Association of Hexokinase and Voltage Dependent Anion Channel in Cardiac Hypertrophy
Bibliographic record
Abstract
Glycolysis is accelerated in pathologic cardiac hypertrophy but the cellular mechanism(s) responsible are not yet fully understood. Binding of hexokinase (HK), a key enzyme in control of glycolysis in heart and other tissues, to mitochondrial voltage dependent anion channels (VDAC) is viewed as a major factor responsible for insulin‐induced glucose uptake in skeletal muscle and acceleration of glycolysis in malignant neoplasms. We hypothesized that binding of HK to VDAC is increased in pathologically hypertrophied heart muscle cells and hearts. Binding of HK to VDAC was assessed by co‐immunoprecipitation and immunoblot analysis of lysates from cultured H9c2 cells hypertrophied by exposure to arginine vasopressin (AVP) and from hypertrophied hearts in mice with an abdominal aortic constriction (AAC). Cells unexposed to AVP and sham‐operated mice served as Controls. AVP‐treated H9c2 cells and hearts from mice with an AAC were hypertrophied 20 to 30% with rates of glycolysis 30 to 50% greater than Controls. Co‐immunprecipitation experiments indicated that association of HK with VDAC was significantly increased in hypertrophied H9c2 cells and in hypertrophied mouse heart tissue. This supports the concept that binding of HK to VDAC is increased in the setting of pathologic cardiac hypertrophy and may play a role in the acceleration of glycolysis observed. Supported by the Canadian Institutes of Health Research
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".