Simulating Nephrocalcinosis in a jar: An in vitro temperature-dependent model of CO₂-induced precipitation relevant to intensive aquaculture
Bibliographic record
Abstract
Nephrocalcinosis (“kidney stones”) is a persistent health issue in intensive aquaculture systems, affecting fish welfare and productivity. However, its causes and risk factors remain poorly understood. Dissolved CO 2 concentrations are typically elevated (hypercapnia) in intensive aquaculture systems, causing elevated blood PCO 2 levels. Fish compensate for this hypercapnia-induced “respiratory acidosis” by elevating plasma bicarbonate (HCO 3 − ) to restore pH. However, if returned to normocapnia (normal CO 2 ), fish experience an alkalosis which we propose may induce nephrocalcinosis. We used an in vitro model to test whether a rapid rise in pH in the pre-urine, caused by an abrupt PCO 2 drop when fish are transferred from hypercapnic to normocapnic levels, could be the trigger for kidney stone formation. Synthetic fish pre-urine was sequentially subjected to: (1) elevated PCO 2 (1–3 %) to mimic hypercapnia-induced respiratory acidosis; (2) HCO 3 − addition to simulate metabolic compensation; and (3) a rapid reduction in PCO 2 mimicking events that occur routinely during fish culture and handling ( e.g. , transfer, sorting/grading, fasting, vaccination). These exposures were conducted at 6, 12, and 18 °C. Precipitation in the pre-urine, measured by turbidity, was rapid and sustained following the drop in PCO 2 (step 3 above). The magnitude of precipitation was strongly associated with pH rise (ΔpH), particularly at higher temperatures. These findings demonstrate that abrupt decreases in PCO 2 can promote mineral precipitation under physiologically relevant ionic conditions, and that elevated temperature exacerbates this effect. The results support the hypothesis that nephrocalcinosis may result, in part, from acid-base disturbances commonly encountered under intensive aquaculture conditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".