Emerging respiratory infections - Host-pathogen interactions of Streptococcus pyogenes and SARS-CoV-2
Bibliographic record
Abstract
This thesis investigates the interaction between pathogens and the human host to improve understanding of airway infections at individual level (disease severity) and population level (epidemics). The COVID-19 pandemic vividly demonstrated how rapid spread of a novel pathogen can disrupt social, economic and health systems. Measures implemented to control SARS-CoV-2 also impacted other infectious diseases. After lifting these measures, an increase in severe group A streptococcal infections was observed in 2022. Re-instalment of national Streptococcus pyogenes surveillance, which included genetic analysis of the bacterium, enabled the detection of emerging variants driving the surge in the Netherlands. Laboratory models that mimic human infection can be used to study the characteristics of novel variants. We demonstrate that airway epithelial cultures derived from stem cells (mini-airways) can be infected with S. pyogenes and used to study the host immune responses. The second part of this thesis focuses on the immune responses to SARS-CoV-2. Absence of a virus-specific T-cell response is predictive of severe disease, underscoring the crucial role of adaptive immunity in controlling SARS-CoV-2 infection. Adaptive immunity can also be induced by vaccination. Vaccine breakthrough infections emerged shortly after the introduction of COVID-19 vaccines. We show that these breakthrough infections could not be attributed to absence of an immune response after vaccination (vaccine failure) or viral mutations (vaccine escape). Our research contributes to better understanding of infectious diseases and development of public health measures, vaccines, and treatments to combat future epidemics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".