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Record W3013028291 · doi:10.1111/php.13264

Singlet Oxygen Luminescence Image in Blood Vessels During Vascular‐Targeted Photodynamic Therapy

2020· article· en· W3013028291 on OpenAlexaff
Lisheng Lin, Huiyun Lin, Yi Shen, Defu Chen, Ying Gu, Brian C. Wilson, Buhong Li

Bibliographic record

VenuePhotochemistry and Photobiology · 2020
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersNational Natural Science Foundation of China
KeywordsLuminescenceSinglet oxygenPhotodynamic therapyOptical imagingMaterials scienceChemistryOxygenPhotochemistryOpticsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Abstract Singlet oxygen (1O2) is widely regarded as the main cytotoxic substance that induces the biological damage for photodynamic therapy (PDT). In this study, the previously developed near‐infrared (NIR) optical imaging system was optimized for fast imaging of 1O2 luminescence. The optical imaging system enables direct imaging of 1O2 luminescence in blood vessels within 2 s during vascular‐targeted PDT (V‐PDT), which makes this system extremely practical for in vivo studies. The dependence of RB concentration on 1O2 luminescence image was investigated for V‐PDT, and the data imply that 1270 nm signal is attributed to 1O2 luminescence. The imaging system operates with a field of view of 9.60 × 7.68 mm2 and a spatial resolution of 30 μm, which holds the potential to elucidate the correlation between cumulative 1O2 luminescence and vasoconstriction for V‐PDT.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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