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Record W4308681427 · doi:10.1002/mabi.202200372

Polysaccharides for Medical Technology: Properties and Applications

2022· review· en· W4308681427 on OpenAlex
Pongpat Sukhavattanakul, Penwisa Pisitsak, Sarute Ummartyotin, Ravin Narain

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacromolecular Bioscience · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChitosanPolysaccharideChitinSelf-healing hydrogelsPectinCelluloseHyaluronic acidDrug deliveryNanotechnologyChemistryTissue engineeringPolymer scienceMaterials scienceBiomedical engineeringEngineeringOrganic chemistryBiochemistryMedicine

Abstract

fetched live from OpenAlex

Over the past decade, the use of polysaccharides has gained tremendous attention in the field of medical technology. They have been applied in various sectors such as tissue engineering, drug delivery system, face mask, and bio-sensing. This review article provides an overview and background of polysaccharides for biomedical uses. Different types of polysaccharides, for example, cellulose and its derivatives, chitin and chitosan, hyaluronic acid, alginate, and pectin are presented. They are fabricated in various forms such as hydrogels, nanoparticles, membranes, and as porous mediums. Successful development and improvement of polysaccharide-based materials will effectively help users to enhance their quality of personal health, decrease cost, and eventually increase the quality of life with respect to sustainability.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.295
Teacher spread0.257 · 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