Evidence map›Paper›PMID 41937975›Full record

ReviewReproductive medicine and biology

Toward Implantation-Assisting Technologies: Lessons From In Vivo and Ex Vivo Models.

Takehiro Hiraoka, Yasushi Hirota, Masahito Ikawa

Abstract readReview
In one paragraph

Review in Reproductive medicine and biology. 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

3 authors.

Takehiro HiraokaDepartment of Obstetrics and Gynecology, Graduate School of Medicine The University of Tokyo Tokyo Japan.ORCID https://orcid.org/0009-0006-4030-3278
Yasushi HirotaDepartment of Obstetrics and Gynecology, Graduate School of Medicine The University of Tokyo Tokyo Japan.
Masahito IkawaResearch Institute for Microbial Diseases Osaka University Osaka Japan.ORCID https://orcid.org/0000-0001-9859-6217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite advances in assisted reproductive technology (ART), embryo implantation remains inefficient and represents a major barrier to successful pregnancy. Recurrent implantation failure persists even after transfer of high-quality embryos, reflecting an incomplete understanding of the molecular mechanisms governing implantation. Methods: This review synthesizes current knowledge from genetically modified mouse models and an ex vivo system using authentic uterine tissue. Implantation is organized as a hierarchical, multistep process comprising acquisition of uterine receptivity, embryo attachment, and trophoblast invasion. Main Findings: Uterine receptivity is acquired through the action of progesterone signaling. Embryo attachment requires FOXA2-mediated uterine gland maturation and activation of the LIF-STAT3 signaling axis. Subsequent invasion is driven by coordinated epithelial clearance, stromal differentiation, and embryonic activation. Disruption of these stage-specific mechanisms leads to implantation failure. To overcome experimental limitations inherent to in vivo models, an ex vivo uterine system has been developed that preserves native tissue architecture and enables direct manipulation of embryo-uterine interactions. Conclusion: Conceptualizing implantation as a hierarchical process reveals discrete regulatory checkpoints and identifies implantation as a biologically tractable target. Integration of mechanistic insights with ex vivo platforms supports the development of implantation-assisting technologies based on transient, trophectoderm-targeted interventions in next-generation reproductive medicine.

Identifiers

PMID41937975
PMCPMC13045413

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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.