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Record W4365515649 · doi:10.3390/pharmaceutics15041235

Clinical Translation of Long-Acting Drug Delivery Systems for Posterior Capsule Opacification Prophylaxis

2023· review· en· W4365515649 on OpenAlexaff
Xinyang Li, Liang Chen, Yexuan Guo, Jing Su, Xi Chen, Robert B. Macgregor, Ruixue Zhang, Hong Yan

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversity of Toronto
FundersKey Research and Development Projects of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsDrug deliveryMedicineDrugPosterior capsule opacificationIntraocular lensesIntraocular lensOphthalmologySurgeryPharmacologyPhacoemulsificationVisual acuityNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Posterior capsule opacification (PCO) remains the most common cause of vision loss post cataract surgery. The clinical management of PCO formation is limited to either physical impedance of residual lens epithelial cells (LECs) by implantation of specially designed intraocular lenses (IOL) or laser ablation of the opaque posterior capsular tissues; however, these strategies cannot fully eradicate PCO and are associated with other ocular complications. In this review, we critically appraise recent advances in conventional and nanotechnology-based drug delivery approaches to PCO prophylaxis. We focus on long-acting dosage forms, including drug-eluting IOL, injectable hydrogels, nanoparticles and implants, highlighting analysis of their controlled drug-release properties (e.g., release duration, maximum drug release, drug-release half-life). The rational design of drug delivery systems by considering the intraocular environment, issues of initial burst release, drug loading content, delivery of drug combination and long-term ocular safety holds promise for the development of safe and effective pharmacological applications in anti-PCO therapies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.353
GPT teacher head0.492
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2023
Admission routes1
Has abstractyes

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