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Record W4414015848 · doi:10.11159/icbes25.134

Robotic Laser Microscope Systems for Tissue-Level Investigation: Applications in Collagen and Drosophila Brain Tissues

2025· article· en· W4414015848 on OpenAlexvenueno aff
Jiayu Hu, X. Ye, Huiying Huang, Zachary Wang, Veronica Gomez‐Godinez, Linda Shi

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
FundersUniversity of California, San Diego
KeywordsBrain tissueMicroscopeLaserBiomedical engineeringMaterials scienceComputer scienceOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Short pulsed Laser-induced microscope systems present a powerful tool for inducing precise and localized damage in live cells to investigate fundamental biomechanics and cellular response pathways.Traditionally applied to live-cell damage in research areas such as DNA repair, cellular biomechanics, chromatin structure during mitosis, and mitotic checkpoint regulation, and neurodegenerative diseases, we explore the application of two platforms-femtosecond laser ablation and laser-induced shockwaves (LIS)-for analysing on tissue level changes in collagen fibers and brain tissues from Drosophila.Our collagen fiber study demonstrates a measurable 30% increase in tissue thickness surrounding the ablation zone, captured using quantitative phase imaging (QPI).Concurrently, LIS investigation of fly brain morphology reveals that total volume, rather than shape, determines resistance to mechanical shock.These studies reinforce the adaptability of laser-induced microscope systems across biological scales, highlighting their application in tissue engineering, neurodegenerative diseases, and regenerative medicine.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.242
Teacher spread0.235 · 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 teacher head, not a consensus.

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

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

Citations0
Published2025
Admission routes1
Has abstractyes

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