A SELECTION OF TRANSGENIC ANIMAL MODELS USED IN BIOMEDICAL RESEARCH
Bibliographic record
Abstract
The term transgenic animal refers to an animal whose genetic composition has been altered by an addition of foreign DNA. The introduced DNA is called a transgene and the overall process is called transgenic technology. These terms now include the use of living organisms or their parts to make or modify products, to change the characteristics of plants or animals, or to develop micro-organisms forspecific uses that currently include several plants and a number of animal species. During the last two decades, transgenic animal model has been an essential mainstay tool in refining our understanding to gene regulation and function of both biological systems and human diseases. The aims of this review article are 1) to elaborate on how transgenic technology is being used to develop the next genera-tion of animal models and 2) to provide an update of the recent advances and a possible structure design for future studies. This review covers the most used animal models of some human disease and specifically discusses two studies conducted on a mouse model of experimental autoimmune encephalomyelitis (EAE) that reproduced specific features of the histopathology and neurobiology ofMultiple Sclerosis (MS). This report is presented with the hope to provide both educational and practical basis for the use of these informative animal models.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".