Cell Extraction Automation in Single Cell Surgery using the Aspiration Method
Bibliographic record
Abstract
Biological cell micromanipulation is the precise in-vitro study and handling of individual cells, where small errors can be disastrous. An important example is embryo biopsy, in which a blastomere is extracted from a cleavage-stage embryo for genetic profiling, without damaging the embryo and affecting its viability. Today, the success rates of manually performed biopsies are relatively low due to human errors, leading to excessive embryo damage and prolong surgery times. In this paper, the automation of the extraction of a blastomeres from an early-stage embryo using the aspiration method and image feedback is presented. Computer-controlled micromanipulators combined with computer vision algorithms are used for automated extraction of a predefined number of cells, and detecting the extraction event. Preliminary proof of concept experiments to extract a single cell from a 2-cell cleavage-stage embryos obtained success rates ranging from 80 − 95% for different extraction stages, providing a set of tools for moving towards a fully automated single-cell surgery procedures.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".