miRNA Signatures in Sera of Patients with Active Pulmonary Tuberculosis.

Several studies showed that assessing levels of specific circulating microRNAs (miRNAs) is a non-invasive, rapid, and accurate method for diagnosing diseases or detecting alterations in physiological conditions. We aimed to identify a serum miRNA signature to be used for the diagnosis of tuberculosis (TB). To account for variations due to the genetic makeup, we enrolled adults from two study settings in Europe and Africa. The following categories of subjects were considered: healthy (H), active pulmonary TB (PTB), active pulmonary TB, HIV co-infected (PTB/HIV), latent TB infection (LTBI), other pulmonary infections (OPI), and active extra-pulmonary TB (EPTB). Sera from 10 subjects of the same category were pooled and, after total RNA extraction, screened for miRNA levels by TaqMan low-density arrays. After identification of "relevant miRNAs", we refined the serum miRNA signature discriminating between H and PTB on individual subjects. Signatures were analyzed for their diagnostic performances using a multivariate logistic model and a Relevance Vector Machine (RVM) model. A leave-one-out-cross-validation (LOOCV) approach was adopted for assessing how both models could perform in practice. The analysis on pooled specimens identified selected miRNAs as discriminatory for the categories analyzed. On individual serum samples, we showed that 15 miRNAs serve as signature for H and PTB categories with a diagnostic accuracy of 82% (CI 70.2-90.0), and 77% (CI 64.2-85.9) in a RVM and a logistic classification model, respectively. Considering the different ethnicity, by selecting the specific signature for the European group (10 miRNAs) the diagnostic accuracy increased up to 83% (CI 68.1-92.1), and 81% (65.0-90.3), respectively. The African-specific signature (12 miRNAs) increased the diagnostic accuracy up to 95% (CI 76.4-99.1), and 100% (83.9-100.0), respectively. Serum miRNA signatures represent an interesting source of biomarkers for TB disease with the potential to discriminate between PTB and LTBI, but also among the other categories

SUPPORTI PLURILINGUE DI NUOVA GENERAZIONE PER MIGLIORARE LA QUALITÀ DELLA COMUNICAZIONE MEDICO-PAZIENTE

Introduzione: L’emergenza nazionale immigrazione, causata dalle condizioni geopolitiche attuali, ha determinato un crescente numero di immigrati presenti nei centri di accoglienza temporanea del territorio italiano. In questo contesto un team multidisciplinare, che nasce dalla collaborazione tra la Struttura Complessa di Igiene dell’AOU di Sassari, il Servizio di Igiene e sanità pubblica della ASL-1 e l’Università di Sassari è impegnato in un progetto volto al miglioramento degli scambi comunicativi. Metodi: Nell’ambito degli interventi di screening per malattie infettive, i medici preposti alla raccolta del consenso per il test HIV nei centri di accoglienza della provincia di Sassari saranno dotati di supporti di mediazione linguisticoculturale a struttura dialogica e corredati da tavole illustrate con traduzione plurilingue. L’impatto di tale iniziativa sarà verificato con metodi empirici (in particolare attraverso l’osservazione attiva e la somministrazione di questionari ed interviste) tenendo conto di diverse variabili quali l’efficacia della comunicazione e il tempo impiegato per ottenere il consenso. Risultati: Partendo dalle pregresse esperienze nazionali ed internazionali, si intende verificare il potenziale dei supporti creati ai fini di migliorare la qualità della comunicazione medico-paziente. I dati raccolti permetteranno di svilupparne ulteriormente la qualità in termini di efficacia comunicativa. Conclusioni: Questo progetto mira ad essere il capofila di una serie di iniziative volte alla produzione di materiali validati di mediazione linguistico-culturale a disposizione degli operatori, con l’intento di realizzare un sistema di accoglienza, assistenza medica e sorveglianza sanitaria quanto più esteso ed accurato possibile rivolto ai parlanti non italofoni (migranti, turisti, studenti Erasmus incoming ecc.) Dichiarazione conflitto di interesse: nessuno

Criteria for the inclusion in the study population.

<p>Criteria for the inclusion in the study population.</p

Diagnostic performances of the serum miRNA signatures in the Relevance Vector Machine (RVM).

<p>The Table reports diagnostic performances already corrected for the leave-one-out cross validation (LOOCV).</p

Serum miRNAs showing different levels in healthy (H) and pulmonary active tuberculosis (PTB) subjects from the two populations included in the study.

*<p>miRNAs showing a p-adj <0.05.</p

Receiver Operating Characteristic (ROC) based on Akaike information criterion (AIC) logistic regression for the 15-miRNA signature.

<p>AUC: area under the curve. The AIC model identified as the best performing miRNA signature the following: miR-let-7e, miR-146a, miR-16, miR-25, miR-365, miR-451, miR-885-5p, miR-223*.</p

Receiver Operating Characteristic (ROC) based on Akaike information criterion (AIC) logistic regression for the 10-miRNA signature specific for <i>TBnew</i> population.

<p>AUC: area under the curve. The AIC model identified as the best performing miRNA signature the following: miR-let-7e, miR-192, miR-25, miR-451.</p

Receiver Operating Characteristic (ROC) based on Akaike information criterion (AIC) logistic regression for the 12-miRNA signature specific for <i>TB CHILD</i> population.

<p>AUC: area under the curve.</p

Comparison between the serum miRNA signature found in the present and the miRNAs reported in the previous studies.

<p>Comparison between the serum miRNA signature found in the present and the miRNAs reported in the previous studies.</p

Summary of candidate serum miRNAs selected as relevant to discriminate among the categories according to the analysis of pooled specimens.

<p>Population and category of pooled specimens are reported on the circumference. The thickness of the ribbons connecting two categories is proportional to the number of miRNAs potentially interesting in the discrimination between the categories linked.</p