HMGB1 is a Central Driver of Dynamic Pro-inflammatory Networks in Pediatric Acute Liver Failure induced by Acetaminophen
Bibliographic record
Abstract
Acetaminophen (APAP) overdose (APAPo) is predominant in the NIH Pediatric Acute Liver Failure (PALF) Study. We assayed multiple inflammatory mediators in serial serum samples from 13 PALF survivors with APAPo + N-acetylcysteine (NAC, the frontline therapy for APAPo), 8 non-APAPo + NAC, 40 non-APAPo non-NAC, and 12 non-survivors. High Mobility Group Box 1 (HMGB1) was a dominant mediator in dynamic inflammation networks in all sub-groups, associated with a threshold network complexity event at d1-2 following enrollment that was exceeded in non-survivors vs. survivors. We thus hypothesized that differential HMGB1 network connectivity after day 2 is related to the putative threshold event in non-survivors. DyNA showed that HMGB1 is most connected in non-survivors on day 2-3, while no connections were observed in APAPo + NAC and non-APAPo + NAC survivors. Inflammatory dynamic networks, and in particular HMGB1 connectivity, were associated with the use of NAC in the context of APAPo. To recapitulate hepatocyte (HC) damage in vitro, primary C57BL/6 HC and HC-specific HMGB1-null HC were treated with APAP + NAC. Network phenotypes of survivors were recapitulated in C57BL/6 mouse HC and were greatly altered in HMGB1-null HC. HC HMGB1 may thus coordinate a pro-inflammatory program in PALF non-survivors (which is antagonized by NAC), while driving an anti-inflammatory/repair program in survivors.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".