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Record W7115811005

BROADLY PROTECTIVE APPROACHES FOR PANDEMIC PREVENTION

2024· dissertation· en· W7115811005 on OpenAlexaffabout

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPandemicDiseasePopulationOseltamivirVaccinationNeuraminidaseEffectorInfectious disease (medical specialty)Neuraminidase inhibitor
DOInot available

Abstract

fetched live from OpenAlex

Pandemics are periodic events characterized by rapid and widespread transmission of infectious disease affecting a significant proportion of the population over a large geographical area. Zoonotic strains of influenza viruses and coronaviruses have both caused pandemics in the recent past. Although vaccination is the often the most effective way to prevent infection or serious outcomes of infection, vaccine development, production, distribution, and deployment are all time- consuming and logistically challenges processes. Alternative readily deployed approaches must be quickly executed to mitigate the toll of future pandemics, especially during the early phases. The work described in this thesis describes some of these approaches. Firstly, I describe the process by which I performed genome-wide CRISPR-Cas9 knockout screens using SARS-CoV-2 variants of concern to discover crucial host factors as targets for broad-acting antivirals. I found that all variants rely on similar host pathways to replicate in the glial cell line used for the screen. I identified BCL-xL, a regulator of apoptosis, as a potential target for a broad- acting antiviral. I show that chemical inhibition of BCL-xL results in accelerated cell death in infected cells in vitro, but improved clinical signs and disease mortality of SARS-CoV-2 in our murine infection model. Secondly, I describe a unique mechanism for cooperative antiviral combination therapy. I demonstrate that chemical inhibition of neuraminidase by oseltamivir improved immune effector cell activation by hemagglutinin stalk-binding antibodies. Combination therapy of oseltamivir and stalk-binding antibodies also improved clinical signs and disease mortality of influenza in our murine infection model compared to monotherapy in both prophylactic and therapeutic contexts. Finally, I show that non-pharmaceutical public health interventions used to restrict the spread of COVID-19 were also effective against several other infectious diseases. I used an interrupted time- series analysis on Ontario public health administrative data during the early COVID-19 pandemic period and found a drastic and sustained decline in outpatient visits for diseases that are typically caused by viruses that transmit by droplet or aerosol. The three projects described in this thesis outlines broadly-protective and distinct strategies to curb the spread of novel respiratory viruses. These new tools may be leveraged to improve the response and to mitigate the burden of future pandemics.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.003

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 head0.061
GPT teacher head0.306
Teacher spread0.245 · 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 designTheoretical or conceptual
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 routes2
Has abstractyes

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