Both antigen-specific and non-specific mechanisms may be involved in the pathogenesis of oral lichen planus (OLP). Antigen-specific mechanisms in OLP include antigen presentation by basal keratinocytes and antigen-specific keratinocyte killing by CD8 + cytotoxic T-cells. Non-specific mechanisms include mast cell degranulation and matrix metalloproteinase (MMP) activation in OLP lesions. These mechanisms may combine to cause T-cell accumulation in the superficial lamina propria, basement membrane disruption, intra-epithelial T-cell migration, and keratinocyte apoptosis in OLP. OLP chronicity may be due, in part, to deficient antigen-specific TGF-␤1-mediated immunosuppression. The normal oral mucosa may be an immune privileged site (similar to the eye, testis, and placenta), and breakdown of immune privilege could result in OLP and possibly other autoimmune oral mucosal diseases. Recent findings in mucocutaneous graft-versus-host disease, a clinical and histological correlate of lichen planus, suggest the involvement of TNF-␣, CD40, Fas, MMPs, and mast cell degranulation in disease pathogenesis. Potential roles for oral Langerhans cells and the regional lymphatics in OLP lesion formation and chronicity are discussed. Carcinogenesis in OLP may be regulated by the integrated signal from various tumor inhibitors (TGF-␤1, TNF-␣, IFN-␥, IL-12) and promoters (MIF,. We present our recent data implicating antigen-specific and non-specific mechanisms in the pathogenesis of OLP and propose a unifying hypothesis suggesting that both may be involved in lesion development. The initial event in OLP lesion formation and the factors that determine OLP susceptibility are unknown.

Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

Abstract:The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions. Langenburg, and M. D. Klein, "Raman spectroscopy for neoplastic tissue differentiation: a pilot study," J. Pediatr. Surg. 39(6), 953-956 (2004). 12. K. Kong, C. Kendall, N. Stone, and I. Notingher, "Raman spectroscopy for medical diagnostics--From in-vitro biofluid assays to in-vivo cancer detection," Adv. Drug Deliv. Rev. 89, 121-134 (2015). 13. P. Crow, J. S. Uff, J. A. Farmer, M. P. Wright, and N. Stone, "The use of Raman spectroscopy to identify and characterize transitional cell carcinoma in vitro," BJU Int. 93(9), 1232-1236 (2004). 14. A. Sikirzhytskaya, V. Sikirzhytski, and I. K. Lednev, "Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood," J. Biophotonics 7(1-2), 59-67 (2014 L. Martin, "Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients," J. Biophotonics 7(3-4), 200-209 (2014). 18.