Experimental Determination of Net Protein Charge and <i>A</i> tot and <i>K</i> a of Nonvolatile Buffers in Canine Plasma
Bibliographic record
Abstract
Acid-base abnormalities frequently are present in sick dogs. The mechanism for an acid-base disturbance can be determined with the simplified strong ion approach, which requires accurate values for the total concentration of plasma nonvolatile buffers (A(tot)) and the effective dissociation constant for plasma weak acids (K(a)). The aims of this study were to experimentally determine A(tot) and K(a) values for canine plasma. Plasma was harvested from 10 healthy dogs; the concentrations of quantitatively important strong ions (Na+, K+, Ca2+, Mg2+, Cl-, L-lactate) and nonvolatile buffer ions (total protein, albumin, phosphate) were determined; and the plasma was tonometered with CO2 at 37 degrees C. Strong ion difference (SID) was calculated from the measured strong ion concentrations, and nonlinear regression was used to estimate values for A(tot) and K(a), which were validated with data from an in vitro and in vivo study. Mean (+/- SD) values for canine plasma were A(tot) = (17.4 +/- 8.6) mM (equivalent to 0.273 mmol/g of total protein or 0.469 mmol/g of albumin); K(a) = (0.17 +/- 0.11) x 10(-7); pK(a) = 7.77. The calculated SID for normal canine plasma (pH = 7.40; P(CO2) = 37 mm Hg; [total protein] = 64 g/L) was 27 mEq/L. The net protein charge for normal canine plasma was 0.25 mEq/g of total protein or 0.42 mEq/g of albumin. Application of the experimentally determined values for A(tot), K(a), and net protein charge should improve understanding of the mechanism for complex acid-base disturbances in dogs.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".