Epigenetics As The Driving Force In Long-Term Immunosuppression
Bibliographic record
Abstract
Epigenetics is an emerging frontier of biology, with the potential for deciphering the intricate molecular and transcriptional cellular programs, therefore contributing to explain the pathological evolution of sepsis, one of the most elusive syndromes in medicine. The evolution of sepsis depends not only on the pathogen which originated the infection but also on the genetic and epigenetic background of the host. Short-term mortality of sepsis and septic shock is high, being considered a public health concern worldwide. Immunosuppression is the predominant driving force for morbidity and mortality in late deaths and long-term deaths of survivors from a sepsis episode. In this regard, apoptosis of immune cells and complex epigenetic reprogramming in immune and progenitor cells may contribute to the immunoparalysis observed in post-septic patients, who are prone to the apparition of new, opportunistic infections. Here, we review the literature and expose the most relevant results which explain the epigenetic programs contributing to the progression of sepsis. Furthermore, we revisit the role of circulating histones in the pathogenesis of sepsis and septic shock and finally we discuss about the therapeutic potential of epigenetic drugs in the treatment of 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".