RETRACTED ARTICLE: Molecular and therapeutic effect of CRISPR in treating cancer
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Cancer has become one of the common causes of mortality around the globe due to mutations in the genome which allows rapid growth of cells uncontrollably without repairing DNA errors. Cancers could arise due alterations in DNA repair mechanisms (errors in mismatch repair genes), activation of oncogenes and inactivation of tumor suppressor genes. Each cancer type is different and each individual has a unique genetic change which leads them to cancer. Studying genetic and epigenetic alterations in the genome leads to understanding the underlying features. CAR T therapy over other immunotherapies such as monoclonal antibodies, immune checkpoint inhibitors, cancer vaccines and adoptive cell therapies has been widely used to treat cancer in recent days and gene editing has now become one of the promising treatments for many genetic diseases. This tool allows scientists to change the genome by adding, removing or altering genetic material of an organism. Due to advance in genetics and novel molecular techniques such as CRISPR, TALEN these genes can be edited in such a way that their original function could be replaced which in turn improved the treatment possibilities and can be used against malignancies and even cure cancer in future along with CAR T cell therapy due to the specific recognition and attacking of tumor.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".