Central illustration: cumulative major adverse cardiac events (MACE) and bioresorbable vascular scaffold (BVS) thrombosis rates after 1, 2, 3, 4 and 5 years.相似文献
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
Since recent reports have shown that (-)-Epigallocatechin-3-gallate (EGCG) could be used for treating proliferative and inflammatory disorders, we explored its use for the management of corneal chemical burns.
Materials and methods
Initially, EGCG was assayed on the rabbit corneal epithelial cell line RCE1(5T5) to establish the best testing conditions, and to avoid unwanted outcomes in the experimental animals. Then, we studied its effects on cell proliferation, cell cycle progression and cell differentiation. Afterwards, we instilled EGCG in experimental grade II corneal alkali burns in mice, three times a day up to 21 days, and evaluated by slit lamp examination and histological sections of corneal epithelial, corneal endothelial and stromal edema, as well as the presence of inflammatory cells and neovascularization.
Results
EGCG reduced cell growth and led to a decline in the proportion of proliferative cells in a concentration dependent manner. At 10 μM, EGCG promoted cell differentiation, an effect not related with apoptosis or cytotoxicity. When 10 μM EGCG was instilled in corneal alkali burns in mice three times a day up to 21 days, EGCG significantly reduced corneal opacity and neovascularization. The improved clinical appearance of the cornea was associated to a controlled epithelial growth; epithelial morphology was similar to that observed in normal epithelium and contrasted with the hyperproliferative, desquamating epithelium observed in control burn wounds. EGCG reduced corneal, stromal and endothelial edema, and wound inflammation.
Conclusion
This work constitutes the first evidence for the use of EGCG in the acute phase of a corneal alkali burn, representing a possible novel alternative to improve patient outcomes as an add-on therapy. 相似文献
