ArticleVaccines2022
Establishment of an Artificial Neural Network Model Using Immune-Infiltration Related Factors for Endometrial Receptivity Assessment.
Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Gradient Boosting Machine Learning Model for Defective Endometrial Receptivity Prediction by Macrophage-Endometrium Interaction Modules.Frontiers in immunology · 2022Pooled it
- Alternative splicing and differential gene expression during changes in endometrial receptivity in patients with recurrent implantation failure.Scientific reports · 2026Article
- A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 2025Review
- The Combination of R848 with Sorafenib Enhances Antitumor Effects by Reprogramming the Tumor Immune Microenvironment and Facilitating Vascular Normalization in Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine.International journal of molecular sciences · 2022Review
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 at 2 institutions in 1 country.
Funding
Abstract
backgroundA comprehensive clinical strategy for infertility involves treatment and, more importantly, post-treatment evaluation. As a component of assessment, endometrial receptivity does not have a validated tool. This study was anchored on immune factors, which are critical factors affecting embryonic implantation. We aimed at establishing novel approaches for assessing endometrial receptivity to guide clinical practice.
methodsImmune-infiltration levels in the GSE58144 dataset (
resultsDysregulation of M1 to M2 macrophage ratio is an important factor contributing to defective endometrial receptivity. M1/M2 related gene modules were enriched in three biological processes in macrophages: antigen presentation, interleukin-1-mediated signalling pathway, and phagosome acidification. Their hub genes were significantly altered in patients and associated with ribosomal, lysosomal, and proteasomal pathways. The established model exhibited an excellent predictive value in both datasets, with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945-1).
conclusionsM1/M2 polarization influences endometrial receptivity by regulating three gene modules, while the established ANN model can be used to effectively assess endometrial receptivity to inform pregnancy and individualized clinical management strategies.
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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.