Fishing for causes and cures of motor neuron disorders
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.
Bibliographic record
Abstract
Motor neuron disorders (MNDs) are a clinically heterogeneous group of neurological diseases characterized by progressive degeneration of motor neurons, and share some common pathological pathways. Despite remarkable advances in our understanding of these diseases, no curative treatment for MNDs exists. To better understand the pathogenesis of MNDs and to help develop new treatments, the establishment of animal models that can be studied efficiently and thoroughly is paramount. The zebrafish (Danio rerio) is increasingly becoming a valuable model for studying human diseases and in screening for potential therapeutics. In this Review, we highlight recent progress in using zebrafish to study the pathology of the most common MNDs: spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and hereditary spastic paraplegia (HSP). These studies indicate the power of zebrafish as a model to study the consequences of disease-related genes, because zebrafish homologues of human genes have conserved functions with respect to the aetiology of MNDs. Zebrafish also complement other animal models for the study of pathological mechanisms of MNDs and are particularly advantageous for the screening of compounds with therapeutic potential. We present an overview of their potential usefulness in MND drug discovery, which is just beginning and holds much promise for future therapeutic development.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it