In vitro α-amylase inhibitory effect of TLC isolates of Aloe megalacantha baker and Aloe monticola Reynolds
Why this work is in the frame
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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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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) | 0.000 | 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) | 0.000 | 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 it