Simvastatin-Loaded Polymeric Nanoparticles: Targeting Inflammatory Macrophages for Local Adipose Tissue Browning in Obesity Treatment
Bibliographic record
Abstract
Obesity is defined as chronic, low-grade inflammation within specific tissues. Given the escalating prevalence of obesity among individuals of all ages, obesity has reached epidemic proportions, posing an important public health challenge. Despite significant advancements in treating obesity, conventional approaches remain largely ineffective or involve severe side effects, thus underscoring the pressing need to explore and develop treatment approaches. Targeted and local immunomodulation using nanoparticles (NPs) can influence fat production and utilization processes. Statins, known for their anti-inflammatory properties, show the potential for mitigating obesity-related inflammation. A localized delivery option offers several advantages over oral and parenteral delivery methods. Here, we developed simvastatin (Sim) encapsulated within PLGA NPs (Sim-NP) for localized delivery of Sim to adipose tissues (ATs) for immunomodulation to treat obesity. In vitro experiments revealed the strong anti-inflammatory effects of Sim-NPs, which resulted in enhanced modulation of macrophage (MΦ) polarization and induction of AT browning. We then extended our investigation to an in vivo mouse model of high-fat-diet (HFD)-induced obesity. Sim-NP administration led to the controlled release of Sim within AT, directly impacting MΦ activity and inducing AT browning while inducing weight loss. Our findings demonstrated that Sim-NP administration effectively inhibited the progression of obesity-related inflammation, controlled white fat production, and enhanced AT modulation. These results highlight the potential of Sim-NP as a potent nanotherapy for treating obesity by modulating the immune system.
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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) | 0.000 | 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 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".