Applications of immunomodulatory nanoparticles in dentistry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Oral inflammatory diseases such as periodontitis, peri-implantitis, and oral mucositis contribute significantly to tooth loss, impaired oral function, and systemic comorbidities. These conditions are driven by dysregulated immune responses, leading to persistent inflammation and poor tissue regeneration. Conventional treatments mainly target microbial reduction but overlook immune imbalance, limiting long-term efficacy. Immunomodulation offers a promising strategy to restore homeostasis and promote repair. Nanoparticles present a versatile platform for immunotherapy owing to their tunable size, surface chemistry, and capacity to target immune cells or respond to pathological cues. This review (PubMed, Scopus, Web of Science, 2015-2025) explores immunomodulatory nanoparticles in dentistry, grouped as organic (lipid-based, polymeric, self-assembled), inorganic (metallic, metal oxide, ceramic), exosome- and extracellular vesicle-derived, and hybrid systems. These platforms modulate macrophage polarization, cytokine production, and T cell balance to control inflammation and support regeneration. Advanced biomimetic designs further integrate antimicrobial, antioxidative, and pro-regenerative features. Despite encouraging preclinical data, translation faces challenges, including limited understanding of immune-nanoparticle interactions, safety issues, regulatory hurdles, lack of predictive models, and absence of standardized characterization protocols. Future directions include smart, personalized, and biomimetic systems, improved in vivo models, companion diagnostics, and harmonized evaluation standards, positioning these nanotechnologies as transformative tools in precision dental medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it