Bibliographic record
Abstract
Background: Many human diseases lack validated animal models, hampering translational progress in several important disease areas. New and improved models can often be developed by recapitulating phenotypes related to those associated with human diseases. Objectives: Our lab has been interested in generating and characterizing several transgenic (Tg) animal models. These Tg animals were used to explore, on the cellular and molecular levels, the pathological mechanisms underlying several diseases, including immunological, neurodegenerative diseases, viral infections, cardiovascular disease, and cancer. Methods: Transgenes construction, analyzing techniques, and generation of Tg mice are as described in literature. Results: Examples of research projects that utilized animal models in our lab include: - Infectious diseases such as the acquired immunodeficiency syndrome (AIDS), which is caused by the human immunodeficiency virus type 1 (HIV-1). HIV-1 causes disease only in humans and chimpanzees. Thus, a major obstacle to explore the cellular and molecular mechanisms by which HIV-1 acts in vivo and to study how HIV-1 causes disease has been the lack of a small and cheap animal model. However, we have overcome the block to disease induction in rodents by generating Tg mice that express HIV-1 in the same immune cells that are normally infected in AIDS patients. These mice develop a severe AIDS-like disease leading to early death. Several pathological phenotypes, remarkably similar to those observed in HIV-1 infected individuals, were found. These included signs of growth retardation and wasting, atrophy of all lymphoid organs, preferential loss of CD4+ T cells, and interstitial nephritis and pneumonitis. More details will be presented. - Cancers such as leukemia and lymphoma as caused by murine leukemia viruses (MuLV) in inoculated mice. Their proviruses integrate in the vicinity of various genes involved in growth regulation, and act as insertion mutagens. We are using this retroviral insertion mutagenesis approach to identify novel oncogenes involved in T- or B-cell lymphomas induced by various MuLVs. We have succeeded in identifying novel oncogenes responsible for the onset of cancer. Among these oncogenes are members of Notch family, which are now under intense investigation to understand their role in inducing cancer. The details of these studies will be presented. Conclusions: It is likely that these novel Tg models will prove valuable in evaluating new pharmacological drugs, vaccines and disease therapies.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".