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Quantification of perfusion in mouse ovarian grafts for fertility preservation

2010· article· en· W3168185259 on OpenAlexaff
Anna Trujillo, Eujin Kim, Catherine Theodoropoulos, Roger G. Gosden

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsFujiFilm VisualSonics (Canada)
Fundersnot available
KeywordsPerfusionMedicineSterilityFertility preservationOvaryOvarian tissueAngiogenesisHormoneSurgeryFertilityInternal medicineUrologyBiology

Abstract

fetched live from OpenAlex

A serious side effect for some young women undergoing cancer treatment is premature ovarian failure and sterility. To preserve fertility ovarian cortex may be harvested, cryogenically preserved and re‐implanted in patients who return to full health. In the absence of vascular anastomosis, tissue is ischemic and the grafting will only be successful if there is rapid reperfusion to minimize loss of follicles. As a model of the clinical procedure, mouse ovaries were grafted to skeletal muscle and monitored non‐invasively using MicroMarker contrast agent and high‐frequency ultrasound (Vevo770, VisualSonics). Vascular perfusion and relative blood volume approached the levels of intact ovaries after seven days. The results demonstrate that microultrasound can monitor reperfusion in small ischemic grafts and is potentially useful for studying the effects of drugs and hormones as well as measuring the progress of angiogenesis. The study was funded by VisualSonics Inc.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.243
Teacher spread0.221 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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