Highlights This study with MALDI-TOF comprises, as far as we know, the first report describing the performance of this technology with COVID-19 diagnosis. This work would encourage researchers to explore the potential of MALDI-TOF MS to assess the feasibility of this technology, as a rapid and reproducible screening tool for diagnosis of SARS-CoV-2. According to our preliminary results, mass spectrometry-based methods combined with multivariate analysis showed potential as a complementary diagnostic tool.

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To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.

Coronavirus disease 2019 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid, sensitive and specific diagnosis of SARS-CoV-2 by fast and unambiguous testing is widely recognized to be critical in responding to the ongoing outbreak. Since the current testing capacity of RT-PCR-based methods is being challenged due to the extraordinary demand of supplies, such as RNA extraction kits and PCR reagents worldwide, alternative and/or complementary testing assays should be developed. Here, we exploit the potential of mass spectrometry technology combined with machine learning algorithms as an alternative fast tool for SARS-CoV-2 detection from nasopharyngeal swabs samples. According to our preliminary results, mass spectrometry-based methods combined with multivariate analysis showed an interesting potential as a complementary diagnostic tool and further steps should be focused on sample preparation protocols and the improvement of the technology applied.was not certified by peer review)