(1) Background: A considerable number of systematic reviews, with substantial heterogeneity regarding their methods and included populations, on the impact of COVID-19 on infected pregnant women and their neonates, has emerged. The aim was to describe the obstetric-perinatal and neonatal outcome of infected pregnant women and their newborns during the COVID-19 pandemic; (2) Methods: Three bibliographical databases were searched (last search: 10 September 2020). Quality assessment was performed using the AMSTAR-2 tool. Primary outcomes included mode of delivery, preterm delivery/labor, premature rupture of membranes (PROM/pPROM) and abortions/miscarriages. Outcomes were mainly presented as ranges. A separate analysis, including only moderate and high-quality systematic reviews, was also conducted. The protocol was registered with PROSPERO (CRD42020214447); (3) Results: Thirty-nine reviews were analyzed. Reported rates, regarding both preterm and term gestations, varied between 52.3 and 95.8% for cesarean sections; 4.2–44.7% for vaginal deliveries; 14.3–63.8% specifically for preterm deliveries and 22.7–32.2% for preterm labor; 5.3–12.7% for PROM and 6.4–16.1% for pPROM. Maternal anxiety for potential fetal infection contributed to abortion decisions, while SARS-CoV-2-related miscarriages could not be excluded. Maternal ICU admission and mechanical ventilation rates were 3–28.5% and 1.4–12%, respectively. Maternal mortality rate was <2%, while stillbirth, neonatal ICU admission and mortality rates were <2.5%, 3.1–76.9% and <3%, respectively. Neonatal PCR positivity rates ranged between 1.6% and 10%. After accounting for quality of studies, ranges of our primary outcomes remained almost unchanged, while among our secondary outcomes, maternal ICU admission (3–10%) and mechanical ventilation rates (1.4–5.5%) were found to be relatively lower; (4) Conclusions: Increased rates of cesarean sections and preterm birth rates were found, with iatrogenic reasons potentially involved. In cases of symptomatic women with confirmed infection, high maternal and neonatal ICU admission rates should raise some concerns. The probability of vertical transmission cannot be excluded. Further original studies on women from all trimesters are warranted.

To evaluate in vitro maturation (IVM) in sub-fertile women with polycystic ovarian syndrome (PCOS) undergoing in vitro fertilisation (IVF), by comparing outcomes with a control group of non-PCOS. A search strategy was developed for PubMed and studies reporting rates of the following outcomes (live birth; clinical pregnancy; implantation; cycle cancellation; oocyte maturation; oocyte fertilization; miscarriage) between patients with PCOS, PCO and controls undergoing IVM were deemed eligible. The review was conducted in accordance to the PRISMA guidelines and included studies quality was assessed through the Newcastle-Ottawa Quality scale. ORs with their corresponding 95% CIs were calculated for the main analysis and subgroup analyses were performed for PCOS cases vs. controls and PCOS vs. PCO cases. Alternative analyses were performed for live birth and clinical pregnancy, based on cycles and on women. Subgroup analyses for FSH stimulation, hCG priming and type of procedure (IVF/ICSI) were undertaken for all meta-analyses encompassing at least four study arms. Random effects models were used to calculate pooled effect estimates. Eleven studies were identified. A total of 268 PCOS patients (328 cycles), 100 PCO patients (110 cycles) and 440 controls (480 cycles) were included in the meta-analysis. A borderline trend towards higher birth rates among PCOS patients emerged (pooled OR = 1.74, 95%CI: 0.99–3.04) mainly reflected at the subgroup analysis vs. controls. Clinical pregnancy (pooled OR = 2.37, 95%CI: 1.53–3.68) and implantation rates (pooled OR = 1.73, 95%CI: 1.06–2.81) were higher, while cancellation rates lower (pooled OR = 0.18, 95%CI: 0.06-0.47) among PCOS vs. non-PCOS subjects; maturation and miscarriage rates did not differ between groups, while a borderline trend towards lower fertilization rates among PCOS patients was observed. The present meta-analysis provides preliminary evidence on the effectiveness of IVM as a treatment option when offered in sub-fertile PCOS women, as the latter present at least as high outcome rates as those in non-PCOS.

Endometriosis-related infertility describes a case of deteriorated fecundity when endometriosis is diagnosed. Numerous mechanisms have been proposed in an effort to delineate the multifaceted pathophysiology that induces impairment of reproductive dynamics in patients with endometriosis. In this critical analysis, authors present the plethora of molecular events that are entailed and elaborate on how they potentially impair the oocyte’s and embryo’s competence in patients with endometriosis. Reactive oxygen species, dysregulation of the immune system and cellular architectural disruption constitute the crucial mechanisms that detrimentally affect oocyte and embryo developmental potential. The molecular level impairment of the reproductive tissue is discussed, since differentiation, proliferation and apoptosis constitute focal regulatory cellular functions that appear severely compromised in cases of endometriosis. Mapping the precise molecular mechanisms entailed in endometriosis-related infertility may help delineate the complex nature of the disorder and bring us a step closer to a more personalized approach in understanding, diagnosing and managing endometriosis-related infertility.

Predicting the outcome of in-vitro fertilization (IVF) treatment is an extremely semantic issue in reproductive medicine. Discrepancies in results among reproductive centres still exist making the construction of new systems capable to foresee the desired outcome a necessity. As such, artificial neural networks (ANNs) represent a combination of a learning, self-adapting, and predicting machine. In this review hypothesis paper we summarize the past efforts of the ANNs systems to predict IVF outcomes. This will be considered together with other statistical models, such as the ensemble techniques, Classification And Regression Tree (CART) and regression analysis techniques, discriminant analysis, and case based reasoning systems. We also summarize the various inputs that have been employed as parameters in these studies to predict the IVF outcome. Finally, we report our attempt to construct a new ANN architecture based on the Learning Vector Quantizer promising good generalization: a system filled by a complete data set of our IVF unit, formulated parameters most commonly used in similar studies, trained by a network expert, and evaluated in terms of predictive power.