Yasmin Jayasinghe

STUDY QUESTION: Do premenarcheal girls have a high proportion of morphologically abnormal ovarian follicles? SUMMARY ANSWER: A higher prevalence of follicles with an abnormal number of oocytes and nuclei was found in premenarcheal ovarian tissue compared with postmenarcheal tissue. WHAT IS KNOWN ALREADY: Morphological aberrations of ovarian follicles, including multi-nuclear oocytes and multi-ovular follicles, have been documented across species, including humans. These specific morphological aberrations, together with other types of abnormal follicles, are more prevalent in the ovaries of children and decline in number with age. However, detailed studies of the prevalence of multi-nuclear and multi-ovular follicles in paediatric ovarian tissue are limited. STUDY DESIGN, SIZE, DURATION: This was an observational study of the ovaries of children and adolescents who underwent ovarian cryopreservation prior to chemotherapy. PARTICIPANTS/MATERIALS, SETTING, METHODS: The study recruited participants aged 0–18 years who underwent ovarian tissue cryopreservation at a single centre. Haematoxylin and eosin-stained slides of ovarian biopsies were scanned and analysed to determine follicle number and aberrations in morphology and the cortical area was measured. Abnormal follicles were defined as those with an abnormal number of oocytes or oocyte nuclei. Relationships with menarcheal status (premenarcheal; postmenarcheal) and age were assessed. MAIN RESULTS AND THE ROLE OF CHANCE: A total of 149 participants’ ovarian samples were evaluated in the study, of whom 67 (45%) had follicles with abnormal numbers of oocytes or oocyte nuclei. Amongst 117 participants who had adequate follicle numbers, these abnormal follicle types accounted for 3.8% (902/23 891) of the total number of follicles and were significantly more common in premenarcheal girls (52/80; 65.0%) compared with postmenarcheal girls (15/37; 40.5%; P = 0.013). Ovular abnormalities (in 76.7% of abnormal follicles) were more common than nuclear abnormalities (in 23.3% of abnormal follicles; P < 0.001). Bi-ovular primordial follicles were the most common abnormality, comprising 54% of the total abnormal follicle population and were present in 41.9% of participants. LIMITATIONS, REASONS FOR CAUTION: Small ovarian biopsies were analysed, which may not fully represent the whole ovary. There is also a limited number of participants in each menarcheal group. WIDER IMPLICATIONS OF THE FINDINGS: Our study highlights that although follicle numbers are high in the prepubertal ovary, there was also a high proportion of follicles containing abnormal numbers of oocytes and oocyte nuclei. Further longitudinal and functional studies are recommended to determine whether this impacts fertility and the longevity of function following ovarian tissue grafting for restoration of fertility in these participants. Our work supports a separate multioocytic follicle class within the NICHD follicle classification, and we suggest expanding it to include a distinct multinuclear follicle category (oocyte contains more than one nucleus), as these likely represent different aberrations in oogenesis or folliculogenesis. This classification with clear definitions and nomenclature may improve consistency in reporting and facilitate future research. FUNDING: This work was supported by an MRFF Grant (MRFAR000308) supporting the funding of coauthors M.J. and M.A. Y.J. is supported through a Dame Kate Campbell Fellowship, The University of Melbourne. Co-authors V.G.-L. and J.Y.M. are supported through National Institute of Health (NIH) Z1A HD009005. DISCLOSURES: No conflict of interests are declared. TRIAL REGISTRATION NUMBER: n/a.

Dysmenorrhea and heavy menstrual bleeding (HMB) are common gynecological conditions that substantially affect physical, emotional, and social well-being of young women (ACOG Committee on Practice B...

Comprehensive sexuality education (CSE) equips youth with knowledge, life skills and attitudes to achieve healthy respectful relationships, and reach their

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk-prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2,679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817-0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666-0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

Background: Menstrual cups offer a cost‐effective and environmentally sustainable product for many young people. While clinical trials have shown their safety and effectiveness, no studies have investigated their performance in real‐world use.Aims: To describe the acceptability and functionality (continuation, discomforts, leakage, and adverse events) of menstrual cups and investigate the supportive role of product knowledge.Materials and Methods: A cross‐sectional online survey of 530 people aged 15–24 living in Australia who had ever used a menstrual cup, recruited via a menstrual cycle tracking application.Results: More than half of participants (55%) were still using their first menstrual cup at the time of the survey, 16% had switched to an alternative cup, and 29% had discontinued use. In their first cycle of use, 54% of participants reported leakage and 25% reported pain or discomfort with the cup in place. Many participants (45%) reported being unable to remove the cup on their first attempt, with subsequently 17% requiring help to remove it, and 2% reported displacement of an intra‐uterine device during removal. These figures decreased for subsequent cycles. Half of the participants were aware prior to using a cup that different cup models may offer a better fit for different individuals. This knowledge was associated with decreased odds of needing help to remove the cup (adjusted odds ratio 0.57, 95% CI 0.35–0.94) or discontinuing use (adjusted relative risk ratio 0.66, 95% CI 0.44–1.00).Conclusions: Difficulties using menstrual cups are common in real‐world use and higher than reported in clinical trials. Improved education provision may support more positive user experiences.