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Record W2008188698 · doi:10.1262/jrd.51.161

Acceleration of Follicular Development by Administration of Vascular Endothelial Growth Factor in Cycling Female Rats

2005· article· en· W2008188698 on OpenAlexaff
Koji Iijima, Jin-Yi Jiang, Takashi Shimizu, Hiroshi Sasada, Eimei Sato

Bibliographic record

VenueJournal of Reproduction and Development · 2005
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsAngiogenesisFollicular phaseOvulationInternal medicineEndocrinologyVascular endothelial growth factorVascular endothelial growth factor ABiologyAndrologyMedicineVEGF receptorsHormone

Abstract

fetched live from OpenAlex

To address the role of follicular angiogenesis in the determination of ovulatory follicles and the effects of different vascular endothelial growth factor (VEGF) isoforms on follicular angiogenesis and development, mature female rats were treated with an angiogenic inhibitor (TNP-470), and also with VEGF 120 or 164 at different dosages (0.4, 0.8, 4.0 or 8.0 microg/kg body weight) for 3 days during the estrous cycle. Ovarian follicular angiogenesis, the population of large follicles and ovulation were examined. VEGF 120 (0.8 microg/kg) and 164 (8.0 microg/kg) treatments stimulated follicular angiogenesis in the theca interna layer, while TNP-470 treatment showed severe depression of follicular angiogenesis, and completely inhibited ovulation. After administration of VEGF 120 or 164, the number of healthy preovulatory follicles and ovulated oocytes increased significantly, concomitantly with a decrease in the number of atretic preovulatory follicles. The oocytes ovulated had normal fertilizability and developed to term with the same litter size as in the control rats. Our findings suggest that follicular angiogenesis may be a determinant of follicular development during the periovulatory phase, and that VEGF isoforms may play different important roles in regulating follicular 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)0.001
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 score0.155
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.290
Teacher spread0.257 · 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

Citations45
Published2005
Admission routes1
Has abstractyes

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