Pathology and genetics of tumours of the urinary system and male genital organs

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Problem-Based Learning: An Approach to Medical Education

Tenascin C is an extracellular matrix protein previously linked to breast cancer metastasis. Here the authors uncover how tenascin C promotes the fitness of metastasis-initiating cells by sustaining the stem and survival signaling pathways NOTCH and Wnt through specific regulation of Msi1 and Lgr5, respectively.

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Breast cancer cells produce tenascin C as a metastatic niche component to colonize the lungs

In this review, we discuss the structure and function of the extracellular matrix protein family of tenascins with emphasis on their involvement in human pathologies. The article is divided into the following sections: Introduction: the tenascin family of extracellular matrix proteins; Structural roles: tenascin-X deficiency and Ehlers–Danlos syndrome; Tenascins as modulators of cell adhesion, migration, and growth; Role of tenascin-C in inflammation; Regulation of tenascins by mechanical stress: implications for wound healing and regeneration; Association of tenascin-C with cancer: antibodies as diagnostic and therapeutic tools; Conclusion and perspectives. Copyright © 2003 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/path.1415

Tenascins: regulation and putative functions during pathological stress.

Although cadavers constitute the gold standard for teaching anatomy to medical and health science students, there are substantial financial, ethical, and supervisory constraints on their use. In addition, although anatomy remains one of the fundamental areas of medical education, universities have decreased the hours allocated to teaching gross anatomy in favor of applied clinical work. The release of virtual (VR) and augmented reality (AR) devices allows learning to occur through hands-on immersive experiences. The aim of this research was to assess whether learning structural anatomy utilizing VR or AR is as effective as tablet-based (TB) applications, and whether these modes allowed enhanced student learning, engagement and performance. Participants (n = 59) were randomly allocated to one of the three learning modes: VR, AR, or TB and completed a lesson on skull anatomy, after which they completed an anatomical knowledge assessment. Student perceptions of each learning mode and any adverse effects experienced were recorded. No significant differences were found between mean assessment scores in VR, AR, or TB. During the lessons however, VR participants were more likely to exhibit adverse effects such as headaches (25% in VR P < 0.05), dizziness (40% in VR, P < 0.001), or blurred vision (35% in VR, P < 0.01). Both VR and AR are as valuable for teaching anatomy as tablet devices, but also promote intrinsic benefits such as increased learner immersion and engagement. These outcomes show great promise for the effective use of virtual and augmented reality as means to supplement lesson content in anatomical education. Anat Sci Educ 10: 549-559. © 2017 American Association of Anatomists.

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The effectiveness of virtual and augmented reality in health sciences and medical anatomy.

Immunohistochemical expression of extracellular matrix components tenascin, fibronectin, collagen type IV and laminin in breast cancer: their prognostic value and role in tumour invasion and progression.

It has become increasingly apparent that no single method for teaching anatomy is able to provide supremacy over another. In an effort to consolidate and enhance learning, a modernized anatomy curriculum was devised by attempting to take advantage of and maximize the benefits from different teaching methods. Both the more traditional approaches to anatomy teaching, as well as modern, innovative educational programs were embraced in a multimodal system implemented over a decade. In this effort, traditional teaching with lectures and dissection was supplemented with models, imaging, computer-assisted learning, problem-based learning through clinical cases, surface anatomy, clinical correlation lectures, peer teaching and team-based learning. Here, we review current thinking in medical education and present our transition from a passive, didactic, highly detailed anatomy course of the past, to a more interactive, as well as functionally and clinically relevant anatomy curriculum over the course of a decade.

Modernization of an anatomy class: From conceptualization to implementation. A case for integrated multimodal-multidisciplinary teaching.

In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application of a distance-dependent rule on the resulted centroids; and the application of classification algorithms. In our method, we have examined the performance of an unsupervised (fuzzy C-means) and a supervised (support vector machines) classification technique. In both classification techniques, the effect of the refinement step improves the performance of the clustering algorithm. The proposed method was evaluated using 38 cytological images of conventional Pap smears containing 5617 recognized squamous epithelial cells. The results are very promising, even in the case of images with high degree of cell overlapping.

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Automated Detection of Cell Nuclei in Pap Smear Images Using Morphological Reconstruction and Clustering

https://www.cs.uoi.gr/~cnikou/Publications/J19_Web_Plissiti_Nikou_Charchanti-Pattern_Recognition_Letters-2011.pdf

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

Classification of cervical cells in Pap smear images is a challenging task due to the limitations these images exhibit and the complexity of the morphological changes in the structural parts of the cells. This procedure is very important as it provides fundamental information for the detection of cancerous or precancerous lesions. For this reason several algorithms have been proposed in order to classify normal and abnormal cells in such images. However, it is a common phenomenon that each research group usually creates its own dataset of images, as well-established datasets are not publicly available. To overcome this obstacle and to assist the research progress in this field, we present an annotated image database of Pap smear images, in which the cells are categorized in five different classes, depending on their cytomorphological features. The area of the cytoplasm and the nucleus in each image is manually defined by experts and salient features of intensity, texture and shape are calculated for each region of interest. Several experiments have been performed for the classification of these images and they include feature and image based classification schemes. In this direction, methods based on support vector machines and deep neural networks are tested and the performance of each classifier is presented in order to constitute a reference point for the evaluation of future classification techniques.

Antonia Charchanti

Papers

Immunohistochemical expression of extracellular matrix components tenascin, fibronectin, collagen type IV and laminin in breast cancer: their prognostic value and role in tumour invasion and progression.

Modernization of an anatomy class: From conceptualization to implementation. A case for integrated multimodal-multidisciplinary teaching.

Automated Detection of Cell Nuclei in Pap Smear Images Using Morphological Reconstruction and Clustering

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

Sipakmed: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images