Design and Control of a Piezo Drill for Robotic Piezo-Driven Cell Penetration
Bibliographic record
Abstract
Cell penetration is an indispensable step in many cell surgery tasks. Conventionally, cell penetration is achieved by passively indenting and eventually puncturing the cell membrane, during which undesired large cell deformation is induced. Piezo drills have been developed to penetrate cells with less deformation. However, existing piezo drills suffer from large lateral vibration or are incompatible with standard clinical setup. Furthermore, it is challenging to accurately determine the time instance of cell membrane puncturing; thus, the time delay to stop piezo pulsing causes cytoplasm stirring and cell damage. This letter reports a new robotic piezo-driven cell penetration technique, in which the piezo drill device induces small lateral vibrations and is fully compatible with standard clinical setup. Techniques based on corner-feature probabilistic data association filter and motion history images were developed to automatically detect cell membrane breakage by piezo drilling. Experiments on hamster oocytes confirmed that the system is capable of achieving a small cell deformation of 5.68 ± 2.74 μm (vs. 54.29 ± 10.21 μm by passive approach) during cell penetration. Automated detection of membrane breakage had a success rate of 95.0%, and the time delay between membrane breakage and piezo-vibration stoppage was 0.51 ± 0.27 s vs. 2.32 ± 0.98 s by manual stoppage of piezo pulsing. This reduced time delay together with smaller cell deformation led to higher oocyte post-penetration survival rate (92.5% vs. 77.5% by passive approach, n = 80 cells).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".