Abstract 11474: Ti <sub>3</sub> C <sub>2</sub> T <sub>x</sub> MXene Nanosheets for Immunomodulation and Prevention of Allograft Vasculopathy
Bibliographic record
Abstract
Allograft vasculopathy is an aggressive form of accelerated atherosclerosis that manifests uniquely in transplanted hearts, lungs and kidneys. Activated blood vessel endothelial cells (ECs) stimulate alloreactive CD4 + and CD8 + T-lymphocytes to result in sustained inflammation. MXenes, an emerging class of transition metal carbides, have recently been shown to have unique immunomodulatory properties that may be leveraged to treat allograft vasculopathy. In this study, we present the synthesis, characterization and application of novel two-dimensional titanium carbide MXene (Ti 3 C 2 T x ) nanosheets for immunomodulation. MXene nanosheets (MNSs) were selectively etched from bulky Ti 3 AlC 2 MAX phase using hydrofluoric acid. The resultant MNSs are 2 to 5 μm in size and are surface modified with carboxyl, hydroxyl and amine functional groups for biological interactions. Using an in vitro co-culture system, we found that MNSs interact with activated human ECs to reduce activation and pro-inflammatory Th1 polarization of allogeneic CD4 + lymphocytes. Mechanistically, we showed that treatment with MNSs significantly decreased expression of the co-stimulatory molecule CD86 and altered the ratio of endothelial surface co-stimulatory to co-inhibitory molecules. Furthermore, when applied in an in vivo rat model of allograft vasculopathy, treatment with MNSs reduced lymphocyte infiltration and preserved medial smooth muscle cell integrity within transplanted vessel segments. Taken together, these findings suggest that these novel MNSs have potential as an effective treatment to prevent allograft vasculopathy.
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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) | 0.002 | 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".