Immunomodulatory effects of therapeutic preparations of human albumin
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Albumin is the most abundant protein in plasma and is considered to be immunologically inert. However, we recently observed that therapeutic human albumin preparations, used as protein control in studies involving high doses of IVIg, modulated the MHC II-restricted activation of antigen-specific T cells. In the present work, we characterized this effect in more details. MATERIALS AND METHODS: An in vitro antigen presentation assay using mouse cells was used to evaluate the effect of therapeutic human albumin preparations on the activation of ovalbumin-specific T cells. Flow cytometry and quantitative real-time PCR were used to monitor the expression of genes involved in this process. RESULTS: Therapeutic human albumin preparations increased T cell activation in a dose-dependent manner. The effect was explained by an increase in the expression of MHC II and of two other genes (CIITA and H2-M) involved in antigen presentation by murine monocytic cells. Similarly, the expression of HLA-DR on the surface of human monocytic cells was increased following incubation with therapeutic human albumin preparations. CONCLUSION: Altogether, these results reveal a possible physiological role of albumin in immunological processes, leading to an increased ability of antigen presenting cells to trigger T cell activation. This immunomodulatory effect needs to be considered, at least in studies in which albumin is used as a presumably inert control protein.
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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 | 0.000 |
| 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".