ReviewJournal of biosciences2022
Identifying critical transitions in complex diseases.
Review in Journal of biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- From Static Risk Estimates to Dynamic Clinical Trajectories: A Conceptual Analysis of Perioperative Medicine.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface.International journal of molecular sciences · 2026Article
- Characterising Epigenetic Tipping Points using a Spectral Dimension Reduction Approach.Bulletin of mathematical biology · 2026Article
- Percolation Forces in Lung Inflammation: Determining the Path to Emphysema or Fibrosis.Biomedicines · 2026Review
- Network-level organization of systemic inflammation reflects early Alzheimer's-like behavioral changes.Scientific reports · 2025Article
- Network-Level Organization of Systemic Inflammation Reflects Early Alzheimer's-Like Behavioral Changes.Research square · 2025Article
- DAMP-ing IBD: Extinguish the Fire and Prevent Smoldering.Digestive diseases and sciences · 2025Review
- Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.Royal Society open science · 2024Article
- Systematic analysis and optimization of early warning signals for critical transitions using distribution data.iScience · 2023Article
- A general hypothesis of multistable systems in pathophysiology.F1000Research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mortality and the burden of diseases worldwide continue to reach substantial numbers with societal development and urbanization. In the face of decline in human health, early detection of complex diseases is indispensable, albeit challenging. In this review, we document the research carried out thus far on the appearance of complex diseases marked by a critical transition or a sudden shift from a healthy state to a disease state. The theory of resilience and critical slowing down can provide practical tools to forecast the onset of various fatal and perpetuating diseases. However, critical transitions in diseases across diverse temporal and spatial scales may not always be preceded by critical slowing down. In this backdrop, an in-depth study of the underlying molecular mechanisms provides dynamic network biomarkers that can forecast potential critical transitions. We have put together the theory of complex diseases and resilience, and have discussed the need for advanced research in developing early warning signals in the field of medicine and health care. We conclude the review with a few open questions and prospects for research in this emerging field.
Indexed as
Identifiers
36210727PMC9018973What OpenQuestion holds
Registered trials
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.