MétaCan
Menu
Back to cohort
Record W4406991848 · doi:10.3390/brainsci15020134

Modeling ALS with Patient-Derived iPSCs: Recent Advances and Future Potentials

2025· review· en· W4406991848 on OpenAlexaff
Ladan Dawoody Nejad, Erik P. Pioro

Bibliographic record

VenueBrain Sciences · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAmyotrophic lateral sclerosisInduced pluripotent stem cellNeuroscienceDiseaseDrug discoveryMedicineBiologyBioinformaticsEmbryonic stem cellPathology

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is a terminal complex neurodegenerative disease, with 10–15% of cases being familial and the majority being sporadic with no known cause. There are no animal models for the 85–90% of sporadic ALS cases. More creative, sophisticated models of ALS disease are required to unravel the mysteries of this complicated disease. While ALS patients urgently require new medications and treatments, suitable preclinical in vitro models for drug screening are lacking. Therefore, human-derived induced pluripotent stem cell (hiPSC) technology offers the opportunity to model diverse and unreachable cell types in a culture dish. In this review, we focus on recent hiPSC-derived ALS neuronal and non-neuronal models to examine the research progress of current ALS 2D monocultures, co-cultures, and more complex 3D-model organoids. Despite the challenges inherent to hiPSC-based models, their application to preclinical drug studies is enormous.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.339
Teacher spread0.310 · 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 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

Citations8
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBrain SciencesSame topicPluripotent Stem Cells ResearchFrench-language works237,207