Sex and kidney ACE2 expression in primary focal segmental glomerulosclerosis: A NEPTUNE study
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting enzyme 2 (ACE2) has been implicated in the pathogenesis of experimental kidney disease. ACE2 is on the X chromosome, and in mice, deletion of ACE2 leads to the development of focal segmental glomerulosclerosis (FSGS). The relationship between sex and renal ACE2 expression in humans with kidney disease is a gap in current knowledge. METHODS: We studied renal tubulointerstitial microarray data and clinical variables from subjects with FSGS enrolled in the Nephrotic Syndrome Study Network (NEPTUNE) study. We compared relationships between ACE2 expression and age, estimated glomerular filtration rate (eGFR), urinary albumin to creatinine ratio (UACR), interstitial fibrosis, tubular atrophy, and genes implicated in inflammation and fibrosis in male and female subjects. RESULTS: ACE2 mRNA expression was lower in the tubulointerstitium of males compared to females (P = 0.0026). Multiple linear regression analysis showed that ACE2 expression was related to sex and eGFR but not to age or treatment with renin angiotensin system blockade. ACE2 expression is also related to interstitial fibrosis, and tubular atrophy, in males but not in females. Genes involved in inflammation (CCL2 and TNF) correlated with ACE2 expression in males (TNF: r = -0.65, P < 0.0001; CCL2: r = -0.60, P < 0.0001) but not in females. TGFB1, a gene implicated in fibrosis correlated with ACE2 in both sexes. CONCLUSIONS: Sex is an important determinant of ACE2 expression in the tubulointerstitium of the kidney in FSGS. Sex also influences the relationships between ACE2, kidney fibrosis, and expression of genes involved in kidney inflammation.
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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) | 0.002 | 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".