In vivo assessment of pediatric kidney function using multi-parametric and multi-nuclear functional magnetic resonance imaging: challenges, perspectives, and clinical applications
Bibliographic record
Abstract
Abstract The conventional methods for assessing kidney function, such as glomerular filtration rate and microalbuminuria, provide only partial insight into kidney function. Multi-parametric and multi-nuclear functional resonance magnetic imaging (MRI) techniques are innovative approaches to unraveling kidney physiology. Multi-parametric MRI includes various sequences to evaluate kidney perfusion, tissue oxygenation, and microstructure characterization, including fibrosis—a key pathological event in acute and chronic kidney disease and in transplant patients—without the need for invasive kidney biopsy. Multi-nuclear MRI detects nuclei other than protons. 23Na MRI enables visualization of the corticomedullary gradient and assessment of tissue sodium storage, which can be particularly relevant for personalized medicine in salt-wasting tubular disorders. Meanwhile, 31P-MRS measures intracellular phosphate and ATP variations, providing insights into oxidative metabolism in the muscle during exercise and recovery. This technique can be useful for detecting subclinical ischemia in chronic kidney disease and in tubulopathies with kidney phosphate wasting. These techniques are non-invasive and do not involve radiation exposure, making them especially suitable for longitudinal and serial assessments. They enable in vivo evaluation of kidney function on a whole-organ basis within a short acquisition time and with the ability to distinguish between medullary and cortical compartments. Therefore, they offer considerable potential for pediatric patients. In this review, we provide a brief overview of the main imaging techniques, summarize available literature data on both adult and pediatric populations, and examine the perspectives and challenges associated with multi-parametric and multi-nuclear MRI. Graphical Abstract
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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.
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".