LR12 Promotes Liver Repair by Improving the Resolution of Inflammation and Liver Regeneration in Mice with Thioacetamide‐ (TAA‐) Induced Acute Liver Failure
Bibliographic record
Abstract
Background. Triggering receptor expressed on myeloid cells‐1 (TREM‐1) controls the mobilization of inflammatory cells in response to injury and consequently enhances liver damage. LR12 is a TREM‐1 inhibitory peptide. However, the role of LR12 in acute liver failure (ALF) has remained elusive. This study was aimed at indicating whether LR12 could promote liver repair in mice with thioacetamide‐ (TAA‐) induced ALF. Methods. BALB/c mice were intraperitoneally injected with TAA, followed by intravenous injection of LR12. Damage and regeneration of the liver were assessed. LO2 cells and macrophages were used to assess the therapeutic effects of LR12. Results. Mice treated with TAA for 24 h developed ALF, while liver inflammation was alleviated after LR12 treatment. Moreover, LR12 promoted hepatocyte regeneration in mice with TAA‐induced ALF. In vitro, the supernatant from TAA+LR12‐treated macrophages promoted the proliferation of LO2 cells. Cytokine protein microarray analysis suggested that LR12 promoted the secretion of C‐C chemokine ligand 20 (CCL20) from macrophages. Besides, neutralization of CCL20 blocked the effects of LR12, thus inhibited the proliferation of LO2 cells in vitro, aggregated the liver inflammation, and restrained hepatocyte regeneration in ALF mice in vivo. Furthermore, we also found that LR12 activated the p38 mitogen‐activated protein kinase (MAPK) pathway in hepatocytes through promoting the secretion of CCL20 from macrophages. Conclusions. LR12 could improve the resolution of inflammation and liver regeneration in mice with TAA‐induced ALF by promoting the secretion of CCL20 from macrophages and activating the p38 MAPK pathway. Therefore, LR12 could be an attractive therapeutic target for the treatment of ALF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".