Low‐power laser stimulation of tissue engineered cartilage tissue formed on a porous calcium polyphosphate scaffold
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Forming cartilage tissue in vitro that resembles native tissue is one of the challenges of cartilage tissue engineering. The aim of this study was to determine whether low-power laser stimulation would improve the formation of cartilage tissue in vitro. STUDY DESIGN/MATERIALS AND METHODS: Bovine articular chondrocytes were seeded on the top surface of porous calcium polyphosphate substrates. After 2 days, laser stimulation was applied daily at a wavelength of 650 nm using a laser diode with energy densities of either 1.75 or 3 J/cm(2) for 4 weeks. Proteoglycan and collagen synthesis and matrix content were determined. Cartilage tissue morphology was evaluated histologically. RESULTS: Histologically, there was no difference in the appearance or cellularity of the tissues that formed in the presence or absence of laser stimulation at either dosage. There were no differences in DNA content between treated and untreated constructs and live-dead assay confirmed that this treatment was not toxic to the cells. Laser stimulation at 3 J/cm(2) enhanced matrix synthesis resulting in significantly more tissue formation than laser stimulation at 1.75 J/cm(2) or untreated cultures. CONCLUSION: Short exposures to low-power laser stimulation using a laser diode with 3 J/cm(2) dose improves cartilage tissue formation.
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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".