Experimental Determination of Net Protein Charge and <i>A</i> tot and <i>K</i> a of Nonvolatile Buffers in Canine Plasma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it