\nINVESTIGATION OF HOW ENDOPLASMIC RETICULUM STRESS CAUSES INSULIN RESISTANCE AND NEUROINFLAMMATION \n \n
Bibliographic record
Abstract
Endoplasmic reticulum (ER) stress is caused by the accumulation of mis/unfolded proteins in the ER. ER stress signalling pathways termed the unfolded protein response are employed to alleviate ER stress through increasing the folding capacity and decreasing the folding demand of the ER as well as removing mis/unfolded proteins. However, ER stress signalling pathways induce diverse cellular changes beyond changes to the ER. This study aims to further investigate some of these ER stress-mediated events. \n \nER stress can cause activation of JNK. Prolonged ER stress-mediated JNK activation is reported to promote apoptosis whilst both acute and long-lasting JNK activation is proposed to cause insulin resistance. To begin with it is reported in this thesis that acute ER stress-induced JNK activation, which is dependent on IRE1α and TRAF2, promotes survival. In contrast to other studies, this thesis provides evidence that acute ER stress-mediated JNK activation does not inhibit insulin signalling during ER stress in several cell lines. However, prolonged ER stress, in four different cell lines, does inhibit insulin signalling in a JNK independent manner. This study argues that ER-stress-induced insulin resistance during prolonged ER stress involves inhibition of trafficking of newly synthesised insulin receptors through the secretory pathway to the plasma membrane. \n \nFinally ER stress can activate inflammatory signalling pathways other than JNK and thus ER stress may promote inflammation. Neuroinflammation and ER stress are reported in Parkinson’s disease (PD) yet a link between them has so far not been investigated. Using a cellular model of PD, it is reported in this thesis that ER stress has the potential to activate neuroinflammation in PD. \n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".