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Record W4382863323 · doi:10.1039/9781837671441

Transition Metal-containing Dendrimers in Biomedicine

2023· book· en· W4382863323 on OpenAlexaff
Alaa S. Abd‐El‐Aziz, Amal Youssef, Ahmad Abd‐El‐Aziz

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsBiomedicineDendrimerNanotechnologyDrug deliveryCombinatorial chemistryChemistryComputer scienceBiochemical engineeringMaterials scienceEngineeringOrganic chemistryBioinformaticsBiology

Abstract

fetched live from OpenAlex

There has been increasing research into designing transition metal-containing dendrimers as innovative materials, especially in the field of biomedicine and pharmaceutical science. They have applications in biosensors and drug-delivery systems, and are now one of the leading classes in the design of therapeutics for drug-resistant diseases. This book introduces readers to a number of classes of metal-containing dendrimers, before moving onto their design and synthesis. Their applications in biomedicine are then discussed, before highlighting future research targets in this growing field. It emphasizes the synthetic strategies to design transition metal-containing dendrimers, and discusses the type of laboratory work used to examine these types of dendrimers in the fields of medicine and pharmacology, including their antimicrobial, anticancer, anti-inflammatory and antiviral activities. Transition Metal-Containing Dendrimers in Biomedicine brings chemistry, biology, pharmaceutical science and medical fields together to design these future materials which will have global benefits.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.054

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 studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.017

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 head0.022
GPT teacher head0.257
Teacher spread0.235 · 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
GenreOther

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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