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Record W2296593873

Combinational Multiphoton Scanning Microscopy and Multiphoton Surgery of Mouse Arteries

2011· dissertation· en· W2296593873 on OpenAlexfundno aff
Samira Karimelahi

Bibliographic record

VenueTSpace · 2011
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsTwo-photon excitation microscopyFluorescence-lifetime imaging microscopyFemtosecondMaterials scienceLaserBiomedical engineeringFluorescenceFluorescence microscopeOpticsMicroscopyAblationMicroscopeLaser-induced fluorescenceMicrofluidicsNanotechnologyMedicinePhysics
DOInot available

Abstract

fetched live from OpenAlex

Preliminary investigations were carried out in order to explore the potential of laser-stimulated capillary growth in a blood vessel-on-a-chip. To fulfill the project objective, a series of experiments in both directions of two photon \nfluorescence imaging and laser-semitransparent materials interaction were performed. A purpose-built two-photon \nfluorescence imaging resolution was tested by imaging 1 micron diameter fluorescent beads. Also, the potential of fluorescence imaging in the waveguide writing eld as well as the biological eld was studied. Further, for laser ablation on the mouse artery loaded in the microfluidic channel, the processing window was found such that the damage induced by femtosecond laser just a effects the artery, not the other interfaces of the microfluidic chip. At the end, the result of laser trepanning on the mouse artery wall combined with two photon fluorescence imaging was shown. These results will be useful for more advanced\nbiological study such as angiogenesis.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.320
Teacher spread0.306 · 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
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
Published2011
Admission routes1
Has abstractyes

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