L-type (loaf) cervical secretion crystallization directionality biomarker study

Abstract

Background: Cervical discharge is considered a hydrogel, which is part liquid and part solid. Under physiological conditions, the characteristic fern-leaf arborization is proportional to the circulating estradiol concentration and maximal in the preovulatory phase. Crystallization of the cervical secretion is a process of dehydration with the arrangement of sodium and potassium chloride crystals around a main axis composed of mucin. L-type (Loaf) (L) is proportional to the circulating estradiol concentration and is higher in the Fertile Window (FW). Currently, most studies comparing the FW with cervical discharge include the biophysical fertile window (BFW) which is 6 days before the estimated day of ovulation (EDO). Methods: Samples were taken from the vulvovaginal region within the FW using the Creighton Model System for Fertility Care (CrMS). Optical images of the samples were digitized to perform a binarization, skeletonization, and crystallization directionality study of the scanned branches. These samples taken from the vulvovaginal secretion were not collected from the endocervix. They were recorded by the Vaginal Secretion Recording System (VDRS) using CrMS. The FW was taken into account from 6 days before the peak (P) day according to CrMS. A statistical study was performed by comparing vulvovaginal crystallization samples including -6 day EDO in FW. Results: Out of 29 samples, 58.6% deemed true positives, 34% were true negatives, 6.8% represented 2 false positives, and no cases were false negatives (p < 0.001). Conclusions: Our analysis of the statistical distribution of the branching angles of the crystallization of cervical secretions allowed us to detect a significant contribution of 90-degree frecuencies, indicating, the existence of an L-type pattern in the FW samples that we studied. The skeletonization of the studied images together with a FW indicator allowed us to identify this crystallization pattern. We therefore propose this feature detection mechanism as a novel biomarker of fertility.