Reverse Transcriptase In Situ PCR: New Methods in Cellular Interrogation
Bibliographic record
Abstract
The advent of the reverse transcriptase polymerase chain reaction (RT-PCR) technique represents a quantum leap in sensitivity over preceding methods of detecting mRNA transcripts, such as Northern blotting. With the arrival of such sensitive techniques, it has become possible to amplify RNA transcripts from very small amounts of template nucleic acid, thus opening new avenues of research that were previously off limits because of difficulties in obtaining adequate quantities and quality of RNA (,). However, RT-PCR suffers from the same limitations as its predecessor because the isolation of RNA necessitates the destruction of the cells/tissue involved, thus preventing the identification of the specific cell source of the mRNA (). Conversely, in situ hybridization allows the specific localization of mRNA to the cells of origin, but the methodology is much less sensitive than RT-PCR (). A methodology that combines the best attributes of in situ hybridization (specific cellular localization) and RT-PCR (high sensitivity) would be desirable. RT in situ PCR provides these attributes, allowing for the location and detection of low copy RNA species, amplified within individual intact cells ().
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".