Soluble CD127 potentiates IL‐7 activity in vivo in healthy mice
Bibliographic record
Abstract
Abstract Introduction Soluble forms of cytokine receptors can be involved in the endogenous regulation of cytokine activity. Soluble interleukin 7 receptor α (sCD127) naturally binds IL‐7, therefore there is interest in its potential application as an immunotherapeutic agent to regulate IL‐7. With the hypothesis that sCD127 enhances IL‐7 activity, thus promoting T‐cell proliferation in vivo, we sought to assess the effect of sCD127, IL‐7 or IL‐7 + sCD127 treatment on CD4+ and CD8+ T‐cells in the blood and spleen of mice. Methods Peripheral blood mononuclear cells and splenocytes were prepared, and analyzed for T‐cell number, phenotype and proliferation (Ki67+) by flow cytometry. Results IL‐7 treatment induced T‐cell proliferation, increased T‐cell number, and triggered T‐cell differentiation each of which was enhanced with the addition of sCD127. IL‐7 + sCD127 treatment significantly increased spleen weight over that seen with IL‐7 treatment alone. More pronounced proliferation and a greater increase in cell number was observed in CD8+ T‐cells relative to the effect on CD4+ T‐cells. Conclusions These findings suggest that the addition of sCD127 enhances IL‐7‐mediated T‐cell proliferation and suggests a potential therapeutic use for sCD127.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".