ReviewFrontiers in psychiatry2026
AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.
Review in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- FuzzyFed-CNN: secure and explainable multimodal federated learning for early Alzheimer's diagnosis.Frontiers in artificial intelligence · 2026Article
- Advanced biomaterials and digital twins for precision psychiatry: neuroimmune modulation and AI-guided therapeutics.Frontiers in bioengineering and biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel AI-powered "gut-brain-sleep" digital-twin nursing ecosystem (G-B-S DT-N) that translates microbiome and neuroimmune signals into precision interventions for sleep and mood disorders. The ecosystem integrates four key layers: Microbiome Dynamics, Neuro-immune Interface, Sleep-Cognition-Emotion Circuits, and Person-Nurse-Environment Triad. These layers leverage multi-omics data, EEG sleep microstructure, real-time sensors, and EMR feeds to create a dynamic, patient-specific architecture. Uncertainty-aware explainable AI (XAI) modules ensure privacy and interpretability, enabling causal inference through advanced machine learning techniques. Adaptive care pathways, including precision pre-/post-biotic delivery and circadian light prescriptions, are optimized via nurse-in-the-loop reinforcement learning. The digital twin is operationalized through a five-step closed-loop workflow in hospital and community settings. Quantum-accelerated simulations and a proposed RCT (D-TWIN-RCT) will assess efficacy compared to standard care. Social, legal, and ethical frameworks protect data sovereignty and autonomy. This ecosystem offers a scalable solution for managing complex comorbidities, positioning nursing as a key driver of microbiome-precision medicine.
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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.