<scp>MPLA</scp> shows attenuated pro‐inflammatory properties and diminished capacity to activate mast cells in comparison with <scp>LPS</scp>
Bibliographic record
Abstract
BACKGROUND: Monophosphoryl lipid A (MPLA), a nontoxic TLR4 ligand derived from lipopolysaccharide (LPS), is used clinically as an adjuvant in cancer, hepatitis, and malaria vaccines and in allergen-specific immunotherapy. Nevertheless, its cell-activating effects have not been analyzed in a comprehensive direct comparison including a wide range of different immune cells. Therefore, the objective of this study was the side-by-side comparison of the immune-modulating properties of MPLA and LPS on different immune cells. METHODS: Immune-activating properties of MPLA and LPS were compared in human monocytes and mast cells (MCs), a mouse endotoxin shock model (ESM), and mouse bone marrow (BM)-derived myeloid dendritic cells (mDCs), T cells (TCs), B cells, and MCs. RESULTS: In a mouse in vivo ESM and a human ex vivo monocyte activation test (MAT), MPLA induced the same cytokine secretion pattern as LPS (ESM: IL-6, IL-12, TNF-α; MAT: IL-1β, IL-6, TNF-α), albeit at lower levels. Mouse mDCs and ex vivo isolated B cells stimulated with MPLA required a higher threshold to induce TRIF-dependent cytokine secretion (IL-1β, IL-6, IL-10, and TNF-α) than did LPS-stimulated cells. In mDC:DO11.10 CD4 TC cocultures, stimulation with MPLA, but not with LPS, resulted in enhanced OVA-specific IL-4 and IL-5 secretion from DO11.10 CD4 TCs. Unexpectedly, in both human and mouse MCs, MPLA, unlike LPS, did not elicit secretion of pro-inflammatory cytokines. CONCLUSIONS: Compared to LPS, MPLA induced a qualitatively similar, but less potent pro-inflammatory immune response, but was unable to activate human or mouse MCs.
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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 | 0.001 |
| 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".