Human beings as islands of stability: Monitoring body states using breath profiles

Maiti, Kiran Sankar; Lewton, Michael; Fill, Ernst E.; Apolonski, Alexander

doi:10.1038/s41598-019-51417-0

Cited by 29 publications

(43 citation statements)

References 56 publications

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“…As none of the participants in the C22_14 dataset were smokers this cluster is relatively well isolated from the network ( Supplementary Figure 11 ). Nonetheless, breath profiles including CO and NO have been proposed for monitorization of whole body states ( Maiti et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Physiological Network From Anthropometric and Blood Test Biomarkers

et al. 2021

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Currently, research in physiology focuses on molecular mechanisms underlying the functioning of living organisms. Reductionist strategies are used to decompose systems into their components and to measure changes of physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence -and collapse- of biological functions of the organism as a whole is often a less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers. We employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p-value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p-value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether Complex Inference Networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for determination and quantification of health and disease.

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Section: Discussionmentioning

confidence: 99%

Physiological Network From Anthropometric and Blood Test Biomarkers

et al. 2021

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“…The instrument and its performance for real breath samples are described in refs. [13,14], and we therefore only present its key parameters here. We used a Fourier spectrometer Bruker Vertex 70 operating in a spectral range of 500-4000 cm −1 in conjunction with a 4 meter 2 liter "White cell" (Bruker) that collects breath for the measurement, and a liquid nitrogen cooled MCT detector.…”

Section: The Spectroscopic and Breath Collection Techniquesmentioning

confidence: 99%

“…The participants were chosen based on our previous results [14], in order to properly match the diseased group. We found that a smoking habit as well as high methane emission in breath of all the volunteers [14] do not affect the data in the spectral range shown in Figure 1. Note: Gross Motor Function Classification System distinguishes a patient's degree of self-initiated movement abilities, and varies from level I (easy) to level IV (difficult).…”

Section: A Description Of the Patients And The Reference Groupmentioning

confidence: 99%

“…Possible reasons for this in regard to the first two techniques were discussed in refs. [13,14]. Recently, we found that the breath content can be measured in a reliable manner by means of mid-infrared spectroscopy [13].…”

Section: Introductionmentioning

confidence: 99%

“…As the first step in this direction, brain abnormality must be detected. In this paper, we suggest obtaining first evidence of abnormality via breath, due to the ease of sampling and measurement steps, accompanied by its high total accuracy [13,14]. Breath provides physiological analysis based on metabolites and could be added to the morphological and behavioral diagnostics, in order to make the analysis of the state of the patient with CP more complete.…”

Section: Introductionmentioning

confidence: 99%

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Breath indeed carries significant information about a disease: Potential biomarkers of cerebral palsy

Maiti

Roy

Lampe

et al. 2020

Journal of Biophotonics

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Objective and reliable noninvasive medical diagnostics of a large variety of diseases is still a dream. As a step in the direction of realization, a spectroscopic breath study of cerebral palsy (CP) was performed. Principal component analysis revealed data clustering for a healthy group and CP individuals was observed, with a P-value below 10 −5. Learning algorithms resulted in 91% accuracy in distinguishing the groups. With the help of manual analysis of absorption spectral features of breath samples, two volatile organic compounds were identified that demonstrate significant deviations in the groups. These represent two esters of propionic acid (PPAE). A transportation scheme was hypothesized that links the gut where propionic acid (PPA) and PPAE are produced, the brain of CP patients, through which PPA and PPAE transmit, and the lungs where PPAE releases. The results show a possibility to detect one more brain-related disorder via breath, in this case CP.

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Advances in Electrochemical Sensors for Detecting Analytes in Biofluids

Lee

Kim

Soltis

et al. 2023

Advanced Sensor Research

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More than half of all Americans suffer from chronic diseases, the leading causes of death and disability. However, prompt treatment of chronic diseases can lead to better patient outcomes and a reduced burden on the healthcare system. This highlights the urgent need for electrochemical (EC) sensors that provide non‐invasive, real‐time monitoring of disease‐indicating biomarkers. Due to their high sensitivity, high selectivity, and cost‐effectiveness, EC biosensors have recently shown tremendous promise for individualized health monitoring. This review explains the working principles of EC biosensors. It summarizes the recent advances and improvements of EC biosensors for detecting biomarkers in different biofluids, including tears, saliva, breath, urine, and sweat. Through a comprehensive overview of EC biosensor technologies, this article is expected to aid the development of flexible and wearable EC biosensing systems that have the potential to provide continuous, long‐term health monitoring for both clinical and at‐home use.

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Human beings as islands of stability: Monitoring body states using breath profiles

Cited by 29 publications

References 56 publications

Physiological Network From Anthropometric and Blood Test Biomarkers

Physiological Network From Anthropometric and Blood Test Biomarkers

Breath indeed carries significant information about a disease: Potential biomarkers of cerebral palsy

Advances in Electrochemical Sensors for Detecting Analytes in Biofluids

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