Lipoprotein Nanoplatform for Targeted Delivery of Diagnostic and Therapeutic Agents
Bibliographic record
Abstract
Low-density lipoprotein (LDL) provides a highly versatile natural nanoplatform for delivery of visible or near-infrared fluorescent optical and magnetic resonance imaging (MRI) contrast agents and photodynamic therapy and chemotherapeutic agents to normal and neoplastic cells that overexpress low-density lipoprotein receptors (LDLRs). Extension to other lipoproteins ranging in diameter from about 10 nm (high-density lipoprotein [HDL]) to over a micron (chylomicrons) is feasible. Loading of contrast or therapeutic agents onto or into these particles has been achieved by protein loading (covalent attachment to protein side chains), surface loading (intercalation into the phospholipid monolayer), and core loading (extraction and reconstitution of the triglyceride/cholesterol ester core). Core and surface loading of LDL have been used for delivery of optical imaging agents to tumor cells in vivo and in culture. Surface loading was used for delivery of gadolinium-bis-stearylamide contrast agents for in vivo MRI detection in tumor-bearing mice. Chlorin and phthalocyanine near-infrared photodynamic therapy agents (≤ 400/LDL) have been attached by core loading. Protein loading was used to reroute the LDL from its natural receptor (LDLR) to folate receptors and could be used to target other receptors. A semisynthetic nanoparticle has been constructed by coating magnetite iron oxide nanoparticles with carboxylated cholesterol and overlaying a monolayer of phospholipid to which apolipoprotein A1 or E was adsorbed for targeting HDL or adsorbing synthetic amphipathic helical peptides ltargeting LDL or folate receptors. These particles can be used for in situ loading of magnetite into cells for MRI-monitored cell tracking or gene expression.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".