Aldehyde fixative solutions alter the water relaxation and diffusion properties of nervous tissue
Bibliographic record
Abstract
Chemically-fixed nervous tissues are well-suited for high-resolution, time-intensive MRI acquisitions without motion artifacts, such as those required for brain atlas projects, but the aldehyde fixatives used may significantly alter tissue MRI properties. To test this hypothesis, this study characterized the impact of common aldehyde fixatives on the MRI properties of a rat brain slice model. Rat cortical slices immersion-fixed in 4% formaldehyde demonstrated 21% and 81% reductions in tissue T(1) and T(2), respectively (P < 0.001). The T(2) reduction was reversed by washing slices with phosphate-buffered saline (PBS) for 12 h to remove free formaldehyde solution. Diffusion MRI of cortical slices analyzed with a two-compartment analytical model of water diffusion demonstrated 88% and 30% increases in extracellular apparent diffusion coefficient (ADC(EX)) and apparent restriction size, respectively, when slices were chemically-fixed with 4% formaldehyde (P <or= 0.021). Further, fixation with 4% formaldehyde increased the transmembrane water exchange rate 239% (P < 0.001), indicating increased membrane permeability. Karnovsky's and 4% glutaraldehyde fixative solutions also changed the MRI properties of cortical slices, but significant differences were noted between the different fixative treatments (P < 0.05). The observed water relaxation and diffusion changes help better define the validity and limitations of using chemically-fixed nervous tissue for MRI investigations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".