Evidence map›Paper›PMID 40296637›Full record

ReviewCurrent molecular medicine2026

Hemolysis Analysis and Hemolysis-related MicroRNA Candidates for Serum/Plasma Samples.

Rongxin He, Yuntao Zhou

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current molecular medicine, 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

2 authors.

Rongxin HeCollege of Medicine, North China University of Science and Technology, Tangshan (063000), Hebei, China.
Yuntao ZhouTangshan Gongren Hospital, North China University of Science and Technology, Tangshan (063000), Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hemolysis is a major challenge in the screening and validation of serum or plasma miRNA biomarkers for human diseases. Over the past decade, numerous studies have focused on hemolysis detection at both the pre-analytical and postanalytical stages to minimize bias in miRNA quantification. Both conventional and advanced hemolysis determination methods have played important roles in quality control in hemolysis assessment and risk prediction during the plasma or serum miRNA quantification process. This review discusses the advantages of these methods and provides an interactive summary of prior knowledge on hemolysissensitive miRNAs and their potential applications in disease diagnosis. Furthermore, the review highlights the advancements in machine learning technologies that enhance classifier predictions and hemolysis risk model evaluations, particularly during the post-analytical stage. Finally, it discusses the ongoing development, standardization, and potential applications of these approaches, which will contribute to a more comprehensive and interpretable framework for the discovery and validation of plasma or serum miRNA biomarkers.

Indexed as

HemolysisMicroRNAsBiomarkersHumansPlasmaBiomarkersMicroRNAsbiomarkerdiagnoseHemolysismicroRNAplasmaserum

Identifiers

What OpenQuestion 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 OpenQuestion graph.