Theranostic porphyrin nanoparticles identify atherosclerosis via multimodal imaging and elicit atheroprotective effects
Bibliographic record
Abstract
Background Porphyrin-lipid nanoparticles (Por-NPs) have unrealized potential for atherosclerosis. Por-NPs incorporate porphyrin-lipid which permits fluorescence imaging and chelates Copper-64 ( 64 Cu) for positron emission tomography (PET) imaging. Their outer shell contains a short peptide ‘R4F’ that enables macrophage targeting and therapeutic effects. Accordingly, this study investigates the simultaneous diagnostic and therapeutic properties of Por-NPs in atherosclerosis. Results In vitro , Por-NPs were found to be internalised by immortalised bone marrow-derived macrophages (iBMDMs), visualised via fluorescence microscopy and flow cytometry. Por-NPs also increased cholesterol efflux from [ 3 H]-cholesterol-loaded iBMDMs, (49%, P <0.05). Incubation of iBMDMs with Por-NPs reduced mRNA levels of inflammatory mediators Il1b (88%), Il18 (54%) Ccl5 (75%) and Ccl17 (92%), and protein secretion of IL-1β (69%), CCL5 (82%) and CCL17 (94%), P <0.05. Por-NPs suppressed inflammasome components Nlrp3 (69%) and Asc (36%), P <0.05. Studies using siRNA deletion of SR-B1 and methyl-β-cyclodextrin, revealed the anti-inflammatory properties of Por-NPs were independent of SR-B1 and cholesterol efflux. However, Por-NPs suppressed activation of inflammatory transcription factor NF-κB (53%, P <0.05). In vivo , in Apoe -/- mice, serial non-invasive PET imaging showed 64 Cu-labelled Por-NPs localised in hearts and detected increases in plaque size longitudinally with high-cholesterol diet. Por-NP fluorescence was visualised in aortic sinus plaques, co-localised with CD68 + macrophages, and by fluorescence IVIS imaging in aortic arch plaque. In two murine models, Por-NP-treated mice had smaller early-stage (22%) and unstable plaques (52%). Por-NP-treated mice had fewer circulating (32%) and aortic (81%) monocytes, and lower mRNA levels of aortic arch Rela (26%) and Nfkb1 (27%), P <0.05. Conclusions Por-NPs detect plaques using multiple imaging modalities and exhibit atheroprotective effects, presenting as novel nanoscale theranostics for atherosclerosis.
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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 | 0.000 |
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 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".