[Application of microdissection combined with RNA linear amplification].
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Microdissection has become indispensable for the selective analysis of stroma-free tumor cell. However, To obtain sufficient RNA from microdissected cells is difficult. The study was designed to seek a specific way to separate nasopharyngeal carcinoma (NPC) cells from stromal cells and to amplify the RNA from microdissected NPC cells. METHODS: NPC cells were obtained using microdissection from frozen NPC tissue sections, then RNA was extracted from the microdissected NPC cells and reverse transcribed in vitro. The expression levels of beta-actin and GADPH in amplified RNA were detected using RT-PCR. RESULTS: About 20,000-40,000 NPC cells were obtained, RNA was extracted from the cells, about 0.5-2.5 kb RNA fragments were obtained after RNA linear amplification and beta-actin and GADPH levels were integral. CONCLUSION: Microdissection combined with RNA linear amplification can be used to successfully obtain pure NPC cells, the integrity of amplified RNA is good and can be used in further research.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".