Two-photon luminescence lifetime imaging microscopy (LIM) to follow up cell metabolism and oxygen consumption during theranostic applications
Bibliographic record
Abstract
A common property during tumor development is altered energy metabolism, which could lead to a switch from oxidative phosphorylation and glycolysis. The impact of this switch for theranostic applications could be significant. Interestingly altered metabolism could be correlated with a change in the fluorescence lifetimes of both NAD(P)H and FAD. However, as observed in a variety of investigations, the situation is complex and the result is influenced by parameters like oxidative stress, pH or viscosity. Besides metabolism, oxygen levels and consumption has to be taken into account in order to understand treatment responses. For this, correlated imaging of phosphorescence and fluorescence lifetime parameters has been investigated by us and used to observe metabolic markers simultaneously with oxygen concentrations. The technique is based on time correlated single photon counting to detect the fluorescence lifetime of NAD(P)H and FAD by FLIM and the phosphorescence lifetime of newly developed phosphors and photosensitizers by PLIM. For this, the photosensitizer TLD1433 from Theralase, which is based on a ruthenium (II) coordination complex, was used. TLD1433 which acts as a redox indicator was mainly found in cytoplasmatic organelles. The most important observation was that TLD1433 can be used as a phosphor to follow up local oxygen concentration and consumption during photodynamic therapy. Oxygen consumption was accompanied by a change in cell metabolism, observed by simultaneous FLIM/PLIM. The combination of autofluorescence-FLIM and phosphor-PLIM in luminescence lifetime microscopy provides new insights in light induced reactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".