Extraction of RNA Using Fine-Needle Aspiration Samples Stored Under Different Conditions
Bibliographic record
Abstract
BACKGROUND: : Diagnosis of lung cancer is achieved by fine-needle aspiration (FNA) techniques such as computed tomography-guided FNA and transbronchial needle aspiration. The purpose of this study was to establish an optimal storing method for molecular testing in FNA samples. METHODS: : We performed FNA using a 21-gauge needle in surgically resected lung cancer samples. The aspirates were stored according to the following protocol: in group 1, the aspirate was snap frozen with liquid nitrogen and in group 2, the aspirate was mixed with RNA later. After sample collection from both groups, these samples were stored at -80°C for 6 months. RNA was extracted from each sample using a commercially available RNA extraction kit, and the quality and quantity of RNA were measured. Quantitative real-time reverse transcriptase polymerase chain reaction (RT-PCR) was performed for human actin β (hACTB) and keratin 19 (KRT19). RESULTS: : FNA was performed from 7 lung cancers. RNA was extracted from all samples. The median total amount of extracted RNA was 33.9 μg for group 1 and 35.8 μg for group 2. The mean RNA integrity number was 3.5 for group 1 and 6.3 for group 2. RT-PCR for hACTB and KRT19 could be successfully performed in all samples; however, the relative gene expression value showed intrasample variation. CONCLUSIONS: : Samples obtained from computed tomography-guided FNA or transbronchial needle aspiration may be used for genetic profiling of lung cancer. RNA can be extracted from FNA samples and can be used for RT-PCR. RNA quality may affect mRNA expression analysis; therefore, an optimal FNA sample storing protocol is essential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".