On-Chip DO sensor based on phosphorescence lifetime spectroscopy for investigation of activation parameters in photodynamic therapy of cancers
Bibliographic record
Abstract
A 3D-printed optofluidic chip with an embedded dissolved oxygen sensor, empowered with a time-resolved phosphorescence lifetime spectroscopy platform, is developed for indirect analysis of reactive oxygen species (ROS) dynamics in photodynamic therapy (PDT). This platform is implemented for continuous evaluation of ROS production/elimination through real-time measurement of photoluminescence (PL) lifetime ( τ ) during PDT treatment, revealing the key contributions of the photosensitizer (PS), excitation laser, and the medium in ROS generation during this process. Rose Bengal (RB) is utilized as a PS to demonstrate this system's capability to analyze and tune the PS activation parameters such as PS concentration, laser exposure time, and power. In addition, the platform provides important information on the medium activation duration, the maximum changes in the PL lifetime ( ∆ τ max ), and the time to reach ∆ τ max . For the Dulbecco's modified eagle medium high glucose (DMEM HG) containing fetal bovine serum (FBS), RB, and A375 human melanoma cell line as a representative example, these parameters are 1070 s, 4.4 μs, and 780 s respectively. Two ROS scavengers of sodium pyruvate (SP) and terephthalic acid (TA) are used to demonstrate that more than 90 % of the change in the τ corresponds to OH• and H 2 O 2 radicals, confirming the correlation between ROS generation/elimination and τ variations. Moreover, this system is compared with conventional absorption and photoluminescent methods based on 1,3-diphenylisobenzofuran (DPBF) indicator. Unlike DPBF and similar indicators, this on-chip system besides providing real-time data on the dynamics of activation and deactivation of the PSs, enables distinguishing the contribution of various parameters, and is not consumed during the measurement and can be reused multiple times. Therefore, the developed platform is potentially beneficial for on-chip drug analysis and development in PDT therapy, as well as other biomedical applications. • Development of on-chip DO sensor based on PL lifetime spectroscopy for PDT analysis. • Continuous evaluation of ROS dynamics during PDT for the A375 cell line. • Revealing the key contributions of PS, excitation laser, and medium in the PDT process.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".