Efficient gene transfection to lung cancer cells via Folate-PEI-Sorbitol gene transporter
Bibliographic record
Abstract
Lung cancer is known to be one of the fatal diseases in the world and is experiencing treatment difficulties. Many treatments have been discovered and implemented, but death rate of patients with lung cancer continues to remain high. Current treatments for cancer such as chemotherapy, immunotherapy, and radiotherapy have shown considerable results, yet they are accompanied by side effects. One effective method for reducing the cytotoxicity of these treatments is via the use of a nanoparticle-mediated siRNA delivery strategy with selective silencing effects and non-viral vectors. In this study, a folate (FA) moiety ligand-conjugated poly(sorbitol-co-PEI)-based gene transporter was designed by combining low-molecular weight polyethyleneimine (LMW PEI) and D-sorbitol with FA to form FPS. Since folate receptors are commonly overexpressed in various cancer cells, folate-conjugated nanoparticles may be more effectively delivered to selective cancer cells. Additionally, siOPA1 was used to induce apoptosis through mitochondrial fusion. The OPA1 protein stability level is important for maintaining normal mitochondrial cristae structure and function, conserving the inner membrane structure, and protecting cells from apoptosis. Consequently, when FPS/siOPA1 was used for lung cancer in-vitro and in-vivo, it improved cell viability and cellular uptake.
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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 | 0.000 |
| 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".