Comparison of a Selection of Rapid Automated DNA and RNA Extraction Technologies for Detection of Somatic or Constitutional Gene Abnormalities in Cancer Diagnosis
Bibliographic record
Abstract
Molecular analyses of large numbers of patient samples are increasingly used for diagnostic applications as well as for understanding cancer biology. Their accuracy depends on the quality and quantity of nucleic acids extracted from human cells or tissues. To optimize these preanalytical steps, we evaluated several automated technologies for nucleic acid purification from clinical samples. Three automated platforms were compared. DNA was extracted from peripheral blood leukocytes from five normal individuals, and its quality was assessed by D-HPLC and sequencing after PCR. Clinical samples from acute leukemia patients were used for automated RNA extractions; results were compared to our standard manual technique. RNA qualification was done using capillary gel electrophoresis and analysis of the Abelson gene transcript by real-time PCR. One robot produced higher total output for both DNA and RNA. While the quality of DNAs obtained from the three workstations allowed implementation of their analysis for detection of germinal mutations, important differences were observed in the quality of RNAs. One robot isolated RNA with similar quality and quantity to the manual technique, but the resulting products displayed low concentrations. A robust technique had therefore to be evaluated and validated to allow the implementation of this workstation within the daily diagnostic practices. This study is of interest at a time when hospital-based laboratories are bringing molecular signatures to the clinic.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".