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Treated human immunodeficiency virus (HIV) infection is characterized by ectopic fat deposition, a persistent inflammatory state, and increased cardiometabolic risk. In this secondary analysis of a placebo controlled trial of rosuvastatin among 147 HIV+ subjects (median age 46; 78% men) on stable antiretroviral therapy, we aimed to evaluate longitudinal associations between computed tomography (CT) measures of pericardial fat (PCF) volume and density, insulin resistance, and inflammation. We measured PCF volume and density (mean attenuation in Hounsfield units) by noncontrast gated CT at baseline and week 96. Homeostatic model of insulin resistance was calculated from fasting insulin and glucose at entry, 24, 48, and 96 weeks. At baseline, insulin resistance correlated positively with PCF volume and negatively with density. Similarly divergent correlations of volume and density were observed with waist:hip ratio, nadir CD4+ count, and duration of antiretroviral therapy. In a linear mixed model, PCF density was associated with insulin resistance independent of PCF volume, body mass index, metabolic syndrome, and biomarkers of immune activation and systemic inflammation; however, baseline PCF measures were not associated with longitudinal changes in insulin resistance. Soluble CD163, a marker of monocyte activation, positively correlated with PCF volume and was associated with insulin resistance in linear models. Statin treatment assignment did not affect PCF volume or density change (both p > 0.8). In conclusion, the quantity and quality (i.e., radiodensity) of PCF are differentially related to insulin resistance and inflammation in patients with treated HIV infection., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Context Accurately distinguishing between benign thyroid nodules (BTNs) and papillary thyroid cancers (PTCs) with current conventional methods poses a significant challenge. Objective We identify DNA methylation markers of immune response–related genes for distinguishing BTNs and PTCs. Methods In this study, we analyzed a public reduced representative bisulfite sequencing dataset and revealed distinct methylation patterns associated with immune signals in PTCs and BTNs. Based on these findings, we developed a diagnostic classifier named the Methylation-based Immune Response Signature (MeIS), which was composed of 15 DNA methylation markers associated with immune response–related genes. We validated MeIS's performance in 2 independent cohorts: Z.S.'s retrospective cohort (50 PTC and 18 BTN surgery-leftover samples) and Z.S.'s preoperative cohort (31 PTC and 30 BTN fine-needle aspiration samples). Results The MeIS classifier demonstrated significant clinical promise, achieving areas under the curve of 0.96, 0.98, 0.89, and 0.90 in the training set, validation set, Z.S.'s retrospective cohort, and Z.S.'s preoperative cohort, respectively. For the cytologically indeterminate thyroid nodules, in Z.S.'s retrospective cohort, MeIS exhibited a sensitivity of 91% and a specificity of 82%; in Z.S.'s preoperative cohort, MeIS achieved a sensitivity of 84% and a specificity of 74%. Additionally, combining MeIS and BRAF V600E detection improved the detecting performance of cytologically indeterminate thyroid nodules, yielding sensitivities of 98% and 87%, and specificities of 82% and 74% in Z.S.'s retrospective cohort and Z.S.'s preoperative cohort, respectively. Conclusion The 15 markers we identified can be employed to improve the diagnostic of cytologically indeterminate thyroid nodules. [ABSTRACT FROM AUTHOR]

Aim: To explore the application of the combined use of baseline salivary biomarkers and clinical parameters in predicting the outcome of scaling and root planing (SRP). Materials and methods: Forty patients with advanced periodontitis were included. Baseline saliva samples were analysed for interleukin‐1β (IL‐1β), matrix metalloproteinase‐8 and the loads of Porphyromonas gingivalis, Prevotella intermedia, Aggregatibacter actinomycetemcomitans and Tannerella forsythia. After SRP, pocket closure and further attachment loss at 6 months post‐treatment were chosen as outcome variables. Models to predict the outcomes were established by generalized estimating equations. Results: The combined use of baseline clinical attachment level (CAL), site location and IL‐1β (area under the curve [AUC] = 0.764) better predicted pocket closure than probing depth (AUC = 0.672), CAL (AUC = 0.679), site location (AUC = 0.654) or IL‐1β (AUC = 0.579) alone. The combination of site location, tooth loss, percentage of deep pockets, detection of A. actinomycetemcomitans and T. forsythia load (AUC = 0.842) better predicted further clinical attachment loss than site location (AUC = 0.715), tooth loss (AUC = 0.530), percentage of deep pockets (AUC = 0.659) or T. forsythia load (AUC = 0.647) alone. Conclusion: The combination of baseline salivary biomarkers and clinical parameters better predicted SRP outcomes than each alone. The current study indicates the possible usefulness of salivary biomarkers in addition to tooth‐related parameters in predicting SRP outcomes. [ABSTRACT FROM AUTHOR]