Alteration of metabolic activity regulates mitochondrial temperature in diagnosis in HepG2 hepatocellular carcinoma cells
Bibliographic record
Abstract
Oxidative phosphorylation (OXPHOS) is a key player in mitochondrial bioenergetic functions. In hepatocellular carcinoma (HCC), OXPHOS slows down or switches to glycolysis via what is known as the Warburg effect. The altered respiration in cancer was reported to affect mitochondrial temperature. We investigated the impact of the metabolic switch on the mitochondrial temperature in HepG2 HCC cell line. Metformin (N, N-dimethylbiguanide) treatment was used to suppress glycolysis to emulate lower metabolically active cells (Met-HepG2). The mitochondrial temperature was assessed using mito-thermo yellow (MTY) absorbing mitochondrial radiant heat. Mito-tracker green (MTG) fluorescent dye was used to confirm mitochondrial localization. Our data showed lower MTY dye intensity in the Met-HepG2 treated group, indicating a significant increase in mitochondrial temperature compared to untreated HepG2 cells (NT-HepG2). Genotypic analysis of the metabolic respiration gene expression showed significant down-regulation in glycolytic genes (ERR-gamma, HK2, PGK, ALDOC, TPI1, IDH1, and PKM2) in the Met-HepG2 cells compared to the NT-HepG2 cells. OXPHOS as evidenced by ATP, ROS, and NADPH production was significantly up-regulated in the Met-HepG2 group compared to the NT-HepG2 group. Transmission electron microscopy showed fewer mitochondria with swollen elongated appearance, as a marker for activated OXPHOS in the Met-HepG2 group. These data show a correlation between HepG2 altered metabolism and mitochondrial temperature and suggest that less metabolically active HepG2 cells are correlated with higher mitochondrial temperature, providing evidence for a possible role of mitochondrial temperature in diagnosis of HCC.
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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".