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Post-Translational Modifications in Tau and Their Roles in Alzheimer'sPathology

2024· review· en· W4394833100 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Alzheimer Research · 2024
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNeuroscienceTau pathologyTau proteinPosttranslational modificationNeurodegenerationNeuroprotectionChemistryDiseaseAlzheimer's diseaseBiologyMedicinePathologyBiochemistry

Abstract

fetched live from OpenAlex

Microtubule-Associated Protein Tau (also known as tau) has been shown to accumulate into paired helical filaments and neurofibrillary tangles, which are known hallmarks of Alzheimer's disease (AD) pathology. Decades of research have shown that tau protein undergoes extensive post-translational modifications (PTMs), which can alter the protein's structure, function, and dynamics and impact the various properties such as solubility, aggregation, localization, and homeostasis. There is a vast amount of information describing the impact and role of different PTMs in AD pathology and neuroprotection. However, the complex interplay between these PTMs remains elusive. Therefore, in this review, we aim to comprehend the key post-translational modifications occurring in tau and summarize potential connections to clarify their impact on the physiology and pathophysiology of tau. Further, we describe how different computational modeling methods have helped in understanding the impact of PTMs on the structure and functions of the tau protein. Finally, we highlight the tau PTM-related therapeutics strategies that are explored for the development of AD therapy.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.304
GPT teacher head0.504
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it