11 May 2021
Researchers used AI to identify which daily changing clinical parameters best predict intervention responses in critically ill COVID-19 patients.
The investigators used machine learning to predict which patients might get worse and not respond positively to being turned onto their front in intensive care units (ICUs) – a technique known as proning that is commonly used in this setting to improve oxygenation of the lungs.
This dynamic understanding is vitally important when trying to understand a new life-threatening disease and to know when and in whom each intervention works. Dr Brijesh Patel Department of Surgery and Cancer
While the AI model was used on a retrospective cohort of patient data collected during the pandemic’s first wave, the study demonstrates the ability of AI methods to predict patient outcomes using routine clinical information used by ICU medics.
Abstract
Surveillance of the SARS-CoV-2 epidemic has mainly relied on case reporting which is biased by health service performance, test availability and test-seeking behaviors. We report a community-wide national representative surveillance program in England involving self-administered swab results from 594,000 individuals tested for SARS-CoV-2, regardless of symptoms, from May to beginning of September 2020. The epidemic declined between May and July 2020 but then increased gradually from mid-August, accelerating into early September 2020 at the start of the second wave. When compared to cases detected through routine surveillance, we report here a longer period of decline and a younger age distribution. Representative community sampling for SARS-CoV-2 can substantially improve situational awareness and feed into the public health response even at low prevalence.
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Credit: Imperial College London / Thomas Angus
Psilocybin, the active compound in magic mushrooms, may be at least as effective as a leading antidepressant medication in a therapeutic setting.
This is the finding of a study carried out by researchers at the Centre for Psychedelic Research at Imperial College London.
In the most rigorous trial to date assessing the therapeutic potential of a psychedelic compound, researchers compared two sessions of psilocybin therapy with a six-week course of a leading antidepressant (a selective serotonin uptake inhibitor called escitalopram) in 59 people with moderate-to-severe depression.
The results, published today in the
New England Journal of Medicine, show that while depression scores were reduced in both groups, the reductions occurred more quickly in the psilocybin group and were greater in magnitude.
Abstract
While it is now widely accepted that host inflammatory responses contribute to lung injury, the pathways that drive severity and distinguish coronavirus disease 2019 (COVID-19) from other viral lung diseases remain poorly characterized. We analyzed plasma samples from 471 hospitalized patients recruited through the prospective multicenter ISARIC4C study and 39 outpatients with mild disease, enabling extensive characterization of responses across a full spectrum of COVID-19 severity. Progressive elevation of levels of numerous inflammatory cytokines and chemokines (including IL-6, CXCL10, and GM-CSF) were associated with severity and accompanied by elevated markers of endothelial injury and thrombosis. Principal component and network analyses demonstrated central roles for IL-6 and GM-CSF in COVID-19 pathogenesis. Comparing these profiles to archived samples from patients with fatal influenza, IL-6 was equally elevated in both conditions whereas GM-CSF was prominent only in