Emerging Self‐Assembled Nanoparticles Constructed from Natural Polyphenols for Intestinal Diseases
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
Intestinal diseases like inflammatory bowel disease (IBD) and colorectal cancer originate from inflammation and disruption of mucosal barriers. Polyphenols can mitigate intestinal inflammation through antioxidant, anti‐inflammatory, and microbiome modulation effects. However, the poor solubility and stability of polyphenols restrict therapeutic delivery. Self‐assembly provides a nanoscale platform to overcome these limitations. Polyphenol‐based nanoparticles (PNPs) are formed via coordination of polyphenols with metals like iron, copper, and zinc based on the catechol/galloyl groups. Templeted assembly with amphiphilic block copolymers can also direct polyphenol self‐assembly into nanostructures. PNPs prepared by these mild, aqueous methods exhibit enhanced stability, pH‐responsive disassembly, high cargo‐loading capacity, and targeted accumulation in inflamed intestinal tissues. PNPs can load with hydrophobic polyphenols, drugs, genes, proteins, or probiotics and demonstrate therapeutic potential in preclinical IBD, colorectal cancer, and microbiome disorder models. Ongoing challenges include augmenting prebiotic effects, multidrug encapsulation, and engineering PNPs as biotherapeutics. Future directions involve tailored polyphenol–polymer covalent assemblies and investigating PNPs interactions with enterocytes, immune cells, and microbiota. Overall, PNPs prepared by facile self‐assembly combine the bioactivities of polyphenols with advanced delivery functionality, presenting new opportunities for combination and microbiota‐based therapies for complex intestinal diseases.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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