Maltase Glucoamylase (MGAM): Putting Diabetes in its Place! (LB85)
Bibliographic record
Abstract
By 2050 1 in 3 Americans will have type 2 diabetes, a disease characterized by excessively high blood glucose levels. Typically, diabetes is treated with regular insulin injections and blood sugar monitoring. For centuries, Aryuvedic medicine has had a massive following, in part due to its natural remedies for diabetes deriving from an Indian herb, Salacia reticulata . These extracts were found to contain α‐glucosidase inhibitors, a class of anti‐diabetic drugs that competitively inhibit intestinal starch‐digesting enzymes such as human Maltase Glucoamylase (MGAM). As MGAM is responsible for catalyzing the final glucose‐releasing step of starch digestion, inhibiting MGAM would delay glucose absorption into the bloodstream. The Ashbury SMART (Students Modeling a Research Topic) Team modeled MGAM in complex with two inhibitors, salacinol and Acarbose, (PDBID 2QMJ & 3L4Z) using Jmol and 3D printing technology. The binding of ‐glucosidase inhibitor salacinol, derived from Salacia reticulata extracts and Acarbose, a currently prescribed α‐glucosidase inhibitor will be compared in the MGAM active site. Notably, both inhibitors interact with MGAM residues Asp327 and His600 through ring hydroxyl groups, however salacinol contributes additional electrostatic interaction between its sulfonium center and the catalytic nucleophile Asp443. Grant Funding Source : Supported by grants from the NIH‐SEPA and NIH‐CTSA.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".