A Novel Method to Characterize in vitro Interactions between Photosensitizers and Antimicrobials using a Modified Checkerboard Assay
Bibliographic record
Abstract
Antimicrobial photodynamic therapy (aPDT) combines a photosensitizer with light and molecular oxygen to generate reactive oxygen species that damage microbial cells. Using complimentary mechanisms of action, the sublethal cellular damages caused by aPDT have shown priming effects that enhance microbial sensitivity to standard antimicrobial chemotherapy, offering a potent and resistance-free approach. To determine the types of interaction between aPDT and antimicrobial compounds—whether synergistic, additive, indifferent, or antagonistic—we developed a modified checkerboard assay based on ASM and CLSI guidelines. First, a two-dimensional concentration gradient of the PS and the antimicrobial agent is prepared and irradiated in 96-well plates. Following an incubation period, optical density data obtained by a plate reader is used in a custom-made calculator that automatically determines the minimum inhibitory concentrations and fractional inhibitory concentration indexes. This approach provides basis for a standard method that objectively characterizes the type of interaction between aPDT and antimicrobial compounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".