In vitro α-amylase inhibitory effect of TLC isolates of Aloe megalacantha baker and Aloe monticola Reynolds
Bibliographic record
Abstract
About 425 million adults had diabetes mellitus globally in 2017. Type 2 diabetes accounts for the enormous majority of diabetes cases and it is gradually growing which is predicted to increase by 48% in 2045. Imbalanced cellular carbohydrate and lipid metabolism cause an increase in postprandial blood glucose level which eventually leads to the onset and progression of type 2 diabetes mellitus. The lack of effective and safe carbohydrate hydrolyzing enzyme inhibitors contributes to the increasing prevalence. Thus, this study was targeted to assess the α-amylase inhibitory potential of isolates obtained from Aloe megalacantha Baker and Aloe monticola Reynolds, which are among the commonly used folkloric remedies for the management of diabetes mellitus. The α-amylase inhibitory effect of Aloe megalacantha Baker and Aloe monticola Reynolds were evaluated using the 3,5-dinitro salicylic acid method. 2, 2-Diphenyl-2-picrylhydrazyl free radical scavenging property was also used to test the antioxidant effect of both plants. Results were analysed using GraphPad Prism software version 8. The more polar isolates (AM 1 and AG 1 ) were possessed stronger α-amylase inhibition activity than the leaves latex and the other strains (AM 2 and AG 2 ). Leaf latex of A. megalacantha , AM 1 , AM 2 , leaf latex of A. monticola , AG 1 , and AG 2 were found to have an IC 50 value of 74.76 ± 1.98, 37.83 ± 3.31, 96.75 ± 1.98, 78.10 ± 1.88, 56.95 ± 1.88 and 64.03 ± 3.60 μg/mL, respectively ( P < 0.001 ). The leaf latexes of A. megalacantha and A. monticola showed a significant ( P < 0.001 ) free radical hunting property with an IC 50 value of 890.1 ± 1.73 and 597.5 ± 2.02 μg/mL, respectively. Hence, the outcomes of the present investigation partly justify the acclaimed use of Aloe megalacantha and Aloe monticola for the treatment of diabetes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".