Temporal variations in maternal treatment requirements and early neonatal outcomes in patients with gestational diabetes

Funder: NIHR Cambridge Biomedical Research CentreAbstract: Aims: There is seasonal variation in the incidence of gestational diabetes (GDM) and delivery outcomes of affected patients. We assessed whether there was also evidence of temporal variation in maternal treatment requirements and early neonatal outcomes. Methods: We performed a retrospective analysis of women diagnosed with GDM (75 g oral glucose tolerance test, 0 h ≥ 5.1; 1 h ≥ 10.0; 2 h ≥ 8.5 mmol/L) in a UK tertiary obstetric centre (2015–2019) with a singleton infant. Data regarding demographic characteristics, total insulin requirements and neonatal outcomes were extracted from contemporaneous electronic medical records. Linear/logistic regression models using month of the year as a predictor of outcomes were used to assess annual variation. Results: In all, 791 women (50.6% receiving pharmacological treatment) and 790 neonates were included. The likelihood of requiring insulin treatment was highest in November (p < 0.05). The average total daily insulin dose was higher at peak (January) compared to average by 19 units/day (p < 0.05). There was no temporal variation in neonatal intensive care admission, or neonatal capillary blood glucose. However, rates of neonatal hypoglycaemia (defined as <2.6 mmol/L) were highest in December (40% above average; p < 0.05). Conclusions: Women with GDM diagnosed in winter are more likely to require insulin treatment and to require higher insulin doses. Neonates born to winter‐diagnosed mothers had a corresponding increased risk of neonatal hypoglycaemia. Maternal treatment requirements and neonatal outcomes of GDM vary significantly throughout the year, even in a relatively temperate climate

Nicotinic Receptors Underlying Nicotine Dependence: Evidence from Transgenic Mouse Models.

Nicotine underlies the reinforcing properties of tobacco cigarettes and e-cigarettes. After inhalation and absorption, nicotine binds to various nicotinic acetylcholine receptor (nAChR) subtypes localized on the pre- and postsynaptic membranes of cells, which subsequently leads to the modulation of cellular function and neurotransmitter signaling. In this chapter, we begin by briefly reviewing the current understanding of nicotine's actions on nAChRs and highlight considerations regarding nAChR subtype localization and pharmacodynamics. Thereafter, we discuss the seminal discoveries derived from genetically modified mouse models, which have greatly contributed to our understanding of nicotine's effects on the reward-related mesolimbic pathway and the aversion-related habenulo-interpeduncular pathway. Thereafter, emerging areas of research focusing on modulation of nAChR expression and/or function are considered. Taken together, these discoveries have provided a foundational understanding of various genetic, neurobiological, and behavioral factors underlying the motivation to use nicotine and related dependence processes, which are thereby advancing drug discovery efforts to promote long-term abstinence

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Comparing diffusion-weighted and T2-weighted MR imaging for the quantification of infarct size in a neonatal rat hypoxic-ischemic model at 24 h post-injury

Purpose: In a neonatal rat model of hypoxic-ischemic (HI) brain injury, using T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), we aim to determine the best MRI method of lesion quantification that reflects infarct size. Materials and methods: Twenty 7-day-old rats underwent MRI 24 h after HI brain injury was induced. Lesion size relative to whole brain was measured using T2WI and apparent diffusion coefficient (ADC) maps, applying thresholds of 60%, 70% and 80% contralateral control hemisphere mean ADC, and at day 10 post-HI on pathology with TTC staining. Multiple linear regression analysis was used to study the relationships between lesion size at MRI and pathology. Results: Lesion size measurement using all MRI methods significantly correlated with infarct size at pathology; using T2WI, r = 0.808 (p < 0.001), using 80% ADC, 70% ADC and 60% ADC thresholds, r = 0.888 (p < 0.001), 0.761, (p < 0.001) and 0.569 (p = 0.014), respectively. Eighty percent ADC threshold was found to be the only significant independent predictor of final infarct volume (adjusted R 2 = 0.775). Conclusion: At 24 h post-HI, lesion size on DWI, using 80% ADC threshold is the best predictor of final infarct volume. Although T2WI performed less well, it has the advantage of superior spatial resolution and is technically less demanding. These are important considerations for experiments which utilize MRI as a surrogate method for lesion quantification in the neonatal rat HI model. © 2006 ISDN.link_to_subscribed_fulltex