TEMPO-Oxidized Cellulose Nanofiber-Alginate Hydrogel as a Bioink for Human Meniscus Tissue Engineering
Bibliographic record
Abstract
Objective: The avascular inner regions of the knee menisci cannot self-heal. As a prospective treatment, functional replacements can be generated by cell-based 3D bioprinting with an appropriate cell source and biomaterial. To that end, human meniscus fibrochondrocytes (hMFC) from surgical castoffs of partial meniscectomies as well as cellulose nanofiber-alginate based hydrogels have emerged as a promising cell source and biomaterial combination. The objectives of the study were to first find the optimal formulations of TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl)-oxidized cellulose nanofiber/alginate (TCNF/ALG) precursors for bioprinting, and then to use them to investigate redifferentiation and synthesis of functional inner meniscus-like extracellular matrix (ECM) components by expanded hMFCs. Methods: The rheological properties including shear viscosity, thixotropic behavior recovery, and loss tangent of selected TCNF/ALG precursors were measured to find the optimum formulations for 3D bioprinting. hMFCs were mixed with TCNF/ALG precursors with suitable formulations and 3D bioprinted into cylindrical disc constructs and crosslinked with CaCl 2 after printing. The bioprinted constructs then underwent 6 weeks of in vitro chondrogenesis in hypoxia prior to analysis with biomechanical, biochemical, molecular, and histological assays. hMFCs mixed with a collagen I gel were used as a control. Results: The TCNF/ALG and collagen-based constructs had similar compression moduli. The expression of COL2A1 was significantly higher in TCNF/ALG. The TCNF/ALG constructs showed more of an inner meniscus-like phenotype while the collagen I-based construct was consistent with a more outer meniscus-like phenotype. The expression of COL10A1 and MMP13 were lower in the TCNF/ALG constructs. In addition, the immunofluorescence of human type I and II collagens were evident in the TCNF/ALG, while the bovine type I collagen constructs lacked type II collagen deposition but did contain newly synthesized human type I collagen.
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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".