Immunothrombosis in Severe COVID-19 and Bacterial Sepsis, and Barriers to Biosampling-Based Translational Research
Bibliographic record
Abstract
Infection, immunity, and blood coagulation are interconnected through a process known as immunothrombosis. In cases of serious illness, such as severe COVID-19 or bacterial sepsis (life-threatening organ dysfunction caused by dysregulated host response to infection), immunothrombosis can worsen disease severity and negatively impact patient outcomes. COVID-19 exposed millions to a life-threatening illness that is linked to immunothrombosis. Despite shared features, the similarities and differences between COVID-19 and sepsis are poorly understood. In this thesis, we demonstrate pathophysiological differences in immunothrombosis between critically-ill COVID-19 patients and non-COVID septic patients with pneumonia. We also demonstrate that immunothrombosis severity was reduced in patients recruited later in the pandemic. Neutrophil extracellular traps (NETs) are prominent drivers of immunothrombosis that are elevated in sepsis. NETs are structures composed of chromatin and antimicrobial molecules released by neutrophils in response to infection. While studies have identified NETs as targets for improving septic patient outcomes, results have been conflicting and have reported that NETs limit the spread of infection while also worsening survival. We demonstrate that PAD4-deficient mice show worsened survival in a fecal-induced peritonitis model of abdominal sepsis, suggesting that NETs have a protective effect. Septic PAD4-/- mice had higher bacterial loads, lower IL-6 levels, elevated lung myeloperoxidase levels, and exacerbated lung/liver injury compared with septic wild-type mice. The investigations conducted in this thesis involved a study design that emphasized the translation of studies in the laboratory to their practical application in the clinic. Translational research is growing in Canada, but a lack of infrastructure may hinder the ability of clinical research units to effectively conduct studies. We conducted a survey to identify barriers to biosampling-based translational research in the critical care setting in Canada. These studies contribute to understanding the relationship between immunothrombosis and disease, and highlight the role of translational research in investigating them.
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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 | 0.117 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".