| 1. |
- Smol, T., et al.
(författare)
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MED13L-related intellectual disability: involvement of missense variants and delineation of the phenotype
- 2018
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Ingår i: Neurogenetics. - : SPRINGER. - 1364-6745 .- 1364-6753. ; 19:2, s. 93-103
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Tidskriftsartikel (refereegranskat)abstract
- Molecular anomalies in MED13L, leading to haploinsufficiency, have been reported in patients with moderate to severe intellectual disability (ID) and distinct facial features, with or without congenital heart defects. Phenotype of the patients was referred to "MED13L haploinsufficiency syndrome." Missense variants in MED13L were already previously described to cause the MED13L-related syndrome, but only in a limited number of patients. Here we report 36 patients with MED13L molecular anomaly, recruited through an international collaboration between centers of expertise for developmental anomalies. All patients presented with intellectual disability and severe language impairment. Hypotonia, ataxia, and recognizable facial gestalt were frequent findings, but not congenital heart defects. We identified seven de novo missense variations, in addition to protein-truncating variants and intragenic deletions. Missense variants clustered in two mutation hot-spots, i.e., exons 15-17 and 25-31. We found that patients carrying missense mutations had more frequently epilepsy and showed a more severe phenotype. This study ascertains missense variations in MED13L as a cause for MED13L-related intellectual disability and improves the clinical delineation of the condition.
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| 2. |
- Azevedo, Flavio, et al.
(författare)
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Social and moral psychology of COVID-19 across 69 countries
- 2023
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Ingår i: Scientific Data. - : NATURE PORTFOLIO. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.
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| 3. |
- Van Bavel, Jay J., et al.
(författare)
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National identity predicts public health support during a global pandemic
- 2022
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Ingår i: Nature Communications. - : Nature Portfolio. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic. Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.
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| 4. |
- Belov, Vladimir, et al.
(författare)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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