Bibliographic record
Abstract
Tau protein stabilizes microtubules in neuronal cells and maintains cell structure and function [1]. Post‐translational modifications of tau, such as phosphorylations, lead to tau detachment from microtubules and subsequent cell death. Modified tau aggregates into cytotoxic structures, leading to neurodegeneration. Currently, neurodegeneration remains without a cure, but several molecular strategies have been employed towards targeting phosphorylation and aggregation of tau protein. Small molecules have been tested as potential aggregation inhibitors. For example, dopamine receptor agonist compounds were effective inhibitors of tau aggregation in vitro [2]. The large molecules, such as antitau antibodies, were also explored for their ability to inhibit phosphorylation of tau or its aggregation. The antitau antibodies, targeting R‐repeat epitopes, effectively reduced tau aggregation in vitro [3], and modulated tau phosphorylation by GSK‐3β protein kinase [4]. The details regarding the inhibition with various small and large molecules will be described, and new therapeutic strategies discussed. References [1] M.D. Weinggarten, A.H. Lockwood, S.Y. Hwo, M.W. Kirschener, Proc. Natl. Acad. Sci., 1975, 72, 1858‐1862. [2] Ziu, I., Rettig, I., Luo, D., Dutta, A., McCormick, T.M., Wu, C., Martic, S. Bioorg. Med. Chem. 2020, 28, 115667. [3] Esteves, J.O.V., Trzeciakiewicz, H., Loeffler, D.A., Martic, S. Biochemistry , 2015 , 54, 293‐302. [4] Loeffler, D. A., Smith, L. M., Klaver, A. C., Martic, S. Experimental Gerontol. 2015, 67, 15‐18.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".