ABSTRACT
The SARS-Cov-2 pandemic has forced all countries worldwide to rapidly develop and implement widespread testing to control and manage the Coronavirus Disease 2019 (COVID-19). RT-qPCR is the gold standard molecular diagnostic method for COVID-19, mostly in automated testing platforms. These systems are accurate and effective, but also costly, time-consuming, high technological, infrastructure dependent and currently suffer from commercial reagent supply shortages. The reverse-transcription loop-mediated isothermal amplification (RT-LAMP) can be used as alternative testing method. Here, we present a novel versatile (real-time and colorimetric) RT-LAMP for the simple (one-step) and rapid (as soon as 9 min) detection of SARS-CoV-2 and demonstrate the assay on RT-qPCR-positive clinical samples. We further transformed the RT-LAMP into a dry format for room-temperature storage suitable for potentially ready-to-use COVID-19 diagnosis. After further testing and validation, the Dry-RT-LAMP could be easily applied both in developed and in low-income countries yielding rapid and reliable results.
Subject(s)
COVID-19ABSTRACT
Background: Passive immunotherapy with convalescent plasma (CP) is a potential treatment for COVID-19 for which evidence from controlled clinical trials is lacking. Methods: We conducted a multi-center, randomized clinical trial in patients hospitalized for COVID-19. All patients received standard of care treatment, including off-label use of marketed medicines, and were randomized 1:1 to receive one dose (250-300 mL) of CP from donors with IgG anti-SARS-CoV-2. The primary endpoint was the proportion of patients in categories 5, 6 or 7 of the COVID-19 ordinal scale at day 15. Results: The trial was stopped after first interim analysis due to the fall in recruitment related to pandemic control. With 81 patients randomized, there were no patients progressing to mechanical ventilation or death among the 38 patients assigned to receive plasma (0%) versus 6 out of 43 patients (14%) progressing in control arm. Mortality rates were 0% vs 9.3% at days 15 and 29 for the active and control groups, respectively (log-rank p=0.056). No significant differences were found in secondary endpoints. At inclusion, patients had a median time of 8 days (IQR, 6-9) of symptoms and 49,4% of them were positive for anti-SARS-CoV-2 IgG antibodies. Conclusions: Convalescent plasma could be superior to standard of care in avoiding progression to mechanical ventilation or death in hospitalized patients with COVID-19. The strong dependence of results on a limited number of events in the control group prevents drawing firm conclusions about CP efficacy from this trial. (Funded by Instituto de Salud Carlos III; NCT04345523).
Subject(s)
COVID-19 , DeathABSTRACT
SARS-CoV-2 enters cells via ACE-2, which binds the spike protein with moderate affinity. Despite a constant background mutational rate, the virus must retain binding with ACE2 for infectivity, providing a conserved constraint for SARS-CoV-2 inhibitors. To prevent mutational escape of SARS-CoV-2 and to prepare for future related coronavirus outbreaks, we engineered a de novo trimeric ACE2 (T-ACE2) protein scaffold that binds the trimeric spike protein with extremely high affinity (KD < 1 pM), while retaining ACE2 native sequence. T-ACE2 potently inhibits all tested pseudotyped viruses including SARS-CoV-2, SARS-CoV, eight naturally occurring SARS-CoV-2 mutants, two SARSr-CoVs as well as authentic SARS-CoV-2. The cryo-EM structure reveals that T-ACE2 can induce the transit of spike protein to "three-up" RBD conformation upon binding. T-ACE2 thus represents a promising class of broadly neutralizing proteins against SARS-CoVs and mutants.
Subject(s)
Severe Acute Respiratory SyndromeABSTRACT
BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.