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Record W3169736453 · doi:10.1093/ndt/gfab130.002

FC 054FUNCTIONAL SODIUM MAGNETIC RESONANCE IMAGING OF THE HUMAN KIDNEY

2021· article· en· W3169736453 on OpenAlexaff
Sandrine Lemoine, Alireza Akbari, Taylor Marcus, Christopher W. McIntyre

Bibliographic record

VenueNephrology Dialysis Transplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineUrineRenal functionOsmotic concentrationKidney diseaseMagnetic resonance imagingSodiumKidneyUrine osmolalityUrinary systemUrologyIngestionInternal medicineRadiology

Abstract

fetched live from OpenAlex

Abstract Background and Aims Maintenance of a cortico-medullary concentration gradient (CMG) required for urine concentration, is one of most important tubular function. However, we are lacking of functional tubular parameters to explore this function. The only tool available to assess it currently, is urinary osmolarity that is an indirect and nonspecific maker of CMG. In this study, we explore the ability of 23NaMRI in measuring 1) the dynamics of CMG for the first time compared to urinary osmolarity after a water load 2) the CMG in kidney disease. Method We conducted an exploratory pilot study for 10 healthy controls with water load then 5 cardiorenal patients with kidney disease. 1) Healthy controls were asked to be fasting since midnight. Urines sample were collected to measure fasting osmolarity and a first MRIscan were performed to acquire baseline anatomical and sodium images. Once the baseline was completed, healthy participants were asked to ingest water (15 mL/kg) within 15 minutes. Four subsequent sodium pictures were acquired an hour after water ingestion. Urine samples were obtained after each sodium acquisition every 15 min during one hours. 2) Cardiorenal patients underwent an MRI scan, provided a spot urine sample and have blood work collected. All MR experiments were carried out on a GE MR750 3T (GE Healthcare, WI). A custom-built two-loop (18cm in diameter) butterfly radiofrequency surface coil tuned for 23Na frequency (33.786 MHz) was used to acquire renal 23Na images. Results Mean age of the 10 healthy controls was 41.8 ± 15.3 years, mean body mass index (BMI) was 24.3 ± 3.8 kg/m2. Mean water intake was 1092 ± 233 mL, total water excreted was 1250 ± 301 mL . Mean age of the 5 cardiorenal patients was 76.6 ± 12.2 years, mean BMI was 28.1 ± 6.9 kg/m2. eGFR was 54 ± 37 mL/min/1.73m2. Urinary osmolarity was 498 ± 145 mosm/L and medulla/cortex ratio was 1.35 ± 0.11. Sodium imaging was successfully acquired in all volunteers. In the morning fasting, medulla/cortex ratio was 1.55 ± 0.11 regarding to a urinary osmolarity to 814 ± 121 mosm/L. Mean ± SD fasting urinary osmolarity dropped significantly to 73 ± 14 mosm/L for maximal dilution, 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. 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. We were able to measure medulla/cortex ratio in patients with CKD with a mean SNR of 20.45 ± 9.45. Conclusion We explored CMG dynamically every 15 min and we were able to discriminate significant changes after a water load. We were also able to provide efficient 23NaMRI pictures in cardiorenal patients with kidney disease. CMG exploration would provide a relevant assessment of tubular dysfunction independently of glomerular alteration and thus could be of prognostic value.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.010
GPT teacher head0.271
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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