Standardization of zebrafish drug testing parameters for muscle diseases
Bibliographic record
Abstract
Skeletal muscular diseases predominantly affect skeletal and cardiac muscle, resulting in muscle weakness, impaired respiratory function and decreased lifespan. These harmful outcomes lead to poor health-related quality of life and carry a high healthcare economic burden. The absence of promising treatments and new therapies for muscular disorders requires new methods for candidate drug identification and advancement in animal models. Consequently, the rapid screening of drug compounds in an animal model that mimics features of human muscle disease is warranted. Zebrafish are a versatile model in preclinical studies that support developmental biology and drug discovery programs for novel chemical entities and repurposing of established drugs. Due to several advantages, there is an increasing number of applications of the zebrafish model for high-throughput drug screening for human disorders and developmental studies. Consequently, standardization of key drug screening parameters, such as animal husbandry protocols, drug compound administration and outcome measures, is paramount for the continued advancement of the model and field. Here, we seek to summarize and explore critical drug treatment and drug screening parameters in the zebrafish-based modeling of human muscle diseases. Through improved standardization and harmonization of drug screening parameters and protocols, we aim to promote more effective drug discovery programs.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".