Toward dynamic isotopomer analysis in the rat brain <i>in vivo</i>: automatic quantitation of <sup>13</sup>C NMR spectra using LCModel
Bibliographic record
Abstract
The LCModel method was adapted to analyze localized in vivo (13)C NMR spectra obtained from the rat brain in vivo at 9.4 T. Prior knowledge of chemical-shifts, J-coupling constants and J-evolution was included in the analysis. Up to 50 different isotopomer signals corresponding to 10 metabolites were quantified simultaneously in 400 microl volumes in the rat brain in vivo during infusion of [1,6-(13)C(2)]glucose. The analysis remained accurate even at low signal-to-noise ratio of the order of 3:1. The relative distribution of isotopomers in glutamate, glutamine and aspartate determined in vivo in 22 min was in excellent agreement with that measured in brain extracts. Quantitation of time series of (13)C spectra yielded time courses of total (13)C label incorporation into up to 16 carbon positions, as well as time courses of individual isotopomer signals, with a temporal resolution as low as 5 min (dynamic isotopomer analysis). The possibility of measuring in vivo a wealth of information that was hitherto accessible only in extracts is likely to expand the scope of metabolic studies in the intact brain.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".