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Record W2578044514 · doi:10.7150/thno.17596

Precise Spatially Selective Photothermolysis Using Modulated Femtosecond Lasers and Real-time Multimodal Microscopy Monitoring

2017· article· en· W2578044514 on OpenAlexafffund
Yimei Huang, Harvey Lui, Jianhua Zhao, Zhenguo Wu, Haishan Zeng

Bibliographic record

VenueTheranostics · 2017
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsVancouver Coastal Health Research InstituteVancouver Coastal HealthBC Cancer Agency
FundersCanadian Institutes of Health ResearchBC HydroVGH and UBC Hospital FoundationCanadian Dermatology Foundation
KeywordsFemtosecondLaserMicroscopeCollateral damageAbsorption (acoustics)OpticsMicroscopyMaterials scienceBiomedical engineeringOptoelectronicsPhysicsMedicine

Abstract

fetched live from OpenAlex

The successful application of lasers in the treatment of skin diseases and cosmetic surgery is largely based on the principle of conventional selective photothermolysis which relies strongly on the difference in the absorption between the therapeutic target and its surroundings. However, when the differentiation in absorption is not sufficient, collateral damage would occur due to indiscriminate and nonspecific tissue heating. To deal with such cases, we introduce a novel spatially selective photothermolysis method based on multiphoton absorption in which the radiant energy of a tightly focused near-infrared femtosecond laser beam can be directed spatially by aiming the laser focal point to the target of interest. We construct a multimodal optical microscope to perform and monitor the spatially selective photothermolysis. We demonstrate that precise alteration of the targeted tissue is achieved while leaving surrounding tissue intact by choosing appropriate femtosecond laser exposure with multimodal optical microscopy monitoring in real time.

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 score0.308
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.351
Teacher spread0.321 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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