Elisa detection of parathyroid hypertensive factor in normotensive and hypertensive individuals
Bibliographic record
Abstract
Preliminary human studies suggest that parathyroid hypertensive factor (PHF) may be a useful marker in the detection and treatment of essential hypertension. In particular, PHF positive patients have been shown to respond most favorably to treatment with calcium channel blockers. Anti-PHF monoclonal antibodies were developed using standard procedures, and used to screen plasma samples from normotensive and hypertensive (untreated) patients for PHF levels. The anti-PHF antibodies were capable of inactivating PHF biological activity (blood pressure bioassay) ex vivo. There was no detectable cross-reactivity with human or rat parathyroid hormone (PTH). PHF levels measured with ELISA were correlated with bioassay measurement of PHF levels in the same samples. The monoclonal antibodies reacted equally with rat and human PHF. In normotensive patients, the average level of circulating PHF as measured with monoclonal anti-PHF ELISA is 0.1819 ± 0.0207 Units/ml (N=49), and was not correlated with mean arterial pressure (r=0.0488, not significant). In hypertensive patients (mean arterial pressure ≥ 95 mm Hg) who were not on any medications (N=27), circulating PHF levels were elevated up to three fold, and positively correlated with mean arterial pressure (r=0.394, significant). These results support previous studies describing the correlation between PHF and hypertension and indicate that the selective monoclonal antibodies may provide an improved ELISA method for clinical assessment of hypertension. Furthermore, the detection of significant levels of PHF in normotensive individuals as well as in hypertensives could suggest that PHF has a physiological function and may be classified as a hormone.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".