Targeted drug delivery strategies to treat lung metastasis
Bibliographic record
Abstract
BACKGROUND: Most cancer patients die of metastatic disease, and in a high proportion of cases, from lung metastasis. Methods to target therapy to metastatic disease in general and specifically to lung metastasis are required. OBJECTIVE: To describe the current and potential tools for the treatment of lung metastasis. METHODS: Literature search tools were used with no predefined limitations to encompass the main tumor targeting methods. Methods in standard clinical use, in clinical trials and in preclinical development are reviewed. Data about treatment of lung metastasis and solid tumors are emphasized. RESULTS: Physically targeting therapies to lung metastasis is feasible by aerosol-carried agents, magnetic targeting and intravascular devices. Biological targeting includes methods such as polymers and liposomes, which are based on the principle of enhanced permeability and retention of large molecules in tumor vascular field. Ligand-targeted treatments depend on cancer-specific antibodies or receptors. Few of these methods are in clinical trials or in standard clinical use. However, promising techniques are in advanced preclinical or early clinical studies. The authors believe that targeted treatments will be one the major anticancer tools in the near future.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".