A functional cell-based bioassay for assessing adrenergic autoantibody activity in postural tachycardia syndrome
Bibliographic record
Abstract
Background Activating autoantibodies (AAb) to adrenergic receptors (AR) have previously been reported in patients with postural tachycardia syndrome (POTS). These AAb may contribute to a final common pathway for overlapping disease processes, reflecting a possible autoimmune contribution to POTS pathophysiology. In prior studies, measurement of AAb activity was inferred from costly, low-throughput, and laborious physiological assays. In the present study, we developed and validated an alternative cell-based bioassay for measuring AAb activity in serum by means of pre-treatment with monoamine oxidase (MAO). Methods A total of 37 POTS patients and 61 sex-matched healthy control participants were included. Serum was pre-treated with MAO to remove endogenous catecholamines that could falsely inflate AR activation by AAb. A receptor-transfected cell-based bioassay was used to detect presence of α1AR-AAb and β1AR-AAb in serum. Results MAO effectively degraded catecholamines as demonstrated by suppression of norepinephrine-induced α1AR activation in POTS (6.4 ± 0.7 vs. 5.5 ± 0.9; P = 0.044) and in controls (4.1 ± 0.5 vs. 3.9 ± 0.6; P = 0.001). Mean activity values were greater in the POTS vs. Controls for α1AR-AAb (6.2 ± 1.2 vs. 5.3 ± 1.0; P < 0.001) and β1AR-AAb (5.7 ± 1.8 vs. 4.1 ± 0.9; P < 0.001). Compared to controls, more POTS patients were positive for α1AR-AAb activity (22% vs 4%; P = 0.007) and β1AR-AAb activity (52% vs. 2%; P < 0.001). Conclusions The co-presence of norepinephrine in serum samples can artifactually elevate α1AR and β1AR activity, which can be avoided by serum pre-treatment with MAO. Using this novel bioassay, we show that POTS patients have increased α1AR-AAb and β1AR-AAb activity compared to healthy controls in the largest POTS cohort reported to-date.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".