Lessons from altitude: cerebral perfusion insights and their potential translational clinical significance

Toward a Reasoned Classification of Diseases Using Physico-Chemical Based Phenotypes

Background: Diseases and health conditions have been classified according to anatomical site, etiological, and clinical criteria. Physico-chemical mechanisms underlying the biology of diseases, such as the flow of energy through cells and tissues, have been often overlooked in classification systems.

Objective: We propose a conceptual framework toward the development of an energy-oriented classification of diseases, based on the principles of physical chemistry.

Methods: A review of literature on the physical chemistry of biological interactions in a number of diseases is traced from the point of view of the fluid and solid mechanics, electricity, and chemistry.

Results: We found consistent evidence in literature of decreased and/or increased physical and chemical forces intertwined with biological processes of numerous diseases, which allowed the identification of mechanical, electric and chemical phenotypes of diseases.

Discussion: Biological mechanisms of diseases need to be evaluated and integrated into more comprehensive theories that should account with principles of physics and chemistry. A hypothetical model is proposed relating the natural history of diseases to mechanical stress, electric field, and chemical equilibria (ATP) changes. The present perspective toward an innovative disease classification may improve drug-repurposing strategies in the future.

Wearable physiological sensors and real-time algorithms for detection of acute mountain sickness

This is a minireview of potential wearable physiological sensors and algorithms (process and equations) for detection of acute mountain sickness (AMS). Given the emerging status of this effort, the focus of the review is on the current clinical assessment of AMS, known risk factors (environmental, demographic, and physiological), and current understanding of AMS pathophysiology. Studies that have examined a range of physiological variables to develop AMS prediction and/or detection algorithms are reviewed to provide insight and potential technological roadmaps for future development of real-time physiological sensors and algorithms to detect AMS. Given the lack of signs and nonspecific symptoms associated with AMS, development of wearable physiological sensors and embedded algorithms to predict in the near term or detect established AMS will be challenging. Prior work using [Formula: see text], HR, or HRv has not provided the sensitivity and specificity for useful application to predict or detect AMS. Rather than using spot checks as most prior studies have, wearable systems that continuously measure SpO2 and HR are commercially available. Employing other statistical modeling approaches such as general linear and logistic mixed models or time series analysis to these continuously measured variables is the most promising approach for developing algorithms that are sensitive and specific for physiological prediction or detection of AMS.