Interferon gamma limits Th1 adhesion to inflamed endothelium: a nitric oxide regulatory feedback mechanism
Bibliographic record
Abstract
CD4+ T helper cell (TH1 and TH2) localization to a site of inflammation is important for the development, maintenance and regulation of an immune response. The factors that regulate TH1 and TH2 cell recruitment into tissue are not fully understood. In the present study we examined the effect of different cytokine microenvironments on the recruitment of TH1 and TH2 cells into tissue. TH1 and TH2 cells were generated from isolated splenic ova‐specific CD4+ T cells which were cultured for 7 days with antigen and polarizing antibodies. Endothelial interactions of fluorescently labelled TH1 or TH2 lymphocytes were observed via intravital microscopy in the cytokine‐treated cremaster muscle. These data show that TNFα alone is sufficient to maximally recruit TH1 cells. Surprisingly treatment with TNFα + IFNγ significantly decreased TH1 adhesion and emigration in comparison to TNFα treatment alone. The decreased adhesion of TH1 cells in response to TNFα + IFNγ reflected a decreased ability to bind to ICAM‐1 and was iNOS dependent. This phenomenon was not observed with TH2 cells. Blockade of IFNγ or iNOS function during the TH1‐mediated contact hypersensitivity response resulted in an acceleration and exacerbation of the inflammatory response, suggesting that these mediators may also play a key immunomodulatory role in controlling early leukocyte recruitment during adaptive immune responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".