Discovered in pig brains in 1982,the brain-derived neurotrophic factor(BDNF)is one of the most studied and characterized neurotrophins in the central nervous *** recent years,BDNF has received considerable attention f...
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Discovered in pig brains in 1982,the brain-derived neurotrophic factor(BDNF)is one of the most studied and characterized neurotrophins in the central nervous *** recent years,BDNF has received considerable attention for its importance in the development and maintenance of normal brain *** is because BDNF plays an important role in crucial functions of the nervous system,such as the survival,differentiation,and maturation of neurons and glial cells as well as the actions of neuroprotection in adverse conditions,such as glutamatergic overstimulation,cerebral ischemia,hypoglycemia,and neurotoxicity(Kowiański et al.,2018).
Stress, encompassing psychological, physical, and physiological challenges, is an important factor affecting an individual's well-being and potentially leading to psychiatric, neurodegenerative, immune, and metabo...
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Stress, encompassing psychological, physical, and physiological challenges, is an important factor affecting an individual's well-being and potentially leading to psychiatric, neurodegenerative, immune, and metabolic disorders. However, not everyone exposed to stress develops these conditions, highlighting the concept of resilience. Resilience is a dynamic process categorized into four dimensions: pre-existing resilience capacity, ongoing resilience processes, post-stress resilience outcomes, and recovery from psychopathologies. These dimensions involve genomic, cellular, and systemic interactions influenced by genetic factors, early life experiences, adult life experiences in addition to community/environmental factors, and health behaviors. The biological response to stress encompasses endocrine, autonomic, immunological, and behavioral components, modulated by stressor characteristics and individual traits. Due to the limitations in studying stress and resilience in humans, translational models using rodents and cell cultures are essential. Rodent models include acute, chronic, and traumatic stress paradigms, aiding the study of stress-related behavioral and molecular outcomes. Additionally, early life stress models, such as prenatal stress and maternal separation, provide insights into developmental impacts. In this review, first, rodent models for lifelong stress exposure will be summarized considering their validity, advantages, and limitations. Subsequently, an overview of models designed to enhance resilience capacity and process in rodents, and later the behavioral models employed to study the outcomes of resilience will be given. Lastly, the focus will be shifted to cell culture and iPSCs models. Finally, future considerations focused on improving translational models used to study stress and resilience will be discussed. It is aimed to provide an overview of designs for translational stress and resilience models to access more effective translational biom
IntroductionInteracting with a continuously changing environment may necessitate cancelling a programmed movement, such as a step, after an environmental change to avoid errors and possible falls. This cancellation is...
IntroductionInteracting with a continuously changing environment may necessitate cancelling a programmed movement, such as a step, after an environmental change to avoid errors and possible falls. This cancellation is successful if it occurs during the few milliseconds from the Anticipatory Postural Adjustments (APA) that precede the step onset, a period in which the center of mass (CoM) and the center of pressure (CoP) can still be aligned. Here we studied the inhibition of steps by the means of Stop Signal Task (SST), a behavioral task matched with a theoretical model developed to study the behavioral and physiological correlates of simple response inhibition [1], e.g., pressing a button. Aim of the present work was investigating if and how the inhibition of a complex multi-effectors movement as in step, accomplishes the assumptions of the model developed for simpler movements and if it is they are reflected in the time evolution of CoM and CoP. Methods12 healthy subjects (age: 33.3±7.1 y; weight: 69.3±17 kg; height: 169.7±8.2 cm) were required to initiate 300 walks from a stationary position (Go trials; GT) in response to the onset of an ahead pointing arrow (Go signal) on a pc monitor, and to interrupt the movement when in 30% of the trials (Stop trials; ST), a road stop signal (Stop signal) replaced the arrow after a variable time, determined using a behavioral tracking staircase algorithm [2]. A stereo-photogrammetric motion analysis system (SMART-DX 6000: BTS) and eight dynamometer platforms (Kistler 9286B; Kistler) were used to detect the kinematic and dynamic parameters of gait initiation. Ad-hoc analysis algorithms were developed by the MATLAB software (R2019b 9.7; MathWorks) to track the time evolution of CoM and CoP trajectories and capture the related reaction times (RT) for GT, error stop trials (eST), and correct stop trials (cST), and to conduct the statistical analysis. ResultsFig. 1 displays the CoM (left upper panels) and CoP (left lower panels) t
Background: Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correla...
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