Sildenafil attenuates nonsteroidal anti-inflammatory-induced gastric ulceration in mice via antioxidant and antigenotoxic mechanisms.

Sildenafil (SIL) has potential as an interesting gastroprotective drug. However, the pathways of its protective effect still needs to be clarified, and its use as a potential gastroprotective agent validated. This study aims to evaluate the effects of SIL via modulation of oxidative stress in a NSAID-induced gastric lesion model. Male Swiss mice were divided into six groups: control (CON, water), nonsteroidal anti-inflammatory drug (NSAID, water), proton pump inhibitor (PPI, 30 mg/kg of lansoprazole), SIL 5 (5 mg/kg), SIL 25 (25 mg/kg) and SIL 50 (50 mg/kg). The animals were treated by gavage (a single dose) after 24 hours of fasting, and gastric lesions were performed after 30 minutes, with indomethacin (40 mg/kg, by gavage). After 6h, the animals were killed and the stomach was removed to evaluate reactive oxygen species (ROS) production, oxidation of macromolecules, quantification of antioxidant enzymes, DNA fragmentation, apoptosis and macroscopic and histologic analysis of gastric lesions. SIL exerts a dose-dependent gastroprotective effect against NSAID-induced mucosal injury, also reducing cytoplasmic levels of ROS and consequent oxidative damage to macromolecules. In addition, SIL increases nitric oxide bioavailability, antioxidant enzymes and gastric cellular viability, as well as restoring important factors involved in gastroprotection. Our results demonstrate that different doses of SIL prevent indomethacin-induced gastric ulcer in mice via different, but complementary antioxidant, antigenotoxic and antiapoptotic mechanisms.

High-throughput image analysis in the diagnosis of papillary thyroid carcinoma.

BACKGROUND: Thyroid cancer is the most common endocrine cancer, and its incidence has been increasing worldwide in the past decades. The increasing demand in medicine for rapid and accurate diagnosis enabled the application of digital imaging analysis in order to increase workflow efficiency and accurate analyses. The present study aimed to automatically differentiate papillary thyroid carcinoma from normal thyroid cells using high-throughput image analysis. METHODS: Images of cellular specimens were taken with a digital camera and were subsequently analyzed. Other software was used for machine-learning-based cellular diagnostics. RESULTS: The two different classes were correctly identified with high sensitivity and specificity. CONCLUSION: The data created offers great potential for an automated diagnosis. Diagn. Cytopathol. 2016;44:574-577. © 2016 Wiley Periodicals, Inc.