Metabolomic alterations in human brain microvascular endothelial cells induced by traumatic injury
Bibliographic record
Abstract
INTRODUCTION: Altered metabolic pathways are critical in the progression of traumatic brain injury (TBI). Identifying differentially abundant metabolites (DAMs) from specific cell types can offer valuable diagnostic and prognostic insights. OBJECTIVE: This study aimed to characterize the metabolomic profile of injured human brain microvascular endothelial cells (hBMEC) at 2-, 12-, 24-, and 48 h post-injury. METHODS: Using an in vitro TBI model, we analyzed metabolites in cell culture media through a combination of direct injection mass spectrometry and a custom reverse-phase LC-MS/MS assay. We evaluated 644 metabolites at each time point. RESULTS: Phosphatidylcholines were significantly upregulated across all time intervals. At 2- and 12 h post-injury, the most significantly upregulated metabolites included sphingomyelin (OH) C22:1, ethylmalonic acid, and methylhistidine, while guanosine and the combination of butyric acid + isobutyric acid were the most downregulated. At 24 and 48 h, deoxyadenosine and inosine, respectively, emerged as the most upregulated metabolites, with butyric acid + isobutyric acid and quinoline-4-carboxylic acid showing the greatest downregulation. CONCLUSION: Metabolomic profiling identified various DAMs after traumatic injury that are linked to human endothelial dysfunction. Future experiments should expand the number of metabolites measured to determine the underlying signaling pathways.
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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 | 0.001 |
| 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".