Cellular Energy Sensing and Metabolism—Implications for Treating Diabetes: The 2017 Outstanding Scientific Achievement Award Lecture
Bibliographic record
Abstract
The Outstanding Scientific Achievement Award recognizes distinguished scientific achievement in the field of diabetes, taking into consideration independence of thought and originality. Gregory R. Steinberg, PhD, professor of medicine, Canada Research Chair, J. Bruce Duncan Endowed Chair in Metabolic Diseases, and codirector of the Metabolism and Childhood Obesity Research Program at McMaster University, Hamilton, Ontario, Canada, received the prestigious award at the American Diabetes Association’s 77th Scientific Sessions, 9–13 June 2017, in San Diego, CA. He presented the Outstanding Scientific Achievement Award Lecture, “Cellular Energy Sensing and Metabolism—Implications for Treating Diabetes,” on Monday, 12 June 2017. The survival of all cells is dependent on the constant challenge to match energetic demands with nutrient availability, a task that is mediated through a highly conserved network of metabolic fuel sensors that orchestrate both cellular and whole-organism energy balance. A mismatch between cellular energy demand and nutrient availability is a key factor contributing to the development of type 2 diabetes; thus, understanding the fundamental mechanisms by which cells sense nutrient availability and demand may lead to the development of new treatments. Glucose-lowering therapies, such as caloric restriction, exercise, and metformin, all induce an energetic challenge that results in the activation of the cellular energy sensor AMP-activated protein kinase (AMPK). Activation of AMPK in turn suppresses lipid synthesis and inflammation while increasing glucose uptake, fatty acid oxidation, and mitochondrial function. In contrast, high levels of nutrient availability suppress AMPK activity while also increasing the production of peripheral serotonin, a gut-derived endocrine factor that suppresses β-adrenergic–induced activation of brown adipose tissue. Identifying new ways to manipulate these two ancient fuel gauges by activating AMPK and inhibiting peripheral serotonin may lead to the development of new therapies for treating type 2 diabetes.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".