pH control mechanisms of tumor survival and growth
Bibliographic record
Abstract
A distinguishing phenotype of solid tumors is the presence of an alkaline cellular feature despite the surrounding acidic microenvironment. This phenotypic characteristic of tumors, originally described by Otto Warburg, arises due to alterations in metabolism of solid tumors. Hypoxic regions of solid tumors develop due to poor vascularization and in turn regulate the expression of numerous genes via the transcription factor HIF-1. Ultimately, the tumor microenvironment directs the development of tumor cells adapted to survive in an acidic surrounding where normal cells perish. The provision of unique pH characteristics in tumor cells provides a defining trait that has led to the pursuit of treatments that target metabolism, hypoxia, and pH-related mechanisms to selectively kill cancer cells. Numerous studies over the past decade involving the cancer-specific carbonic anhydrase IX have re-kindled an interest in pH disruption-based therapies. Although an acidification of the intracellular compartment is established as a means to induce normal cell death, the defining role of acid-base disturbances in tumor physiology and survival remains unclear. The aim of this review is to summarize recent data relating to the specific role of pH regulation in tumor cell survival. We focus on membrane transport and enzyme studies in an attempt to elucidate their respective functions regarding tumor cell pH regulation. These data are discussed in the context of future directions for the field of tumor cell acid-base-related 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".