Urinary excretion of both D‐ and L‐lactate using a calf‐infusion model
Bibliographic record
Abstract
D‐lactic acidosis is recognized as a complication of gastrointestinal diseases in humans and ruminants. Absorbed D‐lactate can be excreted via the kidney; however, competition between D‐ and L‐lactate in the renal mechanisms may effect its elimination. The objective of this study was to measure D‐ and L‐lactate clearance in a calf infusion model. Five calves (age 14–31 days, weight 59 ± 8 kg) were infused with DL‐ and L‐lactic acid (300 mmol/L, 2 mL/kg/h for 8 hours) in a randomized cross‐over design. Calves were fitted with intravenous catheters for infusion and blood collection, and with suprapubic bladder catheters for urine collection. Samples for blood gas analysis, serum and urine D‐ and L‐lactate concentration, urine volume and serum and urine creatinine were collected every 2 hours for 24 hours. D‐ and L‐lactate clearances were calculated for each 2 hour interval and compared to creatinine clearance. With infusion of DL‐lactate, average serum D‐lactate concentration at steady state was 2.9 ± 1.6 mmol/L (range 1.8–5.7 mmol/L), and average urine D‐lactate concentration was 4.0 ± 1.7 mmol/L. Average D‐lactate clearance during steady state was 7.4 ± 3.4 mL/min. Percent excretion of infused D‐lactate increased during the 8 hour infusion, with minimum and maximum excretion averaging 0.6 ± 0.1% and 10.4 ± 4.4%. L‐lactate clearance could not be calculated, likely due to rapid metabolism of infused L‐lactate. Therefore, L‐lactate elimination is more efficient than D‐lactate since it can both be metabolized and excreted from the body. (Supported by NSERC Canada)
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".