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Record W4225391630 · doi:10.1089/hum.2022.023

Gene Therapy for Fibrodysplasia Ossificans Progressiva: Feasibility and Obstacles

2022· review· en· W4225391630 on OpenAlexaff
Elisabeth M. W. Eekhoff, Ruben D. de Ruiter, Bernard J. Smilde, Ton Schoenmaker, Teun J. de Vries, Coen Netelenbos, Edward C. Hsiao, Christiaan Scott, Nobuhiko Haga, Zvi Grunwald, Carmen L. De Cunto, Maja Di Rocco, Patricia Delai, Robert J. Diecidue, Vrisha Madhuri, Tae‐Joon Cho, Rolf Morhart, Clive Friedman, Michael Zasloff, Gerard Pals, Jae‐Hyuck Shim, Guangping Gao, Frederick S. Kaplan, Robert J. Pignolo, Dimitra Micha

Bibliographic record

VenueHuman Gene Therapy · 2022
Typereview
Languageen
FieldMedicine
TopicHeterotopic Ossification and Related Conditions
Canadian institutionsWestern University
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsFibrodysplasia ossificans progressivaHeterotopic ossificationGenetic enhancementEndochondral ossificationMedicineDiseaseHeterotopic boneBioinformaticsGenePathologyBiologySurgeryGeneticsAnatomyCartilage

Abstract

fetched live from OpenAlex

Fibrodysplasia ossificans progressiva (FOP) is a rare and devastating genetic disease, in which soft connective tissue is converted into heterotopic bone through an endochondral ossification process. Patients succumb early as they gradually become trapped in a second skeleton of heterotopic bone. Although the underlying genetic defect is long known, the inherent complexity of the disease has hindered the discovery of effective preventions and treatments. New developments in the gene therapy field have motivated its consideration as an attractive therapeutic option for FOP. However, the immune system's role in FOP activation and the as-yet unknown primary causative cell, are crucial issues which must be taken into account in the therapy design. While gene therapy offers a potential therapeutic solution, more knowledge about FOP is needed to enable its optimal and safe application.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.254
GPT teacher head0.425
Teacher spread0.171 · 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 designOther design
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

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

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