Retrieving high-quality genomic DNA from formalin-fixed paraffin-embedded tissues for multiple molecular analyses
Bibliographic record
Abstract
Formalin-fixed paraffin-embedded (FFPE) tissues represent the biggest source of archival materials for molecular biology research and pathology investigations. Nevertheless, fixation by formalin may cause denaturation and modification of macromolecules constraining DNA quality and its downstream applications. In this study, we developed a fast, simple, and cost-effective phenol/chloroform-based protocol for the extraction of high-quality DNA from 101 FFPE colorectal cancer tissue blocks that can be used in multiple molecular studies. DNA samples extracted using this phenol/chloroform protocol and the QIAamp DNA FFPE Tissue kit were evaluated for the quantity, quality, and amplificability. Spectrophotometer analyses revealed significantly higher quality and quantity of DNA samples obtained with the phenol/chloroform protocol as compared to those of the QIAamp DNA FFPE Tissue kit. In addition, the amplificability of these samples as assessed by conventional and multiplex polymerase chain reaction (PCR), followed by sequencing and fragment analyses presented an absolute success rate. Additionally, it is able to amplify a DNA fragment of 725 base-pairs at an adequate amount for downstream applications. This fast, simple, and cost-effective method may facilitate the molecular analyses of a large number of FFPE specimens that best suits the needs of the overall study design in terms of the quality and quantity of the extracted DNA.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".