Hypothermia-induced hyperphosphorylation: a new model to study tau kinase inhibitors
Bibliographic record
Abstract
Tau hyperphosphorylation is one hallmark of Alzheimer's disease (AD) pathology. Pharmaceutical companies have thus developed kinase inhibitors aiming to reduce tau hyperphosphorylation. One obstacle in screening for tau kinase inhibitors is the low phosphorylation levels of AD-related phospho-epitopes in normal adult mice and cultured cells. We have shown that hypothermia induces tau hyperphosphorylation in vitro and in vivo. Here, we hypothesized that hypothermia could be used to assess tau kinase inhibitors efficacy. Hypothermia applied to models of biological gradual complexity such as neuronal-like cells, ex vivo brain slices and adult non-transgenic mice leads to tau hyperphosphorylation at multiple AD-related phospho-epitopes. We show that Glycogen Synthase Kinase-3 inhibitors LiCl and AR-A014418, as well as roscovitine, a cyclin-dependent kinase 5 inhibitor, decrease hypothermia-induced tau hyperphosphorylation, leading to different tau phosphorylation profiles. Therefore, we propose hypothermia-induced hyperphosphorylation as a reliable, fast, convenient and inexpensive tool to screen for 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".