Fibroma é um tipo de tumor benigno do tecido conjuntivo fibroso. Pode ser considerado uma hiperplasia reacional do tecido conjuntivo em resposta a traumas e irritação.
Índice
1Características clínicas
2Características histopatológicas
3Tratamento
4Referências
Características clínicas |
Geralmente são nodulares, com consistência firme, assintomáticos, coloração semelhante à da mucosa, base séssil, superfície lisa, e com até 2 cm de diâmetro.
Localização mais comum: Mucosa jugal, ao longo da linha de oclusão, língua e mucosa labial.
Características histopatológicas |
Massa nodular de tecido conjuntivo fibroso coberto por epitélio escamoso estratificado.
Tratamento |
Excisão cirúrgica conservadora. Recorrência rara.
Referências |
NEVILLE, B.W. et al.Patologia Oral & Maxilofacial. Rio de Janeiro: Guanabara Koogan S.A., 1998.
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Patologia: Tumores, neoplasia e oncologia (C00-D48, 140-239)
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I found a lot of questions abount appendices and ToC. Many users want appendices to be grouped in an Appendix part, however some problems arise with ToC, hyperref, PDF viewer bookmarks, and so on. There are different solutions which require extra packages, command patching and other extra code, however none of them satisfies me. I almost found an easy way to accomplish a good result, where appendices are added to bookmarks in the right way and hyperref links point to the right page. However, the number of the "Appendix" part page is wrong (it's the number of appendix A). Is there any EASY way to fix that? This is a MWE: documentclass{book} usepackage[nottoc,notlot,notlof]{tocbibind} usepackage{hyperref} begin{document} frontmatter tableofcontents mainmatter part{First} chapter{...
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In the sklearn.model_selection.cross_val_predict page it is stated: Generate cross-validated estimates for each input data point. It is not appropriate to pass these predictions into an evaluation metric. Can someone explain what does it mean? If this gives estimate of Y (y prediction) for every Y (true Y), why can't I calculate metrics such as RMSE or coefficient of determination using these results?
python scikit-learn cross-validation
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edited Nov 28 '18 at 17:52
desertnaut
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I have written a function using curl to generate the token. I check whether the token exists; if not, then I execute the function, otherwise I skip this function and proceed to next. But I am not sure that it will work if a token is expired. Is there any command to identify the expired token and generates the new one by calling this function? #!/bin/ksh export V_TOKEN="gen_token_${V_DATE}.txt" #### Calling function to generate the token function callPOST { curl -X POST -H 'Content-Type: application/x-www-form-url' -d 'grant_type=password&username=usr01&password=pwd...
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