Isolation of Biopsy-Derived, Human Cervical Keratinocytes Propagated as Monolayer and Organoid Cultures
Bibliographic record
Abstract
The successful isolation and propagation of patient-derived keratinocytes from cervical lesions constitute a more appropriate model of cervical disease than traditional cervical cancer-derived cell lines such as SiHa and CaSki. Our aim was to streamline the growth of patient-obtained, cervical keratinocytes into a reproducible process. We performed an observational case series study with 60 women referred to colposcopy for a diagnostic biopsy. Main outcome measures were how many samples could be passaged at least once (n = 11), and where enough cells could be established, to precisely define their proliferation profile over time (n = 3). Altering cell culture conditions over those reported by other groups markedly improved outcomes. We were also successful in making freeze backs which could be resuscitated to successfully propagate multi-layered, organoids from cervical keratinocytes (n = 3). For best results, biopsy-intrinsic factors such as size and tissue digestion appear to be major variables. This seems to be the first systematic report with a well characterized and defined sample size, detailed protocol, and carefully assessed cell yield and performance. This research is particularly impactful for constituting a sample repository-on-demand for appropriate disease modelling and drug screening under the umbrella of personalized health.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".