Functional insights into the cellular response triggered by a bile-acid platinum compound conjugated to biocompatible ferric nanoparticles using quantitative proteomic approaches
Bibliographic record
Abstract
At present, bioferrofluids are employed as powerful multifunctional tools for biomedical applications such as drug delivery, among others. The present study explores the cellular response evoked when bile-acid platinum derivatives are conjugated with bioferrofluids by testing the biological activity in osteosarcoma (MG-63) and T-cell leukemia (Jurkat) cells. The aim of this work is to evaluate the biocompatibility of a bile-acid platinum derivative conjugated with multi-functional polymer coated bioferrofluids by observing the effects on the protein expression profiles and in intracellular pathways of nanoparticle-stimulated cells. To this end, a mass spectrometry-based approach termed SILAC has been applied to determine in a high-throughput manner the key proteins involved in the cellular response process (including specific quantitatively identified proteins related to the vesicular transport, cellular structure, cell cycle, biosynthetic process, apoptosis and regulation of the cell cycle). Finally, biocompatibility was evaluated and validated by conventional strategies also (such as flow cytometry, MTT, etc.).
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 | 0.000 | 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 | 0.000 |
| 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".