Metabolic Study of Cancer Cells Using a pH Sensitive Hydrogel Nanofiber Light Addressable Potentiometric Sensor
Bibliographic record
Abstract
We report a simple, fast, and cost-effective approach that measures cancer cell metabolism and their response to anticancer drugs in real time. Using a Light Addressable Potentiometric Sensor integrated with pH sensitive hydrogel nanofibers (NF-LAPS), we detect localized changes in pH of the media as cancer cells consume glucose and release lactate. NF-LAPS shows a sensitivity response of 74 mV/pH for cancer cells. Cancer cells (MDA MB231) showed a response of ∼0.4 unit change in pH compared to virtually no change observed for normal cells (MCF10A). We also observed a drop in pH for the multidrug-resistant cancer cells (MDA-MB-435MDR) in the presence of doxorubicin. However, inhibition of the metabolic enzymes such as hexokinase and lactate dehydrogenase-A suggested an improvement in the efficacy of doxorubicin by decreasing the level of acidification. This approach, based on extracellular acidification, enhances our understanding of cancer cell metabolic modes and their response to chemotherapies, which will help in the development of better treatments, including choice of drugs and dosages.
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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 | 0.001 | 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".