Heparin interference with reverse transcriptase polymerase chain reaction of RNA extracted from lungs after ischemia-reperfusion
Bibliographic record
Abstract
The reverse transcriptase polymerase chain reaction (RT-PCR) is a rapid and sensitive method for detecting gene expression. However, when we used this technique to study gene expression of cytokines in ischemic and ex-vivo-reperfused rat lungs as a model for transplantation, significant inhibition of RT-PCR reaction was observed. To optimize RT-PCR conditions, RNA was extracted from rat lungs after flushing, preservation, and reperfusion. RNA was further purified and PCR conditions were modified with various strategies. We found that heparinase I pretreatment completely overcame the inhibitory effects of RT-PCR using RNA extracted from lung tissues after ischemia-reperfusion. With this treatment, a dramatic increase in tumor necrosis factor-a (TNF-a) mRNA was revealed from lung tissues after ischemia-reperfusion. This result suggests that residual heparin in lung tissue interferes with RT-PCR. Because heparinization is routinely used during clinical and experimental organ transplantation, we recommend the treatment of RNA samples with heparinase prior to RT-PCR.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".