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Record W4364856662 · doi:10.1109/tase.2023.3265424

Automated Piezo-Assisted Sperm Immobilization

2023· article· en· W4364856662 on OpenAlexafffund
Changsheng Dai, Songlin Zhuang, Guanqiao Shan, Hang Liu, Yong Wang, Changhai Ru, Yu Sun

Bibliographic record

VenueIEEE Transactions on Automation Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSpermPipetteMaterials scienceBiomedical engineeringAnatomyBiologyEngineeringChemistry

Abstract

fetched live from OpenAlex

Sperm immobilization is a crucial procedure in clinical cell surgery for infertility treatment. Current immobilization is implemented by tapping the sperm tail with a glass micropipette, but its effectiveness is restricted by sperm orientation and ineffective membrane ablation. Ineffective ablation leads to limited release of oocyte activating factors and lowers fertilization rate; and sperm swim in small angles relative to the micropipette tip cannot be tapped due to the risk of damaging the sperm’s genetic materials contained in the sperm head. This paper reports automated piezo-assisted sperm immobilization with enhanced efficacy of cell membrane ablation and sperm orientation control. The designed piezo drill consists of two orthogonal vibration modules to generate controlled micropipette vibration along axial and lateral axes. Through stiffness modeling, the flexure joints guide the motion of the central beam of each vibration module. To achieve sperm orientation control, whirl flow is induced by both axial and lateral vibration of the micropipette tip. To immobilize sperm, only micropipette’s axial vibration is generated to prevent lateral vibration from damaging sperm head. A visual servoing scheme is developed by decoupling sperm wiggling from positioning error for immobilization. Experimental results showed that sperm orientation control by the piezo drill achieved an error of 1.4$^{\circ}$and a time cost of 2.5 s. Visual servoing with sperm wiggling decoupling achieved a positioning error of 1.7$\mu$m. Furthermore, the piezo-assisted sperm immobilization technique led to effective membrane ablation. With membrane-impermeable stains, it took 5.6 s for the immobilized sperm to be stained after piezo-assisted immobilization, significantly less than 49.2 s by conventional micropipette tapping.Note to Practitioners—This work tackled the challenge of ineffective membrane ablation and orientation limit in clinical cell surgeries. Conventional manual immobilization suffers from low membrane ablation efficacy, which leads to limited release of oocyte activating factors and lowers fertilization rate. Moreover, sperm swim in small angles relative to the micropipette tip cannot be tapped due to the risk of damaging the sperm’s genetic materials contained in the sperm head. In this paper, we propose automation techniques for effective membrane ablation and orientation control of sperm. A clinically compatible piezo drill is developed to generate controllable micropipette motion along both axial and lateral directions. The whirl flow generated by micropipette vibration is employed to rotate sperm, which greatly increased the number of available sperm for immobilization. A visual servoing controller is developed to keep the sperm at the center of field of view for immobilization by decoupling sperm wiggling from positioning error. The developed methods can be generalized to the manipulation of other types of cells. The piezo drill can be used for effective membrane ablation of oocyte, embryo, yeast cell and so on. The orientation control strategy leveraging piezo-induced whirl flow is applicable to non-contact rotation of a variety of microorganism.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.009
GPT teacher head0.220
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations18
Published2023
Admission routes2
Has abstractyes

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