Contrast Imaging Ultrasound Detects Abnormalities in the Marmoset Ovary
Bibliographic record
Abstract
The development of a functional vascular tree within the primate ovary is critical for reproductive health. To determine the efficacy of contrast agents to image the microvascular environment within the primate ovary, contrast ultrasonography was performed in six reproductive-aged female common marmosets (Callithrix jacchus) during the late luteal phase of the cycle, following injection of Sonovue™. Regions of interest (ROIs), representing the corpus luteum (CL) and noncorpus luteum ovarian tissue (NCLOT), were selected during gray-scale B-mode ultrasound imaging. The magnitude of backscatter intensity of CL and NCLOT ROIs were calculated in XnView, post hoc: subsequent gamma-variate modeling was implemented in Matlab to determine perfusion parameters. Histological analysis of these ovaries revealed a total of 11 CL, nine of which were identified during contrast ultrasonography. The median enhancement ratio was significantly increased in the CL (5.54AU; 95% CI -2.21-68.71) compared to the NCLOT (2.82AU; 95% CI 2.73-15.06; P < 0.05). There was no difference in time parameters between the CL and NCLOT. An additional avascular ROI was identified in the ovary of Animal 5, both histologically and by ultrasonography. This cystic ROI displayed a markedly lower enhancement ratio (0.79AU) and higher time parameters than mean CL and NCLOT, including time to peak and time to wash out. These data demonstrate, for the first time, the ability of commercially available contrast agents, to differentiate structures within the nonhuman primate ovary. Contrast-enhanced ultrasonography has a promising future in reproductive medicine.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".