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Of Designer Mice and Men

2007· article· en· W2944067780 on OpenAlexaboutno aff
Richard G. Ellenbogen

Bibliographic record

VenueNeurosurgery · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

The three scientists were told their seemingly crazy idea could not work. Nevertheless, the concept of creating designer mice has earned these unrelenting three the 2007 Nobel Prize in Physiology or Medicine. Mario Capecchi, a Howard Hughes Medical Institute investigator at the University of Utah in Salt Lake City, Oliver Smithies of the University of North Carolina, Chapel Hill, and Martin Evans of Cardiff University, United Kingdom, have won the 2007 Prize for pioneering the techniques to create knockout mice (Science 318: 178–179, 2007). These mice are bred to delete (knockout) a certain gene. What is the relevance of these mutated mammals? In essence, knockout mice have permitted scientists to understand the roles of thousands (over 11,000 so far) of mammalian genes. By doing so, these scientists have created an industry, in which there are now laboratory models of human disease, a vehicle to test countless therapeutic options not previously possible. Capecchi, one of the more colorful recipients of the Prize was discouraged by early National Institutes of Health (NIH) reviews of the impossibility of such a concept in mammals. However, for this immigrant, a survivor of WWII torn Italy whose mother survived Dachau, “impossible” was just another avenue to succeed in his adopted country whose roads were paved with opportunity.Figure. 2007: Laureates of the Nobel Prize in Physiology and Medicine.Capecchi and Smithies, working at the University of Wisconsin, Madison, showed that targeting specific genes in mammalian cells via recombination could be successful. But the early work was limited to cells in culture. Now Martin Evans, initially at the University of Cambridge, U.K., adds to the mix. Evans led a group who, in 1981, reported growing embryonic stem (ES) cells from mouse embryos. Evans and his team eventually demonstrated that they could produce live mice by injecting cultured ES cells into a developing embryo. The result is called a chimera, an animal whose tissues are a combination of the ES cells and host embryo cells. When these chimeras mate, some of their resulting animals carry the stem cells' genes with the specific knockouts throughout their bodies. The genius of Capecchi and Smithies was that they realized ES cells offered an opportunity to generate research animals with a specific and desired mutation in every cell. Researchers could target genes in ES cells, select the cells that carried the mutation, and then use them to create chimeras. Through techniques of breeding, scientists produced mice that lacked the two working copies of a specific gene. Interestingly, there was no formal collaboration between the three scientists. Evans “brought the ES cells to my lab in his own pocket,” Smithies says. Capecchi, as a visiting scientist, spent time in Evans's lab learning the chimera technique. What have these scientists wrought? The current state of the art and industry is as follows: Several large-scale projects plan to knockout every gene in the mouse genome and make the resulting mice commercially available to the scientific masses. This is no less than a 100 million dollar effort and is comparable in magnitude to the Human Genome Project. Europe and Canada have agreed to a huge effort to produce more than 30,000 knockouts. The NIH will soon announce the Knockout Mouse Project (KOMP), which will create an additional 10,000 genes to the knockout list. China is gearing up to make 100,000 mutants, with the goal of creating 20,000 lines of mice, each with a different gene knocked out. The end game is obvious. We will hopefully soon learn more about what each gene does and what our potential therapeutic options are when genetic mutation produces human disease. Richard G. Ellenbogen, M.D. Science Times Principal

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.011
Scholarly communication0.0100.011
Open science0.0020.007
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0320.017

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.

Opus teacher head0.016
GPT teacher head0.261
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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