Induced Pluripotent Stem Cells—Bringing Humanity One Step Closer to Curing Cancer
Bibliographic record
Abstract
Cancer develops when healthy cells experience a mutation, allowing for rapid and abnormal growth. Mutagens, such as radiation and carcinogens, allow fast-growth variant cells to be positively selected and thus propagate the development of cancer. Radiation and chemotherapy are prevailing, but non-ideal forms of cancer treatment as they can harm healthy cells in the body. Stem cells can be used to replace the healthy cells that were lost, but there are ethical concerns regarding the acquisition of embryonic stem cells (ESCs), or technicalities in obtainment and usage of adult stem cells (ASCs). Thus, the discovery of induced pluripotent stem cells (iPSCs) allows for the use of ASCs that are given the pluripotent characteristics of ESCs. In 2018, Kooreman and his colleagues from Stanford University coaxed iPSCs to display the epitopes of breast cancer. After exposing mice with breast cancer to iPSCs, 70% of the mice had a decreased tumour size compared to control mice. Thus, iPSCs may work as a vaccine for cancer and potentially treat and cure the disease. Further research is required to study the feasibility of the use of iPSCs for human breast cancer.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".