De-sialylated and sialylated IgG anti-dsDNA antibodies respectively worsen and mitigate experimental mouse lupus proteinuria and possible mechanisms
Bibliographic record
Abstract
BACKGROUND: The role of sialylated and de-sialylated (de-SIA) IgG anti-dsDNA antibodies in experimental mouse lupus remains unclear. AIM OF THE STUDY: To examine how sialylated and de-SIA IgG anti-dsDNA antibodies affect lupus mouse proteinuria and possible mechanisms. METHODS: Blood was serially obtained from pristane-induced female BALB/c lupus mice to assess correlations between alpha-2,6-sialic cid (SIA) ratios of serum IgG anti-dsDNA and proteinuria. Kidney C3 staining was correlated with blood IgG anti-dsDNA. Sialylated IgG anti-dsDNA with its de-SIA form was administered to determine the effect on lupus proteinuria and in vitro consequences. RESULTS: We observed that the SIA contents of IgG anti-dsDNA were lower in Week 16/1+ (Week 16 with 1 + proteinuria) and W24/2 + mice than those in W16 (no proteinuria) and W24/1 + mice, respectively (P < 0.005 for both). C3 staining densities in the kidney correlated inversely with the α-2,6-SIA content of plasma IgG anti-dsDNA (r = -0.660). Highly sialylated A52L1 IgG anti-dsDNA injection mitigated lupus proteinuria significantly from PBS injection; however, its de-SIA form worsened proteinuria (aggravation of proteinuria: the latter vs. the former [sialylated A52L1 IgG anti-dsDNA] with an infinite odds ratio). Highly sialylated A52L1 IgG anti-dsDNA resulted in higher interleukin (IL)-10/IL-12 ratios, higher transforming growth factor-β1 levels, and lower tumor necrosis factor-α levels in sera than its de-SIA from. CONCLUSION: We concluded that a low SIA/serum IgG anti-dsDNA ratio indicated a high severity of nephritis in pristane-induced lupus mice. Highly sialylated IgG anti-dsDNA, in contrast to the de-SIA form, alleviated the severity of lupus proteinuria.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".