Evidence map›Paper›PMID 31956651›Full record

ArticleFrontiers in bioengineering and biotechnology2019

Predicting Drug Release From Degradable Hydrogels Using Fluorescence Correlation Spectroscopy and Mathematical Modeling.

Saahil Sheth, Emily Barnard, Ben Hyatt, Muruhan Rathinam, Silviya Petrova Zustiak

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

25 citing papers in PubMed, 57 citations in OpenAlex.

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  12. Diffusion-Based 3D Bioprinting Strategies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
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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

5 authors at 2 institutions in 1 country.

Saahil ShethBiomedical Engineering, Saint Louis University, St. Louis, MO, United States.
Emily BarnardMathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, United States.
Ben HyattMathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, United States.
Muruhan RathinamMathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, United States.
Silviya Petrova ZustiakBiomedical Engineering, Saint Louis University, St. Louis, MO, United States.
University of Maryland, Baltimore County · USSaint Louis University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting release from degradable hydrogels is challenging but highly valuable in a multitude of applications such as drug delivery and tissue engineering. In this study, we developed a simple mathematical and computational model that accounts for time-varying diffusivity and geometry to predict solute release profiles from degradable hydrogels. Our approach was to use time snapshots of diffusivity and hydrogel geometry data measured experimentally as inputs to a computational model which predicts release profile. We used two model proteins of varying molecular weights: bovine serum albumin (BSA; 66 kDa) and immunoglobulin G (IgG; 150 kDa). We used fluorescence correlation spectroscopy (FCS) to determine protein diffusivity as a function of hydrogel degradation. We tracked changes in gel geometry over the same time period. Curve fits to the diffusivity and geometry data were used as inputs to the computational model to predict the protein release profiles from the degradable hydrogels. We validated the model using conventional bulk release experiments. Because we approached the hydrogel as a black box, the model is particularly valuable for hydrogel systems whose degradation mechanisms are not known or cannot be accurately modeled.

Indexed as

computationdegradabilitydiffusiondrug deliveryhydrogelrelease

Identifiers

PMID31956651
PMCPMC6951421
OpenAlexW2990196442

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.