Differential effect of hypoxia and acidity on lung cancer cell and fibroblast metabolism
Bibliographic record
Abstract
This study examined the metabolic response of lung cancer cells and normal lung fibroblasts to hypoxia and acidity. GLUT1 and HXKII mRNA/protein expression was up-regulated under hypoxia in the MRC5 fibroblasts and in the A549 and H1299 lung cancer cell lines, indicating intensified glucose absorption and glycolysis. Under hypoxia, the LDHA mRNA and LDH5 protein levels increased in the cancer cells but not in the fibroblasts. Acidity suppressed the above-mentioned hypoxia effect. PDH-kinase-1 (PDK1 mRNA and protein) and inactive phosphorylated-PDH protein levels were induced under hypoxia in the cancer cells, whereas these were reduced in the MRC5 lung fibroblasts. In human tissue sections, the prevalent expression patterns supported the contrasting metabolic behavior of cancer cells vs. tumor fibroblasts. The monocarboxylate/lactate transporter 1 (MCT1) was up-regulated in all the cell lines under hypoxic conditions, but it was suppressed under acidic conditions. The mitochondrial DNA (mtDNA) content per cell decreased significantly in the A549 cancer cell line under hypoxia, but it increased in the MRC5 fibroblasts. Taking into account these findings, we suggest that, under hypoxia, cancer cells intensify the anaerobic direction in glycolysis, while normal fibroblasts prefer to seek energy by intensifying the aerobic use of the available oxygen.
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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".