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Record W1502085787 · doi:10.1002/9781118775882.ch5

Dendrimer Bioconjugates: Synthesis and Applications

2014· other· ca· W1502085787 on OpenAlexaff
Ali Nazemi, Elizabeth R. Gillies

Bibliographic record

Venuenot available
Typeother
Languageca
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsWestern University
Fundersnot available
KeywordsDendrimerNanotechnologyChemistryCombinatorial chemistryMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

This chapter explores how bioconjugation chemistry can be used to covalently attach biologically relevant molecules to dendrimers. It also explores how the specific conjugation chemistries are determined based on the application, the chemical functionalities available on the molecules of interest and those on the dendrimer's focal point or periphery. First, the chapter discusses the conjugation of drug molecules. This is followed by carbohydrates, imaging agents, oligonucleotides, and peptides/proteins. The impact of the conjugation chemistry on the biological properties of the resulting molecules is illustrated through selected examples. Among the currently studied drug delivery systems, dendrimers have emerged as an attractive class of materials, mainly because of their well-defined structures. Doxorubicin (DOX) is a chemotherapeutic commonly used in the treatment of hematological malignancies. Deoxyribonucleic acid (DNA) is a polymer composed of a sequence of nucleotide repeat units that provide a sugar– phosphate backbone and pendant nucleobases.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.009
GPT teacher head0.232
Teacher spread0.224 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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