Bibliographic record
Abstract
Philip Yu is an undergraduate student researcher passionate about translational research in various fields. The following is an independent experimental study on genetic control of the cellular processes that regulate protein expression and transport in neurodegeneration. The study was conducted under the supervision of Dr. Derrick Gibbings in the Department of Cellular and Molecular Medicine at the University of Ottawa. Throughout the study, Philip conducted all experiments involved, in addition to collecting and processing the generated data. The misfolding of the protein tau contributes to the development of Alzheimer’s disease (AD). Misfolded tau is thought to propagate through a homeostatic degradation process known as autophagy, resulting in the export of cellular materials to the extracellular space via extracellular vesicles, called exosomes. By inhibiting the ATG7 and p62 genes necessary for autophagy to occur, the effects on the amount of exosomal and intracellular tau can be observed. Following the analysis of western blot and protein assay data, it was determined that the inhibition of the ATG7 and p62 genes results in a 70% and 60% reduction in the concentration of tau found in exosomes, respectively. These results suggest potential therapeutic applications of autophagic gene inhibition for the treatment of AD.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".