The Myeloid-Kidney Interface in Health and Disease
Bibliographic record
Abstract
Kidney homeostasis is highly dependent upon the correct functioning of myeloid cells. These cells form a distributed surveillance network throughout the kidney, where they play an integral role in the response to organ threat. Dysregulation of resident proinflammatory and profibrotic macrophages leads to kidney structural damage and scarring after kidney injury. Fibrosis throughout the kidney parenchyma contributes to the progressive functional decline observed in CKD, independent of the etiology. Circulating myeloid cells bearing intrinsic defects also affect the kidney substructures, such as neutrophils activated by autoantibodies that cause GN in ANCA-associated vasculitis. The kidney can also be affected by disorders of myelopoiesis, including myeloid leukemias (acute and chronic myeloid leukemias) and myelodysplastic syndromes. Clonal hematopoiesis of indeterminate potential is a common, newly recognized premalignant clinical entity characterized by clonal expansion of hyperinflammatory myeloid lineage cells that may have significant kidney sequelae. A number of existing therapies in CKD target myeloid cells and inflammation, including glucocorticoid receptor agonists and mineralocorticoid receptor antagonists. The therapeutic indications for these and other myeloid cell-targeted treatments is poised to expand as our understanding of the myeloid-kidney interface evolves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".