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Several lines of evidence have suggested that central nervous system development and function depend upon a supply of cholesterol that comes from low density lipoproteins (LDL-C). These studies test this hypothesis directly by measuring in vivo the uptake of LDL-C in nine regions of the central nervous system at five different stages of development in the fetal and neonatal sheep. The concentration of LDL-C in the plasma decreased from 49 mg/dl in the fetus 90 days before birth (-90 days) to only 10 mg/dl at -13 days. By 17 days postnatal this value increased to nearly 60 mg/dl. Throughout the period of development between -90 days (very early fetus) and 17 days (late neonatal animal) the weight of the brain increased 32-fold (from 2.3 to 73.6 g) and the content of cholesterol rose 100-fold (from 8.6 to 876 mg), yet there was no detectable LDL-C uptake in any of nine areas of the central nervous system at any stage of development (clearances of < 2 microliters/h per g). This was true even in the -90 day fetus prior to closure of the blood brain barrier. In contrast, LDL-C clearance by the adrenal gland increased dramatically (from 91 to 348 microliters/h per g) as it also did in the liver (from 36 to 85 microliters/h per g) during fetal development. These studies strongly suggest, therefore, that cholesterol carried in LDL plays little or no role in the process of sterol acquisition during brain development or in cholesterol turnover in the mature central nervous system. Changes in circulating LDL-C concentration, therefore, should have no effect on brain function.

Abstract Diffuse midline gliomas (DMGs) are aggressive pediatric brain tumors with dismal prognosis due to therapy-resistant tumor growth and invasion. We performed the first integrated histologic/genomic/proteomic analysis of 21 foci from three pontine DMG cases with supratentorial dissemination. Histone H3.3-K27M was the driver mutation, usually at high variant allele fraction due to recurrent chromosome 1q copy number gain, in combination with germline variants in ATM, FANCM and MYCN genes. Both previously reported and novel recurrent copy number variations and somatic pathogenic mutations in chromatin remodeling, DNA damage response and PI3K/MAPK growth pathways were variably detected, either in multiple or isolated foci. Proteomic analysis showed global upregulation of histone H3, lack of H3-K27 trimethylation, and further impairment of polycomb repressive complex 2 by ASXL1 downregulation. Activation of oncogenic pathways resulted from combined upregulation of N-MYC, SOX2, p65/p50 NF-κB and STAT3 transcription factors, EGFR, FGFR2, PDGFRα/β receptor tyrosine kinases, and downregulation of PHLPP1/2, PTEN and p16/INK4A tumor suppressors. Upregulation of SMAD4, PAI-1, CD44, and c-SRC in multiple foci most likely contributed to invasiveness. This integrated comprehensive analysis revealed a complex spatiotemporal evolution in diffuse intrisic pontine glioma, recommending pontine and cerebellar biopsies for accurate populational genetic characterization, and delineated common signaling pathways and potential therapeutic targets. It also revealed an unsuspected activation of a multitude of oncogenic pathways, including cancer cell reprogramming, explaining the resistance of DMG to current therapies.

Abstract Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722–0.773) and an average precision of 0.233 (95% CI 0.194–0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.