The Effect of Rubus idaeus Polyphenols Extract in Induced Endometriosis in Rats.

Endometriosis is a common gynecological condition with a complex physio-pathological background. This study aimed to assess the role of Rubus idaeus leaf extract (RiDE) as a potential therapeutic agent in reducing the size of the endometriotic lesions and modulate the plasma expression of MMP-2, MMP-9, and TGF-ß1. The endometriotic lesions were induced in a rat model by the autologous transplant of endometrium. Thirty-six female rats, Wistar breed, with induced endometriosis, were divided into four groups and underwent treatment for 28 days. The CTRL group received 0.5 mL/day of the vehicle; the DG group received 1 mg/kg b.w./day dienogest; the RiDG group received 0.25 mL/kg b.w./day RiDE and the D+RiDG group received 1 mg/kg b.w./day dienogest and 0.25 mL/kg b.w./day RiDE, respectively. Rats' weight, endometriotic lesion diameter and grade, and plasma levels of MMP-2, MMP-9, and TGF-ß1 were assessed before and after treatment. The administration of RiDE in association with dienogest vs. dienogest determined a lower weight gain and a reduction in diameter of the endometriotic lesions. RiDE administration restored MMP2 and MMP9 plasma levels to initial conditions. Rubus idaeus extract may help in reducing dienogest-associated weight gain, lower the size of endometriotic lesions, and have anti-inflammatory effects through MMP2 and MMP9 reduction.

Ultrasonography in the Differentiation of Endometriomas from Hemorrhagic Ovarian Cysts: The Role of Texture Analysis.

The ultrasonographic (US) features of endometriomas and hemorrhagic ovarian cysts (HOCs) are often overlapping. With the emergence of new computer-aided diagnosis techniques, this is the first study to investigate whether texture analysis (TA) could improve the discrimination between the two lesions in comparison with classic US evaluation. Fifty-six ovarian cysts (endometriomas, 30; HOCs, 26) were retrospectively included. Four classic US features of endometriomas (low-level internal echoes, perceptible walls, no solid components, and less than five locules) and 275 texture parameters were assessed for every lesion, and the ability to identify endometriomas was evaluated through univariate, multivariate, and receiver operating characteristics analyses. The sensitivity (Se) and specificity (Sp) were calculated with 95% confidence intervals (CIs). The texture model, consisting of seven independent predictors (five variations of difference of variance, image contrast, and the 10th percentile; 100% Se and 100% Sp), was able to outperform the ultrasound model composed of three independent features (low-level internal echoes, perceptible walls, and less than five locules; 74.19% Se and 84.62% Sp) in the diagnosis of endometriomas. The TA showed statistically significant differences between the groups and high diagnostic value, but it remains unclear if the textures reflect the intrinsic histological characteristics of the two lesions.