Determinants of C1q Binding in the Single Antigen Bead Assay
Bibliographic record
Abstract
BACKGROUND: A modified single antigen bead (SAB) assay measuring C1q binding to human leukocyte antigen antibodies has recently been introduced. The aim of this study was to investigate the determinants of C1q binding on SAB. METHODS: Sera from 73 sensitized patients were analyzed by the generic IgGpan as well as IgG subclass specific SAB assays and correlated with the standard and an anti-human globulin (AHG) enhanced C1q test. RESULTS: Among 2,665 SABs with positive IgGpan results (mean fluorescence intensity [MFI]>500), strong complement-binding IgG1 and IgG3 subclasses accounted for a median of 99% (interquartile range, 76%-100%) of the total IgG amount. IgGpan MFI alone showed a very strong association with standard C1q positivity (r=0.72), which was superior to a model including all IgG subclass MFI (r=0.62). Combining all IgG subclass MFI and IgGpan MFI only marginally improved the prediction of standard C1q positivity compared with IgGpan MFI alone (Δr=0.02). IgGpan MFI greater than 14,154 predicted standard C1q positivity, with 92% sensitivity and 96% specificity. Notably, 1,840 (93%) of the 1,974 C1q-negative SABs contained human leukocyte antigen antibodies with strong complement-binding IgG1 and IgG3 subclasses. Anti-human globulin significantly enhanced the signal in the C1q assay, but the association of AHG C1q positivity with IgGpan MFI was less strong (r=0.51). CONCLUSION: C1q binding on SAB is strongly associated with IgGpan MFI. IgG subclass information only marginally improves prediction of C1q binding likely because complement-binding IgG1 and IgG3 subclasses dominate regarding frequency and relative amounts. A negative C1q assay result does not indicate the absence of strong complement-binding IgG subclasses.
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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".