Boosting Energy Deprivation by Synchronous Interventions of Glycolysis and Oxidative Phosphorylation for Bioenergetic Therapy Synergetic with Chemodynamic/Photothermal Therapy
Bibliographic record
Abstract
Abstract Bioenergetic therapy is emerging as a promising therapeutic approach. However, its therapeutic effectiveness is restricted by metabolic plasticity, as tumor cells switch metabolic phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) to compensate for energy. Herein, Metformin (MET) and BAY‐876 (BAY) co‐loaded CuFe 2 O 4 (CF) nanoplatform (CFMB) is developed to boost energy deprivation by synchronous interventions of glycolysis and OXPHOS for bioenergetic therapy synergetic with chemodynamic/photothermal therapy (CDT/PTT). The MET can simultaneously restrain glycolysis and OXPHOS by inhibiting hexokinase 2 (HK2) activity and damaging mitochondrial function to deprive energy, respectively. Besides, BAY blocks glucose uptake by inhibiting glucose transporter 1 (GLUT1) expression, further potentiating the glycolysis repression and thus achieving much more depletion of tumorigenic energy sources. Interestingly, the upregulated antioxidant glutathione (GSH) in cancer cells triggers CFMB degradation to release Cu + /Fe 2+ catalyzing tumor‐overexpressed H 2 O 2 to hydroxyl radical (∙OH), both impairing OXPHOS and achieving GSH‐depletion amplified CDT. Furthermore, upon near‐infrared (NIR) light irradiation, CFMB has a photothermal conversion capacity to kill cancer cells for PTT and improve ∙OH production for enhanced CDT. In vivo experiments have manifested that CFMB remarkably suppressed tumor growth in mice without systemic toxicity. This study provides a new therapeutic modality paradigm to boost bioenergetic‐related therapies.
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) | 0.001 | 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".