Characterization of the response of taurine protons to PRESS at 9.4 T for Resolving choline and Determining taurine <i>T</i><sub>2</sub>
Bibliographic record
Abstract
Point-resolved spectroscopy (PRESS), characterized by two TEs (TE1 and TE2 ), can be employed to perform animal magnetic resonance spectroscopy (MRS) studies at 9.4 T. Taurine (Tau) and choline (Cho) are relevant metabolites that can be measured by MRS. In this work, the response of the J-coupled protons of Tau as a function of PRESS TE1 and TE2 was characterized at 9.4 T to achieve two objectives. The first was to determine two TE1 and TE2 combinations that could be used to obtain T2 -corrected measures of Tau (3.42 ppm) that were minimally influenced by J coupling. The second was to exploit the Tau J coupling to find a timing combination that minimized the 3.25-ppm Tau signal to enable the Cho (3.22 ppm) resonance to be resolved from the overlapping Tau signal. The response of Tau protons was investigated both numerically and experimentally. It was numerically determined that the timings {TE1 , TE2 } = {17 ms, 10 ms} and {TE1 , TE2 } = {80 ms, 70 ms} yielded similar 3.42-ppm Tau resonance areas (5% difference), rendering them suitable for Tau T2 determination. {TE1 , TE2 } = {25 ms, 50 ms} was found to yield minimal 3.25-ppm Tau signal, reducing its interference with Cho. The efficacy of the timings was demonstrated on phantom solutions and in vivo in four Sprague Dawley rats. LCModel was employed to analyse the in vivo spectra and Tau T2 values were estimated by fitting the Tau peak areas obtained with {TE1 , TE2 } = {17 ms, 10 ms} and {TE1 , TE2 } = {80 ms, 70 ms} to a monoexponentially decaying function. An average Tau T2 of 106 ms (standard deviation, 12 ms) was obtained. LCModel analysis of rat spectra obtained with {TE1 , TE2 } = {25 ms, 50 ms} demonstrated negligible levels of Tau signal, compared with that obtained with short TE.
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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 | 0.001 |
| 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".