Immunoengineering with Ginseng Polysaccharide Nanobiomaterials through Oral Administration in Mice
Bibliographic record
Abstract
Plant polysaccharides (PS) such as American ginseng polysaccharide (GPS) have drawn immense interest in the field of immunoengineering, as they offer a way to actively control immune cell behavior and stimulation. These pharmacological activities have been limited by PS’s inherent physicochemical properties including large molecular size, heterogeneity, and poor solubility. In this work, we hypothesized that by nanosizing and encapsulating GPSs, we could enhance their immunomodulation by increased penetration and absorption through the GI tract. Herein, GPS nanoparticles (NPs) of average size 20 nm (± 4 nm) were prepared using a microfluidic approach, then encapsulated within porous nanospheres (diameter 180 ± 10 nm) of biodegradable gelatin to enhance their oral delivery. To locate the GPS NPs inside the gelatin, we encapsulated fluorescent-labeled GPS in gelatin and analyzed using confocal microscopy. An in vitro investigation on tumor induced macrophage cell lines showed a concentration dependent enhanced immunostimulation with the encapsulated GPS NPs. The immunomodulation was then studied for different formulations of GPS through oral gavage in Swiss albino mice. The results showed that the production of proinflammatory mediators in blood samples was significantly increased for the encapsulated GPS in a dose- and time-dependent manner compared to other GPS treatments. This study shows that GPS and potentially other PS systems’ immunomodulation properties can be significantly enhanced for use in simple oral drug delivery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".