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Record W4389180791 · doi:10.1039/d1cs00269d

Nuclear localization signal-tagged systems: relevant nuclear import principles in the context of current therapeutic design

2023· review· en· W4389180791 on OpenAlexafffund
Ritabrita Goswami, Aarohi Gupta, Olga Bednova, Gaël Coulombe, Dipika V. Patel, Vincent M. Rotello, Jeffrey V. Leyton

Bibliographic record

VenueChemical Society Reviews · 2023
Typereview
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of OttawaUniversité de Sherbrooke
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsContext (archaeology)Current (fluid)SIGNAL (programming language)Nuclear engineeringComputer sciencePhysicsEngineeringBiology

Abstract

fetched live from OpenAlex

Nuclear targeting of therapeutics provides a strategy for enhancing efficacy of molecules active in the nucleus and minimizing off-target effects. 'Active' nuclear-directed transport and efficient translocations across nuclear pore complexes provide the most effective means of maximizing nuclear localization. Nuclear-targeting systems based on nuclear localization signal (NLS) motifs have progressed significantly since the beginning of the current millennium. Here, we offer a roadmap for understanding the basic mechanisms of nuclear import in the context of actionable therapeutic design for developing NLS-therapeutics with improved treatment efficacy.

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 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)0.003
metaresearch head score (Gemma)0.000
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 score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.205
GPT teacher head0.380
Teacher spread0.175 · 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 teacher head, not a consensus.

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

Citations31
Published2023
Admission routes2
Has abstractyes

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