Towards mentoring as feminist praxis in early childhood education and care in England

Following our contribution to a study of mentoring in seven European countries, we explored epistemological and ontological inconsistencies within mainstream mentoring systems and their regulated practice in England. We considered how feminist mentoring praxis can unsettle conceptualisations of mentoring relationships and challenge inequity in the early education systems and the practice of teaching young children. Predominantly female, early childhood educators suffer from low status in England, and their working lives may be controlled and policed through inequitable systems. On entering the workforce, trainees encounter a reductionist policy milieu where mentoring structures and normative assessment arrangements contribute to inequity. Mentors play pivotal roles in inducting trainees into their worlds of work with young children. Mentoring relationships can determine whether trainees accept the status quo. Principles derived from feminist praxis enable mentors to practise an ‘engaged pedagogy’, co-constructing knowledge, subverting hierarchies and contesting taken-for-granted aspects of policy and practice

β-catenin is a molecular switch that regulates transition of cell-cell adhesion to fusion

When a sperm and an oocyte unite upon fertilization, their cell membranes adhere and fuse, but little is known about the factors regulating sperm-oocyte adhesion. Here we explored the role of β-catenin in sperm-oocyte adhesion. Biochemical analysis revealed that E-cadherin and β-catenin formed a complex in oocytes and also in sperm. Sperm-oocyte adhesion was impaired when β-catenin-deficient oocytes were inseminated with sperm. Furthermore, expression of β-catenin decreased from the sperm head and the site of an oocyte to which a sperm adheres after completion of sperm-oocyte adhesion. UBE1-41, an inhibitor of ubiquitin-activating enzyme 1, inhibited the degradation of β-catenin, and reduced the fusing ability of wild-type (but not β-catenin-deficient) oocytes. These results indicate that β-catenin is not only involved in membrane adhesion, but also in the transition to membrane fusion upon fertilization

The Liverpool alcohol-related liver disease algorithm identifies twice as many emergency admissions compared to standard methods when applied to Hospital Episode Statistics for England

BackgroundEmergency admissions in England for alcohol-related liver disease (ArLD) have increased steadily for decades. Statistics based on administrative data typically focus on the ArLD-specific code as the primary diagnosis and are therefore at risk of excluding ArLD admissions defined by other coding combinations.AimTo deploy the Liverpool ArLD Algorithm (LAA), which accounts for alternative coding patterns (e.g., ArLD secondary diagnosis with alcohol/liver-related primary diagnosis), to national and local datasets in the context of studying trends in ArLD admissions before and during the COVID-19 pandemic.MethodsWe applied the standard approach and LAA to Hospital Episode Statistics for England (2013-21). The algorithm was also deployed at 28 hospitals to discharge coding for emergency admissions during a common 7-day period in 2019 and 2020, in which eligible patient records were reviewed manually to verify the diagnosis and extract data.ResultsNationally, LAA identified approximately 100% more monthly emergency admissions from 2013 to 2021 than the standard method. The annual number of ArLD-specific admissions increased by 30.4%. Of 39,667 admissions in 2020/21, only 19,949 were identified with standard approach, an estimated admission cost of £70 million in under-recorded cases. Within 28 local hospital datasets, 233 admissions were identified using the standard approach and a further 250 locally verified cases using the LAA (107% uplift). There was an 18% absolute increase in ArLD admissions in the seven-day evaluation period in 2020 versus 2019. There were no differences in disease severity or mortality, or in the proportion of admissions with decompensation of cirrhosis or alcoholic hepatitis.ConclusionsThe LAA can be applied successfully to local and national datasets. It consistently identifies approximately 100% more cases than the standard coding approach. The algorithm has revealed the true extent of ArLD admissions. The pandemic has compounded a long-term rise in ArLD admissions and mortality

Primary biliary cirrhosis

Primary biliary cirrhosis (PBC) is a chronic and slowly progressive cholestatic liver disease of autoimmune etiology characterized by injury of the intrahepatic bile ducts that may eventually lead to liver failure. Affected individuals are usually in their fifth to seventh decades of life at time of diagnosis, and 90% are women. Annual incidence is estimated between 0.7 and 49 cases per million-population and prevalence between 6.7 and 940 cases per million-population (depending on age and sex). The majority of patients are asymptomatic at diagnosis, however, some patients present with symptoms of fatigue and/or pruritus. Patients may even present with ascites, hepatic encephalopathy and/or esophageal variceal hemorrhage. PBC is associated with other autoimmune diseases such as Sjogren's syndrome, scleroderma, Raynaud's phenomenon and CREST syndrome and is regarded as an organ specific autoimmune disease. Genetic susceptibility as a predisposing factor for PBC has been suggested. Environmental factors may have potential causative role (infection, chemicals, smoking). Diagnosis is based on a combination of clinical features, abnormal liver biochemical pattern in a cholestatic picture persisting for more than six months and presence of detectable antimitochondrial antibodies (AMA) in serum. All AMA negative patients with cholestatic liver disease should be carefully evaluated with cholangiography and liver biopsy. Ursodeoxycholic acid (UDCA) is the only currently known medication that can slow the disease progression. Patients, particularly those who start UDCA treatment at early-stage disease and who respond in terms of improvement of the liver biochemistry, have a good prognosis. Liver transplantation is usually an option for patients with liver failure and the outcome is 70% survival at 7 years. Recently, animal models have been discovered that may provide a new insight into the pathogenesis of this disease and facilitate appreciation for novel treatment in PBC

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

Applying phase conjugate arrays to sonar

A phase conjugate array (time reversal mirror) is a method of focusing acoustic energy spatially and temporally in a complex ocean propagation environment. Signals from a probe source are received on an array, time-reversed and re-transmitted. Multipath components are directed back along the paths over which they arrived, in the reverse order they were received. Numerical models have been developed to predict the focusing achievable with different array configurations in different types of environment. The effect of a time dependent rough surface has also been calculated. The models are described and examples of results obtained presented. An alternative focusing method based on inverse filters has been developed which may be an important enhancement to the basic reversal method. The relationship between the two methods and the advantages of each are explained. The models of focusing performance need validation using data taken in real environments. The type of experimental system required to provide this validation is described. The focusing techniques can be used in a number of ways to improve the performance of sonar and underwater communication systems. Examples of new types of sonar that exploit the focusing techniques to deliver improved performance are described.</p

Development of automated neural network prediction for echocardiographic left ventricular ejection fraction

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey’s method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline’s accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson’s correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function