Screening of Potential Probiotic <i>Lactobacillaceae</i> and Their Improvement of Type 2 Diabetes Mellitus by Promoting PI3K/AKT Signaling Pathway in db/db Mice
Bibliographic record
Abstract
Abstract The study aimed to isolate Lactobacillaceae strains with in vitro hypoglycemic activity and probiotic properties and to determine their antidiabetic abilities in vivo . Lactiplantibacillus plantarum 22, L. plantarum 25, Limosilactobacillus fermentum 11, and L. fermentum 305 with high in vitro hypoglycemic activity were screened from 23 strains of Lactobacillaceae isolated from human feces and identified by 16S rDNA sequencing. The fasting blood glucose (FBG) of the mice was recorded weekly. After 12 weeks, liver, kidney, and pancreas tissues were stained with hematoxylin and eosin (H&E) to observe histomorphology; the inflammatory factors were assayed by Quantitative Real-time PCR; PI3K and AKT were measured by Western blot; the short-chain fatty acids (SCFAs) were determined by LC-MS/MS. Inhibitory activities of L. plantarum 22, L. plantarum 25, L. fermentum 11, and L. fermentum 305 against α-amylase were 62.29 ± 0.44%, 51.81 ± 3.65%, 58.40 ± 1.68%, and 57.48 ± 5.04%, respectively. Their inhibitory activities to α-glucosidase were 14.89 ± 0.38%, 15.32 ± 0.89%, 52.63 ± 3.07%, and 51.79 ± 1.13%, respectively. Their survival rate after simulated gastrointestinal test were 12.42 ± 2.84%, 9.10 ± 1.12%, 5.86 ± 0.52%, and 8.82 ± 2.50% and their adhesion rates to Caco-2 cell were 6.09 ± 0.39%, 6.37 ± 0.28%, 6.94 ± 0.27%, and 6.91 ± 0.11%, respectively. The orthogonal tests of bacterial powders of the four strains showed that the maximum inhibitory activities to α-amylase and α-glucosidase were 93.18 ± 1.19% and 75.33 ± 2.89%, respectively. The results showed that the mixture of Lactobacillaceae could lower FBG, reduce inflammation, and liver, kidney, and pancreas damage, promote PI3K/AKT signaling pathway, and increase the content of SCFAs. The combination of L. plantarum 22, L. plantarum 25, L. fermentum 11, and L. fermentum 305 can potentially improve type 2 diabetes mellitus (T2DM).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".