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Spontaneous recognition memory tasks are widely used to assess cognitive function in rodents and have become commonplace in the characterization of rodent models of neurodegenerative, neuropsychiatric and neurodevelopmental disorders. Leveraging an animal's innate preference for novelty, these tasks use object exploration to capture the what, where and when components of recognition memory. Choosing and optimizing objects is a key feature when designing recognition memory tasks. Although the range of objects used in these tasks varies extensively across studies, object features can bias exploration, influence task difficulty and alter brain circuit recruitment. Here, we discuss the advantages of using 3D-printed objects in rodent spontaneous recognition memory tasks. We provide strategies for optimizing their design and usage, and offer a repository of tested, open-source designs for use with commonly used rodent species. The easy accessibility, low-cost, renewability and flexibility of 3D-printed open-source designs make this approach an important step toward improving rigor and reproducibility in rodent spontaneous recognition memory tasks.
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http://dx.doi.org/10.1523/ENEURO.0319-21.2021 | DOI Listing |
Front Public Health
September 2025
Department of Personnel Strategies, Institute of Management, Collegium of Management and Finance, SGH Warsaw School of Economics, Warsaw, Poland.
Introduction: Organizational resilience is of paramount importance for coping with adversity, particularly in the healthcare sector during crises. The objective of the present study was to evaluate the impact of resilience-based interventions on the well-being of healthcare employees during the pandemic. In this study, resilience-based interventions are defined as organizational actions that strengthen a healthcare institution's capacity to cope with crises-such as ensuring adequate personal protective equipment and staff testing, clear risk-communication, alternative care pathways (e.
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September 2025
Department of Health Care Sciences, Marie Cederschiöld University, Stockholm, Sweden.
Purpose: This study investigates how older foreign-born adults in Sweden experience and navigate social connectedness as a determinant of wellbeing.
Methods: Employing Glaser's grounded theory methodology, we collected qualitative data through individual ( = 1) and focus group ( = 5) interviews with 23 participants aged 60 + representing four distinct cultural-linguistic groups: Arabic, Finnish, Spanish, and Chinese speakers.
Results: The analysis identified "" as the core category, encompassing three dimensions: (1) , (2) , and (3) .
Patterns (N Y)
July 2025
University of Washington, Department of Astronomy, Seattle, WA, USA.
Machine learning and artificial intelligence promise to accelerate research and understanding across many scientific disciplines. Harnessing the power of these techniques requires aggregating scientific data. In tandem, the importance of open data for reproducibility and scientific transparency is gaining recognition, and data are increasingly available through digital repositories.
View Article and Find Full Text PDFCurr Alzheimer Res
September 2025
Department of Life Science and Bioinformatics, Assam University, Silchar, 788011, Assam, India.
Introduction: Arsenic, a metalloid, is well associated as a risk factor for the development and progression of neurodegenerative diseases, including Alzheimer's Disease (AD), which is characterized by impairment in cognition. However, specific effects of arsenic on Acetylcholinesterase (AChE) activity and inflammatory markers in different brain regions, as well as its impact on behaviour, are not yet fully understood.
Methods: Arsenic was administered (20 mg/kg by gavage for 4 weeks) to male and female mice, and its effects on behaviour were assessed by using the object recognition memory test and lightdark box test.
Comput Biol Med
September 2025
Postgraduate Program in Computing, Center for Technological Development, Federal University of Pelotas, Pelotas, 96010-610, Rio Grande do Sul, Brazil.
In the task of image classification for emotion recognition, facial expression data is commonly used. However, electrical brain signals generated by neural activity provide data with greater integrity. We can capture these signals non-invasively using electroencephalogram (EEG) recording devices.
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