Circulating Brain-Reactive Autoantibody Profiles in Military Breachers Exposed to Repetitive Occupational Blast
Bibliographic record
Abstract
Military breachers are routinely exposed to repetitive low-level blast overpressure, placing them at elevated risk for long-term neurological sequelae. Mounting evidence suggests that circulating brain-reactive autoantibodies, generated following CNS injury, may serve as both biomarkers of cumulative damage and drivers of secondary neuroinflammation. In this study, we compared circulating autoantibody profiles in military breachers (n = 18) with extensive blast exposure against unexposed military controls (n = 19). Using high-sensitivity immunoassays, we quantified IgG and IgM autoantibodies targeting glial fibrillary acidic protein (GFAP), myelin basic protein (MBP), and pituitary (PIT) antigens. Breachers exhibited significantly elevated levels of anti-GFAP IgG (p < 0.001) and anti-PIT IgG (p < 0.001) compared to controls, while anti-MBP autoantibody levels remained unchanged. No significant differences were observed for any IgM autoantibody measurements. These patterns suggest that repetitive blast exposure induces a chronic, adaptive immune response rather than a short-lived acute phase. The elevated IgG autoantibodies highlight the vulnerability of astrocytes, myelin, and the hypothalamic–pituitary axis to ongoing immune-mediated injury following repeated blast insults, likely reflecting sustained blood–brain barrier disruption and neuroinflammatory processes. Our findings underscore the potential of CNS-targeted IgG autoantibodies as biomarkers of cumulative brain injury and immune dysregulation in blast-exposed populations. Further research is warranted to validate these markers in larger, more diverse cohorts, and to explore their utility in guiding interventions aimed at mitigating neuroinflammation, neuroendocrine dysfunction, and long-term neurodegenerative risks in military personnel and similarly exposed groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".