An integrated microfluidic chip for chromosome enumeration using fluorescence in situ hybridization
Bibliographic record
Abstract
Fluorescence in situ hybridization (FISH) is a powerful technique for probing the genetic content of individual cells at the chromosomal scale. Conventional FISH techniques provide a sensitive diagnostic tool for the detection of chromosomal alterations on a cell-by-cell basis; however, the cost-per-test in terms of reagent and highly qualified labour has prevented its wide-spread utilization in clinical settings. Here, we address the inefficient use of labour with the first integrated and automated on-chip FISH implementation, one that requires only minutes of setup time from the technician. Our microfluidic chip has lowered the reagent use by 20-fold, decreased the labour time by 10-fold, and substantially reduced the amount of support equipment needed. We believe this cost-effective platform will make sensitive FISH techniques more accessible for routine clinical usage.
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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".