121 THREE-DIMENSIONAL ASSESSMENT OF EARLY CORPUS LUTEUM VASCULARITY IN BUFFALO (BUBALUS BUBALIS)
Notice bibliographique
Résumé
The aim of the study was to develop an objective method to assess the vascular flow to the early corpus luteum (CL) in buffaloes using colour Doppler ultrasound data. Our hypothesis was that 3-dimensional (3D) volumetric analysis of vascularity would demonstrate lower variability between animals compared with conventional 2-dimensional (2D) analysis of single images. Wave emergence and ovulation was synchronized in buffalo (n = 16) using prostaglandin-GnRH based protocols. Colour Doppler ultrasonography (MyLab5, 7.5-MHz linear array, colour gain 65%) was performed daily from Day -2 to 4 (Day 0 = ovulation). Video clips of the ovaries (20 s at 18–28 frames per second, AVI) were recorded by slow and uniform free-hand movement of the transducer. Day 4 CL was used for analysis of vascular area and volume. For 2D vascularity assessment, 3 images (800 × 652 pixels, RGB, BMP) of each CL (at maximum apparent vascularity) were acquired through the clip image function on the ultrasound machine and analysed by ImageJ (Fiji) software (NIH, Bethesda, MD, USA). For 3D vascularity assessment, a portion of the video clip encompassing an entire ovary was identified and exported as a series of 2D TIFF images using Videomach software. The ultrasound scale bar was used to calculate the number of pixels per millimetre and to calibrate the X (horizontal) and Y (vertical) dimensions. For 2D analyses, the CL boundary was drawn using the free-hand manual selection tool in Fiji, the area of the CL (mm2) was recorded, and the border was then enlarged by 1.5 mm to include the peripheral vascular region of the CL. The colour threshold was adjusted to select the vascular region. The 2D vascularity score was calculated as the ratio of the coloured area to the enlarged luteal area. For 3D volumetric analyses, each series of TIFF images was imported as an image sequence in Fiji and colour thresholding (similar to 2D analysis) was applied to save a second TIFF series containing luteal vascular regions (coloured areas) only. The remaining volumetric analyses were completed in Imaris software using the ovarian volume (original TIFF series) and luteal vascular volume (second TIFF series) as separate channels. The Z-dimension thickness of each image was estimated by using the dimensions of a follicle within the same ovary (Z-axis diameter = mean diameter along X- and Y-axes). Similar to 2D analyses, the volume of the CL was obtained by drawing a border along the edge of the CL, the CL border was enlarged by 1.5 mm, and a 3D vascularity score was obtained by building a surface on the luteal vascular image and calculating the vascular to luteal volume ratio. The 2D vascularity score differed from 3D vascularity score (0.21 ± 0.02 v. 0.13 ± 0.02, paired t-test P < 0.01); however, variance did not differ (Bartlett’s test P = 0.32). Our initial results support the notion that the described technique of quantifying vascular volume of the corpus luteum may decrease the technical variability during image assessment and therefore better reflect the true vascularity compared with 2D image analyses. Research was supported by a grant from the Natural Sciences and Engineering Research Council of Canada.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».