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Record W4412955496 · doi:10.1080/21678421.2025.2542919

ALSUntangled #80: ISRIB (Integrated stress response InhiBitor)

2025· review· en· W4412955496 on OpenAlexaff
Javier Mascías Cadavid, Anna Mena Bravo, Paul E. Barkhaus, Benjamin Barnes, Michael Benatar, Sarah R. Breevoort, Andrew Brown, Gregory T. Carter, Jesse Crayle, Juliette Foucher, Terry Heiman‐Patterson, Esther Hobson, Carlayne E. Jackson, Sartaj Jhooty, E. Mallon, Christopher McDermott, Gary L. Pattee, Kaitlyn N Pierce, Erik P. Pioro, Dylan Ratner, Michael H. Rivner, Elia Tito, Paul Wicks, Richard Bedlack

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2025
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsVancouver Coastal Health
FundersALS Association
KeywordsIntegrated stress responseStress (linguistics)Fight-or-flight responseChemistryPhilosophy

Abstract

fetched live from OpenAlex

ALSUntangled reviews alternative and off-label treatments for people living with amyotrophic lateral sclerosis (PALS). Here we assess ISRIB, a molecule that attenuates the integrated stress response (ISR). The ISR is an intracellular signaling network through which cells normally respond to stress, but in ALS it appears to be overactive, leading to the formation of "stress granules" which some but not all investigators believe can triggerapoptotic cell death. ISRIB can attenuate the formation of these stress granules while still allowing parts of protein synthesis to continue. Pre-clinical data demonstrate that ISRIB is beneficial in cell models of ALS. A small number of patients taking ISRIB in Spain report symptomatic improvements with little or no side effects, though we have not been able to independently verify these benefits. There are no clinical trials evaluating ISRIB in any condition and questions about its solubility and bioavailability have arisen. Currently, we do not have enough evidence to endorse the use of ISRIB for treating ALS. We support further research in disease models and clinical trials to study pharmacokinetics, safety and 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 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 score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.006

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 head0.055
GPT teacher head0.322
Teacher spread0.267 · 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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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