The solvatochromism of several polar solutes, including some that contain both hydrogen bond-donating and -accepting properties (coumarins 1, 102, 120, 151, 152, and 153; nile red; and 4-aminofluorenone), is analyzed in terms of three models:  the Reichardt single parameter E T N polarity scale, the multiparameter Kamlet−Taft equation, and the reaction field model. We use a “step-forward” procedure to determine which terms of the Kamlet−Taft equation are statistically significant in fitting the data. These equations provide the best fits to the data in almost all cases. We also find a correlation between the parameters s and a, which quantify the effects on the transition energy related to the solvatochromic parameters π* and α, respectively. This relationship suggests that the magnitude of a is not indicative of the strength of the hydrogen-bonding interaction, but rather reflects the additional field produced from the dipole moment of a hydrogen bond-donating molecule that is held in an orientation that roughly parallels the solute dipole.

We report a case of gastric lipoma, a rare benign stomach tumor. There are approximately 200 cases previously described in literature. A male, 62-year-old patient with no clinical complaint presented a tumor lesion in the stomach antrum found in a routine upper endoscopy. A surgical resection (subtotal gastrectomy) was done and the histological examination showed submucosal lipoma without signs of malignancy. This report points to the growth of routine examination in the current clinical practice and the dilemma brought by overdiagnosis.

Saber é prevenir: uma nova abordagem no combate ao câncer de mamaKnowledge is prevention: a novel approach to breast cancer prevention

Background Longitudinal measurement of tumor burden with MRI is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors. Methods A preoperative and postoperative cohort were randomly split into training and testing sets in a 4:1 ratio. A 3D U-Net neural network was trained to automatically segment the tumor on T1 contrast-enhanced and T2/FLAIR images. The product of the maximum bidimensional diameters according to the RAPNO criteria (AutoRAPNO) was determined. Performance was compared to that of two expert human raters who performed assessments independently. Volumetric measurements of predicted and expert segmentations were computationally derived and compared. Results A total of 794 pre-operative MRIs from 794 patients and 1,003 post-operative MRIs from 122 patients were included. There was excellent agreement of volumes between preoperative and postoperative predicted and manual segmentations, with ICCs of 0.912 and 0.960 for the two preoperative and 0.947 and 0.896 for the two postoperative models. There was high agreement between AutoRAPNO scores on predicted segmentations and manually calculated scores based on manual segmentations (Rater 2 ICC=0.909; Rater 3 ICC=0.851). Lastly, the performance of AutoRAPNO was superior in repeatability to that of human raters for MRIs with multiple lesions. Conclusions Our automated deep learning pipeline demonstrates potential utility for response assessment in pediatric brain tumors. The tool should be further validated in prospective studies.