MétaCan
Menu
Back to cohort
Record W1546961109 · doi:10.4137/dti.s378

Recent Advancements in Targeted Delivery of Therapeutic Molecules in Neurodegenerative Disease–-Spinocerebellar Ataxia–-Opportunities and Challenges

2008· article· en· W1546961109 on OpenAlexafffund
Satya Prakash, Meenakshi Malhotra

Bibliographic record

VenueDrug Target Insights · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchFaculty of Medicine, McGill UniversityMcGill University
KeywordsMedicineSpinocerebellar ataxiaDiseaseGene silencingBioinformaticsGenetic enhancementAtaxiaDrug discoveryNanocarriersComputational biologyDrugNeuroscienceGeneBiologyPharmacologyGeneticsPathologyPsychiatry

Abstract

fetched live from OpenAlex

Drug discovery and its methodologies have been very effective in terms of treating cancers and immunological disorders but have not been able to stop genetic diseases as most of the drugs target at the protein level. They merely mitigate the symptoms of the disease. Spinocerebellar ataxia is a neurological genetic disorder that is caused by the formation of an abnormal protein. There have been several reports on ataxic drug development but actual clinical treatment is yet to be achieved. Oligonucleotide therapy called sequence specific siRNA mediated gene silencing has evolved with promising results. This approach emphasizes on suppressing the expression of the diseased gene at mRNA level. However, there is a limitation in delivery of siRNA to the target site. Several methods have been developed over the last decade to enhance the target specific delivery of DNA, siRNA, protein and small drug molecules for therapeutic purpose with less or no side effects. This review discusses the latest upcoming technologies in the field that focus on a number of nonviral nanocarriers for targeted delivery. In this review, we explore the promise and potential of novel therapeutics with interest on ataxia therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.145
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.239
Teacher spread0.191 · 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.

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

Citations3
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueDrug Target InsightsSame topicRNA Interference and Gene DeliveryFrench-language works237,207