The Systemic Cytokine Environment Is Permanently Altered in Multiple Myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is an incurable bone marrow malignancy of the B cell lineage. Utilizing multiplex Luminex technology we measured levels of 25 cytokines in the plasma of normal donors (n = 177), those with monoclonal gammopathy of undetermined significance (n = 8), and MM patients (n = 55) with either active disease, on treatment, or in remission. The cytokine levels were compared between normal donors and MM patients as well as between various phases of MM, and discriminant analysis was used to create a predictive classification model based on the differentially expressed cytokines. Evaluating age- and gender-dependence of cytokine expression, we determined that with age there is a shift toward a pro-inflammatory environment. Moreover, we observed a strong gender bias in cytokine expression. However, the profile of differentially expressed cytokines was heavily skewed toward an anti-inflammatory, pro-tumorigenic response in patients with MM. Significantly, our predictive model placed all patients in remission in the same category as those with active disease. Thus, our study demonstrates that the homeostasis of systemic cytokines is not restored when MM patients enter remission, suggesting that once an individual has cancer, the microenvironment is permanently altered and the system is primed for a relapse.
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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 | 0.001 |
| 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) | 0.000 | 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".