In one paragraphArticle in International journal of molecular sciences, 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
7 authors.
Napat KongtawornProgram in Bioinformatics and Computational Biology, College of Interdisciplinary and Integrative Studies, Chulalongkorn University, Bangkok 10330, Thailand.
Borwornlak ToopradabProgram in Bioinformatics and Computational Biology, College of Interdisciplinary and Integrative Studies, Chulalongkorn University, Bangkok 10330, Thailand.
Duangjai TodsapornCenter of Excellence in Structural and Computational Biology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Poomrapee TinpovongProgram in Bioinformatics and Computational Biology, College of Interdisciplinary and Integrative Studies, Chulalongkorn University, Bangkok 10330, Thailand.ORCID 0009-0005-3252-6415 Rada ThongsuebsaengCenter of Excellence in Structural and Computational Biology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Phornphimon MaitaradCenter of Excellence in Structural and Computational Biology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.ORCID 0000-0003-0035-0070 Thanyada RungrotmongkolProgram in Bioinformatics and Computational Biology, College of Interdisciplinary and Integrative Studies, Chulalongkorn University, Bangkok 10330, Thailand.ORCID 0000-0002-7402-3235 Funding
90th Anniversary of Chulalongkorn University Fund GCUGR1125671132DHigh-Performance Computing Center of Shanghai University and the Shanghai Engineering Re-search Center of Intelligent Computing System 19DZ2252600NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation B38G680006Second Century Fund (C2F), Chulalongkorn University, for a PhD scholarship
6 · The paper itselfAbstract
Liver cancer remains a significant global health burden, requiring the development of precise nucleic acid delivery systems. Lipid nanoparticles (LNPs) are leading candidates; however, their efficiency is governed by the pK
Indexed as
Gene SilencingLipidsLiverMachine LearningNanoparticlesQuantitative Structure-Activity RelationshipBoosting Machine Learning AlgorithmsHumansLiposomesPrediction AlgorithmsPredictive Learning ModelsRandom ForestLipid NanoparticlesLipidsLiposomesgene silencingionizable lipid designlipid nanoparticlesmachine learning QSARpKa prediction
Identifiers
PMID42123652
PMCPMC13164459
What OpenQuestion holds
Textmetadata
LicenceCC BY
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