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Record W1997742639 · doi:10.1159/000179023

Capillary Ultrastructure and Functional Capillary Density

2008· article· en· W1997742639 on OpenAlexaff
Odile Mathieu‐Costello, L H Manciet, Karel Tyml

Bibliographic record

VenueInternational Journal of Microcirculation · 2008
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsWestern University
FundersNational Heart, Lung, and Blood Institute
KeywordsExtensor digitorum longus muscleMicrocirculationAnatomyIschemiaSkeletal musclePerfusionEdemaReactive hyperemiaEndotheliumExtensor digitorum muscleSarcolemmaInternal medicineAtrophyCirculatory systemUltrastructureChemistryBiologyEndocrinologyVasodilationMedicineSoleus muscle

Abstract

fetched live from OpenAlex

We briefly summarize our findings on alterations in capillary structure in skeletal muscle and heart in response to up to 30 min of ischemia. In frog sartorius muscle, reactive hyperemia was absent in atrophy. Increased spatial heterogeneity of red cell velocity in individual capillaries was observed, as were increases in the percentage of capillaries with damaged endothelium and white cell volume density in capillaries. Examination of the effect of aging on the response of the vascular bed to 30 min ischemia in extensor digitorum longus muscle of Fisher 344 rats suggested that the lack of postischemic hyperemia and structural alterations in frog muscle were related to disuse rather than aging per se. However, the specific study of disuse in rat extensor digitorum longus muscle after chronic application of tetrodotoxin revealed both capillary damage and a postischemic hyperemic response. It suggested an effect of the degree of tissue deterioration on the hyperemic response after short-term disuse in rat muscle, compared to longer-term atrophy in frog. Morphometric data in isolated rabbit heart suggested a link between microvascular compression as a result of tissue edema and decreased perfusion after 30 min total ischemia.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.296

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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 designObservational
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

Citations8
Published2008
Admission routes1
Has abstractyes

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