LECT2 Deletion Exacerbates Liver Steatosis and Macrophage Infiltration in a Male Mouse Model of LPS-mediated NASH
Bibliographic record
Abstract
Leukocyte cell-derived chemotaxin 2 (LECT2) is a protein initially isolated as a neutrophil chemotactic factor. We previously found that LECT2 is an obesity-associated hepatokine that senses liver fat and induces skeletal muscle insulin resistance. In addition, hepatocyte-derived LECT2 activates macrophage proinflammatory activity by reinforcing the lipopolysaccharide (LPS)-induced c-Jun N-terminal kinase signaling. Based on these findings, we examined the effect of LECT2 deletion on nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) caused by bacterial translocation. We created the bacterial translocation-mediated NAFLD/NASH model using LECT2 knockout mice (LECT2 KO) with 28 times a low-dose LPS injection under high-fat diet feeding conditions. LECT2 deletion exacerbated steatosis and significantly reduced p38 phosphorylation in the liver. In addition, LECT2 deletion increased macrophage infiltration with decreased M1/M2 ratios. LECT2 might contribute to protecting against lipid accumulation and macrophage activation in the liver under pathological conditions, which might be accomplished via p38 phosphorylation. This study provides novel aspects of LECT2 in the bacterial translocation-mediated NAFLD/NASH model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".