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Record W2133158139 · doi:10.1117/1.3000431

Monitoring angiogenesis noninvasively with near-infrared spectroscopy

2008· article· en· W2133158139 on OpenAlexafffund
Jeff F. Dunn, Qiong Zhang, Ying Wu, Sathyanarayanan Srinivasan, Michael R. Smith, Raymond A. Shaw

Bibliographic record

VenueJournal of Biomedical Optics · 2008
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsNational Research Council Institute for BiodiagnosticsUniversity of Calgary
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsHemoglobinHematocritAngiogenesisNear-infrared spectroscopySpectroscopyCerebral blood volumeBlood volumeNuclear magnetic resonanceChemistryBiomedical engineeringMedicineMaterials scienceInternal medicineCerebral blood flowOpticsPhysics

Abstract

fetched live from OpenAlex

Near-infrared (NIR) spectroscopy is used to quantify cerebral blood volume (CBV) as a marker of angiogenesis (formation of new blood vessels). Rats are exposed to chronic hypoxia for 3 weeks at half atmospheric pressure to stimulate angiogenesis, and second-differential NIR spectroscopy is used to quantify total cerebral hemoglobin before and after angiogenesis. The cerebral hemoglobin (from broadband NIR spectroscopy), and the large vessel hemoglobin and hematocrit (from blood samples), are used to derive values for the calculation of CBV. The total hemoglobin in brain is 46.6+/-1.9 micromoll (mean+/-SD, n=5) preacclimation and increases by 72% postacclimation. CBV is initially 3.26+/-0.41% v/v and increases by 31% with acclimation. Each individual animal shows a measureable increase in CBV. This study indicates that NIR broadband spectroscopy can be used for repeated measurements of CBV and can be applied as a noninvasive method to study 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 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 score0.463
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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
Published2008
Admission routes2
Has abstractyes

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