Characterization of human peritoneal dendritic cell precursors and their involvement in peritonitis
Bibliographic record
Abstract
Scattered evidence suggests that the human peritoneal cavity contains cells of the dendritic cell (DC) lineage but their characterization is missing. Here, we report that the peritoneal cavity of normal subjects and of stable patients on peritoneal dialysis (PD) contains a population of CD14(+) cells that can differentiate into DCs or macrophages. Within this pool, we characterized a CD14(+)CD4(+) cell subset (2.2% of the peritoneal cells) fulfilling the definition of myeloid DC precursors or pre-DC1 cells. These cells expressed high levels of HLA-DR, CD13, CD33, and CD86, and low levels of CD40, CD80, CD83, CD123, CD209, TLR-2 and TLR-4. These cells retained CD14 expression until late stages of differentiation, despite concomitant up-regulation of DC-SIGN (CD209), CD1a, CD80 and CD40. Peritoneal pre-DC1 cells had endocytic capacity that was down-regulated upon LPS/IFN-gamma stimulation, were more potent allo-stimulators than peritoneal CD14(+)CD4(-/lo) cells and monocyte-derived macrophages, and induced Th1 cytokine responses. More importantly, the number of peritoneal pre-DC1 cells increased during PD-associated peritonitis, with a different profile for Gram positive and Gram negative peritonitis, suggesting that these cells participate in the induction of peritoneal adaptive immune responses, and may be responsible for the bias towards Th1 responses during peritonitis.
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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".