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Record W1965946815 · doi:10.4021/jcgo.v1i4-5.47

The Effects of Ascorbic Acid on the Estrogen/Progesteron Levels in the Isolated Rabbit Uterine Muscle

2012· article· en· W1965946815 on OpenAlexvenueno aff
Mehmet Sühha Bostancı, Merih Bayram, Zuhal Keskil

Bibliographic record

VenueJournal of Clinical Gynecology and Obstetrics · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRabbits: Nutrition, Reproduction, Health
Canadian institutionsnot available
Fundersnot available
KeywordsEstrogenEndocrinologyInternal medicineAscorbic acidMedicineHormoneEstrous cycleContext (archaeology)ChemistryBiology

Abstract

fetched live from OpenAlex

Background: The study was carried out to examine the effect of Ascorbic Acid (AsA) on estrogen/progesterone levels in the isolated non-pregnant rabbit uterine muscle.  Methods: Twelve non-pregnant rabbits were randomized into two groups as the rabbits pretreated with AsA (n = 6) and the rabbits not pretreated with AsA (n = 6). After 30 mg/kg AsA was administered intraperitoneally, serum and tissue (uterine smooth muscle) levels of estrogen and progesterone were measured.  Results: AsA at the above mentioned concentration caused a significant increase in tissue estrogen/progesterone ratio (P < 0.01), while did not induce any change in the hormone ratio in the serum. The increase in the tissue estrogen/progesterone ratio was resulted from both the decrease of the progesterone levels and the increase of estrogen levels (P < 0.001).  Conclusion: In this context, the fact that the tissue levels of hormones and autacoids is more important than the serum concentrations should be concidered. The findings of this study indicate that in comparison to the control group, AsA causes decrease in the non-pregnant isolated uterine smooth muscle tissue levels of progesterone and increase in the non-pregnant isolated uterine smooth muscle tissue levels of estrogen while the serum levels keep constant. doi: http://dx.doi.org/10.4021/jcgo47w

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)0.005
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.330
Teacher spread0.272 · 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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Gynecology and ObstetricsSame topicRabbits: Nutrition, Reproduction, HealthFrench-language works237,207