Nanoparticles and cytokine response
Bibliographic record
Abstract
Synthetic nanoparticles (NPs) are non-viral equivalents of viral gene delivery systems that are actively explored to deliver a spectrum of nucleic acids for diverse range of therapies. The success of the nanoparticulate delivery systems, in the form of efficacy and safety, depends on various factors related to the physicochemical features of the NPs, as well as their ability to remain "stealth" in the host environment. The initial cytokine response upon exposure to nucleic acid bearing NPs is a critical component of the host response and, unless desired, should be minimized to prevent the unintended consequences of NP administration. In this review article, we will summarize the most recent literature on cytokine responses to nanoparticulate delivery systems and identify the main factors affecting this response. The NP features responsible for eliciting the cytokine response are articulated along with other factors related to the mode of therapeutic administration. For diseases arising from altered cytokine pathophysiology, attempts to silence the individual components of cytokine response are summarized in the context of different diseases, and the roles of NP features on this respect are presented. We finish with the authors' perspective on the possibility of engineering NP systems with controlled cytokine responses. This review is intended to sensitize the reader with important issues related to cytokine elicitation of non-viral NPs and the means of controlling them to design improved interventions in the clinical setting.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".