Tonsillar abscess formation due to herpes simplex type-1 in a severely immunocompromised stem cell transplant patient with chronic myeloid leukemia

Herpes simplex virus (HSV) causes life-threatening infections in immunocompromised patients such as transplant recipients and patients with hematologic malignancies. We herein describe the case of a patient with chronic myeloid leukemia blastic transformation who developed severe herpetic tonsillitis complicated by tonsillar abscess formation. Abscess formation was determined by computed tomography, whereas tonsillitis due to HSV was confirmed by pathologic and immunohistochemical examinations of the tonsillar biopsy. For molecular confirmation, HSV DNA was amplified by LightCycler PCR and type (HSV-1) determined by melting point analysis. The patient responded promptly to antiviral treatment and there were no signs of recurrent infection at the follow-up. To our knowledge, this case is unique for being the first case of tonsillar abscess formation due to HSV-1, also emphasizing the importance of herpetic infections in the differential diagnosis of oropharyngeal small-sized lesions in the immunocompromised patient population

Tp53 Staining In Tissue Samples Of Chronic Lymphocytic Lymphoma Cases: An Immunohistochemical Survey Of 51 Cases

Objective: Chronic lymphocytic leukemia (CLL) is the most common lymphoproliferative disease in adults. The aim of this study is to find out if the extent of proliferation centers or the immunohistochemical expression of p53 is related to disease prognosis. Materials and Methods: In the scope of this study, 54 biopsy specimens from 51 patients (50 of lymph nodes; the others of spleen, tonsil, orbit, and liver) diagnosed with CLL at the Hacettepe University Department of Pathology in 2000-2013 were reevaluated. The clinical and demographic data of the patients were obtained from our patient database. Biopsy samples were assessed semi-quantitatively for the percentage of proliferation center/total biopsy area (PC/TBA) and an immunohistochemical study was performed on representative blocks of tissues for p53 expression. Results: When the patients were divided into two categories according to Rai stage as high and low (stages 0, 1, and 2 vs. stages 3 and 4), it was seen that patients with low Rai stage had a better prognosis than those with high stages (p=0.030). However, there was no statistically significant correlation between overall survival and PC/TBA ratio or p53 expression levels. Conclusion: In our cohort, PC/TBA ratio and immunopositivity of p53 did not show correlations with overall survival.PubMedWoSScopu

Determining the frequency of iron overload at diagnosis in de novo acute myeloid leukemia patients with multilineage dysplasia or myelodysplasia-related changes: a case control study

Acute myeloid leukemia (AML) with myelodysplasia-related changes (AML-MRC) is a new disease category, which was defined as a separate entity in the World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues. While pre-treatment iron overload in patients with myelodysplastic syndrome has been previously studied, its relationship with AML-MRC has not been studied. We aimed to investigate the relationship between serum iron tests compatible with iron overload and the diagnosis of multilineage dysplasia (MLD) and AML with myelodysplasia-related changes (AML-MRC) in AML patients diagnosed at Hacettepe University Adult Hospital between January 2002 and September 2017. Ninety-three patients who met the criteria were enrolled. Bone marrow aspirate of each patient was re-examined, and dysplasia was investigated; other data were examined from patient's records. The iron overload status at diagnosis and transferrin saturation (TS) values were compared between the groups with and without MLD and those with and without AML-MRC. When iron overload was defined as TS >= 58% and ferritin >= 500 ng/mL, iron overload was observed in 10 (37%) patients with MLD and in 4 (13%) without MLD. The difference is almost statistically significant (p = 0.053). The mean TS value and frequency of iron overload were higher in AML-MRC patients than in non-AML-MRC patients (p < 0.05 for both). A mild positive significant correlation was observed between the dysplasia severity score and TS (r = 0.317, p = 0.032). In patients with AML-MLD and AML-MRC, iron overload occurred regardless of the transfusion status at the time of diagnosis. Morphologic severity of dysplasia may be correlated with higher TS values at the time of diagnosis

DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD

This paper presents a cancer stem cell detection method using region covariance and codifference method. It focuses on detection of Cancer Stem Cell (CSC) in microscopic images which are stained with CD13 marker. Features of CSC images are extracted by using both covariance method and its multiplication free version codifference method and these features are fed into a Support Vector Machine (SVM) for classification. Experimental results are presented

Multiplication free neural network for cancer stem cell detection in H&E stained liver images

Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented

Ebv-Related Diffuse Large B-Cell Lymphoma In A Patient With Angioimmunoblastic T-Cell Lymphoma

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Graph convolutional networks for region of interest classification in breast histopathology

Deep learning-based approaches have shown highly successful performance in the categorization of digitized biopsy samples. The commonly used setting in these approaches is to employ convolutional neural networks for classification of data sets consisting of images all having the same size. However, the clinical practice in breast histopathology necessitates multi-class categorization of regions of interest (ROI) in biopsy samples where these regions can have arbitrary shapes and sizes. The typical solution to this problem is to aggregate the classification results of fixed-sized patches cropped from these images to obtain image-level classification scores. Another limitation of these approaches is the independent processing of individual patches where the rich contextual information in the complex tissue structures has not yet been sufficiently exploited. We propose a generic methodology to incorporate local inter-patch context through a graph convolution network (GCN) that admits a graph-based ROI representation. The proposed GCN model aims to propagate information over neighboring patches in a progressive manner towards classifying the whole ROI into a diagnostic class. The experiments using a challenging data set for a 4-class ROI-level classification task and comparisons with several baseline approaches show that the proposed model that incorporates the spatial context by using graph convolutional layers performs better than commonly used fusion rules