Association between the Cerebral Inflammatory and Matrix Metalloproteinase Responses after Severe Traumatic Brain Injury in Humans
Bibliographic record
Abstract
An increasing number of preclinical investigations have suggested that the degree of expression of the matrix metalloproteinase (MMP) family of endopeptidases may explain some of the variability in neurological damage after traumatic brain injury (TBI). As cytokines are a prominent stimulus for MMP expression in animals, we conducted a prospective multimodal monitoring study and determined their association with temporal MMP expression after severe TBI in eight critically ill adults. High cutoff, cerebral microdialysis (n=8); external ventricular drainage (n=3); and arterial and jugular venous bulb catheters were used to measure the concentration of nine cytokines and eight MMPs in microdialysate, cerebrospinal fluid (CSF), and plasma over 6 days. Severe TBI was associated with a robust central inflammatory response, which was largely similar between microdialysate and CSF. At all time points after injury, this response was predominated by the pro-inflammatory cytokines interleukin-6 (IL-6) and IL-8. Use of univariate generalized estimating equations suggested that the concentration of several MMPs varied with cytokine levels in microdialysate. The largest of these changes included increases in microdialysate concentrations of MMP-8 and MMP-9 with increases in the levels of IL-1α and -2 and IL-1α and -2 and TNF-α, respectively. In contrast, the microdialysate level of MMP-7 decreased with increases in microdialysate concentrations of IL-1β, -2, and -6. These findings support the observations of animal studies that cross-talk exists between the neuroinflammatory and MMP responses after acute brain injury. Further study is needed to determine whether this link between cerebral inflammation and MMP expression may have clinical relevance to the care of patients with TBI.
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".