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Record W7039908726

Non-viral gene therapy : design and characterisation of novel non-viral vectors for improved cellular transfection

2006· dissertation· en· W7039908726 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2006
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGenetic enhancementTransfectionGeneVector (molecular biology)Gene expressionGene targeting
DOInot available

Abstract

fetched live from OpenAlex

L'auteur a accordé une licence non exclusive permettant à la Bibliothèque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par télécommunication ou par l'Internet, prêter, distribuer et vendre des thèses partout dans le monde, à des fins commerciales ou autres, sur support microforme, papier, électronique et/ou autres formats.L'auteur conserve la propriété du droit d'auteur et des droits moraux qui protège cette thèse.Ni la thèse ni des extraits substantiels de celle-ci ne doivent être imprimés ou autrement reproduits sans son autorisation.Conformément à la loi canadienne sur la protection de la vie privée, quelques formulaires secondaires ont été enlevés de cette thèse.Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.and encouragement helped give me the courage to change career paths and begin graduate studies.Their continued support was essential in helping me reach this goal.l will always be grateful for their unwavering love and encouragement.l would also like to thank Shawn Carrigan for being a friend and valued co-worker.In difficult times he was the calm voice of reason that enabled me to keep working and helped me to see the value of my work.Not only did he provide expert editing for each of my manuscripts and acted occasionally as a "helping hand" in the lab, he was also the greatest champion of my work.My supervisors, Dr. Maryam Tabrizian and Dr. Ciriaco Piccirillo, are also deserving of my sincere appreciation.The guidance and insight they provided during my studies are invaluable.With their support, l was able to complete this work, of which l am very proud.Under their supervision, l have been fortunate to gain experience with nurnerous methods of analysis and on several highly technical pieces of equipment.l have also benefited greatly from the opportunities provided by Dr. Tabrizian to attend nurnerous national and international conferences, where l was able to present and share my work with the biomaterials community.l would like to acknowledge several other prof essors, who always had an open do or, advice, and support for me.Dr.

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

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