Lipoprotein Nanoplatform for Targeted Delivery of Diagnostic and Therapeutic Agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it