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Record W4323295390 · doi:10.1002/jmri.28664

<scp>In Vivo MRI</scp> Tracking of Degradable Polyurethane Hydrogel Degradation In Situ Using a Manganese Porphyrin Contrast Agent

2023· article· en· W4323295390 on OpenAlexafffund
Eric Tawagi, Kyle D. W. Vollett, Daniel A. Szulc, J. Paul Santerre, Hai‐Ling Margaret Cheng

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsIn vivoSelf-healing hydrogelsBiomedical engineeringMaterials scienceMagnetic resonance imagingChemistryBiophysicsMedicineRadiologyPolymer chemistryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: A noninvasive method to track implanted biomaterials is desirable for real-time monitoring of material interactions with host tissues and assessment of efficacy and safety. PURPOSE: To explore quantitative in vivo tracking of polyurethane implants using a manganese porphyrin (MnP) contrast agent containing a covalent binding site for pairing to polymers. STUDY TYPE: Prospective, longitudinal. ANIMAL MODEL: Rodent model of dorsal subcutaneous implants (10 female Sprague Dawley rats). FIELD STRENGTH/SEQUENCE: A 3-T; two-dimensional (2D) T1-weighted spin-echo (SE), T2-weighted turbo SE, three-dimensional (3D) spoiled gradient-echo T1 mapping with variable flip angles. ASSESSMENT: A new MnP-vinyl contrast agent to covalently label polyurethane hydrogels was synthesized and chemically characterized. Stability of binding was assessed in vitro. MRI was performed in vitro on unlabeled hydrogels and hydrogels labeled at different concentrations, and in vivo on rats with unlabeled and labeled hydrogels implanted dorsally. In vivo MRI was performed at 1, 3, 5, and 7 weeks postimplantation. Implants were easily identified on T1-weighted SE, and fluid accumulation from inflammation was distinguished on T2-weighted turbo SE. Implants were segmented on contiguous T1-weighted SPGR slices using a threshold of 1.8 times the background muscle signal intensity; implant volume and mean T1 values were then calculated at each timepoint. Histopathology was performed on implants in the same plane as MRI and compared to imaging results. STATISTICAL TESTS: Unpaired t-tests and one-way analysis of variance (ANOVA) were used for comparisons. A P value <0.05 was considered to be statistically significant. RESULTS: Hydrogel labeling with MnP resulted in a significant T1 reduction in vitro (T1 = 517 ± 36 msec vs. 879 ± 147 msec unlabeled). Mean T1 values of labeled implants in rats increased significantly by 23% over time, from 1 to 7 weeks postimplantation (651 ± 49 msec to 801 ± 72 msec), indicating decreasing implant density. DATA CONCLUSION: Polymer-binding MnP enables in vivo tracking of vinyl-group coupling polymers. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 1.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.024
GPT teacher head0.305
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations11
Published2023
Admission routes2
Has abstractyes

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