A Lung Tissue Bank for Gene Expression Studies in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
A bank of surgically resected human lung tissues frozen at -70 degrees C after being inflated with support medium for cutting frozen tissue and a separate group inflated with fixative and embedded in paraffin has been established for studies of chronic obstructive pulmonary disease. The present report concerns the quality of RNA that can be extracted from these frozen and fixed tissue samples and from cells obtained from them by laser capture microdissection. The results show that the RNA yield was 257+/-183 ng/mg and 77+/-56 ng/mg from randomly selected frozen and paraffin-embedded tissue, respectively. Intact 18S and 28S rRNA subunits were present in 11/23 frozen and 2/6 paraffin-embedded specimens. The 375-bp actin and 296-bp glyceraldehdye 3-phosphate dehydrogenase targets were amplified by reverse transcription-PCR from both sources and the 983-bp glyceraldehdye 3-phosphate dehydrogenase and 499-bp nonhousekeeping integrin-linked kinase targets from frozen tissue. The minimal amount of RNA required for reverse transcription-PCR of 296-bp glyceraldehdye 3-phosphate dehydrogenase target was 29 pg from frozen tissue when RNA subunits were present and 144 pg when these subunits were absent compared to 0.8 ng from paraffin-embedded tissue. Ten laser pulses were required to laser capture sufficient cells from frozen tissue to detect amplification of the 375-bp actin target while more pulses were required for equivalent amplification from paraffin-embedded tissue. Storage time had no detectable effect on RNA quality. We conclude that both frozen and paraffin-embedded tissues as well as laser-captured cells are suitable for gene expression studies but frozen tissue offered greater sensitivity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".