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Record W4360869473 · doi:10.3389/fbioe.2023.1170933

Editorial: Cell and therapeutic delivery using injectable hydrogels for tissue engineering applications

2023· editorial· en· W4360869473 on OpenAlexaff
Ghulam Jalani, Muhammad Rizwan, Muhammad Aftab Akram, Mohammad Mujahid

Bibliographic record

VenueFrontiers in Bioengineering and Biotechnology · 2023
Typeeditorial
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsMcGill University
Fundersnot available
KeywordsSelf-healing hydrogelsTissue engineeringBiomedical engineeringVolume (thermodynamics)NanotechnologyChemistryMaterials scienceMedicinePolymer chemistryPhysics

Abstract

fetched live from OpenAlex

Editorial on the Research Topic Cell and therapeutic delivery using injectable hydrogels for tissue engineering applications Hydrogels, crosslinked networks of hydrophilic polymers, resemble soft living tissues and allow incorporation of cells and therapeutic molecules to stimulate in-vitro cellular growth and in-vivo regeneration.In particular, the injectable hydrogels, which can be employed to deliver cells and therapeutics to stimulate in-situ regeneration, are actively being developed (Dimatteo et al., 2018).The injectable hydrogels are particularly suitable for minimally invasive therapies, where the traditional prefabricated scaffolds require open surgery for transplantation, leading to long healing time and increased patient-care costs (Øvrebø et al., 2022).However, challenges around mechanical integrity, reduced functions of encapsulated cells (Rizwan et al., 2021), biocompatibility, and manufacturing persists and must be overcome before such materials can be deployed in clinical settings at large scale.This Research Topic focuses on emerging technologies in the development in the field of polymeric injectable hydrogels as carriers for the delivery of cells, and other therapeutics with target applications in tissue engineering and drug delivery.This featured collection includes 5 original research articles which will be of high interest to the researchers in the area of insitu tissue engineering and regenerative medicine.Moreover, two review articles are also part of this Research Topic which provide systemic analysis hydrogel fabrication strategies for clinical translation and vascularization.In-vivo delivery of the immune cells is challenging while maintaining their functions and viability.Cohen et al. developed a new method involving polyethylene glycol-fibrinogen hydrogel microspheres encapsulating alveolar macrophages and epithelial cells for use in respiratory tract model.When exposed to bacterial endotoxin lipopolysaccharide, cells preserved high viability and secreted moderate levels of TNFα, whereas non-encapsulated cells exhibited a burst TNFα secretion and reduced viability.It also had effect on the morphology of cells.The lipopolysaccharide (LPS)-exposed encapsulated macrophages exhibited elongated morphology and out-migration capability from microspheres.This study shows the feasibility of polymer-encapsulated cell delivery to repair pulmonary damage and for general tissue repair.Injectable, and simultaneously, tissue adhesive hydrogels are exciting hosting materials to carry and deliver, cells, drugs, and biomolecules such as proteins.In this article, Sun et al.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0030.001
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0260.021

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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2023
Admission routes1
Has abstractyes

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