Evidence mapPaperPMID 41953447Full record

ArticleFrontiers in microbiology2026

Gi-MAPS: a quantitative engineering framework for AI-guided pediatric gut microbiome ecological interpretation and digital-twin simulation.

Xingyu Wang, Wanjin Hu, Renxiang Li, Ruikun Sun, Min-Tze Liong, Qinghua Yu, Dongbo Chen

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2026. 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
field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

7 authors.

Xingyu WangLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.
Wanjin HuLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.
Renxiang LiLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.
Ruikun SunLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.
Min-Tze LiongLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.
Qinghua YuLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.
Dongbo ChenLaboratory of Microbiology, Immunology, and Metabolism, DiPROBIO (Shanghai) Co., Limited, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Quantitative and reproducible microbiome analysis is limited by fragmented workflows lacking standardized anaerobic sampling, absolute quantification methods, and transparent AI inference. Patent-documented engineering integration is required for reliable microbiome analytics at population scale. Methods: Gi-MAPS was designed as an end-to-end analytical system integrating several core patented innovations, including (i) a press-activated anaerobic sample-preservation device that maintains ultra-low residual oxygen to protect obligate anaerobes during transport, (ii) a multiplex qPCR assay enabling simultaneous absolute quantification of key HMO-utilizing Results: System validation demonstrated <0.1% residual oxygen stability for anaerobic preservation, detection sensitivity down to five genomic copies per microliter, AUC > 0.97 for ecological maturity estimation, 89% accuracy for disease-risk classification, and 95% concordance for digital-twin forecasting. Execution-layer software copyright modules and filed patents extend automation, visualization, and future application domains. Conclusion: Gi-MAPS provides a patent-anchored, standardized engineering framework whose key novelties lie in oxygen-controlled anaerobic sampling, absolute microbial quantification via multiplex qPCR, and digital-twin ecological simulation, enabling quantitative, function-aware, and prospective microbiome analysis. It establishes a reproducible foundation enabling large-scale cohort deployment, longitudinal ecological monitoring, digital-twin simulation, and future multi-omics integration.

Indexed as

artificial intelligencedigital-twin simulationGi-MAPSHMO-utilizing Bifidobacteriumpediatric gut microbiome

Identifiers

PMID41953447
PMCPMC13055552

What Socratic holds

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Registered trials

None linked

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 Socratic graph.