13C-labelled microdialysis studies of cerebral metabolism in TBI patients
Bibliographic record
Abstract
Human brain chemistry is incompletely understood and better methodologies are needed. Traumatic brain injury (TBI) causes metabolic perturbations, one result of which includes increased brain lactate levels. Attention has largely focussed on glycolysis, whereby glucose is converted to pyruvate and lactate, and is proposed to act as an energy source by feeding into neurons' tricarboxylic acid (TCA) cycle, generating ATP. Also reportedly upregulated by TBI is the pentose phosphate pathway (PPP) that does not generate ATP but produces various molecules that are putatively neuroprotective, antioxidant and reparative, in addition to lactate among the end products. We have developed a novel combination of (13)C-labelled cerebral microdialysis both to deliver (13)C-labelled substrates into brains of TBI patients and recover the (13)C-labelled metabolites, with high-resolution (13)C NMR analysis of the microdialysates. This methodology has enabled us to achieve the first direct demonstration in humans that the brain can utilise lactate via the TCA cycle. We are currently using this methodology to make the first direct comparison of glycolysis and the PPP in human brain. In this article, we consider the application of (13)C-labelled cerebral microdialysis for studying brain energy metabolism in patients. We set this methodology within the context of metabolic pathways in the brain, and (13)C research modalities addressing them.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".