P3‐001: Hypothermia‐induced hyperphosphorylation: A new model to study tau kinase inhibitors
Bibliographic record
Abstract
Tau hyperphosphorylation is a main pathological hallmark of Alzheimer's disease and other tauopathies.We have previously shown that hypothermia can induce tau hyperphosphorylation both in vitro and in vivo. In this work, we tested the hypothesis that hypothermic conditions could be used to screen for tau kinase inhibitors. For this purpose, we used different biological models of gradual complexity such as SH-SY5Y neuronal-like cell cultures, ex vivo metabolically active brain slices, as well as adult non-transgenic mice. Our results show that either direct hypothermia on cells and brain slices,or anesthesia-induced hypothermia in vivo led to tau hyperphosphorylation at multiple epitopes such as AT270(Thr181),tau-1(Ser195/Ser198/Ser199/Ser202), CP13(Ser202), AT8(Ser202/Thr205), AT180(Thr231), MC-6(Ser235), Ser262and PHF-1(Ser396/Ser404).We further show that LiCl and AR-A014418, two Glycogen Synthase Kinase-3(GSK-3) inhibitors, as well as roscovitine a cyclin-dependent kinase 5(Cdk5) inhibitor, can partially or totally prevent hypothermia-induced tau hyperphosphorylation. Importantly, the use of either GSK-3 or Cdk5 inhibitors leads to different pattern of tau phosphorylation reduction, specific of the inhibited kinase, thus allowing us to assess kinase inhibitors specificity. Based on these observations, we propose that hypothermia-induced hyperphosphorylation can be used as a reliable, fast, convenient and inexpensive tool to screen for and characterize tau kinase inhibitors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".