Generation, Characterization, and Use of Monoclonal Antibodies against Parathyroid Hypertensive Factor
Bibliographic record
Abstract
Parathyroid hypertensive factor (PHF) may be useful as a diagnostic marker of salt-sensitive, low-renin hypertension. PHF was discovered in the plasma of salt-sensitive hypertensive humans (1) and was also found to be increased in spontaneously hypertensive rats, DOCA-salt-hypertensive rats, and Dahl-salt-sensitive rats, but not in two-kidney one-clip rats or in Dahl-salt-insensitive rats (2)(3). Studies on the mechanism of PHF action indicate that this substance acts directly on vascular smooth muscle cells to enhance Ca2+ influx (4), likely associated with depolarization of the plasma membrane by inhibition of voltage-gated K+ channels (5). Together these actions will sensitize vascular tissues to other vasoconstrictors, such as norepinephrine and angiotensin II (6). PHF may therefore be a causative factor in the development of hypertension in some individuals. It was found that PHF-positive (salt-sensitive) patients respond best to calcium channel blockers and diuretics and that PHF-negative patients respond better to angiotensin-converting enzyme inhibitors and beta blockers (7). Recently, an enzyme immunoassay for detection of PHF in human plasma has been reported that uses anti-PHF oligoclonal antibodies (8). The present study describes the further development, characterization, and clinical application of monoclonal antibodies (MAbs) against PHF.
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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 | 0.000 |
| 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 | 0.001 | 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".