Sarcoma Subgrouping by Detection of Fusion Transcripts Using NanoString nCounter Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: NanoString technology is an innovative barcode-based system that requires less tissue than traditional techniques and can test for multiple fusion transcripts in a single reaction. The objective of this study was to determine the utility of NanoString technology in the detection of sarcoma-specific fusion transcripts in pediatric sarcomas. DESIGN: Probe pairs for the most common pediatric sarcoma fusion transcripts were designed for the assay. The NanoString assay was used to test 22 specific fusion transcripts in 45 sarcoma samples that had exhibited one of these fusion genes previously by reverse transcription polymerase chain reaction (RT-PCR). A mixture of frozen (n = 18), formalin-fixed, paraffin-embedded (FFPE) tissue (n = 23), and rapid extract template (n = 4) were used for testing. RESULTS: Each of the 22 transcripts tested was detected in at least one of the 45 tumor samples. The results of the NanoString assay were 100% concordant with the previous RT-PCR results for the tumor samples, and the technique was successful using both FFPE and rapid extract method. CONCLUSION: Multiplexed interrogation for sarcoma-specific fusion transcripts using NanoString technology is a reliable approach for molecular diagnosis of pediatric sarcomas and works well with FFPE tissues. Future work will involve validating additional sarcoma fusion transcripts as well as determining the optimal workflow for diagnostic purposes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 it