Quantitative approaches in clinical reproductive endocrinology

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes

The prospect of artificial intelligence to personalize assisted reproductive technology

The Department of Metabolism, Digestion, and Reproduction is funded by grants from the MRC and NIHR. S.H. is supported by the UKRI CDT in AI for Healthcare http://ai4health.io (EP/S023283/1). A.A. is supported by an NIHR Clinician Scientist Award (CS-2018-18-ST2-002). M.V. and K.T.A. are supported by the EPSRC (EP/T017856/1). W.S.D. is supported by an NIHR Senior Investigator Award (NIHR202371).Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.Peer reviewe

Insulin-like peptide 3 (INSL3) in congenital hypogonadotrophic hypogonadism (CHH) in boys with delayed puberty and adult men

Background: Delayed puberty in males is almost invariably associated with constitutional delay of growth and puberty (CDGP) or congenital hypogonadotrophic hypogonadism (CHH). Establishing the cause at presentation is challenging, with “red flag” features of CHH commonly overlooked. Thus, several markers have been evaluated in both the basal state or after stimulation e.g. with gonadotrophin releasing hormone agonist (GnRHa). Insulin-like peptide 3 (INSL3) is a constitutive secretory product of Leydig cells and thus a possible candidate marker, but there have been limited data examining its role in distinguishing CDGP from CHH. In this manuscript, we assess INSL3 and inhibin B (INB) in two cohorts: 1. Adolescent boys with delayed puberty due to CDGP or CHH and 2. Adult men, both eugonadal and having CHH. Materials and methods: Retrospective cohort studies of 60 boys with CDGP or CHH, as well as 44 adult men who were either eugonadal or had CHH, in whom INSL3, INB, testosterone and gonadotrophins were measured. Cohort 1: Boys with delayed puberty aged 13-17 years (51 with CDGP and 9 with CHH) who had GnRHa stimulation (subcutaneous triptorelin 100mcg), previously reported with respect to INB. Cohort 2: Adult cohort of 44 men (22 eugonadal men and 22 men with CHH), previously reported with respect to gonadotrophin responses to kisspeptin-54. Results: Median INSL3 was higher in boys with CDGP than CHH (0.35 vs 0.15 ng/ml; p=0.0002). Similarly, in adult men, median INSL3 was higher in eugonadal men than CHH (1.08 vs 0.05 ng/ml; p<0.0001). However, INSL3 more accurately differentiated CHH in adult men than in boys with delayed puberty (auROC with 95% CI in adult men: 100%, 100-100%; boys with delayed puberty: 86.7%, 77.7-95.7%). Median INB was higher in boys with CDGP than CHH (182 vs 59 pg/ml; p<0.0001). Likewise, in adult men, median INB was higher in eugonadal men than CHH (170 vs 36.5 pg/ml; p<0.0001). INB performed better than INSL3 in differentiating CHH in boys with delayed puberty (auROC 98.5%, 95.9-100%), than in adult men (auROC 93.9%, 87.2-100%). Conclusion: INSL3 better identifies CHH in adult men, whereas INB better identifies CHH in boys with delayed puberty

Exploring the experiences and coping strategies of international medical students

<p>Abstract</p> <p>Background</p> <p>Few studies have addressed the challenges that international medical students face and there is a dearth of information on the behavioural strategies these students adopt to successfully progress through their academic program in the face of substantial difficulties of language barrier, curriculum overload, financial constraints and assessment tasks that require high proficiency in communication skills.</p> <p>Methods</p> <p>This study was designed primarily with the aim of enhancing understanding of the coping strategies, skill perceptions and knowledge of assessment expectations of international students as they progress through the third and fourth years of their medical degree at the School of Medicine, University of Tasmania, Australia.</p> <p>Results</p> <p>Survey, focus group discussion and individual interviews revealed that language barriers, communication skills, cultural differences, financial burdens, heavy workloads and discriminatory bottlenecks were key factors that hindered their adaptation to the Australian culture. Quantitative analyses of their examination results showed that there were highly significant (p < 0.001) variations between student performances in multiple choice questions, short answer questions and objective structured clinical examinations (70.3%, 49.7% & 61.7% respectively), indicating existence of communication issues.</p> <p>Conclusions</p> <p>Despite the challenges, these students have adopted commendable coping strategies and progressed through the course largely due to their high sense of responsibility towards their family, their focus on the goal of graduating as medical doctors and their support networks. It was concluded that faculty needs to provide both academic and moral support to their international medical students at three major intervention points, namely point of entry, mid way through the course and at the end of the course to enhance their coping skills and academic progression. Finally, appropriate recommendations were made.</p

