Protective effect of three glucomannans from different plants against DSS induced colitis in female BALB/c mice
Bibliographic record
Abstract
Glucomannans (GMs) from diverse natural plants have great potentiality in enhancing the host immune system. The protective effects of three GMs on the intestinal mucosal immunity in colitis mice were investigated and compared in this study. The three GMs (KGM, AGP, and DOP) were obtained from Amorphophallus rivieri, Aloe vera, and Dendrobium officinale, respectively, having different weight-averaged molecular weights (Mw), acetyl group content, and molar ratios of mannose to glucose (M/G). The three fractions were administered with or without dextran sodium sulfate (DSS) containing drinking water. Macroscopic observations (health state, crypt depth, and bowel thickness of colon tissue) were conducted. Furthermore, related cytokines and mRNA expressions of TLRs were measured by ELISA and RT-qPCR, respectively. Results showed that the administration of the three GMs improved the health state of colitis mice, such as the recovery of body weight, and the increase of the immune organ index, crypt depth, bowel wall thickness, and total number of immune cells. The integrity of intestinal mucosa was maintained by the increased number of goblet cells and mucin protein production. Further studies showed that GMs kept the balance between pro- and anti-inflammatory cytokines and also regulated the expressions of TLR-2, TLR-4, TLR-6, and TLR-9. The above results suggested that GMs could attenuate the intestinal epithelial injury and regulate the intestinal mucosal immunity. Structural features including the M/G ratio, Mw, and the content of acetyl groups jointly influence the protective effects of GMs on the colitis mice.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".