The use of FTA cards for preserving unfixed cytological material for high‐throughput molecular analysis
Bibliographic record
Abstract
BACKGROUND: Novel high-throughput molecular technologies have made the collection and storage of cells and small tissue specimens a critical issue. The FTA card provides an alternative to cryopreservation for biobanking fresh unfixed cells. The current study compared the quality and integrity of the DNA obtained from 2 types of FTA cards (Classic and Elute) using 2 different extraction protocols ("Classic" and "Elute") and assessed the feasibility of performing multiplex mutational screening using fine-needle aspiration (FNA) biopsy samples. METHODS: Residual material from 42 FNA biopsies was collected in the cards (21 Classic and 21 Elute cards). DNA was extracted using the Classic protocol for Classic cards and both protocols for Elute cards. Polymerase chain reaction for p53 (1.5 kilobase) and CARD11 (500 base pair) was performed to assess DNA integrity. RESULTS: Successful p53 amplification was achieved in 95.2% of the samples from the Classic cards and in 80.9% of the samples from the Elute cards using the Classic protocol and 28.5% using the Elute protocol (P = .001). All samples (both cards) could be amplified for CARD11. There was no significant difference in the DNA concentration or 260/280 purity ratio when the 2 types of cards were compared. Five samples were also successfully analyzed by multiplex MassARRAY spectrometry, with a mutation in KRAS found in 1 case. CONCLUSIONS: High molecular weight DNA was extracted from the cards in sufficient amounts and quality to perform high-throughput multiplex mutation assays. The results of the current study also suggest that FTA Classic cards preserve better DNA integrity for molecular applications compared with the FTA Elute cards.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".