Evidence map›Paper›PMID 41270075›Full record

ArticlePloS one2025

Can physiological network mapping reveal pathophysiological insights into emerging diseases? Lessons from COVID-19.

Cindy Xinyu Ji, Majid Sorouri, Mohammad Abdollahi, Omalbanin Paknejad, Ali R Mani

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Cindy Xinyu JiNetwork Physiology Lab, Division of Medicine, UCL, London, United Kingdom.ORCID https://orcid.org/0009-0000-1741-9842
Majid SorouriLiver and Pancreaticobiliary Research Centre, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad AbdollahiDigestive Diseases Research Centre, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Omalbanin PaknejadDepartment of Pulmonary Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Ali R ManiNetwork Physiology Lab, Division of Medicine, UCL, London, United Kingdom.ORCID https://orcid.org/0000-0003-0830-2022

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Network physiology is a multidisciplinary field that offers a comprehensive view of the complex interactions within the human body, emphasising the critical role of organ system connectivity in health and disease. This approach has the potential to provide pathophysiological insights into complex and emerging diseases. This study aims to evaluate the effectiveness of physiological network mapping in predicting outcomes for COVID-19 patients, using data from the first wave of the pandemic. Routine clinical and laboratory data from 202 patients with COVID-19 were retrospectively analysed. Twenty-one physiological variables representing various organ systems were used to construct organ network connectivity through correlation analysis. Parenclitic network analysis was also employed to measure deviations in individual patients' organ system correlations from the reference physiological interactions observed in survivors. We observed distinct features in the correlation network maps of non-survivors compared to survivors. In non-survivors, there was a significant correlation between the level of consciousness and the liver enzyme cluster, a relationship not present in the survivor group. This relationship remained significant even after adjusting for age and degree of hypoxia. Additionally, a strong correlation along the BUN-potassium axis was identified in non-survivors, suggesting varying degrees of kidney damage and impaired potassium homeostasis in non-survivors. These findings highlight the potential of network physiology as a valuable tool for uncovering complex inter-organ interactions in emerging diseases, with applications that could support clinicians, researchers, and policymakers in future epidemics.

Indexed as

COVID-19AdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSARS-CoV-2

Identifiers

PMID41270075
PMCPMC12637946

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.