Demented flies? using <i>Drosophila</i> to model human neurodegenerative diseases
Bibliographic record
Abstract
The success of biomedical research in the past few decades has led to dramatic improvements in human health and, as a result, increased life expectancy. An unexpected consequence, however, has been an increase in the number of age-related diseases and, in particular, neurodegenerative diseases. Despite their prevalence, a therapeutic void exists in part due to an incomplete understanding of the biochemical pathogenesis of these diseases. A powerful method that can be used to understand the basic mechanisms underlying neurodegenerative diseases is to generate animal models based on manipulating the expression of single genes that are disease causative. This approach has been facilitated by the fact that many neurodegenerative diseases are inherited as autosomal dominant traits such that expression of the mutant gene in a model organism might be expected to recapitulate the disease. During the past few years, the fruit fly, Drosophila melanogaster, has emerged as a powerful tool to model human neurodegenerative diseases. Here, we describe the various approaches utilized to create fly models of human neurodegenerative disease, and how they can aid in our understanding of disease pathogenesis and facilitate drug discovery and testing.
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 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) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".