Nanotechnology – a robust tool for fighting the challenges of drug resistance in non-small cell lung cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it