| 1. |
- Bailey, Liam D., et al.
(författare)
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Bird populations most exposed to climate change are less sensitive to climatic variation
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- The phenology of many species shows strong sensitivity to climate change; however, with few large scale intra-specific studies it is unclear how such sensitivity varies over a species’ range. We document large intra-specific variation in phenological sensitivity to temperature using laying date information from 67 populations of two co-familial European songbirds, the great tit (Parus major) and blue tit (Cyanistes caeruleus), covering a large part of their breeding range. Populations inhabiting deciduous habitats showed stronger phenological sensitivity than those in evergreen and mixed habitats. However, populations with higher sensitivity tended to have experienced less rapid change in climate over the past decades, such that populations with high phenological sensitivity will not necessarily exhibit the strongest phenological advancement. Our results show that to effectively assess the impact of climate change on phenology across a species’ range it will be necessary to account for intra-specific variation in phenological sensitivity, climate change exposure, and the ecological characteristics of a population.
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| 2. |
- Kennedy, Beatrice, 1982-, et al.
(författare)
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App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.
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| 3. |
- Leone, Marc, et al.
(författare)
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Update in antibiotic therapy in intensive care unit : report from the 2019 Nîmes International Symposium
- 2019
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Ingår i: Anaesthesia Critical Care & Pain Medicine. - : Elsevier BV. - 2352-5568. ; 38:6, s. 647-656
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Forskningsöversikt (refereegranskat)abstract
- The 2019 Nîmes International Symposium in Antibiotic Therapy Optimisation aimed at determining the best approaches of a number of the antibiotic management strategies for critically ill patients. Experts reviewed the latest literature relating to requirements for an optimal antibiotic stewardship program, risks of sub-therapeutic dosing of antibiotics in critically ill patients, persisting issues about efficiency of combination therapy and the value of de-escalation, new perspectives of pharmacokinetics, drug toxicities including collateral damages-associated with antibiotics, the place of nebulisation of antibiotics, management of patients receiving extracorporeal therapies and the place of new antibiotics. In this paper, each of these issues is discussed with key messages presented after a brief review of evidence.
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