Quantitative Analysis of Climbing Defects in a Drosophila Model of Neurodegenerative Disorders
Bibliographic record
Abstract
Locomotive defects resulting from neurodegenerative disorders can be a late onset symptom of disease, following years of subclinical degeneration, and thus current therapeutic treatment strategies are not curative. Through the use of whole exome sequencing, an increasing number of genes have been identified to play a role in human locomotion. Despite identifying these genes, it is not known how these genes are crucial to normal locomotive functioning. Therefore, a reliable assay, which utilizes model organisms to elucidate the role of these genes in order to identify novel targets of therapeutic interest, is needed more than ever. We have designed a sensitized version of the negative geotaxis assay that allows for the detection of milder defects earlier and has the ability to evaluate these defects over time. The assay is performed in a glass graduated cylinder, which is sealed with a wax barrier film. By increasing the threshold distance to be climbed to 17.5 cm and increasing the experiment duration to 2 min we have observed a greater sensitivity in detecting mild mobility dysfunctions. The assay is cost effective and does not require extensive training to obtain highly reproducible results. This makes it an excellent technique for screening candidate drugs in Drosophila mutants with locomotion defects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".