No abstract

Background: Sleep deprivation is a universal issue that affects individuals in different ways. While some individuals experience a deficit in performance, others experience resiliency as they maintain high levels of physical and mental activity. Sleep loss is known to cause cognitive dysfunction in areas such as learning and memory, but little is known about neural mechanisms that contribute to resilience to this adverse effect. Methods: An existing database of a learning paradigm in sleep deprived and non-sleep deprived 16 to 18-month old C57BL/6 mice was used to identify fast learners and slow learners based on an R2 value representing the learning curve of each individual mouse. Results: Results showed that sleep deprived mice had more slow learners compared to fast learners whereas non-sleep-deprived mice showed the opposite. Hippocampal immunohistochemistry and digital imaging analysis showed sleep deprived, fast learners expressed lower levels of monocyte chemoattractant protein-1 and histone deacetylase 2 and higher levels of synaptophysin and brain-derived neurotrophic factor compared to sleep-deprived slow learners. Conclusions: These observations provide evidence to suggest that sleep-deprived mice that performed well in a cognitive assay show less hippocampal mediated learning impairment and provide the rationale for further investigations into neurobiological resilience to sleep deprivation with increasing age. Keywords: Sleep deprivation, resiliency, learning impairment, aging, neuropathology, hippocampus

Let (X, φ) be a compact flow without fixed points. We define the packing topological entropy h P top (φ, K) on subsets of X through considering all the possible reparametrizations of time. For fixed-point free flows, we prove the following result: for any non-empty compact subset K of X,, µ is a Borel probability measure onX}, where h µ (φ) denotes the upper local entropy for a Borel probability measure µ on X.

Physical resilience, the capacity to respond to and recover from a stressful event, declines with advancing age. Individuals respond differently to physical stressors across their lifespans. While the biological underpinnings of resilience remain unclear, a plausible determinant is the capacity of an individual’s cellular and molecular levels to return to homeostasis after a physical challenge. Impaired resilience may not only be a consequence of aging but could also be a contributing factor to the aging process. Therefore, resilience at relatively younger ages could be predictive of future health and lifespan. By utilizing standardized physical challenges and measuring stress response patterns, the relative resilience of individuals can be quantified and classified. Current preclinical research suggests that several physical stressors could be used to measure resilience in clinical aging studies. A mechanistic understanding of why some individuals are more resilient to physical stressors than others could help identify protective factors and therapeutic ways to promote healthy aging. Keywords: Physical resilience to aging, physical stressors, heterogeneity, stress response patterns, healthy aging, therapeutic resilience

Effective treatments to prevent or delay age-related learning impairment are not generally available. In a preliminary preclinical study, mice 20 months of age were fed a diet containing 14 ppm rapamycin, an inhibitor of mTOR, for three months and then tested in a spatial navigation task. Mice fed the nonmedicated control diet showed learning impairment while mice fed the rapamycin diet were not learning impaired. This observation provides support for additional preclinical studies and suggests that short-term rapamycin treatment could be a possible strategy for preventing or delaying age-related cognitive impairment in people.