427 research outputs found

    Is a soft tissue graft harvested from the maxillary tuberosity the approach of choice in an isolated site?

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    Soft tissue augmentation procedures are becoming more popular these days. Different soft tissue graft harvesting approaches have been proposed. Nonetheless, the location of the donor site (whether anterior-, lateral-, superficial-, deep-palate or the maxillary tuberosity) can affect the graft shape and its composition. Soft tissue grafts from the maxillary tuberosity are rich in connective tissue fibers, with minimal presence of fatty or glandular components. Clinical, histological, and molecular evidence shows that a soft tissue graft obtained from the maxillary tuberosity has unique properties. In addition, harvesting from this area presents minimal risk for intra- or postoperative complications, leading to reduced patient morbidity. The aim of this commentary is to discuss the advantages and disadvantages of harvesting a soft tissue graft from the tuberosity and to compare it with the traditional palatal graft, while highlighting functional, esthetic, and patient-related outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151301/1/jper10300_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151301/2/jper10300.pd

    Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

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    Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.Comment: Submitted to https://link.springer.com/chapter/10.1007/978-3-030-52791-4_

    The breadth of primary care: a systematic literature review of its core dimensions

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    Background: Even though there is general agreement that primary care is the linchpin of effective health care delivery, to date no efforts have been made to systematically review the scientific evidence supporting this supposition. The aim of this study was to examine the breadth of primary care by identifying its core dimensions and to assess the evidence for their interrelations and their relevance to outcomes at (primary) health system level. Methods: A systematic review of the primary care literature was carried out, restricted to English language journals reporting original research or systematic reviews. Studies published between 2003 and July 2008 were searched in MEDLINE, Embase, Cochrane Library, CINAHL, King's Fund Database, IDEAS Database, and EconLit. Results: Eighty-five studies were identified. This review was able to provide insight in the complexity of primary care as a multidimensional system, by identifying ten core dimensions that constitute a primary care system. The structure of a primary care system consists of three dimensions: 1. governance; 2. economic conditions; and 3. workforce development. The primary care process is determined by four dimensions: 4. access; 5. continuity of care; 6. coordination of care; and 7. comprehensiveness of care. The outcome of a primary care system includes three dimensions: 8. quality of care; 9. efficiency care; and 10. equity in health. There is a considerable evidence base showing that primary care contributes through its dimensions to overall health system performance and health. Conclusions: A primary care system can be defined and approached as a multidimensional system contributing to overall health system performance and health
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