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Record W2805012591 · doi:10.19070/2332-290x-si02002

A Review of Personalised Molecular Medicine for the Treatment of Corneal Disorders

2015· review· en· W2805012591 on OpenAlexaff
Courtney DG, Abhimanyu Thakur, Nesbit MA, Moore Je, Tara Moore

Bibliographic record

VenueInternational Journal of Ophthalmology & Eye Science · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsTranscription activator-like effector nucleaseCRISPRGenome editingCas9RNA interferenceGene silencingBiologyGeneComputational biologyGeneticsGene expressionSmall interfering RNAGenetic enhancementDNARNA

Abstract

fetched live from OpenAlex

As the field of molecular medicine evolves greater numbers of systems are being utilised to ellicit changes in gene expression in an attempt to resolve genetic or environmental disorders.Within this review we focus on 4 of the more common forms of gene therapy utilised to alter gene expression; siRNAs, TALENs, ZFNs and CRISPR/Cas9.Short-interfering RNAs (siRNAs) are one method of transiently down regulating the expression of any target gene through the exploitation of the RNA interference pathway, often referred to as gene silencing [1].The bacterial Type II Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 genome editing system is the latest method of interrupting gene expression through cleavage of target DNA.Its effectiveness at cleaving genomic DNA in mammalian cells in vitro and in vivo [2,3], the specificity that this system exhibits [4, 5] and the relative ease with which targeted systems can be constructed [6], make this method of gene disruption a favourable technique.Transcription activator-like effector nucleases (TALENs) are artificial systems that can be designed and constructed relatively quickly to bind practically anywhere in the genome and cleave double stranded DNA, thus interrupting the expression of any given target gene AbstractThe cornea represents an ideal tissue to elucidate the potential of molecular medicine to treat genetic disorders.It is a small, readily accessible tissue that is easy to monitor treat topically, while a number of corneal disorders are well characterized with regard to a known genetic cause.In addition, due to their genetic basis most of these conditions are bilateral, affecting both corneas, allowing for a clear comparison between treated and untreated tissue using the fellow eye as a control.Currently, molecular techniques utilising siRNAs, CRISPR/Cas9, TALENS and ZFNs are being evaluated by many for their therapeutic potential for corneal disorders.Methodologies are discussed within this review with particular emphasis on the more recent and emerging field of CRISPR/Cas9-based therapeutics.Since the advent of gene therapies, effective delivery has been a challenge.Potential ocular delivery techniques include viral vectors, nanoparticles or physical injection.Studies employing in vitro and in vivo models of corneal disorders have demonstrated promising results for treatment by molecular techniques.However, many of these studies have not translated into the clinic, perhaps due to a lack of good animal models for preclinical testing or failure to develop suitable delivery methods.Until this occurs it is difficult to fully gauge the usefulness of these techniques to treat corneal disorders in human patients over a sustained time period.Here we present a review of current research into molecular-based therapies for genetic corneal disorders and discuss the applicability of these studies to advancing translational research for other genetic disorders.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.045
GPT teacher head0.455
Teacher spread0.410 · 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
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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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