Two Techniques to Create Hypoparathyroid Mice: Parathyroidectomy Using GFP Glands and Diphtheria-Toxin-Mediated Parathyroid Ablation
Bibliographic record
Abstract
Hypoparathyroidism (HP) is a disorder characterized by low levels of PTH which lead to hypocalcemia, hyperphosphatemia, and low bone turnover. The most common cause of the disease is accidental removal of the parathyroid glands during thyroid surgery. Novel therapies for HP are needed, but testing them requires reliable animal models of acquired HP. Here, we demonstrate the generation of two mouse models of acquired HP. In the GFP-PTX model, mice with green fluorescent protein (GFP) expressed specifically in the parathyroids (PTHcre-mTmG) were created by crossing PTHcre+ mice with Rosa-mTmGfl/fl mice. Green fluorescing parathyroid glands are easily identified under a fluorescence dissecting microscope and parathyroidectomy is performed in less than 20 min. After fluorescence-guided surgery, mice are profoundly hypocalcemic. Contrary to the traditional thyro-parathyroidectomy, this precise surgical approach leaves thyroid glands and thyroid function intact. The second model, which does not require surgery, is based on a diphtheria-toxin approach. PTHcre-iDTR mice, which express the diphtheria toxin (DT) receptor specifically in the parathyroids, were generated by crossing the inducible DTR mouse with the PTHcre mouse. Parathyroid cells are thus rendered sensitive to diphtheria toxin (DT) and can be selectively destroyed by systemically injecting mice with DT. The resulting hypocalcemic phenotype is stable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".