A low-cost HPV immunochromatographic assay to detect high-grade cervical intraepithelial neoplasia

Objective To evaluate the reproducibility and accuracy of the HPV16/18-E6 test. Methods The study population was comprised of 448 women with a previously abnormal Pap who were referred to the Barretos Cancer Hospital (Brazil) for diagnosis and treatment. Two cervical samples were collected immediately before colposcopy, one for the hr-HPV-DNA test and cytology and the other for the HPV16/18-E6 test using high-affinity monoclonal antibodies (mAb). Women with a histologic diagnosis of cervical intraepithelial neoplasia grade 2 or 3 were considered to be positive cases. Different strategies using a combination of screening methods (HPV-DNA) and triage tests (cytology and HPV16/18-E6) were also examined and compared. Results The HPV16/18-E6 test exhibited a lower positivity rate compared with the HPV-DNA test (19.0% vs. 29.3%, p<0.001) and a moderate/high agreement (kappa = 0.68, 95% CI: 0.60-0.75). It also exhibited a significantly lower sensitivity for CIN2+ and CIN3+ detection compared to the HPV-DNA test and a significantly higher specificity. The HPV16/18-E6 test was no different from cytology in terms of sensitivity, but it exhibited a significantly higher specificity in comparison to ASCH+. A triage test after HPV-DNA detection using the HPV16/18-E6 test exhibited a significantly higher specificity compared with a triage test of ASCH+ to CIN2+ (91.8% vs. 87.4%, p = 0.04) and CIN3+ (88.6% vs. 84.0%, p = 0.05). Conclusion The HPV16/18-E6 test exhibited moderate/high agreement with the HPV-DNA test but lower sensitivity and higher specificity for the detection of CIN2+ and CIN3+. In addition, its performance was quite similar to cytology, but because of the structural design addressed for the detection of HPV16/18-E6 protein, the test can miss some CIN2/3+ lesions caused by other high-risk HPV types.Cancer Prevention Department, Center for the Researcher Support and Pathology Department of the Barretos Cancer Hospital. This study was supported by CNPq 573799/2008-3 and FAPESP 2008/57889-1info:eu-repo/semantics/publishedVersio

Active Learning for Patch-Based Digital Pathology using Convolutional Neural Networks to Reduce Annotation Costs

Methods to reduce the need for costly data annotations become increasingly important as deep learning gains popularity in medical image analysis and digital pathology. Active learning is an appealing approach that can reduce the amount of annotated data needed to train machine learning models but traditional active learning strategies do not always work well with deep learning. In patch-based machine learning systems, active learning methods typically request annotations for small individual patches which can be tedious and costly for the annotator who needs to rely on visual context for the patches. We propose an active learning framework that selects regions for annotation that are built up of several patches, which should increase annotation throughput. The framework was evaluated with several query strategies on the task of nuclei classification. Convolutional neural networks were trained on small patches, each containing a single nucleus. Traditional query strategies performed worse than random sampling. A K-centre sampling strategy showed a modest gain. Further investigation is needed in order to achieve significant performance gains using deep active learning for this task

End stage renal disease patients have a skewed T cell receptor Vβ repertoire

BACKGROUND: End stage renal disease (ESRD) is associated with defective T-cell mediated immunity. A diverse T-cell receptor (TCR) Vβ repertoire is central to effective T-cell mediated immune responses to foreign antigens. In this study, the effect of ESRD on TCR Vβ repertoire was assessed. RESULTS: A higher proportion of ESRD patients (68.9 %) had a skewed TCR Vβ repertoire compared to age and cytomegalovirus (CMV) – IgG serostatus matched healthy individuals (31.4 %, P < 0.001). Age, CMV serostatus and ESRD were independently associated with an increase in shifting of the TCR Vβ repertoire. More differentiated CD8(+) T cells were observed in young ESRD patients with a shifted TCR Vβ repertoire. CD31-expressing naive T cells and relative telomere length of T cells were not significantly related to TCR Vβ skewing. CONCLUSIONS: ESRD significantly skewed the TCR Vβ repertoire particularly in the elderly population, which may contribute to the uremia-associated defect in T-cell mediated immunity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12979-015-0055-7) contains supplementary material, which is available to authorized users

SMOD - Data Augmentation Based on Statistical Models of Deformation to Enhance Segmentation in 2D Cine Cardiac MRI

Deep learning has revolutionized medical image analysis in recent years. Nevertheless, technical, ethical and financial constraints along with confidentiality issues still limit data availability, and therefore the performance of these approaches. To overcome such limitations, data augmentation has proven crucial. Here we propose SMOD, a novel augmentation methodology based on Statistical Models of Deformations, to segment 2D cine scans in cardiac MRI. In brief, the shape variability of the training set space is modelled so new images with the appearance of the original ones but unseen shapes within the space of plausible realistic shapes are generated. SMOD is compared to standard augmentation providing quantitative improvement, especially when the training data available is very limited or the structures to segment are complex and highly variable. We finally propose a state-of-art, deep learning 2D cardiac MRI segmenter for normal and hypertrophic cardiomyopathy hearts with an epicardium and endocardium mean Dice score of 0.968 in short and long axis.</p

Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learning framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural Network (CNN) architectures, AlexNet and VGG, structured to perform regression, were finetuned and validated on manually graded images from three evaluators. Two different scoring strategies, a criteria-based percentage and an overall general impression, were used. The developed CNN models estimate the average score with a root mean square accuracy ranging between 84% − 93%, indicating the ability to replicate expert valuation. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills

DC-SIGN and CD150 Have Distinct Roles in Transmission of Measles Virus from Dendritic Cells to T-Lymphocytes

Measles virus (MV) is among the most infectious viruses that affect humans and is transmitted via the respiratory route. In macaques, MV primarily infects lymphocytes and dendritic cells (DCs). Little is known about the initial target cell for MV infection. Since DCs bridge the peripheral mucosal tissues with lymphoid tissues, we hypothesize that DCs are the initial target cells that capture MV in the respiratory tract and transport the virus to the lymphoid tissues where MV is transmitted to lymphocytes. Recently, we have demonstrated that the C-type lectin DC-SIGN interacts with MV and enhances infection of DCs in cis. Using immunofluorescence microscopy, we demonstrate that DC-SIGN+ DCs are abundantly present just below the epithelia of the respiratory tract. DC-SIGN+ DCs efficiently present MV-derived antigens to CD4+ T-lymphocytes after antigen uptake via either CD150 or DC-SIGN in vitro. However, DC-SIGN+ DCs also mediate transmission of MV to CD4+ and CD8+ T-lymphocytes. We distinguished two different transmission routes that were either dependent or independent on direct DC infection. DC-SIGN and CD150 are both involved in direct DC infection and subsequent transmission of de novo synthesized virus. However, DC-SIGN, but not CD150, mediates trans-infection of MV to T-lymphocytes independent of DC infection. Together these data suggest a prominent role for DCs during the initiation, dissemination, and clearance of MV infection

Differential effects of age, cytomegalovirus-seropositivity and end-stage renal disease (ESRD) on circulating T lymphocyte subsets

The age- and cytomegalovirus (CMV)-seropositivity-related changes in subsets and differentiation of circulating T cells were investigated in end-stage renal disease (ESRD) patients (n = 139) and age-matched healthy individuals. The results show that CMV-seropositivity is associated with expansion of both CD4+ and CD8+ memory T cells which is already observed in young healthy individuals. In addition, CMV-seropositive healthy individuals have a more differentiated memory T cell profile. Only CMV-seropositive healthy individuals showed an age-dependent decrease in CD4+ naïve T cells. The age-related decrease in the number of CD8+ naïve T cells was CMV-independent. In contrast, all ESRD patients showed a profound naïve T-cell lymphopenia at every decade. CMV-seropositivity aggravated the contraction of CD4+ naïve T cells and increased the number of differentiated CD4+ and CD8+ memory T cells. In conclusion, CMV-seropositivity markedly alters the homeostasis of circulating T cells in healthy individuals and aggravates the T cell dysregulation observed in ESRD patients

A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach.

Objectives: The aims of this study were to assess the discriminative performance of quantitative multiparametric magnetic resonance imaging (mpMRI) between prostate cancer and noncancer tissues and between tumor grade groups (GGs) in a multicenter, single-vendor study, and to investigate to what extent site-specific differences affect variations in mpMRI parameters. Materials and Methods: Fifty patients with biopsy-proven prostate cancer from 5 institutions underwent a standardized preoperative mpMRI protocol. Based on the evaluation of whole-mount histopathology sections, regions of interest were placed on axial T2-weighed MRI scans in cancer and noncancer peripheral zone (PZ) and transition zone (TZ) tissue. Regions of interest were transferred to functional parameter maps, and quantitative parameters were extracted. Across-center variations in noncancer tissues, differences between tissues, and the relation to cancer grade groups were assessed using linear mixed-effects models and receiver operating characteristic analyses. Results: Variations in quantitative parameters were low across institutes (mean [maximum] proportion of total variance in PZ and TZ, 4% [14%] and 8% [46%], respectively). Cancer and noncancer tissues were best separated using the diffusion-weighted imaging-derived apparent diffusion coefficient, both in PZ and TZ (mean [95% confidence interval] areas under the receiver operating characteristic curve [AUCs]; 0.93 [0.89–0.96] and 0.86 [0.75–0.94]), followed by MR spectroscopic imaging and dynamic contrast-enhanced-derived parameters. Parameters from all imaging methods correlated significantly with tumor grade group in PZ tumors. In discriminating GG1 PZ tumors from higher GGs, the highest AUC was obtained with apparent diffusion coefficient (0.74 [0.57–0.90], P < 0.001). The best separation of GG1–2 from GG3–5 PZ tumors was with a logistic regression model of a combination of functional parameters (mean AUC, 0.89 [0.78–0.98]). Conclusions: Standardized data acquisition and postprocessing protocols in prostate mpMRI at 3 T produce equivalent quantitative results across patients from multiple institutions and achieve similar discrimination between cancer and noncancer tissues and cancer grade groups as in previously reported singlecenter studies.publishedVersion© 2019 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CC-BY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal