We sought to assess the accuracy and safety of the ESAT6-CFP10 reagent in diagnosing tuberculosis (TB) disease. An open-label, randomized phase 2a trial was conducted in 56 healthy adults and 88 TB patients at one medical centre and one teaching hospital in China. All participants received 0.1, 0.5, 1 or 2 μg ESAT6-CFP10 in their right forearm. Moreover, 56 healthy volunteers and 56 patients were given tuberculin-purified protein derivative (TB-PPD) in their left forearm. The remaining 32 patients were administered placebo. The main outcome measure was induration diameter. An enzyme-linked immunospot (ELISPOT) assay was conducted before the skin test. The ESAT6-CFP10 test caused a higher positivity rate than placebo (81.2% (26/32) vs. 3.1% (1/32); p <0.001). The median maximum induration diameter after ESAT6-CFP10 injection was 17.0 (interquartile range (IQR), 14.0-21.7) mm, similar to that for TB-PPD (17.5 (IQR, 7.0-30.5) mm). The diagnostic accuracy of ESAT6-CFP10 was superior to that of TB-PPD (area under the receiver operating characteristic curve (AUC), 0.870 (95% confidence interval (CI), 0.796-0.944) vs. 0.686 (95% CI, 0.585-0.786); p <0.001). When analysed in all participants, ESAT6-CFP10 had comparable AUC values to the ELISPOT assay (0.849 (95% CI, 0.835-0.952) vs. 0.908 (95% CI, 0.852-0.965)). Local itching (12/144, 8.3%) and pain (26/144, 18.1%) were the main side effects of ESAT6-CFP10. No serious adverse events were reported. The ESAT6-CFP10 skin test appears to be a safe and promising tool; further testing will confirm its efficacy in identifying TB disease.

Background SARS-CoV-2 can spread both from symptomatic and asymptomatic individuals. Ocular manifestations due to SARS-CoV-2 have been described, being conjunctival inflammation the most common affectation. Evidence shows that conjunctivitis could be the first and/or only manifestation of COVID-19. This study aimed to develop and validate a COVID-19 screening method based on eyes photographs and artificial intelligence. Methods In this multicentre study, 1,200 participants were enrolled from Shanghai Public Health Clinical Center (SPHCC) Fudan University, AIMOMICS LAB and La Fe University and Polytechnic Hospital (LFUPH) of Valencia (Spain). Pictures of participants' ocular surface were taken in four different positions with mobile phone cameras, and a Deep Learning System (DLS) was developed through machine learning to identify characteristic conjunctival inflammation patterns. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committees of SPHCC and LFUPH. Results The area under the receiver-operating-characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated according to the results of our binary classification network. Bootstrapping with 1,000 replicates was used to estimate 95% confidence intervals of the performance metrics, with photography as the resampling unit. On the subject-level classification, the network achieved the AUC of 0.976 (95% CI 0.965-0.988) among Asian population and 0.892 (95% CI 0-763-1.000) among Caucasian population. Conclusions Preliminary results show that this DLS performed well in identifying probable asymptomatic COVID-19 cases through the analysis of participants' eyes pictures. This method could be an innocuous, accessible, low cost and quick COVID-19 screening method. Eventually, it could potentially contribute to pandemic control. Key messages In the context of the COVID-19 pandemic it would be useful to have a screening method to easily and quickly detect asymptomatic individuals, in addition to using temperature control. Preliminary results show that this Deep Learning System (DLS) based on eyes pictures taken with mobile phone cameras could be an innocuous, accessible, low cost and quick COVID-19 screening method.