How Protective Mechanisms Interact to Prevent Overnight Calcium Phosphate Precipitation - An Observational Study to Determine Factors Against Calcium Phosphate Lithogenesis in a Healthy Cohort
Bibliographic record
Abstract
BACKGROUND/AIMS: As restful, non-interrupted sleep is essential for normal mental and physical functioning, the urine flow rate (UFR) overnight remains low. Due to this reduced UFR, the kidneys produce a lower urine volume, which may lead to supersaturation of lithogens in the renal collecting system. The protective mechanisms that prevent the rise in the concentration of the lithogenic substances in urine, such as calcium phosphate, are explored. METHODS: Urine samples were collected from 26 subjects every 2-3 h during daylight with one nocturnal collection; the UFR was calculated in the median time for each collection period. Urinary constituents for calcium phosphate precipitation including electrolytes, calcium, phosphate, citrate, and pH were measured. Comparisons within individuals were done by paired t test. RESULTS: The calcium excretion rate fell significantly overnight (from 2.4 ± 0.2 µmol/min during the daytime to 1.5 ± 0.3 µmol/min, p < 0.05), in parallel with sodium excretion (54 ± 16 µmol/min from its daytime 127 ± 12 µmol/min, p < 0.05), preventing nocturnal calcium concentration from increasing (3.0 ± 0.3 mmol/l daytime to 2.5 ± 0.5 mmol/l overnight), while citrate concentration did not change significantly. The total urine phosphate concentration rose significantly overnight (daytime 18.7 ± 1.4 µmol/min vs. nocturnal 20.9 ± 1.7 µmol/min), but the concentration of divalent phosphate did not increase in the overnight period. CONCLUSIONS: Although the UFR was lower overnight, there was no evidence that the risk of calcium phosphate precipitate formation in healthy subjects was increased.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".