Development and evaluation of an immuno-MALDI (iMALDI) assay for angiotensin I and the diagnosis of secondary hypertension
Bibliographic record
Abstract
Plasma renin activity (PRA) is an essential analytical tool for screening and diagnosis of secondary forms of hypertension. Typically, PRA is measured by competitive radioimmunoassay, but there are significant drawbacks to this technique including non-specificity, long analysis times, narrow calibration range, and the requirement for radionucleotides. In this paper, we report a method for plasma renin activity determination by immuno-MALDI mass spectrometry detection. This method overcomes the issues of non-specificity and long analytical times present with RIA, and does not require the use of radionucleotides. As an initial methodological evaluation, plasma renin activity results obtained by radioimmunoassay, LC/ESI-MS/MS, and immuno-MALDI on 64 samples from an outpatient primary aldosteronism screening program have been compared. A strong correlation was found between immuno-MALDI and radioimmunoassay (R2 = 0.9412, 62/64 within the 95% CI of the Bland-Altman plot), and iMALDI and LC/ESI-MS/MS (R2 = 0.9471, 62/64 within the 95% CI of the Bland-Altman plot). Technical replicates showed a 4.8% CV, while inter- and intra-day replicates showed CVs of 17.3% and 17.2% respectively. We have developed an assay capable of measuring PRA without the use of radionucleotides. This immuno-MALDI approach affords the specificity of MS while avoiding the long analytical run times and technical problems associated with HPLC. With the use of robotic sample preparation to optimize precision, this assay should be adaptable to clinical environments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".