Application of Ti<sub>3</sub>C<sub>2</sub> MXene Quantum Dots for Immunomodulation and Regenerative Medicine
Bibliographic record
Abstract
Abstract Inflammation is tightly linked to tissue injury. In regenerative medicine, immune activation plays a key role in rejection of transplanted stem cells and reduces the efficacy of stem cell therapies. Next‐generation smart biomaterials are reported to possess multiple biologic properties for tissue repair. Here, the first use of 0D titanium carbide (Ti3C2) MXene quantum dots (MQDs) for immunomodulation is presented with the goal of enhancing material‐based tissue repair after injury. MQDs possess intrinsic immunomodulatory properties and selectively reduce activation of human CD4+IFN‐γ+ T‐lymphocytes (control 87.1 ± 2.0%, MQDs 68.3 ± 5.4%) while promoting expansion of immunosuppressive CD4+CD25+FoxP3+ regulatory T‐cells (control 5.5 ± 0.7%, MQDs 8.5 ± 0.8%) in a stimulated lymphocyte population. Furthermore, MQDs are biocompatible with bone marrow‐derived mesenchymal stem cells and induced pluripotent stem cell‐derived fibroblasts. Finally, Ti3C2 MQDs are incorporated into a chitosan‐based hydrogel to create a 3D platform with enhanced physicochemical properties for stem cell delivery and tissue repair. This composite hydrogel demonstrates increased conductivity while maintaining injectability and thermosensitivity. These findings suggest that this new class of biomaterials may help bridge the translational gap in material and stem cell‐based therapies for tissue repair and treatment of inflammatory and degenerative diseases.
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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".