Functional Sodium Magnetic Resonance Imaging of Human Kidney
Bibliographic record
Abstract
Background: Maintenance of a cortico-medullary concentration gradient (CMG) is required for urine concentration. We explored the ability of 23NaMRI in measuring 1) the dynamics of CMG for the first time compared to urinary osmolarity after a water load and 2) the CMG in kidney disease. Methods: We conducted an exploratory pilot study for 10 healthy controls following water load then 5 cardiorenal patients with kidney disease. 1) Fasting healthy controls provided urine samples to measure osmolarity and baseline 23NaMRI scans were performed. They were instructed to ingest water (15 mL/kg) within 15 minutes. Four subsequent sodium images and urine samples were acquired at 15 min intervals starting one hour after water ingestion. 2) Cardiorenal patients underwent an MRI scan, provided a blood and urine sample, but no water loading. Results: Mean age of the 10 healthy controls was 41.8 ± 15.3 years. In the morning fasting, medulla/cortex ratio was 1.55 ± 0.11 with concurrent urinary osmolarity measured at 814 ± 121 mOsm/L. Mean ± SD fasting urinary osmolarity dropped significantly to 73 ± 14 mOsm/L, p=0.001. Mean medulla/cortex ratio dropped significantly to 1.31 ± 0.09 mOsm/L for maximal dilution, p=0.002. Figure 1 displays changes of 23NaMRI pictures before (A) then 1h (B), 1H15 (C), 1h30 (D) and 1h45 (E) after a water load. Urinary osmolarity and medulla/cortex ratio are significantly correlated, r=0.54, p=0.0001. Mean age of the 5 cardiorenal patients was 76.6 ± 12.2 years, eGFR was 54 ± 37 mL/min/1.73m2. Urinary osmolarity was 498 ± 145 mOsm/L and medulla/cortex ratio was 1.35 ± 0.11. We measured corticomedullary gradient in cardiorenal patient with different level of eGFR to show the ability and feasibility to measure this gradient in pathological settings. Conclusions: We explored CMG dynamically every 15 min in healthy controls and demonstrated significant changes after a water load. We were also able to acquire 23NaMRI pictures in cardiorenal patients with kidney disease with plans for future analyses.
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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 | 0.000 | 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) | 0.003 | 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".