Evidence map›Paper›PMID 42841474›Full record

ArticleLab on a chip2026

Data-driven and mechanistic modelling of PLGA nanoparticle formation in hydrodynamic flow focusing microfluidics.

Muhammad Mubashar Saeed, Muhammad Turab, Lasse H E Thamdrup, Anja Boisen, Eadaoin Carthy, Nicholas Dunne, David Kinahan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Lab on a chip, 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.

Muhammad Mubashar SaeedML-Labs Centre for Research Training, Dublin City University, Dublin, Ireland.ORCID http://orcid.org/0009-0008-2814-1020
Muhammad TurabSchool of Computing, Dublin City University, Dublin, Ireland.
Lasse H E ThamdrupIDUN Section, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
Anja BoisenIDUN Section, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
Eadaoin CarthySchool of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland. Muhammad.saeed3@mail.dcu.ie.ORCID http://orcid.org/0000-0001-6505-9503
Nicholas DunneSchool of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland. Muhammad.saeed3@mail.dcu.ie.
David KinahanSchool of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland. Muhammad.saeed3@mail.dcu.ie.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microfluidic devices offer precise control over polymeric nanoparticle (NP) synthesis. However, rational design of process optimization to obtain a target NP size remains challenging because the relationship between operating parameters, mixing dynamics, and particle formation is nonlinear. Here, we integrate mechanistic analysis, statistical design of experiments (DoE), and machine learning (ML) to develop predictive and inverse design frameworks for the synthesis of poly(lactic-

Identifiers

PMID42841474
PMCPMC13644315

What OpenQuestion holds

Textmetadata
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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.