A pH responsive nanocomposite for combination sonodynamic‐immunotherapy with ferroptosis and calcium ion overload via SLC7A11/ACSL4/LPCAT3 pathway
Bibliographic record
Abstract
Abstract Sonodynamic therapy offers a non‐invasive approach to induce the death of tumor cells. By harnessing ultrasound waves in tandem with sonosensitizers, this method produces reactive oxygen species (ROS) that inflict oxidative damage upon tumor cells, subsequently causing their demise. Ferroptosis is a regulatory form of cell death that differs from other forms, characterized by iron accumulation, ROS accumulation, and lipid peroxidation. In the presented research, a nanoparticle formulation, parthenolide/ICG‐CaCO3@lipid (PTL/ICG‐CaCO3@Lip), has been engineered to amplify ferroptosis in tumor cells, positioning it as a potent agent for sonodynamic cancer immunotherapy. This nanoparticle significantly augments ROS levels within tumor cells, inducing oxidative stress that leads to cell death. The therapeutic potential of PTL/ICG‐CaCO3@Lip, both in vivo and in vitro, has been convincingly demonstrated. Furthermore, RNA‐seq analysis insights revealed that PTL/ICG‐CaCO3@Lip facilitates tumor cell ferroptosis by regulating P53 to downregulate SLC7A11 protein expression, thereby inhibiting the glutamate‐cystine antiporter system Xc− and stimulating ACSL4/LPCAT3 pathways. This pioneering work uncovers an innovative strategy for combatting tumors, leveraging enhanced oxidative stress to promote cell ferroptosis, and paves the way for groundbreaking cancer immunotherapeutic interventions.
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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".