Bibliographic record
Abstract
Huntington’s disease is a neurodegenerative disease that have significantly negative impact to cognitive function. In the worldwide range, approximately 5-10 individuals are affected per 100,000 people. At molecular level, the expanded CAG repeats lead to misfolding and aggregation of the huntington protein, which can interfere with cellular metabolism, including transcription, mitochondrial function, and other important physiological processes. Though scientists already have a well-established theory for the pathology of Huntington’s Disease, no effected cure has been developed due to the heavy genetic base of the disease. Despite the genetic barrier to overcome, many therapies have been created to alleviate the symptoms. In this primer, four main therapies are discussed who reduce the mutant huntington protein amount at post-transcriptional level: Antisense Oligonucleotides, Ethyl-Eicosapentaenoic Acid, autophagy modification, and intrabody based immunotherapy. Within each module, it is described how these therapies can reduce the level of mHTT in molecular level and correct the symptom. Development history is also touched upon briefly and discussion about the current status of each approach is made. Clinical prospective and future direction is included at the end as well.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".