E-education in pathology including certification of e-institutions

E–education or electronically transferred continuous education in pathology is one major application of virtual microscopy. The basic conditions and properties of acoustic and visual information transfer, of teaching and learning processes, as well as of knowledge and competence, influence its implementation to a high degree. Educational programs and structures can be judged by access to the basic conditions, by description of the teaching resources, methods, and its program, as well as by identification of competences, and development of an appropriate evaluation system. Classic teaching and learning methods present a constant, usually non-reversible information flow. They are subject to personal circumstances of both teacher and student. The methods of information presentation need to be distinguished between static and dynamic, between acoustic and visual ones. Electronic tools in education include local manually assisted tools (language assistants, computer-assisted design, etc.), local passive tools (slides, movies, sounds, music), open access tools (internet), and specific tools such as Webinars. From the medical point of view information content can be divided into constant (gross and microscopic anatomy) and variable (disease related) items. Most open access available medical courses teach constant information such as anatomy or physiology. Mandatory teaching resources are image archives with user–controlled navigation and labelling, student–oriented user manuals, discussion forums, and expert consultation. A classic undergraduate electronic educational system is WebMic which presents with histology lectures. An example designed for postgraduate teaching is the digital lung pathology system. It includes a description of diagnostic and therapeutic features of 60 rare and common lung diseases, partly in multimedia presentation. Combining multimedia features with the organization structures of a virtual pathology institution will result in a virtual pathology education institution (VPEI), which can develop to a partly automated distant learning faculty in medicine

Systematic Investigation of Expression of G2/M Transition Genes Reveals CDC25 Alteration in Nonfunctioning Pituitary Adenomas

Hungarian Resarch Fund (OTKA PD100648) National Development Agency (KTIA_AIK-2-2012-0010)

Comparative Analyses Based On WSI View Paths Recorded During Multiple Practical Exams In Oral Pathology

INTRODUCTION / BACKGROUND: Data collected about the ways students view Whole Slide Images (WSIs) during their practical exams can be used for many interesting analyses. Tracking such viewing behavior over years enables multiple comparisons and leads to drawing more general conclusions about observed viewing patterns. AIMS: First goal of this work was to collect data about how students view WSIs attached to questions during practical exams in oral pathology conducted over several years. What is important, we were collecting this data for all or for most students participating in the exams. Second objective was to analyze the data using specially prepared methods. This way we could gain interesting insights into students’ viewing behavior. Finally, by analyzing data from a few exams we wanted to compare the observed viewing patterns across multiple years, and find general conclusions which hold true for multiple exams. METHODS: A scalable software-based view path tracking method with centralized database was used to collect data describing how students pan and zoom across WSIs attached to exam questions. The tracking software was active during multiple practical exams in oral pathology conducted in the recent years at Poznan University of Medical Sciences in Poznan, Poland. Data about over 100,000 view fields has been gathered, and we used it in various analyses, including visualizations and numerical calculations. The latter were based on computation of per-view-path metrics, like number of view fields, viewing time, average zoom level, focus on a region of interest, dispersion. Generated overview images and calculated numbers were compared and aggregated for different students, questions, student classes, and exam years. On each level, we split the data into groups of students who answered a question correctly, and those who gave an incorrect answer. We looked for correlations between the calculated metrics and answer correctness, and even attempted to predict students’ answers based on the metrics, using machine learning approaches. RESULTS: The view path tracking implementation has successfully collected data about WSI areas viewed by students during multiple practical exams in oral pathology, and we were able to use this data for multiple analyses. Produced visualizations (static images and animations) provided clear overviews of how individual students viewed WSIs, and which areas of the slides were most often viewed when answering correctly or incorrectly. Calculated metrics enabled more objective comparisons, and aggregation of obtained numbers resulted in finding more general patterns. We found that students who gave incorrect answers tended to view the WSIs for longer time, go through more areas, often more dispersed across a slide, and focus less on the expected region of interest. Analysis of data split by student classes taught by different assistants helped in assessing personal impact of a teacher on his or her students’ results. Finally, thanks to the view path tracking data collected over multiple years, we were able to compare results of the analyses from different exams, to see whether the observations hold true for multiple groups of students and for a longer period of time. This way we found certain consistencies and patterns reoccurring over years, which makes such findings particularly meaningful. Yearly analysis also helped in assessing didactic value of used slides and identifying slides which potentially require more attention during oral pathology classes

Distinct methylation profiles of glioma subtypes

Gliomas are tumors of the central nervous system with a wide spectrum of different tumor types. They range from pilocytic astrocytoma, with a generally good prognosis, to the extremely aggressive malignant glioblastoma. In addition to these 2 types of contrasting neoplasms, several other subtypes can be distinguished, each characterized by specific phenotypic, as well as genotypic features. Recently, the epigenotype, as evident from differentially methylated DNA loci, has been proposed to be useful as a further criterion to distinguish between tumor types. In our study, we screened 139 tissue samples, including 33 pilocytic astrocytomas, 46 astrocytomas of different grades, 7 oligoastrocytomas, 10 oligodendrogliomas, 10 glioblastoma multiforme samples and 33 control tissues, for methylation at CpG islands of 15 different gene loci. We used the semiquantitative high throughput method MethyLight to analyze a gene panel comprising ARF, CDKN2B, RB1, APC, CDH1, ESR1, GSTP1, TGFBR2, THBS1, TIMP3, PTGS2, CTNNB1, CALCA, MYOD1 and HIC1. Seven of these loci showed tumor specific methylation changes. We found tissue as well as grade specific methylation profiles. Interestingly, pilocytic astrocytomas showed no evidence of CpG island hypermethylation, but were significantly hypomethylated, relative to control tissues, at MYOD1. Our results show that glioma subtypes have characteristic methylation profiles and, with the exception of pilocytic astrocytomas, show both locus specific hyper- as well as hypomethylation

Importance of GFAP isoform-specific analyses in astrocytoma

Gliomas are a heterogenous group of malignant primary brain tumors that arise from glia cells or their progenitors and rely on accurate diagnosis for prognosis and treatment strategies. Although recent developments in the molecular biology of glioma have improved diagnosis, classical histological methods and biomarkers are still being used. The glial fibrillary acidic protein (GFAP) is a classical marker of astrocytoma, both in clinical and experimental settings. GFAP is used to determine glial differentiation, which is associated with a less malignant tumor. However, since GFAP is not only expressed by mature astrocytes but also by radial glia during development and neural stem cells in the adult brain, we hypothesized that GFAP expression in astrocytoma might not be a direct indication of glial differentiation and a less malignant phenotype. Therefore, we here review all existing literature from 1972 up to 2018 on GFAP expression in astrocytoma patient material to revisit GFAP as a marker of lower grade, more differentiated astrocytoma. We conclude that GFAP is heterogeneously expressed in astrocytoma, which most likely masks a consistent correlation of GFAP expression to astrocytoma malignancy grade. The GFAP positive cell population contains cells with differences in morphology, function, and differentiation state showing that GFAP is not merely a marker of less malignant and more differentiated astrocytoma. We suggest that discriminating between the GFAP isoforms GFAPδ and GFAPα will improve the accuracy of assessing the differentiation state of astrocytoma in clinical and experimental settings and will benefit glioma classification