The Temporal Pattern of Changes in Serum Biomarker Levels Reveals Complex and Dynamically Changing Pathologies after Exposure to a Single Low-Intensity Blast in Mice
Bibliographic record
Abstract
Time-dependent changes in blood-based protein biomarkers can help identify the -pathological processes in blast-induced traumatic brain injury (bTBI), assess injury severity, and monitor disease progression. We obtained blood from control and injured mice (exposed to a single, low-intensity blast) at 2-h, 1-day, 1-week, and 1-month post-injury. We then determined the serum levels of biomarkers related to metabolism (4-HNE, HIF-1α, ceruloplasmin), vascular function (AQP1, AQP4, VEGF, vWF, Flk-1), inflammation (OPN, CINC1, fibrinogen, MIP-1a, OX-44, p38, MMP-8, MCP-1 CCR5, CRP, galectin-1), cell adhesion and the extracellular matrix (integrin α6, TIMP1, TIMP4, Ncad, connexin-43), and axonal (NF-H, Tau), neuronal (NSE, CK-BB) and glial damage (GFAP, S100β, MBP) at various post-injury time points. Our findings indicate that the exposure to a single, low-intensity blast results in metabolic and vascular changes, altered cell adhesion, and axonal and neuronal injury in the mouse model of bTBI. Interestingly, serum levels of several inflammatory and astroglial markers were either unchanged or elevated only during the acute and subacute phases of injury. Conversely, serum levels of the majority of biomarkers related to metabolic and vascular functions, cell adhesion, as well as neuronal and axonal damage remained elevated at the termination of the experiment (1 month), indicating long-term systemic and cerebral alterations due to blast. Our findings show that the exposure to a single, low-intensity blast induces complex pathological processes with distinct temporal profiles. Hence, monitoring serum biomarker levels at various post-injury time points may provide enhanced diagnostics in blast-related neurological and multi-system deficits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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".