ArticlePloS one2018
Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- Nanomedicines Bearing an Alkylating Cytostatic Drug from the Group of 1,3,5-Triazine Derivatives: Development and Characterization.Pharmaceutics · 2022Article
- D-Lactic Acid as a Metabolite: Toxicology, Diagnosis, and Detection.BioMed research international · 2020Review
- Article
- Correction: Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications.PloS one · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This methodology permits to simulate the performance of different Poly(D,L-lactide-co-glycolide) copolymer formulations (PDLGA) in the human body, to identify the more influencing variables on hydrolytic degradation and, thus, to estimate biopolymer degradation level. The PDLGA characteristic degradation trends, caused by hydrolysis processes, have been studied to define their future biomedical applications as dental scaffolds. For this purpose, the mass loss, pH, glass transition temperature (Tg) and absorbed water mass of the different biopolymers have been obtained from samples into a phosphate-buffered saline solution (PBS) with initial pH of 7.4, at 37°C (human body conditions). The mass loss has been defined as the variable that characterize the biopolymer degradation level. Its dependence relationship with respect to time, pH and biopolymer formulation has been modelled using statistical learning tools. Namely, generalized additive models (GAM) and nonlinear mixed-effects regression with logistic and asymptotic functions have been applied. GAM model provides information about the relevant variables and the parametric functions that relate mass loss, pH and time. Mixed effects are introduced to model and estimate the degradation properties, and to compare the PDLGA biopolymer populations. The degradation path for each polymer formulation has been estimated and compared with respect to the others for helping to use the proper polymer for each specific medical application, performing selection criteria. It was found that the mass loss differences in PDLGA copolymers are strongly related with the way the pH decay versus time, due to carboxylic acid groups formation. This may occur in those environments in which the degradation products remain relatively confined with the non degraded mass. This is the case emulated with the present experimental procedure. The results show that PDLGA polymers degradation degree, in terms of half life and degradation rate, is increasing when acid termination is included, when DL-lactide molar ratio is reduced, decreasing the midpoint viscosity, or when glycolide is not included.
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Registered trials
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.