Combination of human tau and islet amyloid polypeptide exacerbates metabolic dysfunction in transgenic mice
Bibliographic record
Abstract
Amyloid plaques and neurofibrillary tangles composed of hyperphosphorylated tau are important contributors to Alzheimer's disease (AD). Tau also impacts pancreatic beta cell function and glucose homeostasis. Amyloid deposits composed of islet amyloid polypeptide (IAPP) are a pathological feature of type 2 diabetes (T2D). The current study investigates the role of human tau (hTau) in combination with human IAPP (hIAPP) as a potential mechanism connecting AD and T2D. Transgenic mice expressing hTau and hIAPP in the absence of murine tau were generated to determine the impact of these pathological factors on glucose metabolism. Co-expression of hIAPP and hTau resulted in mice with increased hyperglycaemia, insulin resistance, and glucose intolerance. The hTau-hIAPP mice also exhibited reduced beta cell area, increased amyloid deposition, impaired insulin processing, and reduced insulin content in islets. Tau phosphorylation also increased after stimulation with high glucose. In addition, brain insulin content and signalling were reduced, and tau phosphorylation was increased in these animals. These data support a link between tau and IAPP amyloid, which seems to act co-ordinately to impair beta cell function and glucose homeostasis, and suggest that the combined pathological actions of these proteins may be a potential mechanism connecting AD and T2D. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".