Bovine lactoferrin and lactoferrin peptides affect endometrial and cervical cancer cell lines
Bibliographic record
Abstract
Cervical, uterine, and ovarian cancers are the most common malignancies of the female genital tract worldwide. Despite advances in prevention, early diagnosis, effective screening, and treatment programs, mortality remains high. Consequently, it is important to search for new treatments. The activity of bovine lactoferrin (bLF) and LF peptides against several types of cancer has been studied; however, only a few studies report the effect of bLF and LF peptides against cervical and endometrial cancers. In this study, we explored the effect of bLF as well as LF chimera and its constituent peptides LFcin17-30 and LFampin265-284 on the viability of cervical (HeLa, SiHa) and endometrial (KLE, HEC-1A) cancer cell lines. Cell proliferation was quantified with an MTT assay, cell morphological changes and damage were determined by Giemsa and phalloidin-TRITC and DAPI staining, and apoptotic and necrotic cells were identified by Alexa Fluor® 488 Annexin V and propidium iodide staining. Additionally, the effect of combinations of bLF and LF peptides with cisplatin was assessed. bLF and LF peptides inhibited the proliferation of uterine cancer cells and caused cellular morphological changes and damage to cell monolayers. bLF induced apoptosis, LFcin17-30 and LFampin265-284 induced apoptosis and necrosis, and LF chimera induced necrosis. Additionally, bLF and LF chimera showed an additive interaction with cisplatin against uterine cancer cells.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".