Membrane potential differences and GABA<sub>A</sub> receptor expression in hepatic tumor and non-tumor stem cells
Bibliographic record
Abstract
The ability to differentiate tumor initiating stem cells (TISCs) from healthy, normal stem cells (NSCs) could have important diagnostic and therapeutic implications for patients with hepatocellular carcinoma (HCC). The aim of this study was to document and compare cell membrane potentials (PDs) and GABAA receptor subunit expression in hepatic TISCs and NSCs. PD values were determined in CD133(+) Huh-7 TISCs and CD133(+) WBF344 NSCs by single channel microelectrode impalement. GABAA receptor subunit expression was documented using immunohistochemistry (IH) in both cell lines as well as surgically resected HCC and healthy liver tissues. TISCs were significantly depolarized compared with NSCs (-4.0 ± 1.8 versus -11.0 ± 2.4 mV, respectively; p < 0.05). GABAA α6 subunit expression was either absent or markedly attenuated, while γ3 subunit expression was abundant in TISCs and HCC compared with NSCs and healthy liver tissues. Exposure to the GABAA receptor agonist muscimol caused hyperpolarization of TISCs (Δ -4.4 ± 1.1) but depolarization of NSCs (Δ + 5.2 ± 2.3) and attenuation of TISC proliferative activity. We conclude that TISCs and NSCs have significantly different cell membrane potentials and these differences are associated with differences in GABAA receptor subunit expression.
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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".