Bibliographic record
Abstract
Disease modelling has enabled researchers to study a wide range of human diseases in the laboratory, overcoming many challenges. Parkinson Disease (PD) is a progressive neurodegenerative disorder that affects 1 to 2% of the human population over 65 years old, influencing cognitive ability and motor function. It is characterized by the inadequate function or the loss of dopamine-producing neurons of the substantia nigra pars compacta in the human midbrain. Impairment of several genes has been associated with disease progression. Recently, a polymorphism in Oculocerebrorenal Syndrome of Lowe protein (Ocrl), was identified as a risk factor for PD. As Ocrl is very well-conserved between mammals and insects, I have used D. melanogaster to create an Ocrl-dependant model of human PD. Ocrl is the D. melanogaster orthologue of human Ocrl, a PtdIns(4,5)P2 phosphatase encoding gene in which mutant forms can result in the X-linked disorder known as Oculocerebrorenal Lowe Syndrome. Directed manipulation of the single D. melanogaster version of Ocrl in neurons that include dopaminergic neurons was performed in order to produce an in vivo model of the development and progression of a unique version of PD. The directed loss of function of Ocrl in dopaminergic neurons, through the use of RNAi, resulted in a decreased locomotor ability and median lifespan of the flies over time. In complementary experiments, the directed interference of Ocrl expression in the developing eye, led to a reduction in the number of ommatidia and interommatidial bristles. Overexpression of Ocrl using D42 Gal4 and ddc Gal4 decreased lifespan, locomotor ability, the number of ommatidia and interommatidial bristles and increased lifespan by using TH Gal4. Crossing Ocrl with recombinant Ddc-GAL4/CyO; UAS-park RNAi/TM3 reduced lifespan overtime. Further investigation of Ocrl and its role in human disease progression is needed and crucial to our understanding of new therapeutic approaches for research into human disease.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".