Introduction: After initial treatment, differentiated thyroid cancer (DTC) patients are stratified as low and high risk based on clinical/pathological features. Recently, a risk stratification based on additional clinical data accumulated during follow-up has been proposed.Objective: To evaluate the predictive value of delayed risk stratification (DRS) obtained at the time of the first diagnostic control (8–12 months after initial treatment).Methods: We reviewed 512 patients with DTC whose risk assessment was initially defined according to the American (ATA) and European Thyroid Association (ETA) guidelines. At the time of the first control, 8–12 months after initial treatment, patients were re-stratified according to their clinical status: DRS.Results: Using DRS, about 50% of ATA/ETA intermediate/high-risk patients moved to DRS low-risk category, while about 10% of ATA/ETA low-risk patients moved to DRS high-risk category. The ability of the DRS to predict the final outcome was superior to that of ATA and ETA. Positive and negative predictive values for both ATA (39.2 and 90.6% respectively) and ETA (38.4 and 91.3% respectively) were significantly lower than that observed with the DRS (72.8 and 96.3% respectively,P<0.05). The observed variance in predicting final outcome was 25.4% for ATA, 19.1% for ETA, and 62.1% for DRS.Conclusions: Delaying the risk stratification of DTC patients at a time when the response to surgery and radioiodine ablation is evident allows to better define individual risk and to better modulate the subsequent follow-up.

Background: Systemic inflammation is a strong predictor of atrial fibrillation. A key role for electrical remodeling is increasingly recognized, and experimental data suggest that inflammatory cytokines can directly affect connexins resulting in gap‐junction dysfunction. We hypothesized that systemic inflammation, regardless of its origin, promotes atrial electric remodeling in vivo, as a result of cytokine‐mediated changes in connexin expression. Methods and Results: Fifty‐four patients with different inflammatory diseases and elevated C‐reactive protein were prospectively enrolled, and electrocardiographic P‐wave dispersion indices, cytokine levels (interleukin‐6, tumor necrosis factor‐α, interleukin‐1, interleukin‐10), and connexin expression (connexin 40, connexin 43) were measured during active disease and after reducing C‐reactive protein by >75%. Moreover, peripheral blood mononuclear cells and atrial tissue specimens from an additional sample of 12 patients undergoing cardiac surgery were evaluated for atrial and circulating mRNA levels of connexins. Finally, in vitro effects of interleukin‐6 on connexin expression were studied in HL‐1 mouse atrial myocytes. In patients with active inflammatory diseases, P‐wave dispersion indices were increased but rapidly decreased within days when C‐reactive protein normalizes and interleukin‐6 levels decline. In inflammatory disease patients, both P‐wave dispersion indices and interleukin‐6 changes were inversely associated with circulating connexin levels, and a positive correlation between connexin expression in peripheral blood mononuclear cells and atrial tissue was demonstrated. Moreover, interleukin‐6 significantly reduced connexin expression in HL‐1 cells. Conclusions: Our data suggest that regardless of specific etiology and organ localization, systemic inflammation, via interleukin‐6 elevation, rapidly induces atrial electrical remodeling by down‐regulating cardiac connexins. Although transient, these changes may significantly increase the risk for atrial fibrillation and related complications during active inflammatory processes.

Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo‐Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions.

High Grade Gliomas (HGG) have a poor outcome, however, prognostic sub-groups of patients may be individuated by some clinico-biological parameters. It was recently demonstrated that the main respon...

BECAUSE advanced cutaneous melanoma is still incurable, early detection, by means of accurate screening, is an important step toward a reduction in mortality.^1-6 In most cases, the screening tests are simple and only require a brief skin examination by a trained professional.⁷ Of the methods for evaluating pigmented skin lesions (PSL), the ABCD approach is one of the most widely used.^7,8 It is based on the study of color and a few coarse geometric variables. In some studies, it has been found to have high sensitivity (82%-96%) at the expense of specificity (28%-46%).⁹ However, for the many difficult or borderline lesions, diagnostic accuracy is only slightly above 60% even in specialized centers.^4,10