Melanoma, which evolves from melanocytes, is the most malignant skin cancer and is highly fatal, although it only accounts for 4% of all skin cancers. Numerous studies have demonstrated that melanoma has a large tumor mutational burden, which means that melanoma has great potential to achieve immune evasion. Tumor-associated macrophages (TAMs) are an important component of both the immune system and tumor microenvironment. Several studies have demonstrated their double-edged sword effects on melanoma. The present review focuses on the role of TAMs in melanoma development, including regulation of proliferation, invasion, metastasis, angiogenesis and chemical resistance of melanoma. Furthermore, the existing mechanisms of action of the TAM-targeting treatments for melanoma are reviewed. More broadly, the weak points of existing research and the direction of future research are finally identified and described.
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http://dx.doi.org/10.3892/etm.2022.11577 | DOI Listing |
Sleep
January 2025
UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).
View Article and Find Full Text PDFJ Exp Bot
January 2025
Ministry of Education Key Laboratory of Molecular and Cellular Biology; Hebei Research Center of the Basic Discipline of Cell Biology; Hebei Collaboration Innovation Center for Cell Signaling and Environmental Adaptation; Hebei Key Laboratory of Molecular and Cellular Biology; College of Life Sciences, Hebei Normal University, 050024 Shijiazhuang, China.
A well-constructed pollen wall is essential for pollen fertility, which relies on the contribution of tapetum. Our results demonstrate an essential role of the tapetum-expressed protein phosphatase 2A (PP2A) B'α and B'β in pollen wall formation. The b'aβ double mutant pollen grains harbored sticky remnants and tectum breakages, resulting in failed release.
View Article and Find Full Text PDFJ Gerontol B Psychol Sci Soc Sci
January 2025
Linguistics and English as a Second Language, Faculty of Arts, University of Groningen, Groningen, the Netherlands.
Objectives: The complex life experience of speaking two or more languages has been suggested to preserve cognition in older adulthood. This study aimed to investigate this further by examining the relationship between multilingual experience variables and cognitive functioning in a large cohort of older adults in the diversely multilingual north of the Netherlands.
Method: 11,332 older individuals participating in the Lifelines Cohort Study completed a language experience questionnaire.
J Gerontol A Biol Sci Med Sci
January 2025
Discipline of Medical Gerontology, Trinity College Dublin, Ireland.
Background: It has been suggested that dog walking may protect against falls and mobility problems in later life, but little work to date has examined this.The aim of this study was to assess if regular dog walking was associated with reduced likelihood of falls, fear of falling and mobility problems in a large cohort of community-dwelling older people.
Methods: Participants ≥60 years at Wave 5 of The Irish Longitudinal Study on Ageing were included.
MAGMA
January 2025
Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.
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