The Human Male Liver Is Predisposed to Inflammation Via Enhanced Myeloid Responses to Inflammatory Triggers
Bibliographic record
Abstract
Background & Aim: Men have a higher prevalence of liver disease. Liver myeloid cells can regulate tissue inflammation, which drives progression of liver disease. We hypothesized that sex alters the responsiveness of liver myeloid cells, predisposing men to severe liver inflammation. Methods: Luminex was done on plasma from Hepatitis B Virus infected patients undergoing nucleoside analogue cessation in 45 male and female patients. We collected immune cells from the sinusoids of uninfected livers of 53 male and female donors. Multiparametric flow cytometry was used to phenotype and characterize immune composition. Isolated monocytes were stimulated with TLR ligands to measure the inflammatory potential and the expression of regulators of TLR signaling. Results: We confirmed that men experienced more frequent and severe liver damage upon Hepatitis B Virus reactivation, which was associated with inflammatory markers of myeloid activation. No differences were observed in the frequency or phenotype of sinusoidal myeloid cells between male and female livers. However, monocytes from male livers produced more inflammatory cytokines and chemokines in response to TLR stimulation than female monocytes. We investigated negative regulators of TLR signaling and found that TOLLIP was elevated in female liver-derived monocytes. Conclusions: Our data show that enhanced responsiveness of myeloid cells from the male liver predisposes men to inflammation, which was associated with altered expression of negative regulators of TLR signaling.
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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) | 0.007 | 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".