A novel image analysis algorithm reveals that media conditioned with chitosan and platelet-rich plasma biomaterial dose dependently increases fibroblast migration in a scratch assay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Chitosan (CS) and Platelet-Rich Plasma (PRP) both display interesting properties for wound healing applications. A hybrid CS-PRP biomaterial was previously developped, consisting of a freeze dried CS formulation solubilized in PRP that promotes tissue repair and regeneration. The purpose of the current study was to investigate the ability of the CS-PRP biomaterial to stimulate cell migration in vitro . Scratch assays revealed that CS-PRP significantly stimulates the migration rate of cells compared to cells in culture medium but not differently than PRP alone. The increase in the migration rate is dose-dependent at low dose and reaches a plateau corresponding with maximum cell motility. Cell migration rate as a function of the number of platelets that have degranulated in culture medium (to which total concentration of growth factors contributing to cell response is proportionnal), follows a modified Hill model. To analyze photographs taken during the assay and follow cell migration, an open source image analysis algorithm was developed: SAMScratch (Systematic Area Measurement of Scratch - available here: https://github.com/Biomaterials-and-Cartilage-Laboratory/SAM-Scratch) . Compared with other existing analysis tools, the algorithm is precise in the determination of the scratch area and performs equally well with usual and challenging images. This study resulted in the creation of a freely available application for scratch assay analysis and provided evidence that CS-PRP implants hold promise for treatment of wounds.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it