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Monolithic porous carbon materials prepared from polyurethane foam templates
João Pires,André Janeiro,Filipe J. Oliveira,Alexandre C. Bastos,Moisés L. Pinto 한국탄소학회 2016 Carbon Letters Vol.18 No.-
Monolithic carbon foams with hierarchical porosity were prepared from polyurethane templates and resol precursors. Mesoporosity was achieved through the use of soft templating with surfactant Pluronic F127, and macroporosity from the polyurethane foams was retained. Conditions to obtain high porosity materials were optimized. The best materials have high specific surface areas (380 and 582 m2 g–1, respectively) and high electrical conductivity, which make them good candidates for supports in sensors. These materials showed an almost linear dependence between the potential and the pH of aqueous solutions.
Pedro R. F. Rende,Joel Machado Pires,Kátia Sakimi Nakadaira,Sara Lopes,João Vale,Fabio Hecht,Fabyan E. L. Beltrão,Gabriel J. R. Machado,Edna T. Kimura,Catarina Eloy,Helton E. Ramos 대한병리학회 2024 Journal of Pathology and Translational Medicine Vol.58 No.3
Background: Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration. Methods: We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio. Results: This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. Conclusions: The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.