Strategic targeting of non‐small‐cell lung cancer utilizing genetic material‐based delivery platforms of nanotechnology
Bibliographic record
Abstract
Several limitations of conventional cancer treatment such as non-specific targeting, solubility problems, and ineffective entry of chemotherapeutics into cancer cells can be overcome by using nanotechnology targeted drug delivery systems. Some combinations of biomolecules and nanoparticles have proven to be excellent therapeutics for Non-small cell lung cancer (NSCLC) in the last decades. Targeted gene delivery has shown in vivo as well as in vitro promising results with therapeutic efficacy. Gene therapy has shown enhanced transfection efficiency and better targeting potential on several NSCLC cell lines. Still, there are several challenges in nanoparticle-mediated gene therapy, which include stability of biomolecules and nanoparticles during delivery, managing their biodistribution, and reducing the possible cytotoxic effects of the nanoparticles, which need to be solved before clinical trials. Evaluation of therapeutic efficacy of biomolecules and nanoparticle combination in gene therapy must be established to expand the application of nano-gene therapy in cancer treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".