Nebulized recombinant tissue plasminogen activator (rt-PA) for acute COVID-19-induced severe respiratory failure: an exploratory proof of concept trial (preprint)

Nebulized thrombolysis offers locally targeted therapy with potentially lower bleeding risk than systemic administration for coronavirus disease 2019 (COVID-19) respiratory failure. In a proof-of-concept safety study, adult patients with COVID-19-induced respiratory failure and a <300mmHg PaO2/FiO2 (P/F) ratio, requiring invasive mechanical ventilation (IMV) or non-invasive respiratory support (NIRS) received nebulized rt-PA in two cohorts (C1 and C2), alongside standard of care during the first two UK COVID-19 waves. Matched historical controls (MHC; n=18) were used in C1. Safety co-primary endpoints were treatment-related bleeds and fibrinogen reduction to <1.0–1.5 g/L. A dose escalation strategy for improved efficacy with the least safety concerns was determined in C1 for use in C2; patients were stratified by ventilation type to receive 40–60 mg rt-PA per day for ≤14 days. Nine patients in C1 (IMV, 6/9; NIRS, 3/9) and 26 in C2 (IMV, 12/26; NIRS, 14/26) received nebulized rt-PA for a mean (SD) of 6.7 (4.6) and 9.1(4.6) days, respectively. Four bleeding events (one severe and three mild) in three patients were considered treatment-related. No significant fibrinogen reductions were reported. Greater improvement in mean P/F ratio from baseline to end of study was observed in C1 compared with MHC [C1; 154 to 299 vs MHC; 154 to 212). In C2, there was no difference in the baseline P/F ratio of NIRS and IMV patients. However, a larger improvement in P/F ratio was observed in NIRS patients [NIRS; 126 to 240 vs IMV; 120 to 188) and they required fewer treatment days (NIRS; 7.86 vs IMV; 10.5). Nebulized rt-PA appears to be well-tolerated, showing a trend of improved oxygenation and faster recovery in patients with acute COVID-19-induced respiratory failure requiring respiratory support; this effect was more pronounced in the NIRS group. Further investigation is required to study the potential of this novel treatment approach.

On Modeling of COVID-19 for the Indian Subcontinent using Polynomial and Supervised Learning Regression (preprint)

COVID-19, a recently declared pandemic by WHO has taken the world by storm causing catastrophic damage to human life. The novel cornonavirus disease was first incepted in the Wuhan city of China on 31st December 2019. The symptoms include fever, cough, fatigue, shortness of breath or breathing difficulties, and loss of smell and taste. Since the devastating phenomenon is essentially a time-series representation, accurate modeling may benefit in identifying the root cause and accelerate the diagnosis. In the current analysis, COVID-19 modeling is done for the Indian subcontinent based on the data collected for the total cases confirmed, daily recovered, daily deaths, total recovered and total deaths. The data is treated with total confirmed cases as the target variable and rest as feature variables. It is observed that Support vector regressions yields accurate results followed by Polynomial regression. Random forest regression results in overfitting followed by poor Bayesian regression due to highly correlated feature variables. Further, in order to examine the effect of neighbouring countries, Pearson correlation matrix is computed to identify geographic cause and effect.