Bibliographic record
Abstract
Tau, a member of the microtubule-associated proteins, is a known component of the neuronal cytoskeleton; however, in the brain tissue, it is involved in other vital functions beyond maintaining the cellular architecture. The pathologic tau forms aggregates inside the neurons and ultimately forms the neurofibrillary tangles. Intracellular and extracellular accumulation of different tau isoforms, including dimers, oligomers, paired helical filaments and tangles, lead to a highly heterogenous group of diseases named "Tauopathies". About twenty-six different types of tauopathy diseases have been identified that have different clinical phenotypes or pathophysiological characteristics. Although all these diseases are identified by tau aggregation, they are distinguishable based on the specific tau isoforms, the affected cell types and the brain regions. The neuropathological and phenotypical heterogeneity of these diseases impose significant challenges for discovering new diagnostic and therapeutic strategies. Here, we review the recent literature on tau protein and the pathophysiological mechanisms of tauopathies. This article mainly focuses on physiologic and pathologic tau and aims to summarize the upstream and downstream events and discuss the current diagnostic approaches and therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".