Intracellular pH regulation and the acid delusion
Bibliographic record
Abstract
The hydrogen ion concentration ([H + ]) in intracellular cytoplasmic fluid (ICF) must be maintained in a narrow range in all species for normal protein functions. Thus, mechanisms regulating ICF are of fundamental biological importance. Studies on the regulation of ICF [H + ] have been hampered by use of pH notation, failure to consider the roles played by differences in the concentration of strong ions (strong ion difference, SID), the conservation of mass, the principle of electrical neutrality, and that [H + ] and bicarbonate ions [HCO 3 − ] are dependent variables. This argument is based on the late Peter Stewart’s physical–chemical analysis of [H + ] regulation reported in this journal nearly forty years ago (Stewart. 1983. Can. J. Physiol. Pharmacol. 61: 1444–1461. Doi: 10.1139/y83-207 ). We start by outlining the principles of Stewart’s analysis and then provide a general understanding of its significance for regulation of ICF [H + ]. The system may initially appear complex, but it becomes evident that changes in SID dominate regulation of [H + ]. The primary strong ions are Na + , K + , and Cl − , and a few organic strong anions. The second independent variable, partial pressure of carbon dioxide (PCO 2 ), can easily be assessed. The third independent variable, the activity of intracellular weak acids ([A tot ]), is much more complex but largely plays a modifying role. Attention to these principles will potentially provide new insights into ICF pH regulation.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".