Chemical Modification of Cordyceps Polysaccharides and Their Antitumor Activity
Bibliographic record
Abstract
Cordyceps polysaccharides have been frequently mentioned in functional foods and cancer adjuvant therapy in recent years because of their immunomodulatory and anti-tumor activities.However, there are also many problems, such as poor water solubility and low bioavailability, which affect the actual application effect.This study systematically explored various chemical modification methods of Cordyceps polysaccharides (e.g., sulfonation, phosphorylation, selenization, acetylation and nanocarrier grafting), and analyzed the effects of these structural modifications on their anti-tumor activity.The results showed that as long as the modification is appropriate, such as the introduction of functional groups at specific sites, it can not only enhance its ability to induce apoptosis and immune activation, but also help improve tumor targeting and safety.In animal models, a variety of modified polysaccharides showed obvious tumor inhibition effects.In addition, we also discussed its potential as an anti-tumor drug candidate in combination with structure-activity analysis and several typical cases.Of course, from experiments to industry, there are still challenges such as production stability and clinical transformation.This study provides support for the integration of Cordyceps polysaccharides into combined treatment strategies and nano-delivery systems.
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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) | 0.001 | 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".