well as surgically resected primary human HCCs, and to study the impact of TLR3 expression on patient outcomes. Functional analyses were performed in HCC cells, following the restoration of TLR3 by lentiviral transduction. The role of TLR3-triggered apoptosis in HCC was analyzed in vivo in a transgenic mouse model of HCC. Results: Lower expression of TLR3 in tumor compared to nontumor matched tissue was observed at both mRNA and protein levels in primary HCC, and was predictive of shorter recurrencefree survival after surgical resection in both univariate (hazard ratio [HR] 1.79; 95% CI 1.04–3.06; p = 0.03) and multivariate analyses (HR 1.73; CI 1.01–2.97; p = 0.04). Immunohistochemistry confirmed frequent downregulation of TLR3 in human and mouse primary HCC cells. None of the 6 human HCC cell lines analyzed expressed TLR3, and ectopic expression of TLR3 following lentiviral transduction not only restored the inflammatory response but also sensitized cells to TLR3-triggered apoptosis. Lastly, in the transgenic mouse model of HCC, absence of TLR3 expression was accompanied by a lower rate of preneoplastic hepatocyte apoptosis and accelerated hepatocarcinogenesis without altering the tumor immune infiltrate. Conclusion: Downregulation of TLR3 protects transforming hepatocytes from direct TLR3-triggered apoptosis, thereby contributing to hepatocarcinogenesis and poor patient outcome. Lay summary: Hepatocellular carcinoma (HCC) is a heterogeneous disease associated with a poor prognosis. In patients with HCC, TLR3 downregulation is associated with reduced survival. Herein, we show that the absence of TLR3 is associated with a lower rate of apoptosis, and subsequently more rapid hepatocarcinogenesis, without any change to the immune infiltrate in the liver. Therefore, the poor prognosis associated with low TLR3 expression in HCC is likely linked to tumors ability to escape apoptosis. TLR3 may become a promising therapeutic target in TLR3-positive HCC.
مقاله انگلیسی
5 سطح بافت چربی و پلاسما (ANGPTL7 ) شبه آنژیوپویتین 7 با چاقی افزایش می یابد و پس از انجام تمرینات ورزشی کاهش می یابد:
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 17 - تعداد صفحات فایل doc فارسی: 24
هدف ANGPTL7 یکی از اعضای خانواده پروتئین شبه آنژیوپویتین است که از هشت (1+8) پروتئین تشکیل می شوند. شواهد بسیاری پروتئین های ANGPTL7را با چاقی و مقاومت به انسولین مرتبط می دانند. نقش بیولوژیکی ANGPTL7 به جز نقش اخیر آن در پاتوفیزیولوژی گلوکوم هنوز کشف نشده است. این مطالعه را به منظور تاکید بر نقش ANGPTL7 در چاقی و مدولاسیون آن با تمرینات ورزشی و نیز ارتباط بالقوه آن با پنل چربی انجام داده اند. روش ها 144 افراد در این مطالعه حضور داشتند و طی مدت سه ماه تمرینات ورزشی را به پایان رساندند. آنها را طبق BMI شان طبقه بندی کردند یعنی 82 نفر غیر چاق بودند و 62 نفر آنها نیز جزو افراد چاق بودند. سطح ANGPTL7 در پلاسما و بافت چربی توسط ELISA، RTPCR و بافت شیمی ایمنی اندازه گیری شد. نتایج در این مطالعه، نشــان می دهیم که سطح ANGPTL7 در پلاســـمای این افــراد (1249.05± 130.39 pg/mL) در مقایسه با افـراد غیر چاق (930.34 ± 87.27 pg/mL (p-Value =032)افزایش نشان می دهد. تجلی ANGPTL7 در بافت چربی نیز بعد از تمرینات کاهش می یابد. بالاخره سطح گردش ANGPTL7 رابطه معنادار آن را با سطح تری گلیسیرین در افراد چاق (R2 = 0.183, p-Value = 0.03) نشان می دهد. نتیجه گیری برای اولین بار داده های ما نشان می دهد که اضافه وزن سطح ANGPTL7 را در پلاسما و هم در بافت چربی افزایش می دهد. افزایش ANGPTL7 در تنظیم سطح تریگلیسیرین افراد چاق به صورت مستقیم یا با دخالت سایر اعضای پروتئین ANGPTL7 نقش اندکی دارد. تمرینات ورزشی سطح ANGPTL7 را کاهش می دهد این در حالی است که پتانسیل برای هدف گذاری این پروتئین را بعنوان هدف درمانی تنظیم دیس لپیدمی برجسته می نماید.
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