Water-Dispersible Fluorescent Silicon Nanoparticles That Modulate Inflammatory Response in Macrophages
Bibliographic record
Abstract
Silicon nanoparticles (SiNps) are being explored as biomaterials in the design of fluorescent medical probes and immunomodulatory strategies. However, most of the synthesis methods reported in the literature to obtain SiNps show a complicated synthesis process, resulting in nanoparticles dispersed in solvents that are hardly compatible with biological applications. This work reports a simple methodology to synthesize SiNps dispersed in water. Some physicochemical properties such as size, structure, composition, and fluorescence are evaluated. Furthermore, biological properties such as in vitro cytocompatibility and dose-dependent macrophage response are reported. SiNps were demonstrated to be rounded under 10 nm diameter with a Si–Si crystalline structure. Results showed that SiNps are promising as cell fluorescent trackers since they fluoresce under ultraviolet wavelength irradiation. In this report, several immunocytofluorescence and enzyme-linked immunosorbent assay (ELISA) tests strongly suggest that SiNps are not only cytocompatible but also exert an anti-inflammatory effect on macrophages by downregulating the production of pro-inflammatory cytokines and upregulating anti-inflammatory cytokines. All of these results suggest that SiNps synthesized using our methodology are a suitable alternative to be used as an immunomodulatory fluorescent biomaterial.
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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) | 0.000 | 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".