Evidence map›Paper›PMID 42635625›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

NICE: A Two-Step Non-Invasive Framework for Embryo cfDNA Read Enrichment and Quality Assessment.

Xueya Zhou, Shu Ding, Zhenyi Zhang, Qiaoling Shangguan, Jie Qiao, Peijie Zhou, Yidong Chen

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. 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

7 authors.

Xueya Zhou *Center for Machine Learning Research, Center for Quantitative Biology, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, China.
Shu Ding *Center for Machine Learning Research, Center for Quantitative Biology, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, China.
Zhenyi ZhangSchool of Mathematical Sciences, Peking University, Beijing, China.ORCID https://orcid.org/0009-0009-5351-7154
Qiaoling ShangguanCenter for Machine Learning Research, Center for Quantitative Biology, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, China.
Jie QiaoCenter for Machine Learning Research, Center for Quantitative Biology, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, China.ORCID https://orcid.org/0000-0003-2126-1376
Peijie ZhouCenter for Machine Learning Research, Center for Quantitative Biology, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, China.ORCID https://orcid.org/0000-0002-4585-2923
Yidong ChenCenter for Machine Learning Research, Center for Quantitative Biology, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, China.ORCID https://orcid.org/0000-0002-3154-6511

Funding

Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities PKU2025PKULCXQ031Key Clinical Projects of Peking University Third Hospital BYSYZD2025035National Key R&D Program of China 2023YFC2705600 2023YFC2705602National Natural Science Foundation of China 12288101 8206100646 T2321001National Natural Science Foundation of China 82301889Young Elite Scientists Sponsorship Program by CAST 2023QNRC001
6 · The paper itself

Abstract

Non-invasive preimplantation genetic testing (niPGT) using cell-free DNA (cfDNA) extracted from spent embryo culture medium (SECM) has shown great potential, providing economic and practical advantages for embryo ploidy testing and quality assessment, while minimizing the risk of embryo damage. However, maternal DNA contamination, which can result in sex discordance and false-negative findings, remains a critical barrier to its clinical application in embryo prioritization. In this study, we present the NICE (Non-Invasive CfDNA-based Embryo assessment) framework, designed to enable contamination-resistant evaluation and support accurate embryo selection. The workflow integrates a two-step strategy: first, embryonic cfDNA is effectively purified using DECENT-plus-an enhanced version of our previously established deep CNV reconstruction algorithm (DECENT)-that minimizes interference from polar body-derived maternal DNA and improves signal resolution between maternal and embryonic origins. Subsequently, machine learning models based on biometric features extracted from the purified cfDNA are constructed to classify embryo quality, providing intelligent decision support for clinical embryo prioritization. By addressing the challenge of maternal contamination, NICE establishes a more automated, standardized and non-invasive paradigm for embryo quality assessment and underscores the critical role of cfDNA-based analysis in facilitating non-invasive selection of high-quality embryos to improve outcomes in assisted reproductive technology.

Indexed as

assisted reproductive technologycomputational biologygenetic testingmachine learningpreimplantation genetic diagnosis

Identifiers

PMID42635625
PMCPMC13502268

What Socratic holds

Textmetadata
Read underepoch 390

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.