Bibliographic record
Abstract
Type 2 diabetes is a complex disorder characterized by progressive defects in nearly every aspect of metabolic regulation. Despite this complexity, traditional in vivo methodologies have limited experimental examination to a small number of metabolic indices at one or two points in time. As a result the etiology and natural history of this disease remain unclear and much debated. This thesis takes a two pronged approach to this problem. First, a mathematical model is developed to incorporate experimental data from different sources into an integrated representation of metabolic regulation. Bifurcation and simulation analysis of this model are used to investigate mechanisms of metabolic regulation as well as the pathogenesis of type 2 diabetes. Second, new experimental methodologies are developed that greatly improve the practicality of estimating several key metabolic indices in vivo. Applying these methodologies to animal models of type 2 diabetes allowed us to perform a fully dynamic and integrative analysis of the pathogenesis of type 2 diabetes in two commonly used animal models. Overall, data from this thesis suggests that the etiology of type 2 diabetes lies in two distinct abnormalities; rapid development of insulin resistance coupled to impaired P-cell mass adaptation.
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.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".