A portable surface plasmon resonance (<scp>SPR</scp>) sensor for the detection of immunoglobulin <scp>A</scp> in plasma
Bibliographic record
Abstract
BACKGROUND: A life-threatening anaphylactic shock can occur if a patient with undiagnosed immunoglobulin A (IgA) deficiency (i.e., IgA levels <500 ng/mL) receives IgA-containing blood, hence the need for a rapid, point-of-care (POC) method for IgA deficiency screening. Enzyme-linked immunosorbent assay (ELISA) is routinely used to detect IgA, but this method requires trained specialists and ≥24 h to obtain a result. We developed a surface plasmon resonance (SPR)-based protocol to identify IgA-deficient patients or donors within 1 h. MATERIALS AND METHODS: The SPR sensor relies on the detection of IgAs captured by primary antibodies adsorbed on the SPR chip and quantified with secondary antibodies. The sensor was calibrated from 0 to 2000 ng/mL in buffer, IgA-depleted human serum, and plasma samples from IgA-deficient individuals. A critical concentration of 500 ng/mL was set for IgA deficiency. The optimized sensor was then tested on eight plasma samples with known IgA status (determined by ELISA), including five with IgA deficiency and three with normal IgA levels. RESULTS: The limit of detection was estimated at 30 ng/mL in buffer and 400 ng/mL in diluted plasma. The results obtained fully agreed with ELISA among the eight plasma samples tested. The protocol distinguished IgA-deficient from normal samples, even for samples with an IgA concentration closer to critical concentration. DISCUSSION: In conclusion, we developed a reliable POC assay for the quantification of IgA in plasma. This test may permit POC testing at blood drives and centralized centers to maintain reserves of IgA-deficient blood and in-hospital testing of blood recipients.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".