Different PEG‐PLGA Matrices Influence In Vivo Optical/Photoacoustic Imaging Performance and Biodistribution of NIR‐Emitting <i>π</i>‐Conjugated Polymer Contrast Agents
Bibliographic record
Abstract
Abstract The π‐conjugated polymer poly[2,6‐(4,4‐bis‐(2‐ethylhexyl)‐4H‐cyclopenta[2,1‐b;3,4‐b0]‐dithiophene)‐alt‐4,7‐(2,1,3‐benzothiadiazole)] (PCPDTBT) with deep‐red/near‐infrared (NIR) absorption and emission has been investigated as a contrast agent for in vivo optical and photoacoustic imaging. PCPDTBT is encapsulated within poly(ethylene glycol) methyl ether‐block‐poly(lactide‐co‐glycolide) (PEG2kDa–PLGA4kDa or PEG5kDa–PLGA55kDa) micelles or enveloped by the phospholipid, 1,2‐dipalmitoyl‐sn‐glycero‐3‐phosphoethanolamine‐N‐[methoxy(polyethylene glycol)‐2000] (PEG2kDa‐DPPE), to investigate the formulation effect on imaging performance, biodistribution, and biocompatibility. Nanoparticles that meet the quality requirements for parenteral administration are generated with similar physicochemical properties. Optical phantom imaging reveals that both PEG‐PLGA systems exhibit a 30% higher signal‐to‐background ratio (SBR) than PEG2kDa‐DPPE. This trend cannot be observed in a murine HeLa xenograft model following intravenous administration since dramatic differences in biodistribution are observed. PEG2kDa–PLGA4kDa systems accumulate more rapidly in the liver compared to other formulations and PEG2kDa‐DPPE demonstrates a higher tumor localization. Protein content in the “hard” corona differs between formulations (PEG2kDa‐DPPE < PEG2kDa–PLGA4kDa < PEG5kDa–PLGA55kDa), although this observation alone does not explain biodistribution patterns. PEG2kDa‐PLGA4kDa systems show the highest photoacoustic amplitude in a phantom, but also a lower signal in the tumor due to differences in biodistribution. This study demonstrates that formulations for conjugated polymer contrast agents can have significant impact on both imaging performance and biodistribution.
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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".