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Record W2945545459 · doi:10.1126/sciadv.aan9388

Precise closure of single blood vessels via multiphoton absorption–based photothermolysis

2019· article· en· W2945545459 on OpenAlexafffund
Yimei Huang, Zhenguo Wu, Harvey Lui, Jianhua Zhao, Shusen Xie, Haishan Zeng

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal HealthBC Cancer Agency
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaCanadian Dermatology Foundation
KeywordsAbsorption (acoustics)Closure (psychology)LaserComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

We report a novel approach to selectively close single blood vessels within tissue using multiphoton absorption-based photothermolysis (multiphoton photothermolysis) without the need of exogenous agents. The treatment process is monitored by in vivo reflectance confocal microscopy in real time. Closure of single targeted vessels of varying sizes ranging from capillaries to venules was demonstrated. We also demonstrated that deeply situated blood vessels could be closed precisely while preserving adjacent overlying superficial blood vessels. In vivo confocal Raman spectroscopy of the treatment sites confirmed vessel closure as being mediated by local coagulative damage. Partial vessel occlusion could be achieved, and it is accompanied by increased intravascular blood cell speed. Multiphoton photothermolysis under real-time reflectance confocal imaging guidance provides a novel precision medicine approach for noninvasive, precise microsurgery treatment of vascular diseases on a per-vessel/per-lesion basis. The method could also be used for building ischemic stroke models for basic biology study.

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 score0.011
Threshold uncertainty score0.458

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 communication0.0000.001
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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