Multicarrier Nanoplatforms for Precise Targeting of Cellular Energy Centers in Ischemic Heart Disease Therapy
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mitochondria, as the core organelle of cellular energy metabolism and fate regulation, not only maintain the balance between myocardial reactive oxygen species ROS and energy metabolism but also regulate key pathological processes such as apoptosis, calcium homeostasis and mPTP opening, which is an important target for IHD treatment. With the aging of the population and the increase of risk factors (such as hypertension, diabetes, obesity), the incidence and prevalence of IHD are still increasing. Although interventional therapy and surgery have achieved certain results, they still face challenges such as reperfusion injury and poor long-term prognosis. In recent years, nanomedicine has shown significant potential for restoring energy supply and breaking the vicious cycle of oxidative stress by virtue of its precise targeting and regulation ability, especially in mitochondrial targeted intervention. Through strategies such as specific ligand recognition, pathological microenvironment responsive drug release, and biomimetic modification, functionalized nanosystems can accurately deliver therapeutic drugs to diseased mitochondria, thereby effectively interfering with the progress of IHD. However, the existing research has not systematically reviewed and discussed the therapeutic strategies and molecular mechanisms of mitochondrial targeted drugs, which is difficult to meet the urgent needs of scientific research and clinical practice. This paper focuses on the core role of mitochondria in IHD, analyzes the design strategy and mechanism of mitochondrial targeted nanomedicine, summarizes the latest research progress of nanocarriers in this field, and looks forward to the application of artificial intelligence in the development and optimization of nanomedicine, to provide a comprehensive perspective and theoretical basis for the precision and clinical transformation of IHD treatment.
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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 | 0.001 | 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".