<i>In Situ</i> Interfacial Super-Assembly of Nanobiohybrids through Plant for Food-Grade Oral Medicine
Bibliographic record
Abstract
Developing a next-generation oral drug delivery system with enhanced efficacy and limited side effects is highly desired for refractory diseases treatment such as colitis. The bioinspired assembly of drugs within food-grade plants highlights its potential value of this unique hybrid material. Herein, we report the preparation of drug-encapsulated vegetable nanobiohybrid superassembled frameworks as an oral food-grade drug delivery system (SAF-FGDD). The in situ superassembly of SAF-FGDD driven by natural transpiration from living plants is carried out through a sustainable and low-carbon manner, allowing for the assembly of distinct precursors inside edible living plants. As an example, mesalazine, an anti-inflammatory drug, is encapsulated in the frameworks for colitis treatment. The cell activity and feeding experiments of zebrafish and mice demonstrate the excellent efficacy of this SAF-FGDD. Compared with those of the control groups, the disease activity index scores and histological scores of the SAF-FGDD group were significantly decreased by 80% and 98%, respectively. The improved performance is attributed to the biocompatibility and protective effect of SAF-FGDD, allowing for abundant mesalazine to be released and act at the site of the intestine during the process of food digestion. In combination with mature soilless cultivation technology, plant-based organisms with natural structure-forming abilities possess broad commercial prospects in large-scale production of various food-grade functional materials.
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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) | 0.002 | 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".