Evidence map›Paper›PMID 42557467›Full record

ArticleDrug delivery and translational research2026

Dissecting corneal drug permeability under experimental variability and drug cold-start challenges via hybrid machine learning.

Shuya Xu, Nannan Wang, Yong Tao, Chihua Li, He Song, Defang Ouyang

Abstract read
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In one paragraph

Article in Drug delivery and translational research, 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

6 authors.

Shuya Xu *State Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macau, China.
Nannan Wang *State Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macau, China.
Yong TaoDepartment of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Chihua LiState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macau, China.
He SongState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macau, China.
Defang OuyangState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macau, China. defangouyang@um.edu.mo.ORCID http://orcid.org/0000-0002-8052-4773

Funding

Science and Technology Development Fund, Macau SAR File no. 0002/2025/NRP and 0037/2025/RIB1University of Macau Multi-Year Research Grant MYRG-GRG2024-00123-ICMS-UMDF
6 · The paper itself

Abstract

Corneal drug permeability is a key determinant of topical ocular drug delivery efficiency, yet its quantitative prediction remains challenging due to experimental noise and limited generalizability across drugs. Herein, we systematically integrated ex vivo drug and formulation corneal permeability data and developed a hybrid machine learning framework that jointly incorporates intrinsic and formulation-level information to quantitatively characterize corneal drug permeation under high-noise and drug cold-start conditions. We curated the first comprehensive ex vivo corneal apparent permeability coefficient (P

Indexed as

Apparent permeability coefficientArtificial intelligenceCorneal permeabilityMachine learningOphthalmic formulationsPenetration enhancer

Identifiers

PMID42557467

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.