Leveraging Prior Knowledge to Recover Characteristic Immune Regulatory Motifs in Gulf War Illness
Bibliographic record
Abstract
Potentially linked to the basic physiology of stress response, Gulf War Illness (GWI) is a debilitating condition presenting with complex immune, endocrine and neurological symptoms. Here we interrogate the immune response to physiological stress by measuring 16 blood-borne immune markers at 8 time points before, during and after maximum exercise challenge in n=12 GWI veterans and n=11 healthy veteran controls deployed to the same theatre. Immune markers were combined into functional sets and the dynamics of their joint expression described as classical rate equations. These empirical networks were further informed structurally by projection onto prior knowledge networks mined from the literature. Of the 49 literature-informed immune signaling interactions, 21 were found active in the combined exercise response data. However, only 4 signals were common to both subject groups while 7 were uniquely active in GWI and 10 uniquely active in healthy veterans. Feedforward mediation of IL-23 and IL-17 by IL-6 and IL-10 emerged as distinguishing control elements that were characteristically active in GWI versus healthy subjects. Simulated restructuring of the regulatory circuitry in GWI as a result of applying an IL-6 receptor antagonist in combination with either a Th1 (IL-2, IFNγ, TNFα) or IL-23 receptor antagonist predicted a partial rescue of immune response elements previously associated with illness severity. Overall, results suggest that pharmacologically altering the topology of the immune response circuitry identified as active in GWI can inform on strategies that while not curative, may nonetheless deliver a reduction in symptom burden. A lasting and more complete remission in GWI may therefore require manipulation of a broader physiology, namely one that includes endocrine oversight of immune function.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".