Addressing Complications of Complement Activation Product Evaluation in Autoimmune Inflammatory Myopathies
Bibliographic record
Abstract
Objectives Biomarkers are needed for efficient diagnosis and monitoring of autoimmune inflammatory myopathies (AIM). Complement activation products (CAP) reflect the activation state of the complement system, an important mechanism of AIM pathogenesis. However, CAP analysis requires standardized sampling and accurate assay procedures. This study aimed to establish a standardized protocol for measuring blood-based serum CAPs, C3a, C5a, and soluble C5b-9 (sC5b-9) to be utilized in future research studies evaluating novel biomarkers for AIM. Methods Paired plasma (n=21) and serum samples (n=21) from AIM patients, and plasma samples (n=50) from healthy controls (HC), were obtained from the Plasma Services Group (PSG, Moorestown, NJ). Samples were shipped on dry ice, stored at −80°C, and had not undergone any freeze-thaw cycles (non-freeze-thawed [NFT]). Serum samples from AIM patients enrolled in a multi-site Canadian registry were included (n=80) and had undergone 1-3 freeze-thaw (FT) cycles. C3a and C5a concentrations of each sample were measured using meso scale discovery (Meso Scale Diagnostics, Rockville, MD) while sC5b-9 concentrations were measured with an enzyme-linked immunosorbent assay (Quidel Corporation, San Diego, CA). Using t tests, for each CAP we compared the mean concentration of 1) plasma to serum (paired patients who had AIM and have not undergone FT), 2) FT to NFT (unpaired AIM patients and these were serum only), and 3) HC to AIM (NFT plasma samples only). Results When comparing plasma to serum, serum had higher mean concentrations of C3a (mean difference [MD] 7.7 ng/mL [95% confidence interval [CI]: 2.7, 12.8]) and C5a (MD 98.7 ng/mL [95% CI 75, 122.3]) (Table 1). There was no significant difference for sC5b-9. When comparing FT to NFT samples, FT samples had significantly higher C3a (MD 3230.6 ng/mL [95% CI 2919.8, 3541.4]), but lower C5a (MD −96.1 ng/mL [95% CI −108.6, −83.7]). There was no significant difference for sC5b-9. When comparing AIM to HC, HC had significantly lower sC5b-9 mean concentrations than AIM (MD −8228.8 ng/mL [95% CI −10379.7, −6077.9]). There was no difference for C3a between AIM and HC and only a small difference was detected for C5a (MD 3.1 ng/mL [95% CI 0.5, 5.7]). Table 1. Mean concentration of C3a, C5a, and soluble C5b-9 (sC5b-9) for 1) plasma vs. serum from the same autoimmune inflammatory myopathy (AIM) patients with no history of freeze-thaw (non-freeze-thaw, NFT), 2) Freeze-thawed (FT) vs. NFT samples (serum samples from different AIM patients in each group), 3) Healthy controls (HC) vs. AIM (NFT and plasma samples only) Conclusion Comparing the same type of blood specimen and avoiding freeze-thaw cycles are important factors to consider when interpreting CAP levels, particularly for C3a and C5a. Future studies to examine factors that may have contributed to variability, including differences in AIM patient characteristics such as disease subsets and activity are underway.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".