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Record W2086238899 · doi:10.1071/rdv22n1ab220

220 ULTRASOUND BIOMICROSCOPY: A NONINVASIVE APPROACH FOR STUDYING THE DEVELOPMENT OF SMALL FOLLICLES IN THE BOVINE OVARY

2009· article· en· W2086238899 on OpenAlexaffabout
Lukáš Pfeifer, Gregory P. Adams, Roger A. Pierson, L. G. B. Siqueira, Jaswant Singh

Bibliographic record

VenueReproduction Fertility and Development · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of SaskatchewanSaskatchewan Polytechnic
Fundersnot available
KeywordsAntral follicleAntrumUltrasoundOvaryUltrasonographyMedicineTransrectal ultrasonographyNuclear medicineIn vivoBiologyRadiologyInternal medicineProstate

Abstract

fetched live from OpenAlex

Ultrasonography has revolutionized our understanding of the dynamics of antral follicles >3 mm; however, very little is known about the growth patterns of small antral follicles (0.2-2 mm). Ultrasound biomicroscopy (UBM) permits in vivo imaging of tissues with spatial resolution of 50 µm or more, although the depth of penetration is limited to about 2.5 cm. Our objectives were to (1) evaluate the feasibility of UBM for imaging cow v. heifer ovaries in vivo for study of small antral follicles; (2) compare transvaginal v. transrectal imaging approaches; and (3) compare the echotextures in images acquired by UBM and conventional ultrasonography of the wall and antrum (follicular fluid) from follicles >3 mm. Mature cows (n = 5) and prepubertal heifers (11-13 months; n = 5) were examined once irrespective of ovarian status using conventional ultrasonography (Aloka 900, Tokyo, Japan) with a 7.5-MHz transducer via a transrectal approach, and with a 5-MHz transducer via a transvaginal approach. A second series of examinations was performed using an ultrasound biomicroscope (Visualsonics Vevo 660, Toronto, Ontario, Canada) equipped with either a 40-MHz probe (transvaginal) or a 30-MHz probe (transrectal). All examinations were recorded digitally in real time. Spot-analyses of images of the antrum and line-analyses of images of the wall of follicles >3 mm were performed using a custom-developed software program (Synergyne 2 version 2.8, Saskatoon, Saskatchewan, Canada). Data were analyzed by 2-sample t-test or two-way ANOVA. Using the transvaginal approach, more follicles were detected by UBM than by conventional ultrasonography in heifers (40.4 ± 17.4 v. 14.6 ± 5.6; P = 0.01) but not in cows (38.3 ± 16.4 v. 21.7 ± 6.2; P = 0.20). Using the transrectal approach, however, more follicles were detected by conventional ultrasonography than by UBM in both heifers (17.6 ± 4.9 v. 8.6 ± 5.6; P = 0.02) and cows (20.3 ± 7 v. 5.3 ± 6.1; P = 0.04). More small follicles (<3 mm) were detected using the transvaginal approach with UBM than by using conventional ultrasonography in both heifers (32.4 ± 4.24 v. 7.2 ± 1.4; P < 0.0001) and cows (35.0 ± 13.8 v. 10.7 ± 7.5; P = 0.0013). The number of medium (3-5 mm) and large (> 5 mm) follicles detected using the transvaginal approach was similar between UBM and conventional ultrasonography in both heifers and cows (P > 0.90). For transrectal UBM imaging, both distance between the scanhead and the ovary and signal attenuation due to intervening tissues resulted in poor image quality. Lower signal attenuation caused by thinner vaginal walls in heifers than in cows resulted in better quality of UBM imaging in the transvaginal approach. Mean pixel values and heterogeneity of images of the follicle antrum were higher in UBM images than in conventional ultrasonography images. In conclusion, UBM using a transvaginal approach may be used for the in vivo assessment of small ovarian follicles in cattle. However, sequential monitoring of follicular development still needs to be tested and some limitations of the technique, such as the approach, need to be considered. This study was supported by NSERC and CIHR, Canada. Luiz Pfeifer was supported by CAPES foundation, Brazil.

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)0.002
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 score0.911
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.067
GPT teacher head0.262
Teacher spread0.195 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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