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Record W4402711585 · doi:10.1002/jbm.a.37800

Probing the effects of polysaccharide hydrogel composition on the viability and pro‐angiogenic function of human adipose‐derived stromal cells

2024· article· en· W4402711585 on OpenAlexafffund
Fiona E. Serack, Kaylee A. Fennell, Christina Iliopoulos, John T. Walker, John A. Ronald, Brian G. Amsden, David A. Hess, Lauren E. Flynn

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2024
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsQueen's UniversityWestern University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsSelf-healing hydrogelsMesenchymal stem cellHyaluronic acidStromal cellStromal vascular fractionMaterials scienceAdipose tissueTissue engineeringViability assayBiomedical engineeringCell therapyIn vivoChitosanCellCell biologyChemistryCancer researchMedicineBiologyBiochemistryBiotechnologyAnatomy

Abstract

fetched live from OpenAlex

Abstract Cell therapies harnessing the pro‐vascular regenerative capacities of mesenchymal stromal cell (MSC) populations, including human adipose‐derived stromal cells (hASCs), have generated considerable interest as an emerging treatment strategy for peripheral arterial disease (PAD) and its progression to critical limb ischemia (CLI). There is evidence to support that polysaccharide hydrogels can enhance therapeutic efficacy when applied as minimally‐invasive delivery systems to support MSC survival and retention within ischemic tissues. However, there has been limited research to date on the effects of hydrogel composition on the phenotype and function of encapsulated cell populations. Recognizing this knowledge gap, this study compared the pro‐angiogenic function of hASCs encapsulated in distinct but similarly‐modified natural polysaccharide hydrogels composed of methacrylated glycol chitosan (MGC) and methacrylated hyaluronic acid (MHA). Initial in vitro studies confirmed high viability (>85%) of the hASCs following encapsulation and culture in the MGC and MHA hydrogels over 14 days, with a decrease in the cell density observed over time. Moreover, higher levels of a variety of secreted pro‐angiogenic and immunomodulatory factors were detected in conditioned media samples collected from the hASCs encapsulated in the MGC‐based hydrogels compared to the MHA hydrogels. Subsequent testing focused on comparing hASC delivery within the MGC and MHA hydrogels to saline controls in a femoral artery ligation‐induced CLI (FAL‐CLI) model in athymic nu / nu mice over 28 days. For the in vivo studies, the hASCs were engineered to express tdTomato and firefly luciferase to quantitatively compare the efficacy of the two platforms in supporting the localized retention of viable hASCs through longitudinal cell tracking with bioluminescence imaging (BLI). Interestingly, hASC retention was significantly enhanced when the cells were delivered in the MHA hydrogels as compared to the MGC hydrogels or saline. However, laser Doppler perfusion imaging (LDPI) indicated that the restoration of hindlimb perfusion was similar between the treatment groups and controls. These findings were corroborated by endpoint immunofluorescence (IF) staining showing similar levels of CD31 + cells in the ligated limbs at 28 days in all groups. Overall, this study demonstrates that enhanced MSC retention may be insufficient to augment vascular regeneration, emphasizing the complexity of designing biomaterials platforms for MSC delivery for therapeutic angiogenesis. In addition, the data points to a potential challenge in approaches that seek to harness the paracrine functionality of MSCs, as strategies that increase the secretion of immunomodulatory factors that can aid in regeneration may also lead to more rapid MSC clearance in vivo.

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 score0.002

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 head0.049
GPT teacher head0.353
Teacher spread0.304 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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