The Effect of Various Stimulants on Cytokine Secretion Profiles in Freshly Isolated Peripheral Blood Mononuclear Cells (PBMC)
Bibliographic record
Abstract
Abstract Cytokines play critical roles in in cancer, in regulating autoimmune diseases and in chemically induced tissue damage repair. In response to antibody treatment or as a result of corona virus disease (COVID), a cytokine storm may occur where high levels of inflammatory cytokines are produced leading to organ damage. To understand how to regulate cytokine levels, primary cell in vitro assays may assess the effects of small molecule compounds or antibodies. We evaluated OKT3, Polyinosinic-polycytidylic acid (Poly I:C), Resiquimod, CpG oligodeoxynucleotides (CpG ODN), DynaBeads with aCD3/aCD28, Phytohemagglutinin (PHA)-M and lipoplolysaccharide (LPS) on cytokine secretion from 6 normal donors. PBMCs isolated from fresh blood using a density gradient medium were plated at 1 × 105 cells per well in the presence and absence of the test compounds for 48 hours. Supernatants were assessed for interleukin 2 (IL-2), tumor necrosis factor-α (TNFα) and interferon-γ (IFNγ). The remaining cells were assessed for viability to evaluate cytotoxicity. Flow cytometric profiles of T cell subsets were determined. The data generated from the 6 normal donors varied greatly, though the effects of the stimulants followed the same trends. The Dynabeads (CD3/28) generated the highest values of IL-2, IFNγ and TNFα in all donors. OKT3 and PHA-M, Resiquimod and LPS generated lesser amounts of the cytokines whereas Poly I:C and CpG ODN did not induce these cytokines over background levels. Only PHA-M caused a decrease in cell viability. Additionally, the release of cytokines was not associated with activation of T cells. Cytokine release assays provide a robust readout with certain stimuli and may facilitate evaluation of novel test compounds for functional activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".