Cytokine networks in human severe combined immunodeficiency: a model for regulating T-cell homeostasis
Bibliographic record
Abstract
Human T-lymphocyte homeostasis is a complex process wherein the number of T lymphocytes is held relatively constant in normal individuals. How the body senses the numbers of circulating T-cells and regulates their expansion and survival is unclear. We sought to better understand this process by profiling serum cytokines in severe combined immunodeficiency (SCID) before and after T-cell reconstitution with bone marrow transplantation. We used a novel protein microarray technology to simultaneously measure the levels of 78 different soluble proteins in the serum of 10 normal subjects and 31 SCID subjects before and at various times after transplantation. We used a linear mixed analysis of variance model and novel SAS graphics tools to identify factors that were altered in SCID and changed in response to T-cell engraftment. We identified that IL-15 > MCP-1 > HCC4 > IL-7 were over-expressed in SCID patients before transplantation at times when there were few circulating T cells ( P < 0.001 compared with controls). Both IL-7 and IL-15 normalized after successful transplantation with normal levels of circulating T cells, but not after failed transplants. HCC4 and MCP-1 levels remained high despite transplantation. Levels of nine growth factors were decreased and levels of six proinflammatory cytokines were increased after successful transplantation for SCID. Our studies in a human model of aberrant T-lymphocyte homeostasis wherein IL-15 and, to a lesser extent, IL-7 levels inversely correlate with circulating T-lymphocyte levels provide evidence that IL-15 and IL-7 may regulate human T-lymphocyte homeostasis. In addition, the downmodulation of numerous growth factors and upregulation of proinflammatory cytokines after successful T-cell reconstitution identify factors other than IL-15 and IL-7 that may be involved in the sensing and regulation of T-lymphocyte homeostasis in man.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".