II. Modulating leukocyte recruitment to splanchnic organs to reduce inflammation
Bibliographic record
Abstract
A hallmark feature of intestinal inflammation is the recruitment and extravasation of numerous cell types from the blood to the afflicted site. Much of what we know about the mechanisms of leukocyte recruitment to splanchnic organs comes from an extensive series of studies on neutrophils in the mesenteric microvasculature. In this themes article, we highlight the important findings from these experiments but also emphasize some of the limitations. In fact, there is a growing body of evidence that neutrophil recruitment may be quite different in the mesentery than in other splanchnic organs. For example, the molecular mechanisms underlying neutrophil recruitment into the liver are quite different than the mesentery and are dependent on the type of inflammatory disease. We also discuss the effect of modulating leukocyte recruitment to splanchnic organs in chronic inflammation and emphasize that the approaches that have been successful in acute inflammation may be less effective in such conditions as inflammatory bowel disease (IBD). One obvious reason for this observation is the growing body of evidence to suggest that the initiation and maintenance of IBD is, in part, due to dysregulated or inappropriately activated populations of infiltrating T lymphocyte subsets. Therefore, we also discuss some interesting new approaches to limiting lymphocyte recruitment into the inflamed intestine either by targeting T helper (Th)1 vs. Th2 lymphocytes or perhaps by allowing the recruitment of regulatory T cells. Inhibiting specific adhesion molecules or specific chemokine receptors may work in this regard.
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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 | 0.001 |
| 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".