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Our findings suggest an involvement of miRNA-200b and miRNA-429 in the pituitary regulation of human ovulation. Although it is unclear whether this altered miRNA expression profile is a cause or a result of anovulation, the levels of these molecules in the serum of anovulatory women may serve as serum biomarkers for the ovulation process.

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Objective To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour. Design Retrospective Electronic‐Medical‐Record (EMR) ‐based study. Population A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.8%) deliveries with SANO. Methods A gradient boosting model was created, analysing 21 million data points from antepartum features (e.g. gravidity and parity) gathered at admission to the delivery unit, and intrapartum data (e.g. cervical dilatation and effacement) gathered during the first stage of labour. Deliveries were allocated to high‐risk and low‐risk groups based on the Youden index to maximise sensitivity and specificity. Main outcome measures SANO was defined as either umbilical cord pH levels ≤7.1 or 1‐minute or 5‐minute Apgar score ≤7. Results The model for prediction of SANO yielded an area under the receiver operating curve (AUC) of 0.761 (95% CI 0.748–0.774). A third of the cohort (33.5%, n = 24 721) were allocated to a high‐risk group for SANO, which captured up to 72.1% of these cases (odds ratio 5.3, 95% CI 4.7–6.0; high‐risk versus low‐risk groups). Conclusions Data acquired throughout the first stage of labour can be used to predict SANO during the second stage of labour using a machine learning model. Stratifying parturients at the beginning of the second stage of labour in a ‘time out’ session, can direct a personalised approach to management of this challenging aspect of labour, as well as improve allocation of staff and resources. Tweetable abstract Personalised prediction score for severe adverse neonatal outcomes in labour using machine learning model.

Background Physiological selection of spermatozoa for ICSI (PICSI) is a sperm selection method based on sperm binding to hyaluronic acid. Previous studies on the effect of hyaluronic acid binding assays on fertilization and embryo quality have shown inconsistent results. Previous sibling oocyte studies have not found a significant improvement in fertilization or embryo development with hyaluronic acid binding assays. Objective To compare fertilization and embryo development between standard intracytoplasmic sperm injection (ICSI) and PICSI in sibling oocytes. Materials and Methods This is a retrospective analysis of all in vitro fertilization (IVF) cycles between January 2017 and April 2020 in which sibling oocytes were randomly fertilized by both ICSI and PICSI. Fertilization rate and the rate of embryos eligible for transfer were compared. Results Forty‐five IVF cycles, in which 257 oocytes were fertilized with PICSI and 294 with standard ICSI, were compared. Most of the patients included in the study had previous failures of fertilization, poor embryonic development, implantation failure, or miscarriage. All but two of the patients had at least one previous unsuccessful IVF cycle. Both fertilization rates (71% vs. 83%) and transfer eligible embryo rates (38% vs. 51%) were significantly higher in PICSI fertilized oocytes (p = 0.008 and p = 0.01 respectively). Discussion Our study is the largest sibling oocyte study comparing ICSI and PICSI, and the first to find a significant improvement in fertilization and embryo quality with PICSI using sibling oocytes. The fact our cohort included almost exclusively couples with previous unsuccessful IVF cycles might suggest that PICSI should be used in selected cases. Conclusion PICSI improves fertilization rates and transfer eligible embryo rates in sibling oocytes in a selected study group.

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SUMMARYRecent studies demonstrated that human trophoblast stem-like cells (hTS-like cells) can be derived from naïve embryonic stem cells or be induced from somatic cells by the pluripotency factors, OSKM. This raises two main questions; (i) whether human induced TSCs (hiTSCs) can be generated independently to pluripotent state or factors and (ii) what are the mechanisms by which hTSC state is established during reprogramming. Here, we identify GATA3, OCT4, KLF4 and MYC (GOKM) as a pluripotency-independent combination of factors that can generate stable and functional hiTSCs, from both male and female fibroblasts. By using single and double knockout (KO) fibroblasts for major pluripotency genes (i.e. SOX2 or NANOG/PRDM14) we show that GOKM not only is capable of generating hiTSCs from the KO cells, but rather that the efficiency of the process is increased. Through H3K4me2 and chromatin accessibility profiling we demonstrate that GOKM target different loci and genes than OSKM, and that a significant fraction of them is related to placenta and trophoblast function. Moreover, we show that GOKM exert a greater pioneer activity compared to OSKM. While GOKM target many specific hTSC loci, OSKM mainly target hTSC loci that are shared with hESCs. Finally, we reveal a gene signature of trophoblast-related genes, consisting of 172 genes which are highly expressed in blastocyst-derived TSCs and GOKM-hiTSCs but absent or mildly expressed in OSKM-hiTSCs.Taken together, these results imply that not only is the pluripotent state, and SOX2 specifically, not required to produce functional hiTSCs, but that pluripotency-specific factors actually interfere with the acquisition of the hTSC state during reprogramming.

While communication during night shifts between residents and attendings occurs in most shifts, attendings initiate far less contact with residents than is required by the guidelines.