Preservation of neural tissue with a formaldehyde‐free phenol‐based embalming protocol
Bibliographic record
Abstract
The adverse effects formaldehyde fixation has on tissues both gross anatomically and histologically are well documented. Consequently, researchers are seeking alternative embalming techniques that better preserve in vivo characteristics of tissues. Phenol-based embalming is one method that has shown promise in its ability to adequately preserve the in vivo qualities of tissues through preliminary explorations at the gross anatomical level. The literature on phenol-based embalming is currently scarce, especially with regard to its effects on tissues at the microscopic level. For the current study we aimed to document the histologic effects of a formaldehyde-free phenol-based embalming solution on neural tissue, with the hope of providing novel insight into the effects of soft-embalming on tissues at the microscopic level. Cerebral and cerebellar tissue obtained from porcine brains was fixed in phenol- and formaldehyde-based fixatives; the latter served as a control. Fixed samples were processed for histological analysis. The phenol-based embalming solution provided excellent preservation of the cerebral and cerebellar tissue morphology. Of note was the decrease in separation artifact seen in both tissue types relative to the control tissue, as well as anomalous circular artifacts in the white matter. The results of this study indicate that the phenol-based embalming solution preserves neural tissue at the histological level, perhaps superiorly in many aspects when compared to the formaldehyde-fixed samples. Further investigations of both gross anatomy and histology are recommended on the basis of these promising new findings to determine its potential utilities within research and education. Clin. Anat. 32:224-230, 2019. © 2018 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".