RETRACTED: Wheat proteins improve cryopreservation of rat hepatocytes
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Hepatocytes are an important physiological model for in vitro studies of drug metabolism and toxicity. However, fresh hepatocytes are not always available and hence cyopreservation is needed to preserve large quantities until they are needed for these applications. Hepatocytes are extremely sensitive to damage induced by the freeze-thaw process, even after addition of traditional cryoprotectants such as dimethyl sulfoxide (DMSO). Furthermore, they do not proliferate in culture. We previously demonstrated that a crude wheat extract protects rat hepatocytes during cryopreservation and could provide a promising alternative to DMSO. We have considerably improved this novel cryopreservation procedure by using wheat extracts that are partially purified by either ammonium sulphate or acetone precipitation, or by using recombinant wheat freezing tolerance-associated proteins such as WCS120, TaTIL, WCS19, and TaIRI-2. These improved procedures enhance long-term storage (2-12 months) and recovery of large quantities of healthy cells after cryopreservation, and maintain the differentiated functions of rat hepatocytes, compared to freshly isolated cells, as judged by viability (77-93%), adherence (77%) and metabolic functions of major cytochrome P450 isoforms CYP1A1/2, CYP2C6, CYP2D2, and CYP3A1/2. The advantage of using wheat proteins as cryopreservants is that they are non-toxic, natural products that do not require animal serum, and are economical and easy to prepare.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".