Self‐Assembled Integrative Nutrient Carrier Platform Containing Green Tea Catechin for Short Bowel Syndrome Treatment
Bibliographic record
Abstract
Abstract Extensive resection of the small intestine leads to the development of short bowel syndrome (SBS), which reduces the effective absorptive surface area of the intestine and predisposes patients to emaciation, malnutrition, and other severe symptoms. Herein, green tea catechin (−)‐epigallocatechin gallate (EGCG) and ferrous ions (Fe 2+ ) are utilized to construct a nutrient carrier platform that self‐assembles with nutrients to form phenolic‐based nutrient complexes (PNCs). PNCs effectively prolong the residence and absorption time of nutrients in the intestine. Further this platform is applied to integrate full nutrient formula, an enteral nutrition (EN) preparation containing a range of full nutrient components. In an SBS rat model, the prepared phenolic‐based integrative nutrient complexes (PINCs) enhance nutritional status, improve anemia and immune function, as well as facilitate the growth of remaining intestinal villi and crypts, and maintain the integrity of the intestinal barrier. In addition, PINCs enable the modulation of gut microbial dysbiosis, enrich the abundance of beneficial bacteria, and have no toxic effects after the long‐term ingestion. These results provide a proof of principle for the use of polyphenol‐based nanocomplexes as EN preparation, offering a feasible strategy for both nutritional support and therapeutic perspectives for SBS treatment.
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 | 0.001 | 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".