Optimization of Lipid Nanoparticles for saRNA Expression and Cellular Activation Using a Design-of-Experiment Approach.

Lipid nanoparticles (LNPs) are the leading technology for RNA delivery, given the success of the Pfizer/BioNTech and Moderna COVID-19 mRNA (mRNA) vaccines, and small interfering RNA (siRNA) therapies (patisiran). However, optimization of LNP process parameters and compositions for larger RNA payloads such as self-amplifying RNA (saRNA), which can have complex secondary structures, have not been carried out. Furthermore, the interactions between process parameters, critical quality attributes (CQAs), and function, such as protein expression and cellular activation, are not well understood. Here, we used two iterations of design of experiments (DoE) (definitive screening design and Box-Behnken design) to optimize saRNA formulations using the leading, FDA-approved ionizable lipids (MC3, ALC-0315, and SM-102). We observed that PEG is required to preserve the CQAs and that saRNA is more challenging to encapsulate and preserve than mRNA. We identified three formulations to minimize cellular activation, maximize cellular activation, or meet a CQA profile while maximizing protein expression. The significant parameters and design of the response surface modeling and multiple response optimization may be useful for designing formulations for a range of applications, such as vaccines or protein replacement therapies, for larger RNA cargoes.

Efficiency of digital tuition in University education

The past, nearly one and a half year has brought a significant change to the daily lives of humanity. Due to the coronavirus epidemic, many areas of our lives have been overwritten with radical changes that previously seemed unthinkable. In March 2020, traditional tuition had to move and evolve into online education practically overnight. This situation posed a challenge to everyone, teachers, students and university executives alike. The aim of the authors was to explore and formulate the lessons learned of this recent period, so that the benefits and drawbacks of online education can be utilized and used to its highest potential in the upcoming years. Exploration of the topic was carried out with the help of qualitative and quantitative methodology within the framework of the primary research. The quantitative research was executed with the help of a standardized survey. The opinions and experiences of university students were revealed using snowball method on the basis of a non-representative sample. The three main questions of the dissertation are whether it is more difficult to motivate students to learn in the form of digital education, how the efficiency of learning has changed over the last year in the digital form, and finally, did teaching and learning become more difficult overall for both teachers and students? The results were presented using statistical indicators and conclusions were drawn based on the results. The research ended with a proposal for future semesters and a summary.

Pre-Existing and Pragmatically-Derived Immunohematologic Phenotypes Predict All-Cause and COVID-19-Specific Mortality: A 'Population Immunology' Approach (preprint)

Background: Dysregulation of immunohematologic function (IHF) promotes cardiovascular disease and impairs protective responses to cancer and infection. A pragmatic method to identify those as risk due to IHF could improve the precision of preventive interventions and provide insight into the heterogeneity of immunologic capacity. We developed and validated a method to distill complete blood cell count data into distinct IHF profiles of prognostic relevance. Methods: We adapted latent profile analysis methods to simultaneously identify distinct groups of patients with respect to 10 immunohematologic indicators and regress time to all-cause mortality on this latent IHF profile. The model was developed using data from 30274 National Health and Nutrition Examination Survey participants and externally validated in 49851 outpatients in the Veterans Heath Administration (VHA) system and 44142 SARS-CoV-2 positive VHA patients.Findings: Ten distinct IHF profiles were identified. Profile 1, with relative mild pan-leukopenia in absence of red cell abnormalities, was associated with the best long term survival in each setting. Profiles 8-10, featuring anemia/anisocytosis especially in the setting of lymphopenia (Profiles 9-10) were associated with adjusted hazard ratio (HR) estimates of 1.76-2.62 for mortality across the three cohorts, compared to Profile 1. Profiles 6-7, featuring relative neutrophilia, were less common but also independently associated with mortality risk, especially after COVID-19 infection (Profile 7 HR [95% CI]: 2.51 [1.63 – 3.86]). The magnitude of adjusted risk conveyed by IHF profiles was greater than individual clinical risk factors (i.e., smoking, diabetes) or prevalent co-morbidities.Interpretation: Distinct immunohematologic endotypes can be identified during routine blood panels which project to mortality risk on par with a decade of life, additive to demographic and clinical factors. Applications that consider immunohematologic dysfunction may improve prevention of common fatal diseases, including COVID-19.Funding Information:This study was funded in part by The National Institute on Aging (R01AG055480; Dalton and Perzynski), the National Cancer Institute (U01CA260513; Zidar and Chan), and the United States Veteran Administration (COVID19-8900-05; Zidar). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veteran Affairs or the National Institutes of Health.Declaration of Interests: No conflict of interest exists between any of the authors and the contents of this paper.Ethics Approval Statement: The study was approved by the Institutional Review Board of the Louis Stokes Cleveland VAMC.

The Effect of Domestic Air Travel on the Spread of COVID-19 in the U.S. (preprint)

We examine the relationship between domestic air travel and the spread of COVID-19 during the first wave of the pandemic in the U.S. To do this, we link airline passenger flows with COVID-19 infection and mortality data, controlling for county-level population and demographic data. We examine infection and mortality rates in counties receiving more versus fewer airline passengers from four early COVID-19 hotspots: New York City, Boston, Detroit, and New Orleans. We then compare the effect of airline passengers from these four hotspots with the effect of airline passengers from a set of eight cities who did not experience early outbreaks. We find that passengers arriving from COVID-19 hotspots are not positively correlated with local COVID-19 infection and death rates. Passengers arriving from our eight comparison group cities are positively, but very weakly, correlated with local infection and mortality rates. Our results imply that banning domestic air travel may do little to slow the spread of infections.

The Effect of International Travel on the Spread of COVID-19 in the U.S. (preprint)

We examine the relationship between incoming international passengers and COVID-19 case or death counts during the first wave of the pandemic in the U.S. We find passengers from Italy, but not China, were an important source of exposure, and thus increased the early spread of COVID-19 in the U.S. These results suggest stopping travel from Europe earlier likely would have had greater impact on reducing the spread of the virus in the US, compared to the earlier ban on travel from China. Further, banning travel from pandemic hotspots may be smart policy, depending on the specific characteristics of the virus.

Rapid detection of SARS-CoV2 by Ambient Mass Spectrometry Techniques (preprint)

Ambient Ionisation Mass Spectrometry techniques: Desorption Electrospray Ionisation (DESI) and Laser Desorption Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) were used to detect the SARS-CoV-2 in dry nasal swabs. 45 patients were studied from samples collected between April & June 2020 in a clinical feasibility study. Diagnostic accuracy was calculated as 86.7% and 84% for DESI and LD-REIMS respectively. Results can be acquired in seconds providing robust and quick analysis of COVID-19 status which could be carried out without the need for a centralised laboratory. This technology has the potential to provide an alternative to population testing and enable the track and trace objectives set by governments and curtail the effects of a second surge in COVID-19 positive cases. In contrast to current PCR testing, using this technique there is no requirement of specific reagents which can cause devastating delays upon breakdowns of supply chains, thus providing a promising alternative testing method.