Evidence map›Paper›PMID 41256687›Full record

ArticlebioRxiv : the preprint server for biology2025

Single-cell-level digital twins for preterm birth prevention strategies.

Jakob Einhaus, Peter Neidlinger, Olivier Fondeur, Masaki Sato, Alexandra Anronikov, Kotaro Miyazaki, Jonas N Amar, Kazuo Ando, Valentin Badea, Dyani K Gaudilliere and 20 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

30 authors.

Jakob EinhausDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Peter NeidlingerDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Olivier FondeurDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Masaki SatoDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Alexandra AnronikovDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Kotaro MiyazakiDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jonas N AmarDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Kazuo AndoDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Valentin BadeaDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Dyani K GaudilliereDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
Maximilian SabayevDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Dorien FeyaertsDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Maïgane DiopDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Amy S TsaiDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Amelie CambrielDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Edward A GanioDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Romain LagardeDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Emmeline O'KellyDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Ina A StelzerDepartment of Pathology, University of California San Diego, La Jolla, CA, USA.
Julien HedouDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Ronald J WongDepartment of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0003-1205-6936
Yair J BlumenfeldDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
Deirdre J LyellDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
Gary M ShawDepartment of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.ORCID 0000-0001-7393-5120
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Linda GiudiceDepartment of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, CA, USA.
David K StevensonDepartment of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Brice GaudilliereDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Funding

UCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GIUDICE, LINDA C · 2021 to 2025
$7.1M
Neuromodulation of maternal immune adaptations in pregnancyK99HD105016 · NICHD · STANFORD UNIVERSITY · PI STELZER, INA · 2021 to 2022
$263k
Endometrial immune microenvironment dysfunction in endometriosis pathobiologyK99HD115829 · NICHD · STANFORD UNIVERSITY · PI FEYAERTS, DORIEN · 2024 to 2025
$259k
NICHD NIH HHS K99 HD105016NICHD NIH HHS K99 HD115829NICHD NIH HHS P01 HD106414
6 · The paper itself

Abstract

Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face unique ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell-type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCO's digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate, maprotiline, and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naïve CD4

Identifiers

PMID41256687
PMCPMC12621756

What OpenQuestion holds

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Registered trials

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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.