Bibliographic record
Abstract
Ferroptosis is a novel non-apoptotic form of programmed cell death driven by iron-dependent lipid peroxidation, distinct from apoptosis, necrosis, and autophagy. The core of ferroptosis is the accumulation of lipid peroxides (LPO), resulting from the uncontrolled oxidation of polyunsaturated fatty acids catalyzed by intracellular reactive oxygen species (ROS). However, redox homeostasis and iron metabolism homeostasis in tumor cells can regulate ROS and iron ion levels to avoid oxidative stress-induced cell damage, limiting the therapeutic effect and clinical applications of ferroptosis. Fe- and Cu-based metal-organic frameworks (MOFs) not only serve as nanocarriers for various cargoes, including drugs, photosensitizers, inhibitors, inducers, and sensitizers, but also function as iron/copper ion carriers and ferroptosis inducers. In addition to enhancing ferroptosis with Fe-MOFs and Cu-MOFs, the combination therapy of ferroptosis, chemotherapy, photodynamic therapy, and immunotherapy for tumors is achieved. This highlight article reviews the major achievements made in the field of ferroptosis based on Fe-MOFs and Cu-MOFs for tumor therapy over the past 5 years, especially the last 3 years. The future challenges of physiological stability and active targeting of MOF-based delivery systems, as well as the large-scale preparation of Fe-MOFs, and promising prospects of ferroptosis based on Fe-MOFs from clinical translation into practical applications are also outlined.
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.
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) | 0.002 | 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".