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Related Concept Videos

Physiological Foundation of Stress01:24

Physiological Foundation of Stress

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Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
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Introduction to Stress and Lifestyle01:27

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Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
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Applications of Stress01:04

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Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
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Stress Response System01:21

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The stress response system, also known as the fight-or-flight response, is the body's automatic physiological reaction to perceived threats. Hans Selye introduced the concept of General Adaptation Syndrome (GAS) to describe the predictable pattern of changes that occur in response to stress. GAS consists of three sequential stages: alarm, resistance, and exhaustion. This model helps explain how chronic stress can contribute to health problems.
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Psychological Responses to Stress01:20

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Psychological responses to stress encompass the various cognitive and emotional reactions individuals experience when faced with challenging or threatening situations, such as a job loss. Prolonged exposure to stressors can disturb emotional balance, increasing negative emotions (e.g., anxiety and sadness) and diminishing positive emotions (e.g., joy and satisfaction). These persistent emotional shifts are associated with an increased risk of both physical illness and mental health issues, such...
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Components of Stress01:23

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Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
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Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines.

Zeinab Shahbazi1, Yung-Cheol Byun2

  • 1Department of Mathematics Informatics, University of Barcelona, 08007 Barcelona, Spain.

Biology
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Summary

Early life stress during pregnancy can impact maternal and child health. This study used machine learning to detect stress from physiological signals in at-risk pregnant women.

Keywords:
early life stressmachine learningphysiological signalsprediction

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Area of Science:

  • Psychoneuroimmunology
  • Developmental Psychology
  • Computational Biology

Background:

  • Pregnancy and early childhood are critical developmental periods with heightened immunological sensitivity.
  • Exposure to stress during these times can significantly increase health risks for both mother and child.
  • Early Life Stress (ELS) is linked to adverse outcomes in psychological development, metabolism, and cardiovascular health.

Purpose of the Study:

  • To investigate the relationship between recalled childhood or pregnancy hardship and inflammatory imbalance in at-risk pregnant women.
  • To explore the potential of machine learning for detecting stress using physiological signals during pregnancy.
  • To identify strategies for controlling Early Life Stress in pregnant women at risk for future diseases.

Main Methods:

  • Retrospective data collection on childhood and pregnancy hardship.
  • Assessment of inflammatory imbalance in a cohort of 53 ethnically diverse, low-income women.
  • Application of Convolutional Neural Networks (CNNs) for stress detection using short-term physiological data (heart rate, galvanic skin response).

Main Results:

  • Analysis of the association between recalled hardship and inflammatory markers.
  • Evaluation of CNN performance in identifying stress from physiological signals.
  • Identification of specific physiological indicators of stress during pregnancy.

Conclusions:

  • Early Life Stress during pregnancy poses significant risks to maternal and child well-being.
  • Machine learning approaches show promise for objective stress detection in pregnant populations.
  • Further research is needed to develop effective interventions for managing ELS in at-risk pregnant women.