Nanotechnology – a robust tool for fighting the challenges of drug resistance in non-small cell lung cancer
Bibliographic record
Abstract
Genomic and proteomic mutation analysis is the standard of care for selecting candidates for therapies with tyrosine kinase inhibitors against the human epidermal growth factor receptor (EGFR TKI therapies) and further monitoring cancer treatment efficacy and cancer development. Acquired resistance due to various genetic aberrations is an unavoidable problem during EGFR TKI therapy, leading to the rapid exhaustion of standard molecularly targeted therapeutic options against mutant variants. Attacking multiple molecular targets within one or several signaling pathways by co-delivery of multiple agents is a viable strategy for overcoming and preventing resistance to EGFR TKIs. However, because of the difference in pharmacokinetics among agents, combined therapies may not effectively reach their targets. The obstacles regarding the simultaneous co-delivery of therapeutic agents at the site of action can be overcome using nanomedicine as a platform and nanotools as delivery agents. Precision oncology research to identify targetable biomarkers and optimize tumor homing agents, hand in hand with designing multifunctional and multistage nanocarriers that respond to the inherent heterogeneity of the tumors, may resolve the challenges of inadequate tumor localization, improve intracellular internalization, and bring advantages over conventional nanocarriers.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".