Evaluation of Small Sample cDNA Amplification for Microdissected Airway Expression Profiling in COPD
Bibliographic record
Abstract
Small airway obstruction and emphysematous destruction account for the airflow limitation that defines chronic obstructive pulmonary disease (COPD). While laser capture microdissection (LCM) allows gene expression studies in small airways separately from the surrounding parenchyma, tissue size limits the number of genes examined. The present study evaluates the Clontech SMART amplification to test the hypothesis that this amplification provides RNA in sufficient quantity and quality to evaluate large numbers of genes in airways < 2 mm diameter obtained by LCM. Commercial reference RNA was amplified 200-fold and the expression levels of 51 genes relative to the unamplified RNA had a correlation coefficient of 0.84. For two pairs of RNA preparations (commercial placenta versus commercial lung; lung sections prepared for LCM from GOLD 0 (at risk for COPD) versus GOLD 2 (moderate disease) patients linear regression of Delta Ct's (delta cycle thresholds) of unamplified versus amplified RNA gave correlation coefficients of R = 0.95. In RNA from microdissected small airways, expression patterns in all GOLD classes of COPD severity were very similar between unamplified and amplified RNA. We conclude that SMART amplification provides cDNA sufficient for studying large numbers of genes even in laser-captured small airways and this cDNA maintains the relative expression found in corresponding unamplified RNAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 0.001 |
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".