αEβ7, α4β7 and α4β1 integrin contributions to T cell distribution in blood, cervix and rectal tissues: Potential implications for HIV transmission
Bibliographic record
Abstract
Cell surface expression of α4β7, α4β1 and αEβ7 integrins play a key role in T cell distribution. Understanding the contribution of integrins to the density and ratios of CD4+: CD4negT cell at the portals of entry for HIV is of fundamental importance for the advance of more effective HIV prevention strategies. We therefore set out to characterize and compare the expression of α4β7, α4β1 and αEβ7 integrins on systemic, cervical and rectal CD4+ and CD4negT cells isolated from a cohort of healthy Kenyan women at low risk for sexually transmitted infections (STI) (n = 45). Here we show that blood and cervix were enriched in α4+β1+CD4+T cells and α4+β7hiCD4+T cells, whereas the rectum had an equal frequency of α4+β7hiCD4+T cells and αE+β7hiCD4+T cells. Most cervical and rectal αE+β7hiCD4+T cells expressed CCR5 as well as CD69. Interestingly, αEβ7 was the predominant integrin expressed by CD4negT cells in both mucosal sites, outnumbering αE+β7hiCD4+T cells approximately 2-fold in the cervix and 7-fold in the rectum. The majority of αE+β7hiCD4negT cells expressed CD69 at the mucosa. Taken together, our results show unique tissue-specific patterns of integrin expression. These results can help in guiding vaccine design and also the use of therapeutically targeting integrin adhesion as a means to preventing HIV.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".