Aims The influence of the COVID-19 pandemic on attendance to out-of-hospital cardiac arrest (OHCA) has only been described in city or regional settings. The impact of COVID-19 across an entire country with a high infection rate is yet to be explored. Methods The study uses data from 8629 cases recorded in two time-series (2017/2018 and 2020) of the Spanish national registry. Data from a non-COVID-19 period and the COVID-19 period (February 1st–April 30th 2020) were compared. During the COVID-19 period, data a further analysis comparing non-pandemic and pandemic weeks (defined according to the WHO declaration on March 11th, 2020) was conducted. The chi-squared analysis examined differences in OHCA attendance and other patient and resuscitation characteristics. Multivariate logistic regression examined survival likelihood to hospital admission and discharge. The multilevel analysis examined the differential effects of regional COVID-19 incidence on these same outcomes. Results During the COVID-19 period, the incidence of resuscitation attempts declined and survival to hospital admission (OR = 1.72; 95%CI = 1.46–2.04; p < 0.001) and discharge (OR = 1.38; 95%CI = 1.07–1.78; p = 0.013) fell compared to the non-COVID period. This pattern was also observed when comparing non-pandemic weeks and pandemic weeks. COVID-19 incidence impinged significantly upon outcomes regardless of regional variation, with low, medium, and high incidence regions equally affected. Conclusions The pandemic, irrespective of its incidence, seems to have particularly impeded the pre-hospital phase of OHCA care. Present findings call for the need to adapt out-of-hospital care for periods of serious infection risk. Study registration number ISRCTN10437835.

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

No abstract

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>