Improved Phytate Phosphorus Utilization by Japanese Medaka Transgenic for the <i>Aspergillus niger</i> Phytase Gene
Bibliographic record
Abstract
The inefficient digestion of phytate phosphorus by fish has created environmental concerns associated with phosphorus pollution from aquaculture production facilities. To further complicate this situation, phytate is known to chelate minerals and proteins, making them nutritionally unavailable. The enzyme phytase degrades phytate into inorganic phosphorus, which can be directly utilized by fish. As a model to examine the feasibility and efficacy of producing fish capable of degrading phytate, Japanese medaka (Oryzias latipes) transgenic for an Aspergillus niger phytase gene were produced and their ability to utilize phytate phosphorus tested. Cell culture techniques, including transfection, RT-PCR, Northern blot, Western blot, and enzyme activity analysis demonstrated that the protein was expressed, active, and secreted. Survival of transgenic fish was significantly greater on all examined diets than their nontransgenic siblings and up to six-fold higher on a diet with phytate as the main phosphorus source. Similar results were obtained with nontransgenic fish when fed the same diet supplemented with phytase, suggesting that phytase, whether ingested or produced by the fish, is effective in degrading phytate and overcoming many of the known antinutritional factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".