The effect of dose-interval on antibody response to mRNA COVID-19 vaccines: a prospective cohort study.

Background: Vaccination against COVID-19 is highly effective in preventing severe disease and hospitalization, but primary COVID mRNA vaccination schedules often differed from those recommended by the manufacturers due to supply chain issues. We investigated the impact of delaying the second dose on antibody responses to COVID mRNA-vaccines in a prospective cohort of health-care workers in Quebec. Methods: We recruited participants from the McGill University Health Centre who provided serum or participant-collected dried blood samples (DBS) at 28-days, 3 months, and 6 months post-second dose and at 28-days after a third dose. IgG antibodies to SARS-CoV2 spike (S), the receptor-binding domain (RBD), nucleocapsid (N) and neutralizing antibodies to the ancestral strain were assessed by enzyme-linked immunosorbent assay (ELISA). We examined associations between long (≤89 days) versus short (<89 days) between-dose intervals and antibody response through multivariable mixed-effects models adjusted for age, sex, prior covid infection status, time since vaccine dose, and assay batch. Findings: The cohort included 328 participants who received up to three vaccine doses (>80% Pfizer-BioNTech). Weighted averages of the serum (n=744) and DBS (n=216) cohort results from the multivariable models showed that IgG anti-S was 31% higher (95% CI: 12% to 53%) and IgG anti-RBD was 37% higher (95% CI: 14% to 65%) in the long vs. short interval participants, across all time points. Interpretation: Our study indicates that extending the covid primary series between-dose interval beyond 89 days (approximately 3 months) provides stronger antibody responses than intervals less than 89 days. Our demonstration of a more robust antibody response with a longer between dose interval is reassuring as logistical and supply challenges are navigated in low-resource settings.

Cyberinfrastructure for Open Science at the Montreal Neurological Institute.

Data sharing is becoming more of a requirement as technologies mature and as global research and communications diversify. As a result, researchers are looking for practical solutions, not only to enhance scientific collaborations, but also to acquire larger amounts of data, and to access specialized datasets. In many cases, the realities of data acquisition present a significant burden, therefore gaining access to public datasets allows for more robust analyses and broadly enriched data exploration. To answer this demand, the Montreal Neurological Institute has announced its commitment to Open Science, harnessing the power of making both clinical and research data available to the world (Owens, 2016a,b). As such, the LORIS and CBRAIN (Das et al., 2016) platforms have been tasked with the technical challenges specific to the institutional-level implementation of open data sharing, including: Comprehensive linking of multimodal data (phenotypic, clinical, neuroimaging, biobanking, and genomics, etc.)Secure database encryption, specifically designed for institutional and multi-project data sharing, ensuring subject confidentiality (using multi-tiered identifiers).Querying capabilities with multiple levels of single study and institutional permissions, allowing public data sharing for all consented and de-identified subject data.Configurable pipelines and flags to facilitate acquisition and analysis, as well as access to High Performance Computing clusters for rapid data processing and sharing of software tools.Robust Workflows and Quality Control mechanisms ensuring transparency and consistency in best practices.Long term storage (and web access) of data, reducing loss of institutional data assets.Enhanced web-based visualization of imaging, genomic, and phenotypic data, allowing for real-time viewing and manipulation of data from anywhere in the world.Numerous modules for data filtering, summary statistics, and personalized and configurable dashboards. Implementing the vision of Open Science at the Montreal Neurological Institute will be a concerted undertaking that seeks to facilitate data sharing for the global research community. Our goal is to utilize the years of experience in multi-site collaborative research infrastructure to implement the technical requirements to achieve this level of public data sharing in a practical yet robust manner, in support of accelerating scientific discovery.

Differential ability of Ptf1a and Ptf1a-VP16 to convert stomach, duodenum and liver to pancreas.

Determining the functional attributes of pancreatic transcription factors is essential to understand how the pancreas is specified distinct from other endodermal organs, such as liver, stomach and duodenum, and to direct the differentiation of other cell types into pancreas. Previously, we demonstrated that Pdx1-VP16 was sufficient to convert liver to pancreas. In this paper, we characterize the functional ability of another pancreatic transcription factor, Ptf1a, in promoting ectopic pancreatic fates at early stages throughout the endoderm and later during organogenesis. Using the transthyretin promoter to drive expression in the early liver region/bud of transgenic Xenopus tadpoles, we find that Ptf1a-VP16 is able to convert liver to pancreas. Overexpression of the unmodified Ptf1a on the other hand has no effect in liver but is able to convert stomach and duodenum to pancreas. When overexpressed at earlier embryonic stages throughout the endoderm, Ptf1a activity is similarly limited, whereas Ptf1a-VP16 has increased activity. Interestingly, in all instances we find that Ptf1a-VP16 is only capable of promoting acinar cell fates, whereas Ptf1a promotes both acinar and endocrine fates. Lastly, we demonstrate that, similar to mouse and zebrafish, Xenopus Ptf1a is essential for the initial specification of both endocrine and exocrine cells during normal pancreas development.