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Graefe's Archive for Clinical and Experimental Ophthalmology - The purpose was to ascertain if any relation exists between the elevated intraocular pressure (IOP) in patients with...  相似文献   
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European Journal of Epidemiology - With the rising use of machine learning for healthcare applications, practitioners are increasingly confronted with the limitations of prediction models that are...  相似文献   
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Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.  相似文献   
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Odontology - The purpose of this study was to investigate whether the root perforation repair with mineral aggregate-based cements affects the retention of customized fiberglass posts to bovine...  相似文献   
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Objective

Investigate the effect of erosive challenge with hydrochloric acid (HCl) on the surface of five different restorative materials.

Material and methods

Ten plates of five restorative materials (Admira Fusion, Activa BioActive-Restorative, Charisma, Equia Forte HT Fil/EF, Filtek Universal Restorative/FU) were obtained. Half of the plate surfaces was covered with an adhesive tape, creating a control area, and the other side was submitted to the HCl (0.06 M HCl; pH 1.2; at 37 °C; for 30 h). Plates with control and HCl-treated areas were analyzed regarding the surface roughness (Sa), roughness profile (Rv), surface loss (SL), microhardness (MI), and gloss (GL) (n?=?10). Surface morphology was analyzed by SEM and chemical elements were identified by EDX (n?=?5). Data were evaluated by ANOVA and Tukey’s test (α?=?0.05).

Results

Most materials were not affected by HCl. FU showed the lowest Sa and Rv, and the highest GL after HCl. On the other hand, EF presented the highest Sa, Rv, and SL, and the lowest GL. The MI of materials was not changed after HCl. Topographical and chemical alterations were observed after HCl only for EF.

Conclusions

The composites showed minor surface changes after HCl, which was not observed for the glass ionomer cement (EF). FU presented the best performance regarding the parameters evaluated.

Clinical relevance

The effects of erosive challenge with HCl on composites were minimal, while the glass ionomer cements might not be indicated as restorative material for patients with gastroesophageal reflux disease.

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AIDS and Behavior - Social biases may influence providers’ judgments related to pre-exposure prophylaxis (PrEP) and patients’ consequent PrEP access. US primary and HIV care providers...  相似文献   
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