TLR4: the fall guy in sepsis?
Bibliographic record
Abstract
Sepsis and its impact on human health can be traced back to 1000 BC and continues to be a major health burden today. It causes about 11 million deaths world-wide of which, more than a third are due to neonatal sepsis. There is no effective treatment other than fluid resuscitation therapy and antibiotic treatment that leave patients immunosuppressed and vulnerable to nosocomial infections. Added to that, ageing population and the emergence of antibiotic resistant bacteria pose new challenges. Most of the deleterious effects of sepsis are due to the host response to the systemic infection. In the initial phase of infection, hyper activation of the immune system leads to cytokine storm, which could lead to organ failure and this accounts for about 15% of overall deaths. However, the subsequent immune paralysis phase (mostly attributed to apoptotic death of immune cells) accounts for about 85% of all deaths. Past clinical trials (more than 100 in the last 30 years) all targeted the inflammatory phase with little success, predictably, for inflammation is a necessary process to fight infection. In order to identify the regulators of immune cell death during sepsis, we carried out an unbiased, whole genome CRISPR screening in mice and identified Trigger Receptor Expressed in Myeloid-like 4 (Treml4) as the receptor that controls both the inflammatory phase and the immune suppression phase in sepsis (Nedeva et al. (2020) Nature Immunol, doi: 10.1038/s41590-020-0789-z). Characterising the Treml4 gene knockout mice revealed new insights into the relative roles of TLR4 and TREML4 in inducing the inflammatory cytokine storm during sepsis.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".