Evidence map›Paper›PMID 42065651›Full record

ArticleSmall methods2026

Liposome Particle Size Prediction by In-Line Process Analytical Technology (PAT)-Integrated Machine Learning.

Junghu Lee, Nozomi Morishita Watanabe, Noriko Yoshimoto, Seonghyeon Eom, Moon Kyu Kwak, Ho-Sup Jung, Hiroshi Umakoshi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Junghu LeeDivision of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan.
Nozomi Morishita WatanabeDivision of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan.
Noriko YoshimotoDepartment of Applied Chemistry, Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan.ORCID https://orcid.org/0000-0002-3171-9953
Seonghyeon EomNbiocell Inc, Siheung SNU Start-up Campus, Gyeonggi-do, Republic of Korea.
Moon Kyu KwakDepartment of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
Ho-Sup JungNbiocell Inc, Siheung SNU Start-up Campus, Gyeonggi-do, Republic of Korea.
Hiroshi UmakoshiDivision of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan.ORCID https://orcid.org/0000-0002-9241-853X

Funding

Japan Society for the Promotion of Science 21H04628Japan Society for the Promotion of Science 24K21230Ministry of SMEs and Startups RS-2024-0050877National Research Foundation of Korea RS-2024-00356054National Research Foundation of Korea RS-2024-00432685
6 · The paper itself

Abstract

Precise control of liposome size is critical for drug delivery. We developed an in-line PAT-integrated machine learning model that predicts particle size with high accuracy (root mean square error 7.18 nm) using limited experimental data. By integrating physicochemical membrane characteristics, the model demonstrates generalization (root mean square error 7.53 nm) and interpretability, establishing a practical framework for advanced liposome particle size control.

Indexed as

liposomesmachine learningmicrofluidicsnanomedicine manufacturingprocess analytical technologyquality by designsize control

Identifiers

PMID42065651
PMCPMC13288013

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.