FISH and chips: chromosomal analysis on microfluidic platforms
Bibliographic record
Abstract
Interphase fluorescence in situ hybridisation (FISH) is a sensitive diagnostic tool used for the detection of alterations in the genome on cell-by-cell basis. However, the cost-per-test and the technical complexity of current FISH protocols have slowed its widespread utilisation in clinical settings. For many cancers, the lack of a cost-effective and informative diagnostic method has compromised the quality of life for patients. We present the first demonstration of a microchip-based FISH protocol, coupled with a novel method to immobilise peripheral blood mononuclear cells inside microfluidic channels. These first on-chip implementations of FISH allow several chromosomal abnormalities associated with multiple myeloma to be detected with a ten-fold higher throughput and 1/10-th the reagent consumption of the traditional slide-based method. Moreover, the chip test is performed within hours whereas the conventional protocol required days. In addition, two on-chip methods to enhance the hybridisation aspects of FISH have been examined: mechanical and electrokinetic pumping. Similar agitation methods have led to significant improvements in hybridisation efficiency with DNA microarray work, but with this cell-based method the benefits were moderate. On-chip FISH technology holds promise for sophisticated and cost-effective screening of cancer patients at every clinic visit.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".