Correlation between GFAP and UCH-L1 serum concentrations and mitigation of brain insult after MV with diaphragm neurostimulation
Bibliographic record
Abstract
Rationale: Our group has demonstrated hippocampal cell loss after mechanical ventilation (MV) which can be mitigated by temporary transvenous diaphragm neurostimulation (TTDN). GFAP and UCH-L1 serum concentrations are established markers of neural injury. There is a need to investigate strategies to prevent brain insult after MV. Objectives: We investigate whether TTDN plus lung-protective MV for 50 hours mitigates elevation of serum markers for brain injury. Methods: Thirty-eight healthy pigs with non-injured lungs were divided into four groups: MV, TTDN50%+MV, TTDN100%+MV and NV (Figure 1). GFAP and UCH-L1 serum concentrations were analyzed. Results: TTDN100%+MV group had lower concentrations of GFAP and UCH-L1 in comparison to the other groups. Differences in GFAP and UCH-L1 serum concentrations were statistically significant between groups, p<0.0001. Spearman correlation showed a nonlinear, positive, and moderate relationship between serum concentrations of GFAP and UCH-L1, r=0.4773, p=0.0028 (Figure 1A-C). Conclusions: TTDN resulted in lower serum concentrations of established biomarkers of neural injury (GFAP and UCH-L1) after 50 hours of MV. There was a nonlinear, moderate, and positive correlation between GFAP and UCH-L1 serum concentrations.
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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) | 0.000 | 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) | 0.001 | 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".