Establishing the role of the FES tyrosine kinase in the pathogenesis, pathophysiology, and severity of sepsis and its outcomes
Bibliographic record
Abstract
Introduction Sepsis is a result of initial over-activation of the immune system in response to an infection or trauma that results in reduced blood flow and life-threatening end-organ damage, followed by suppression of the immune system that prevents proper clearance of the infection or trauma. Because of this, therapies that not only limit the activation of the immune system early on, but also improve blood flow to crucial organs and reactivate the immune system in late-stage sepsis, may be effective treatments. The tyrosine kinase FES may fulfill this role. FES is present in immune cells and serves to limit immune system activation. We hypothesize that by enhancing FES in early sepsis and inhibiting its effects in late sepsis, the severity and outcome of septic illness can be improved. Methods and analysis In vitro and in vivo modeling will be performed to determine the degree of inflammatory signaling, cytokine production, and neutrophil extracellular trap (NET) formation that occurs in wild-type (WT) and FES knockout (FES-/-) mice. Clinically available treatments known to enhance or inhibit FES expression (lorlatinib and decitabine, respectively), will be used to explore the impact of early vs. late FES modulation on outcomes in WT mice. Bioinformatic analysis will be performed to examine FES expression levels in RNA transcriptomic data from sepsis patient cohorts, and correlate FES expression data with clinical outcomes (diagnosis of sepsis, illness severity, hospital length-of-stay). Ethics and dissemination Ethics approval pending from the Queen’s University Health Sciences & Affiliated Teaching Hospitals Research Ethics Board. Results will be disseminated through scientific publications and through lay summaries to patients and families.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".