Aberrant IgA1 Glycosylation in IgA Nephropathy: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Galactose-deficient IgA1 was evaluated in patients with IgA nephropathy(IgAN) and controls in order to determine the predictive value of galactose-deficient IgA1 in cases of IgA nephropathy. METHODS: PubMed, EMBASE, Cochrane central register of controlled trials, CNKI, CBM disc, and VIP database were searched to identify eligible studies that evaluated a difference in aberrant IgA1 glycosylation in IgAN patients compared with controls. A meta-analysis was conducted to evaluate the impact of galactose-deficient IgA1(Gd-IgA1) levels in different groups. RESULTS: A total of 22 studies (n = 1657) met inclusion criteria. The mean Newcastle-Ottawa Scale (NOS) score was 7.2 and ranged from 6 to 8. The standard mean difference(SMD) in the meta-analysis of 20 studies of the level of Gd-IgA1 in the serum and/or supernatant of cultured cells was higher in the IgAN group compared with healthy controls as well as in those with other renal diseases (SMD = 1.76, 95% CI = 1.18-2.34, P<0.00001; SMD = 1.05, 95% CI = 0.05-2.04, P = 0.04). The data synthesis suggested that IgAN patients had similar levels of serum Gd-IgA1, with no significant differences, compared with first-degree relatives and Henoch-Schonlein purpura nephritis (HSPN) patients (MD = 0.04, 95% CI = 0.00-0.08, P = 0.05; MD = -46.03, 95% CI = -217.70-125.64, P = 0.60). In addition, the combined MD of 5 studies indicated that there were no significant differences in Gd-IgA1 levels among patients with varying severities of IgAN (MD = 0.02, 95% CI = -0.02-0.05, P = 0.28). CONCLUSIONS: The pooled evidence suggests that the level of Gd-IgA1 in the serum or supernatant of cultured cells from peripheral blood or tonsils may be a useful biomarker for predicting IgA nephropathy, though the level of Gd-IgA1 was not significantly associated with disease severity.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".