Fetal growth delay caused by loss of non-canonical imprinting is resolved late in pregnancy and culminates in offspring overgrowth.

Germline epigenetic programming, including genomic imprinting, substantially influences offspring development. Polycomb Repressive Complex 2 (PRC2) plays an important role in Histone 3 Lysine 27 trimethylation (H3K27me3)-dependent imprinting, loss of which leads to growth and developmental changes in mouse offspring. In this study, we show that offspring from mouse oocytes lacking the PRC2 protein Embryonic Ectoderm Development (EED) were initially developmentally delayed, characterised by low blastocyst cell counts and substantial growth delay in mid-gestation embryos. This initial developmental delay was resolved as offspring underwent accelerated fetal development and growth in late gestation resulting in offspring that were similar stage and weight to controls at birth. The accelerated development and growth in offspring from Eed-null oocytes was associated with remodelling of the placenta, which involved an increase in fetal and maternal tissue size, conspicuous expansion of the glycogen-enriched cell population, and delayed parturition. Despite placental remodelling and accelerated offspring fetal growth and development, placental efficiency, and fetal blood glucose levels were low, and the fetal blood metabolome was unchanged. Moreover, while expression of the H3K27me3-imprinted gene and amino acid transporter Slc38a4 was increased, fetal blood levels of individual amino acids were similar to controls, indicating that placental amino acid transport was not enhanced. Genome-wide analyses identified extensive transcriptional dysregulation and DNA methylation changes in affected placentas, including a range of imprinted and non-imprinted genes. Together, while deletion of Eed in growing oocytes resulted in fetal growth and developmental delay and placental hyperplasia, our data indicate a remarkable capacity for offspring fetal growth to be normalised despite inefficient placental function and the loss of H3K27me3-dependent genomic imprinting.

How Can We Improve the Delivery of Urology Teaching to Medical Students Using Modern Educational Techniques?

It is generally well-known that the medical school curriculum is becoming increasingly busy, more so with the COVID-19 pandemic. By itself, urology education will need to adapt to meet the changing circumstances, but it remains uncertain on how best to address this need. In this article, we will discuss several methods that will allow institutions to ease and overcome pressures using modern educational techniques. These methods can be classified based on the aspect of the curriculum they seek to improve, namely core-curricular teaching, anatomy training, virtual reality, and electronic learning opportunities. We anticipate that the implementation of these suggestions will enhance medical school teaching.

Leibovich score is the optimal clinico-pathological system associated with recurrence of non-metastatic clear cell renal cell carcinoma.

PURPOSE: To determine the optimal post-operative risk stratification system associated with survival following surgery for clear cell renal cell carcinoma (ccRCC): tumour grade, tumour stage, Leibovich 2003, Leibovich 2018, Kattan, Stage, size, grade and necrosis (SSIGN) or UCLA Integrated Staging System (UISS) scores. METHODS: 542 patients with non-metastatic ccRCC who underwent nephrectomy 2008-2018 were reviewed. Primary outcome was recurrence-free survival (RFS), with secondary outcomes cancer-specific survival (CSS) and overall survival (OS). RESULTS: All systems were significantly associated with RFS, CSS and OS by Kaplan-Meier and unadjusted Cox-regression. ROC analysis identified that Leibovich 2003, Leibovich 2018A or B and SSIGN were optimally association with 5year RFS (AUC (Area under curve) 0.87, 0.86, 0.86 and 0.86), but Leibovich 2003 or 2018A offered additional information on adjusted regression analysis (HR 1.24, P = 0.02; HR 1.17, P = 0.04). ROC analysis identified that Leibovich 2018B, Leibovich 2003, SSIGN and UISS were equally associated with 5 year OS (AUC 0.76, 0.74, 0.73 and 0.72). UISS added additional explanation of the variance in OS on adjusted regression analysis (HR 1.96, P = 0.002). A novel combination of Leibovich 2003 score and Eastern Co-operative Oncology Group (ECOG) performance status improved 5 year OS association compared to the Leibovich 2003 alone (AUC 0.78, P = 0.001), without affecting association with 5year RFS (AUC 0.87, P = 0.75). CONCLUSIONS: All systems were robust tools associated with RFS, CSS and OS in ccRCC. In our cohort, the Leibovich 2003 and Leibovich 2018A scores may be better associated with RFS compared to other strategies. The UISS, Leibovich 2018B or Leibovich 2003 combined with ECOG performance status may stratify OS better than other modalities.

Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures.

PURPOSE: A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in-patient video-electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time-frequency mapping of data from a movement-recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES. METHODS: Time-frequency mapping was performed on accelerometer traces obtained during 56 convulsive seizure-like events from 35 patients recorded during in-patient video-EEG monitoring. Twenty-six patients had PNES, eight had epileptic seizures, and one had both seizure types. The time-frequency maps were derived from fast Fourier transformations to determine the dominant frequency for sequential 2.56-s blocks for the course of each event. KEY FINDINGS: The coefficient of variation (CoV) of limb movement frequency for the PNES events was less than for the epileptic seizure events (median, 17.18% vs. 52.23%; p < 0.001). A blinded review of the time-frequency maps by an epileptologist was accurate in differentiating between the event types, that is, 38 (92.7%) of 41 and 6 (75%) of 8 nonepileptic and epileptic seizures, respectively, were diagnosed correctly, with seven events classified as "nondiagnostic." Using a CoV cutoff score of 32% resulted in similar classification accuracy, with 42 (93%) of 45 PNES and 10 (91%) of 11 epileptic seizure events correctly diagnosed. SIGNIFICANCE: Time-frequency analysis of data from a wristband movement monitor could be utilized as a diagnostic tool to differentiate between epileptic and nonepileptic convulsive seizure-like events.