Bmc Med
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Ethnic minority groups in England have been disproportionately affected by the COVID-19 pandemic and have lower vaccination rates than the White British population. We examined whether ethnic differences in COVID-19 mortality in England have continued since the vaccine rollout and to what extent differences in vaccination rates contributed to excess COVID-19 mortality after accounting for other risk factors. ⋯ Lower COVID-19 vaccination uptake in several ethnic minority groups may drive some of the differences in COVID-19 mortality compared to White British. Public health strategies to increase vaccination uptake in ethnic minority groups would help reduce inequalities in COVID-19 mortality, which have remained substantial since the start of the vaccination campaign.
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Poor social support during pregnancy has been linked to inflammation and adverse pregnancy and childhood health outcomes. Placental epigenetic alterations may underlie these links but are still unknown in humans. ⋯ The findings suggest that prenatal social support is linked to placental DNA methylation changes in a low-stress setting, including fetal sex-dependent epigenetic changes. Given the relevance of some of these changes in fetal neurodevelopmental outcomes, the findings signal important methylation targets for future research on molecular mechanisms of effect of the broader social environment on pregnancy and fetal outcomes.
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Review Practice Guideline
Recommendations for robust and reproducible preclinical research in personalised medicine.
Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. ⋯ Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.
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The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support. ⋯ High-dimensional, multimodal predictive models of mortality based on routine clinical data offer higher predictive fidelity than simpler models, facilitating individual level prognostication and interventional targeting. The joint contributions of both extracranial and intracranial features highlight the potential importance of optimising somatic as well as neural functions in healthy ageing. Our findings suggest a promising path towards a high-fidelity, multimodal index of frailty.
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Rezivertinib (BPI-7711) is a novel third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI). This phase IIa study was part of a phase I/IIa study (NCT03386955), aimed to evaluate the efficacy and safety of rezivertinib as the first-line treatment for patients with locally advanced or metastatic/recurrent EGFR mutated non-small cell lung cancer (NSCLC). ⋯ Rezivertinib (BPI-7711) showed promising efficacy and a favorable safety profile for the treatment among the locally advanced or metastatic/recurrent NSCLC patients with EGFR mutation in the first-line setting.