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Record W7132947221

Antibodies in Infectious Diseases: HIV Biomarker Discoveries, Complement Enhanced SARS-CoV-2 Neutralization and Vaccine Generation

2024· dissertation· W7132947221 on OpenAlexaboutno aff
Patrick Budylowski

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsNeutralizationAntibodyImmune systemHuman immunodeficiency virus (HIV)Viral entryTiterInfectious disease (medical specialty)HIV vaccine
DOInot available

Abstract

fetched live from OpenAlex

The main barrier towards an HIV cure is the presence of latently HIV-infected cells that make up the viral reservoir which persists through antiretroviral therapy. Although there is some leaky protein transcription in these latent cells, they evade immune surveillance and through homeostatic proliferation, maintain the viral reservoir. To eradicate the viral reservoir, one must be able to target it through the use of a biomarker. In the first part of this thesis, sea lamprey were immunized with latently HIV-infected T cells to generate a library of lamprey antibodies. 1273 antibodies were isolated and screened against HIV infected and non-infected primary cells. 25 antibodies were found to be specific to HIV infected cells which are currently being classified further.Due to the COVID pandemic, HIV research was placed on ‘hold’ in order to develop SARS-CoV-2 neutralization assays and COVID vaccine development efforts. In chapter 3 we report the SARS-CoV-2 neutralization titers of human convalescent sera were consistent across all disease states except for severe COVID-19, which yielded significantly higher neutralization titers. Heat inactivation of human convalescent serum was shown to inactivate complement proteins, and the contribution of the complement system in SARS-CoV-2 neutralization was often over 50% and mainly driven through the classical pathway. In some cases, heat inactivation completely abolished neutralization levels to undetectable levels. This effect was also observed in COVID-19 vaccinees and could be abolished in individuals who were being treated with anti-complement antibodies for other diseases. Safe and effective vaccines are needed to end the COVID-19 pandemic. In the final chapter we report the preclinical development of a lipid nanoparticle formulated SARS-CoV-2 mRNA vaccine. Tests in mice and hamsters indicated that PTX-COVID19-B induced robust humoral and cellular immune responses and completely protected the vaccinated animals from SARS-CoV-2 infection in the lung. Mouse immune sera elicited by PTX-COVID19-B vaccination neutralized SARS-CoV-2 variants of concern, including the Alpha, Beta, Gamma and Delta lineages. No adverse effects were induced by PTX-COVID19-B in either mice or hamsters. Based on these results, PTX-COVID19-B was authorized by Health Canada and had passed a phase 2 clinical trial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.405
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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