Biological evaluation of the MILLIPLEX® map Mouse High Sensitivity T Cell Panel
Bibliographic record
Abstract
Abstract Low levels of inflammation are involved in many clinical and sub-clinical disease states like autoimmune diseases. Measuring picogram levels of cytokines is critical for understanding their pathogenesis. Model organisms, such as mice, offer unique challenges of sample availability and detection of low levels of cytokines. We recently developed a Mouse High Sensitivity T Cell Panel multiplex assay for simultaneous measurement of 18 mouse cytokines using Luminex xMAP® technology. In this study, we analyzed mouse cytokine secretions both in vitro, with PMA-, PHA-, LPS-, Con-A, or calcium ionophore-challenged PBMC samples, and in vivo, using LPS-challenged mice and an aged-mouse model. Typically, the response of mouse PBMCs to in vitro stimulants is difficult to study due to low levels of cytokine secretion. Using the MILLIPLEX® map Mouse High Sensitivity T Cell Panel, distinctly different stimulant-dependent cytokine responses were observed. PMA and LPS induced the secretion of IL-1β, IL-5, IL-10, LIX, MCP-1, MIP-2 and LPS. IL-6 was secreted in response to LPS but not PMA. Th17 cell cytokine IL-17A was secreted in response to PHA and Con A but not LPS. Interestingly, low levels of secretion were seen in response to the combination of all stimulants for GM-CSF, IFNγ, IL-1α and IL-2. The in vivo challenge of LPS significantly increased mouse plasma levels of fourteen cytokines. Small increases were also seen for GM-CSF and IL-2, while IL-5 and IL-12 (p70) trended higher. In a mouse model of aging, sex-specific changes of plasma levels were observed for IL-1, IL-5, IL-10, GM-CSF, IFNγ, KC and MIP-2. Thus, the MILLIPLEX® MAP Mouse High Sensitivity T Cell Panel is a useful tool to study low levels cytokines in mouse models of disease and inflammation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".