Face mask sampling (FMS) for tuberculosis shows lower diagnostic sensitivity than sputum sampling in Guinea.

BACKGROUND: Pulmonary tuberculosis (PTB) diagnosis relies on sputum examination, a challenge in sputum-scarce patients. Alternative non-invasive sampling methods such as face mask sampling (FMS) have been proposed. OBJECTIVE: To evaluate the value of FMS for PTB diagnosis by assessing its agreement with sputum samples processed by GeneXpert MTB/RIF (Ultra)(Xpert) testing, and describe FMS sensitivity and specificity. METHODS: This was a prospective study conducted at the Carrière TB clinic in Guinea. Presumptive TB patients willing to participate were asked to wear a surgical mask containing a polyvinyl alcohol (PVA) strip for thirty minutes. Subsequently, two spot sputum samples were collected, of which one was processed by microscopy on site and the other by Xpert in Guinea's National Reference Laboratory of Mycobacteriology (LNRM). The first 30 FMS were processed at the Supranational Reference Laboratory in Antwerp, Belgium, and the following 118 FMS in the LNRM. RESULTS: One hundred fifty patients participated, of whom 148 had valid results for both mask and sputum. Sputum smear microscopy was positive for 47 (31.8%) patients while sputum-Xpert detected MTB in 54 (36.5%) patients. Among the 54 patients testing sputum-Xpert positive, 26 (48.1%) yielded a positive FMS-Xpert result, while four sputum-Xpert negative patients tested positive for FMS and 90 patients were Xpert-negative for both sputum and mask samples, suggesting a moderate level of agreement (k-value of 0.47). The overall mask sensitivity was 48.1%, with 95.7% specificity. CONCLUSION: In our setting, Xpert testing on FMS did not yield a high level of agreement to sputum sample.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary. This allows for secure and controlled data exchange, while offering flexible datasharing options, considering trustworthiness, risk tolerance, and healthcare interoperability. Empirical studies and participatory research will be conducted to develop a trustworthy and effective data trust model.