Insulin-like peptide 3 (INSL3) in congenital hypogonadotrophic hypogonadism (CHH) in boys with delayed puberty and adult men

Background: Delayed puberty in males is almost invariably associated with constitutional delay of growth and puberty (CDGP) or congenital hypogonadotrophic hypogonadism (CHH). Establishing the cause at presentation is challenging, with “red flag” features of CHH commonly overlooked. Thus, several markers have been evaluated in both the basal state or after stimulation e.g. with gonadotrophin releasing hormone agonist (GnRHa). Insulin-like peptide 3 (INSL3) is a constitutive secretory product of Leydig cells and thus a possible candidate marker, but there have been limited data examining its role in distinguishing CDGP from CHH. In this manuscript, we assess INSL3 and inhibin B (INB) in two cohorts: 1. Adolescent boys with delayed puberty due to CDGP or CHH and 2. Adult men, both eugonadal and having CHH. Materials and methods: Retrospective cohort studies of 60 boys with CDGP or CHH, as well as 44 adult men who were either eugonadal or had CHH, in whom INSL3, INB, testosterone and gonadotrophins were measured. Cohort 1: Boys with delayed puberty aged 13-17 years (51 with CDGP and 9 with CHH) who had GnRHa stimulation (subcutaneous triptorelin 100mcg), previously reported with respect to INB. Cohort 2: Adult cohort of 44 men (22 eugonadal men and 22 men with CHH), previously reported with respect to gonadotrophin responses to kisspeptin-54. Results: Median INSL3 was higher in boys with CDGP than CHH (0.35 vs 0.15 ng/ml; p=0.0002). Similarly, in adult men, median INSL3 was higher in eugonadal men than CHH (1.08 vs 0.05 ng/ml; p<0.0001). However, INSL3 more accurately differentiated CHH in adult men than in boys with delayed puberty (auROC with 95% CI in adult men: 100%, 100-100%; boys with delayed puberty: 86.7%, 77.7-95.7%). Median INB was higher in boys with CDGP than CHH (182 vs 59 pg/ml; p<0.0001). Likewise, in adult men, median INB was higher in eugonadal men than CHH (170 vs 36.5 pg/ml; p<0.0001). INB performed better than INSL3 in differentiating CHH in boys with delayed puberty (auROC 98.5%, 95.9-100%), than in adult men (auROC 93.9%, 87.2-100%). Conclusion: INSL3 better identifies CHH in adult men, whereas INB better identifies CHH in boys with delayed puberty

Respiratory analysis during sleep using a chest-worn accelerometer: A machine learning approach

Objective: There is a great interest in observing breathing patterns during sleep, as sleep disturbances can be caused by respiratory irregularity and cessations. In this paper, we introduce the first steps to an accelerometer-based screening tool for respiratory rate estimation and a novel approach towards detecting breathing cessations such as apnea/hypopnea, by extending and combining established signal processing routines with machine learning. Methods: From a single chest-worn accelerometer, we estimate the respiratory rate based on the inhalation/exhalation movements of the chest and carry out a full overnight validation. On this basis, we build a set of features customized to detect irregular respiratory activity, including a novel feature: the respiratory peak variance (RPV). From thirteen healthy subjects, a classification model was trained, validated, and tested with over 98 h of PSG-labeled accelerometer data. Results: The algorithm estimated the respiratory rate with a mean difference of 1.8 breaths per minute compared to respiratory inductance plethysmography during overnight PSGs. The machine learning classifier detected respiratory cessations with a sensitivity and specificity of 76.05% and 70.05% respectively, with an overall accuracy of 70.95%. Conclusion: We successfully demonstrated the potential of a novel respiratory feature set in a preliminary application with young healthy volunteers for respiratory rate estimation and in identifying apnea/hypopnea events during overnight sleep. Significance: We present a simple and unobtrusive wearable system that can serve as a home screening tool for sleep-related breathing disorders

