Enzymatic detection of formalin-fixed museum specimens for DNA analysis and enzymatic maceration of formalin-fixed specimens
Bibliographic record
Abstract
Abstract.—A simple enzymatic screening method has been developed to detect whether a tissue sample has been preserved with formalin or with ethanol only because such a method is a useful tool for predicting the quality of genetic test results. The method is based on enzymatic digestion at 55 C at neutral pH. The screening method shows that only ethanol-preserved tissue samples are dissolved, whereas formalin-preserved samples remain undissolved. The method was developed by the incorporation of laboratory rats preserved under controlled conditions in either 4% neutral buffered formalin or 96% ethanol. The method was subsequently tested on wild-living preserved specimens and an archived specimen. The protease enzyme used was SavinaseH 16 L, Type EX from Novozymes A/S. The enzymatic screening test demands only simple laboratory equipment. The method is useful for natural history collections in museums where DNA analyses of archived specimens are performed. Wasted time and resources can be avoided through the detection of formalin-fixed specimens because these specimens yield low-quality, damaged DNA. In addition to the screening method, it is shown that formalin-preserved specimens can be macerated by enzymatic digestion under alkaline conditions at 55 C.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".