A62 IDENTIFICATION OF SPECIFIC COLONIC DEEP MUSCLE LAYER MACROPHAGES SUBSETS BY THE CD64 (FCΓRI) MARKER
Bibliographic record
Abstract
Abstract Background Although intestinal muscle layer macrophages have been suggested to play an important role in the colonic transit by interacting with the myenteric plexus neurons, they have not been fully characterized. CD64 (FcγRI) is one of the most generally used markers for intestinal macrophages, but several studies suggested existence of a subpopulation of macrophages that lack CD64. In addition, the muscle layer macrophage subsets currently identified are considered to be same in the small intestine and colon, although this has not been formally tested. Purpose In this study, we aim to identify and characterize the subsets of muscle layer macrophages by CD64 marker. We hypothesize that colon specific CD64-macrophages have a different role from CD64+ conventional macrophages. Method The muscle layers of small intestine (ileum) and colon were separated from SPF mice and cells from each muscle layer were analyzed by flow cytometry and fluorescent staining. The muscle layer macrophages were gated withCD45+, F4/80+, CD11b+ and Ly6c-, and analyzed with CD64 and MHCⅡmarkers by flow cytometry. In additional experiments, fluorescent staining with CD64 and F4/80 was assessed in whole-mount tissue of the separated muscle layer. Result(s) Within the macrophage population from the colon muscle layer, we found not only CD64+ cells, a conventional marker of macrophage, but also CD64- cell population (CD45+, F4/80+, CD11b+, Ly6c-). However, in the small intestine, this CD64- cell population was barely detectable. In addition, colonic CD64+ cells had mostly high expression of MHCⅡ marker, while CD64- cells had low expression of MHCⅡ. A similar pattern was found when we examined intestinal and colonic tissues by immunofluorescent staining. Conclusion(s) We identified a colon-specific CD64- subset of macrophage in muscle layer. Additional experiments are needed to characterize their immunomodulatory properties. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared CLIINICAL PRACTICE
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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) | 0.002 | 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".