Physiology最新文献

The predatory imminence continuum (PIC) of antipredator defensive behavior has been a helpful strategy for modeling anxiety and fear-related disorders in nonclinical research. The PIC is divided into three different sequential stages that reflect defensive behavioral strategy in response to predatory imminence. However, the PIC was experimentally addressed with a series of shock-based fear conditioning experiments rather than predatory threats. In this article, we consider the PIC in a more naturalistic behavioral setting, focusing on analyzing the neural systems of animals responding to terrestrial and aerial predators. Of relevance, there is a sequential engagement of the distinct neural circuits along each phase of the PIC. In the preencounter phase, prefrontal cortical networks are particularly involved in planning and organizing behavioral responses to ambiguous threats. As the predatory cues or the real predator is detected, there is an engagement of amygdalar and hippocampal > hypothalamic pathways in conjunction with the periaqueductal gray, which organize fear responses. This dynamic particularly reveals how specific neural circuits are set into action to subserve distinct defensive responses. Moreover, we further explore the neural circuits governing other fearful situations outside the context of the PIC, including agonistic social encounters and interoceptive challenges. This analysis reveals an interesting overlap between the neural systems responding to these threats and those involved in response to predatory threats. The present review clarifies how defensive circuits respond to natural threats and provides a more realistic view of the neural systems underlying anxiety and fear responses.

Recent developments in artificial intelligence (AI) may significantly alter physiological research and healthcare delivery. Whereas AI applications in medicine have historically been trained for specific tasks, recent technological advances have produced models trained on more diverse datasets with much higher parameter counts. These new, "foundation" models raise the possibility that more flexible AI tools can be applied to a wider set of healthcare tasks than in the past. This review describes how these newer models differ from conventional task-specific AI, which relies heavily on focused datasets and narrow, specific applications. By examining the integration of AI into diagnostic tools, personalized treatment strategies, biomedical research, and healthcare administration, we highlight how these newer models are revolutionizing predictive healthcare analytics and operational workflows. In addition, we address ethical and practical considerations associated with the use of foundation models by highlighting emerging trends, calling for changes to existing guidelines, and emphasizing the importance of aligning AI with clinical goals to ensure its responsible and effective use.

The autonomic nervous system is critical for regulating cardiovascular physiology. The neurocardiac axis encompasses multiple levels of control, including the motor circuits of the sympathetic and parasympathetic nervous systems, sensory neurons that contribute to cardiac reflexes, and the intrinsic cardiac nervous system that provides localized sensing and regulation of the heart. Disruption of these systems can lead to significant clinical conditions. Recent advances have enhanced our understanding of the autonomic control of the heart, detailing the specific neuronal populations involved and their physiologic roles. In this review, we discuss this research at each level of the neurocardiac axis. We conclude by discussing the clinical field of neurocardiology and attempts to translate this new understanding of neurocardiac physiology to the clinic. We highlight the contributions of autonomic dysfunction in prevalent cardiovascular diseases and assess the current status of novel neuroscience-based treatment approaches.

Sleep has been postulated to play an important role in the removal of potentially neurotoxic molecules, such as amyloid-β, from the brain via the glymphatic system. Disturbed sleep, on the other hand, may contribute to the accumulation of neurotoxins in brain tissue, eventually leading to neuronal death. A bidirectional relationship has been proposed between impaired sleep and neurodegenerative processes, which start years before the onset of clinical symptoms associated with conditions like Alzheimer's and Parkinson's diseases. Given the heavy burden these conditions place on society, it is imperative to develop interventions that promote efficient brain clearance, thereby potentially aiding in the prevention or slowing of neurodegeneration. In this review, we explore whether the metabolic clearance function of sleep can be enhanced through sensory (e.g., auditory, vestibular) or transcranial (e.g., magnetic, ultrasound, infrared light) stimulation, as well as pharmacological (e.g., antiepileptics) and behavioral (e.g., sleeping position, physical exercise, cognitive intervention) modulation of sleep physiology. A particular focus is placed on strategies to enhance slow-wave activity during nonrapid eye movement sleep as a driver of glymphatic brain clearance. Overall, this review provides a comprehensive overview on the potential preventative and therapeutic applications of sleep interventions in combating neurodegeneration, cognitive decline, and dementia.

Histone deacetylases (HDACs) are enzymes that catalyze the removal of acetyl groups from key lysine residues on histone and nonhistone proteins and thereby regulate gene transcription. They have been implicated in several biological processes in both healthy and pathological settings. This review discusses the role of HDACs in multiple metabolically active tissues and highlights their contribution to the pathogenesis of tissue-specific maladaptation and diseases. We also summarize the current knowledge gaps and potential ways to address them in future studies.

The spatial organization of chromatin within the eukaryotic nucleus is critical in regulating key cellular functions, such as gene expression, and its disruption can lead to disease. Advances in experimental techniques, such as Hi-C and microscopy, have significantly enhanced our understanding of chromatin's intricate and dynamic architecture, revealing complex patterns of interaction at multiple scales. Along with experimental methods, physics-based computational models, including polymer phase separation and loop-extrusion mechanisms, have been developed to explain chromatin structure in a principled manner. Here, we illustrate genomewide applications of these models, highlighting their ability to predict chromatin contacts across different scales and to spread light on the underlying molecular determinants. Additionally, we discuss how these models provide a framework for understanding alterations in chromosome folding associated with disease states, such as SARS-CoV-2 infection and pathogenic structural variants, providing valuable insights into the role of chromatin architecture in health and disease.

Imaging is ubiquitous with biomedical research and discovery. This article surveys the role of imaging in our understanding of metabolism and introduces photoacoustic imaging as a compelling candidate for providing high-resolution, label-free, and real-time insights into metabolic processes. As a rapidly evolving modality, photoacoustics holds promising preclinical and clinical potential in imaging of metabolism as well as metabolism-related changes.