Molecular Vehicles for Mitochondrial Chemical Biology and Drug Delivery
Bibliographic record
Abstract
The mitochondria within human cells play a major role in a variety of critical processes involved in cell survival and death. An understanding of mitochondrial involvement in various human diseases has generated an appreciable amount of interest in exploring this organelle as a potential drug target. As a result, a number of strategies to probe and combat mitochondria-associated diseases have emerged. Access to mitochondria-specific delivery vectors has allowed the study of biological processes within this intracellular compartment with a heightened level of specificity. In this review, we summarize the features of existing delivery vectors developed for targeting probes and therapeutics to this highly impermeable organelle. We also discuss the major applications of mitochondrial targeting of bioactive molecules, which include the detection and treatment of oxidative damage, combating bacterial infections, and the development of new therapeutic approaches for cancer. Future directions include the assessment of the therapeutic benefit achieved by mitochondrial targeting for treatment of disease in vivo. In addition, the availability of mitochondria-specific chemical probes will allow the elucidation of the details of biological processes that occur within this cellular compartment.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".