Parathyroid hormone and leptin – new peptides, expanding clinical prospects
Bibliographic record
Abstract
There are three injectable and one oral bone-building (i.e., bone anabolic) parathyroid hormone (PTH) peptides. One of the four, Lilly's injectable teriparatide (Forteo), is currently being used, and the other three are in clinical trials. They are being used or assessed only for treating postmenopausal osteoporosis. However, their potential clinical targets now extend far beyond osteoporosis. They can accelerate the mending of even severe non-union fractures; they will probably be used to strengthen the anchorage of pros-theses to bone; they have been shown to treat psoriasis that has resisted other treatments; they can increase the size of haematopoietic stem cell proliferation and accelerate the endogenous repopulation or repopulation by donor transplants of bone marrow depleted by chemotherapeutic drugs; and they may prevent vascular ossification. Leptin, a member of the cytokine superfamily has a PTH-like osteogenic activity and may even partly mediate PTH action. But leptin has two drawbacks that cloud its therapeutic future. First, apart from directly stimulating osteoblastic cells, it targets cells in the hypothalamic ventromedial nuclei and through them it reduces oestrogenic activity by promoting osteoblast-suppressing adrenergic activity. Second, it stimulates vascular and heart valve ossification, which leads to such events as heart failure and diabetic limb amputations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".