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Record W2921632146 · doi:10.1042/etls20180099

Pharmacologic normalization of pathogenic dosage underlying genetic diseases: an overview of the literature and path forward

2019· article· en· W2921632146 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEmerging Topics in Life Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsHaploinsufficiencyNormalization (sociology)Messenger RNAGeneDiseaseGene productComputational biologyBiologyBioinformaticsGene expressionMedicinePhenotypeGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Most monogenic disorders are caused by a pathologic deficit or excess of a single transcript and/or protein. Given that small molecules, including drugs, can affect levels of mRNA and protein, the pharmacologic normalization of such pathogenic dosage represents a possible therapeutic approach for such conditions. Here, we review the literature exploring pharmacologic modulation of mRNA and/or protein levels for disorders with paralogous modifier genes, for haploinsufficient disorders (insufficient gene-product), as well as toxic gain-of-function disorders (surplus or pathologic gene-product). We also discuss challenges facing the development of rare disease therapy by pharmacologic modulation of mRNA and protein. Finally, we lay out guiding principles for selection of disorders which may be amenable to this approach.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.064
GPT teacher head0.385
Teacher spread0.321 · 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