Although negative early life experiences are associated with an increased risk of developing psychopathology, some individuals exposed to childhood adversity demonstrate psychological resilience. Little is known about the neural correlates of resilience, especially in young people. To address this gap, we conducted a systematic review of neuroimaging studies of resilience in youth. The PubMed, Web of Science, Scopus, and PsycINFO databases were searched; 5,482 studies were identified. Following title/abstract screening, and full reading of the remaining articles, 22 studies based on 19 unique datasets were included. We found preliminary evidence that resilience is associated with structural, functional, and connectivity differences in young people, as assessed using structural and functional MRI and diffusion tensor imaging methods. Despite heterogeneity in definitions/assessment of resilience and a limited number of studies, the neuroimaging literature suggests some convergence across modalities regarding brain regions linked to resilience (especially the prefrontal cortex). Future studies would benefit from adopting longitudinal designs, broader conceptualisations of resilience that capture the impact of adversity exposure, and a dimensional approach to psychopathology.
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http://dx.doi.org/10.1016/j.neubiorev.2021.11.001 | DOI Listing |
Reprod Health
January 2025
Department of Global Health, University of Warwick, Coventry, UK.
Objectives: The research objectives were to identify and synthesise prevailing definitions and indices of resilience in maternal, newborn, and child health (MNCH) and propose a harmonised definition of resilience in MNCH research and health programmes in low- and middle-income countries (LMICs).
Design: Scoping review using Arksey and O'Malley's framework and a Delphi survey for consensus building.
Participants: Mothers, new-borns, and children living in low- and middle-income countries were selected as participants.
BMC Public Health
January 2025
Department of Statistics, College of Natural and Computational Sciences, Debre Markos University, P.O. Box 269, Debre Markos, Ethiopia.
Backgrounds: Poverty is a complex and multifaceted global public health issue, particularly prevalent in Ethiopia, including the East Gojjam Zone. Previous studies on poverty have largely relied on unidimensional measures, providing limited evidence on multidimensional poverty (MP). Therefore, this study tried to assess the prevalence and identify the associated factors of MP among rural households in selected woredas of East Gojjam Zone, Northern Ethiopia.
View Article and Find Full Text PDFBMC Public Health
January 2025
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
China witnessed an Omicron COVID-19 outbreak at the end of 2022. During this period, medical crowding and enormous pressure on the healthcare systems occurred, which might result in the occurrence of occupational burnout among healthcare workers (HCWs). This study aims to investigate the prevalence of occupational burnout and associated mental conditions, such as depressive symptoms, anxiety, PTSD symptoms, perceived social support, resilience, and mindfulness among HCWs of the Chinese mainland during the Omicron COVID-19 outbreak, and to explore the potential risk and protective factors influencing occupational burnout of HCWs.
View Article and Find Full Text PDFBMC Plant Biol
January 2025
National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, Henan, 455000, China.
Background: Salinity stress impairs cotton growth and fiber quality. Protoplasts enable elucidation of early salt-responsive signaling. Elucidating crop tolerance mechanisms that ameliorate these diverse salinity-induced stresses is key for improving agricultural productivity under saline conditions.
View Article and Find Full Text PDFCreating the Babel Fish, a tool that helps individuals translate speech between any two languages, requires advanced technological innovation and linguistic expertise. Although conventional speech-to-speech translation systems composed of multiple subsystems performing translation in a cascaded fashion exist, scalable and high-performing unified systems remain underexplored. To address this gap, here we introduce SEAMLESSM4T-Massively Multilingual and Multimodal Machine Translation-a single model that supports speech-to-speech translation (101 to 36 languages), speech-to-text translation (from 101 to 96 languages), text-to-speech translation (from 96 to 36 languages), text-to-text translation (96 languages) and automatic speech recognition (96 languages).
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