Two main types of cervical mucus have been described during the menstrual cycle: oestrogenic and progestative. Each category shows diverse morphological and functional features from the reproductive point of view. Traditionally, this change has been approached by analysing morphological patterns. In fact, a mesh model has been described for cervical mucus, structurally composed of fibrillar subunits with a parallel orientation, together with another model in a characteristic network shape with canalicular units, but the real model is not clear. The objective of our work was to study the different morphological structures of the mucus, as related to the day of follicular rupture (considered as day 0) determined by ultrasound. Cervical mucus samples were obtained from the cervical canal with an ASPIRETTEtrade mark from day -4 to day +1 of the menstrual cycle. Samples were fixed and dried by critical point. The ultrastructure was examined with scanning electron microscopy. The presence of three types of oestrogenic and one type of progestative cervical mucus was confirmed in this period. Our paper shows different types of ultrastructure in the oestrogenic mucus in relation to ovulation, which would help to understand the interaction between male gametes and cervical mucus in migration through the female genital tract.
PMID 16006441 16006441 DOI 10.1093/jmicro/dfh106 10.1093/jmicro/dfh106 Ceric et al. 2005, Ceric 2005
Cite this article
Ceric, F., Silva, D., & Vigil, P. (2005). Ultrastructure of the human periovulatory cervical mucus. Journal of electron microscopy, 54(5), 479-484. https://doi.org/10.1093/jmicro/dfh106
Ceric F, Silva D, Vigil P. Ultrastructure of the human periovulatory cervical mucus. J Electron Microsc (Tokyo). 2005;54(5):479-484. doi:10.1093/jmicro/dfh106
Ceric, F., et al. "Ultrastructure of the human periovulatory cervical mucus." Journal of electron microscopy, vol. 54, no. 5, 2005, pp. 479-484.
The role of minerals in female fertility, particularly in relation to the menstrual cycle, presents a complex area of study that underscores the interplay between nutrition and reproductive health. This narrative review aims to elucidate the impacts of minerals on key aspects of the reproductive system: hormonal regulation, ovarian function and ovulation, endometrial health, and oxidative stress. Despite the attention given to specific micronutrients in relation to reproductive disorders, there is a noticeable absence of a comprehensive review focusing on the impact of minerals throughout the menstrual cycle on female fertility. This narrative review aims to address this gap by examining the influence of minerals on reproductive health. Each mineral's contribution is explored in detail to provide a clearer picture of its importance in supporting female fertility. This comprehensive analysis not only enhances our knowledge of reproductive health but also offers clinicians valuable insights into potential therapeutic strategies and the recommended intake of minerals to promote female reproductive well-being, considering the menstrual cycle. This review stands as the first to offer such a detailed examination of minerals in the context of the menstrual cycle, aiming to elevate the understanding of their critical role in female fertility and reproductive health.
Goodale BM et al., 2019·J Med Internet Res·
Open Access
Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.
The purpose of this review was to determine whether there is evidence that ovulation can occur in women using hormonal contraceptives and whether these drugs might inhibit implantation. We performed a systematic review of the published English-language literature from 1990 to the present which included studies on the hormonal milieu following egg release in women using any hormonal contraceptive method. High circulating estrogens and progestins in the follicular phase appear to induce dysfunctional ovulation, where follicular rupture occurs but is followed by low or absent corpus luteum production of progesterone. Hoogland scoring of ovulatory activity may inadvertently obscure the reality of ovum release by limiting the term "ovulation" to those instances where follicular rupture is followed by production of a threshold level of luteal progesterone, sufficient to sustain fertilization, implantation, and the end point of a positive β-human chorionic gonadotropin. However, follicular ruptures and egg release with subsequent low progesterone output have been documented in women using hormonal contraception. In the absence of specific ovulation and fertilization markers, follicular rupture should be considered the best marker for egg release and potential fertilization. Women using hormonal contraceptives may produce more eggs than previously described by established criteria; moreover, suboptimal luteal progesterone production may be more likely than previously acknowledged, which may contribute to embryo loss. This information should be included in informed consent for women who are considering the use of hormonal contraception. For this study, the authors looked at English-language research articles that focused on how hormonal birth control, such as the birth control pill, may affect very early human embryos. The authors found that abnormal ovulation, or release of an egg followed by abnormal hormone levels, may often occur in women using hormonal birth control. This may increase the number of very early human embryos who are lost before a pregnancy test becomes positive. For women who are thinking about using hormonal birth control, this is important information to consider.
Sohda S et al., 2017·J Med Internet Res·
Open Access
There are many mobile phone apps aimed at helping women map their ovulation and menstrual cycles and facilitating successful conception (or avoiding pregnancy). These apps usually ask users to input various biological features and have accumulated the menstrual cycle data of a vast number of women. The purpose of our study was to clarify how the data obtained from a self-tracking health app for female mobile phone users can be used to improve the accuracy of prediction of the date of next ovulation. Using the data of 7043 women who had reliable menstrual and ovulation records out of 8,000,000 users of a mobile phone app of a health care service, we analyzed the relationship between the menstrual cycle length, follicular phase length, and luteal phase length. Then we fitted a linear function to the relationship between the length of the menstrual cycle and timing of ovulation and compared it with the existing calendar-based methods. The correlation between the length of the menstrual cycle and the length of the follicular phase was stronger than the correlation between the length of the menstrual cycle and the length of the luteal phase, and there was a positive correlation between the lengths of past and future menstrual cycles. A strong positive correlation was also found between the mean length of past cycles and the length of the follicular phase. The correlation between the mean cycle length and the luteal phase length was also statistically significant. In most of the subjects, our method (ie, the calendar-based method based on the optimized function) outperformed the Ogino method of predicting the next ovulation date. Our method also outperformed the ovulation date prediction method that assumes the middle day of a mean menstrual cycle as the date of the next ovulation. The large number of subjects allowed us to capture the relationships between the lengths of the menstrual cycle, follicular phase, and luteal phase in more detail than previous studies. We then demonstrated how the present calendar methods could be improved by the better grouping of women. This study suggested that even without integrating various biological metrics, the dataset collected by a self-tracking app can be used to develop formulas that predict the ovulation day when the data are aggregated. Because the method that we developed requires data only on the first day of menstruation, it would be the best option for couples during the early stages of their attempt to have a baby or for those who want to avoid the cost associated with other methods. Moreover, the result will be the baseline for more advanced methods that integrate other biological metrics.