Using machine learning to determine follicle sizes on the day of trigger most likely to yield oocytes

Study question Which follicle sizes on the day of trigger (DoT) are most likely to yield oocytes after different IVF treatment protocols and trigger types? Summary answer Follicles sized 11-19mm on DoT are most likely to yield oocytes in both 'long' and 'short' protocols after using either hCG or GnRH agonist triggers. What is known already On the DoT, both follicles that are too small, or too large, are less likely to yield oocytes, but the precise range of follicle sizes that are most contributory to oocyte yield remains uncertain. Knowledge of this optimal follicle size range can aid in selecting the DoT and in quantifying the efficacy of the trigger by benchmarking the expected number of oocytes to be retrieved. Machine learning can aid in the analysis of large complex datasets and thus could be used to determine the follicle sizes on the DoT that are most predictive of the number of oocytes retrieved. Study design, size, duration We applied machine learning techniques to data from 8030 patients aged under 35 years who underwent autologous fresh IVF and ICSI cycles between 2011-2021 in a single IVF clinic. The DoT was determined by 2-3 leading follicles reaching ≥ 18mm in size. Follicle sizes from ultrasound scans performed on the DoT (n = 3056), a day prior to DoT (n = 2839), or two days prior to DoT (n = 2135), were evaluated in relation to the number of oocytes retrieved. Participants/materials, setting, methods A two-stage random forest pipeline was developed, with the number of follicles of a certain size on DoT as input, and the number of oocytes retrieved as output. First, a variable preselection model to determine the most contributory follicle sizes. Second, a model to identify the optimal range of follicle sizes to yield oocytes. Both models were trained and cross-validated with fixed hyperparameters. The pipeline was run for each protocol and trigger type independently. Main results and the role of chance The machine learning pipeline identified follicles sized 11-19mm on the DoT as most contributory in IVF/ICSI cycles when using an hCG trigger. After a GnRH agonist trigger, follicles sized 10-19mm were most predictive of the number of oocytes retrieved. To mitigate the role of chance, the statistical methods were further validated by utilizing scans prior to the DoT to rerun the pipelines, as well as a comparison against the true number of retrieved oocytes with linear regression. In ‘short’ protocol cycles triggered with hCG (n = 1581), follicles sized 11-19mm on the DoT were more closely associated with the number of oocytes retrieved (r2=0.58) than either smaller (r2=0.031), or larger (r2=0.051), follicle size ranges (p < 0.0001). The most predictive follicles sizes on the day prior to DoT were 10-18 mm (n = 1421), and 6-17 mm for two days prior to the DoT (n = 1103), consistent with expected median follicle growth rates of 1-2 mm per day. Using fivefold cross-validation, the mean absolute error was 3.47 oocytes for hCG-triggered 'short' protocol patients. Similarly, significant trends were seen across all protocols and trigger types. Limitations, reasons for caution This was a single-center retrospective study and thus the analysis would benefit from further validation by extension to multiple centers using varying clinical practices to ensure model generalizability. Wider implications of the findings This data-driven target could enable greater personalization of treatment by guiding selection of the DoT to optimize oocyte yield. Prospective studies to assess whether this proposed target for follicle size range is preferable to standard methods based on lead follicle size are needed to confirm the implication of this data. Trial registration number not applicabl