Investigation of Porphyrin and Lipid Supramolecular Assemblies for Cancer Imaging and Therapy
Bibliographic record
Abstract
Aerobic life on earth is made possible through the functions of the porphyrin. These colorful and ubiquitous chromophores are efficient at concentrating and converting sunlight into chemical energetic potential which sustain biological life. Humans have had a longstanding fascination with these molecules, especially for their applications in photodynamic therapy. The photophysical properties of porphyrins are highly influenced by their surrounding environment. Intermolecular interactions between these pigments can lead to excited state quenching, energy transfer and large changes to their absorption and fluorescence spectra. This thesis is focused on utilizing molecular self-assembly strategies to develop nanoscale porphyrin and phospholipid structures. The rationale being that intermolecular interactions between porphyrins in these nanostructures can induce changes which can be exploited in novel biomedical imaging and therapeutic applications. Four lipid-based structural platforms are studied including: nanoemulsions, bilayer discs and nanovesicles. In Chapter 1, I provide a background on the photophysics of porphyrins and the effect of intermolecular porphyrin interactions on photophysical properties. I also discuss phospholipids and their self-assembly process. Lastly I review current biomedical photonics techniques and discuss how these strategies can be used in conjugation with porphyrin and lipid supramolecular assemblies. In Chapter 2, I investigate the influence that loading a novel bacteriochlorin photosensitizer into a protein-stabilized lipid emulsion has on its spectral properties. I discovered that while the dye can be incorporated into the lipid emulsion, no changes were observed in its spectral properties. In Chapter 3, an amphipathic alpha-helical protein is used to stabilize and organize porphyrin-lipid molecules into bilayer discs. Close packing between porphyrin molecules causes quenching, which can be reversed by structural degradation of the nanoparticle. This enables application as an activatable photodynamic therapy agent. In Chapter 4, energy transfer between porphyrin-lipid dyes in porphysome nanovesicles is explored as a way to track the structural fate of the nanovesicle
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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".