COVID-19 Vaccination and Reproductive Health: a Comprehensive Review for Healthcare Providers.

With all the current misinformation on social media platforms about the COVID-19 vaccine and its potential effects on fertility, it is essential for healthcare providers to have evidenced-based research to educate their patients, especially those who are trying to conceive, of the risks to mothers and fetuses of being unvaccinated. It is well known that COVID-19 infection puts pregnant women at higher risk of complications, including ICU admission, placentitis, stillbirth, and death. In February of 2021, the American College of Obstetricians and Gynecologists (ACOG), the American Society for Reproductive Medicine (ASRM), and the Society for Maternal-Fetal Medicine (SMFM) released a statement denying any link between COVID vaccination and infertility. ASRM later confirmed and stated that "everyone, including pregnant women and those seeking to become pregnant, should get a COVID-19 vaccine". In this review, we aim to provide a compilation of data that denies any link between vaccination and infertility for healthcare providers to be able to educate their patients based on evidence-based medicine. We also reviewed the effect of COVID-19 virus and vaccination on various parameters and processes that are essential to obtaining a successful pregnancy.

Association Between MitoScore, BMI, and Body Fat Percentage as a Predictive Marker for the Outcome of In-Vitro Fertilization (IVF).

Background Infertility is defined as the inability to establish a pregnancy within 12 months of regular and unprotected sexual intercourse. In response to these problems, assisted reproductive techniques (ARTs) have made profound impacts on the therapeutic management of infertility. However, in-vitro fertilization (IVF) success rates are confounded by several internal and external factors. A relatively new approach to embryo assessment is known as MitoScore (Igenomix, Miami, USA). As a result, we sough to evaluate whether MitoScore can help in predicting in IVF outcomes, and to assess the relationship between MitoScore, BMI, and body fat percentage in determining the success of ARTs. Methods Using retrospective cohort, a study population consisting of 166 women aged 26-43 who were undergoing ART with pre-implantation genetic testing for aneuploidy (PGT-A) was assessed to determine if MitoScore, BMI, and body fat percentage impacted IVF outcomes. Results MitoScore, BMI, and body fat percentage were significantly lower in pregnant women as compared to non-pregnant women. Furthermore, MitoScore was correlated with subclasses of IVF outcomes (delivery, biochemical pregnancy, and spontaneous abortion) and was found to be positively correlated with BMI in patients with biochemical pregnancies. Conclusion Our findings suggest that MitoScore, BMI, and body fat percentage could act as critical parameters in determining the success of ART. However, the association between MitoScore, BMI, and body fat percentage does not appear to be a significant confounding factor to determine pregnancy outcome at this stage. Still, many factors need to be considered to establish the correlation reliably.

Positive effects of thyroid replacement therapy on assisted reproductive technology outcomes in women with subclinical hypothyroidism with positive thyroid peroxidase autoantibodies.

Objective: To study the beneficial effects of thyroid replacement therapy (TRT) on pregnancy outcomes in patients with subclinical hypothyroidism (SCl hypoT) with respect to thyroid peroxidase (TPO) autoantibodies. Design: Retrospective study of 706 patients. Setting: Not applicable. Patients: The study evaluated 706 patients, who were divided into 3 cohorts: euthyroid patients, with pre-in vitro fertilization thyroid-stimulating hormone levels of <2.5 µIU/mL; patients with SCl hypoT, defined as thyroid-stimulating hormone levels of >2.5 µIU/mL and <4 µIU/mL, who were not treated; and patients with SCl hypoT who received TRT. The 3 cohorts were further subclassified into 2 groups, each based on TPO antibody levels. Interventions: The cohorts were compared for the effects of TRT on pregnancy outcomes. Main Outcome Measures: Identification of effects of TRT on assisted reproductive technology outcomes. Results: Patients with SCl hypoT had significantly fewer positive pregnancy outcomes than euthyroid patients. Importantly, low-dose TRT was found to be beneficial in improving IVF success and pregnancy outcomes in patients with SCl hypoT. The original cohort of patients, further classified into 2 subgroups on the basis of antithyroid (TPO) antibodies, showed that low-dose TRT was associated with improved pregnancy outcomes in women with SCl hypoT and TPO-positive antibodies. Conclusions: Our findings demonstrate that low-dose TRT may be beneficial in improving in vitro fertilization success and pregnancy outcomes in women with SCl hypoT and TPO-positive antibodies.

An Artificial Intelligence-Based Algorithm for Predicting Pregnancy Success Using Static Images Captured by Optical Light Microscopy during Intracytoplasmic Sperm Injection.

CONTEXT BACKGROUND: Analysis of embryos for in vitro fertilization (IVF) involves manual grading of human embryos through light microscopy. Recent research shows that artificial intelligence techniques applied to time lapse embryo images can successfully ascertain embryo quality. However, laboratories often capture static images and cannot apply this research in a real-world setting. Further, current models do not predict the outcome of pregnancy. AIMS: To create and assess a convolutional neural network to predict embryo quality using static images from a limited dataset. We considered two classification problems: predicting whether an embryo will lead to a pregnancy or not and predicting the outcome of that pregnancy. SETTINGS AND DESIGN: We utilized transfer learning techniques using a pretrained Inception V1 network. All models were built using the Tensorflow software package. METHODS: We utilized a total of 361 randomly sampled static images collected from four South Florida IVF clinics. Data were collected between 2016 and 2019. STATISTICAL ANALYSIS USED: We utilized deep-learning techniques, including data augmentation to reduce model variance and transfer learning to bolster our limited dataset. We used a standard train/validation/ test dataset split to avoid model overfitting. RESULTS: Our algorithm achieved 0.657 area under the curve for predicting pregnancy versus nonpregnancy. However, our model was unable to meaningfully predict whether a pregnancy led a to live birth. CONCLUSIONS: Despite the limited dataset that achieved somewhat of a lower accuracy than conventional embryo selection, this is the first study that has successfully made IVF predictions from static images alone. Future availability of more data may allow for prospective validation and further generalisability of results.