Low Dose of Ti<sub>3</sub>C<sub>2</sub> MXene Quantum Dots Mitigate SARS‐CoV‐2 Infection
Bibliographic record
Abstract
Abstract MXene QDs (MQDs) have been effectively used in several fields of biomedical research. Considering the role of hyperactivation of immune system in infectious diseases, especially in COVID‐19, MQDs stand as a potential candidate as a nanotherapeutic against viral infections. However, the efficacy of MQDs against SARS‐CoV‐2 infection has not been tested yet. In this study, Ti 3 C 2 MQDs are synthesized and their potential in mitigating SARS‐CoV‐2 infection is investigated. Physicochemical characterization suggests that MQDs are enriched with abundance of bioactive functional groups such as oxygen, hydrogen, fluorine, and chlorine groups as well as surface titanium oxides. The efficacy of MQDs is tested in VeroE6 cells infected with SARS‐CoV‐2. These data demonstrate that the treatment with MQDs is able to mitigate multiplication of virus particles, only at very low doses such as 0,15 µg mL −1 . Furthermore, to understand the mechanisms of MQD‐mediated anti‐COVID properties, global proteomics analysis are performed and determined differentially expressed proteins between MQD‐treated and untreated cells. Data reveal that MQDs interfere with the viral life cycle through different mechanisms including the Ca 2 + signaling pathway, IFN‐ α response, virus internalization, replication, and translation. These findings suggest that MQDs can be employed to develop future immunoengineering‐based nanotherapeutics strategies against SARS‐CoV‐2 and other viral infections.
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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".