Alcohol Impairs the Myeloid Proliferative Response to Bacteremia in Mice by Inhibiting the Stem Cell Antigen-1/ERK Pathway
Bibliographic record
Abstract
Enhancement of stem cell Ag-1 (Sca-1) expression by myeloid precursors promotes the granulopoietic response to bacterial infection. However, the underlying mechanisms remain unclear. ERK pathway activation strongly enhances proliferation of hematopoietic progenitor cells. In this study, we investigated the role of Sca-1 in promoting ERK-dependent myeloid lineage proliferation and the effects of alcohol on this process. Thirty minutes after i.p. injection of alcohol, mice received i.v. challenge with 5 × 10(7) Escherichia coli for 8 or 24 h. A subset of mice received i.v. BrdU injection 20 h after challenge. Bacteremia increased Sca-1 expression, ERK activation, and proliferation of myeloid and granulopoietic precursors. Alcohol administration suppressed this response and impaired granulocyte production. Sca-1 expression positively correlated with ERK activation and cell cycling, but negatively correlated with myeloperoxidase content in granulopoietic precursors. Alcohol intoxication suppressed ERK activation in granulopoietic precursors and proliferation of these cells during bacteremia. Granulopoietic precursors in Sca-1(-/-) mice failed to activate ERK signaling and could not increase granulomacrophagic CFU activity following bacteremia. These data indicate that Sca-1 expression promotes ERK-dependent myeloid cell proliferation during bacteremia. Suppression of this response could represent an underlying mechanism for developing myelosuppression in alcohol-abusing hosts with severe bacterial infection.
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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) | 0.000 | 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 | 0.001 |
| 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".