Stem Cells - Hope or Hype for Reproductive and Regenerative Medicine?
Bibliographic record
Abstract
1. Generally, conditioned media from cells generated to overexpress an antibody are run through a bead column – similar to an immunoprecipitation – and then the bound antibody is eluted off the column. However, how a company generates this antibody can vary (ie, using insect or mammalian cells) as well as the methods used to purify the antibody. This can make a big difference in what additional proteins may be present in the antibody as it is shipped. 2. These experiments in Figure 1 were performed with antibodies, not commercially available, that were generated by 4 different companies. All were raised to detect Receptor X and selected for in vivo use because they inhibit the binding of Receptor X’s ligand. Their use in vivo has been to test whether inhibiting Receptor X can inhibit tumor growth. In general, these antibodies are not different from those commercially available and used in Westerns – yet it is possible that that these antibodies may not be as pure as those you buy commercially.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".