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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".