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Record W3165780855 · doi:10.1039/d0cs01387k

Harnessing the physicochemical properties of DNA as a multifunctional biomaterial for biomedical and other applications

2021· review· en· W3165780855 on OpenAlexafffund
Aishik Chakraborty, Shruthi Polla Ravi, Yasmeen Shamiya, Caroline Cui, Arghya Paul

Bibliographic record

VenueChemical Society Reviews · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiomaterialNanotechnologyDNAMaterials scienceChemistryBiochemistry

Abstract

fetched live from OpenAlex

The biological purpose of DNA is to store, replicate, and convey genetic information in cells. Progress in molecular genetics have led to its widespread applications in gene editing, gene therapy, and forensic science. However, in addition to its role as a genetic material, DNA has also emerged as a nongenetic, generic material for diverse biomedical applications. DNA is essentially a natural biopolymer that can be precisely programed by simple chemical modifications to construct materials with desired mechanical, biological, and structural properties. This review critically deciphers the chemical tools and strategies that are currently being employed to harness the nongenetic functions of DNA. Here, the primary product of interest has been crosslinked, hydrated polymers, or hydrogels. State-of-the-art applications of macroscopic, DNA-based hydrogels in the fields of environment, electrochemistry, biologics delivery, and regenerative therapy have been extensively reviewed. Additionally, the review encompasses the status of DNA as a clinically and commercially viable material and provides insight into future possibilities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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
GenreReview

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

Citations48
Published2021
Admission routes2
Has abstractyes

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