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
Non-small cell lung cancer (NSCLC) is the largest contributor to cancer mortality in the United States. Traditional chemotherapies are toxic and prone to the development of drug-resistance. Recently, several drug candidates were shown to induce lysosomal membrane permeabilization (LMP) in aggressive cancers. This has led to increased interest in lysosome dysregulation as a therapeutic target. However, approaches are needed to overcome two limitations of current lysosomal inhibitors: low specificity and potency. Here, we report the development of a transformable nanomaterial which is triggered to induce LMP of lysosomes in NSCLC. The nanomaterial consists of peptide amphiphiles, which self-assemble into nanoparticles, colocalize with the lysosome, and change conformation to nanofibrils due to lysosomal pH shift, which leads to the disruption of the lysosome, cell death, and cisplatin sensitization. We have found that this cell-penetrating transformable peptide nanoparticle (CPTNP) was cytotoxic to NSCLC cells in the low-micromolar range and it synergized cisplatin cytotoxicity four-fold. Moreover, we demonstrate CPTNP's promising antitumor effect in mouse xenograft models with limited toxicity when given in combination with low dose cisplatin chemotherapy. This is the first example of enhanced LMP via transformable peptide nanomaterial and offers a promising new strategy for cancer therapy.
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 | 0.000 | 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 | 0.001 | 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 it