Synthesis of benzoyl esters of β-amyrin and lupeol and evaluation of their antibiofilm and antidiabetic activities
Bibliographic record
Abstract
Diabetes as well as the enhanced microbial multidrug resistance resulting from biofilm formation, constitutes some of the major health problems around the world. Triterpenoids and their derivatives have been shown to have a great contribution in this domain. A small library of benzoyl esters of lupeol and β-amyrin was synthesized and their structures were characterized by electronic ionization mass spectrometry (EIMS). Their inhibitory potential on pathogenic bacteria biofilms, as well as their inhibitory action on α-amylase and β-glucosidase activities were evaluated. The mass fragmentation patterns from the EIMS data confirm the success of the reactions. The minimal inhibitory concentrations (MIC) varied from 250 to 1000 µg/mL in the antimicrobial activities. Biofilm inhibitory potential of the compounds on S. aureus, E. coli and C. albicans were performed at MIC and sub-MIC concentrations and the results showed concentration-dependent inhibition of biofilms. At MIC, the highest biofilm inhibition was exhibited by compound 7 on S. aureus (60.8 ± 3.2%), compound 3 on E. coli (60.5 ± 2.8%) and compound 8 on C. albicans (56.9 ± 2.5%). For all tested compounds, percentage inhibition of violacein production was 100% at MIC except for the starting compounds 1 and 2. At 24.24 µg/mL the percentage of inhibition varied from 22.9 ± 1.2% to 42.1 ± 1.0% for α-amylase inhibition and at a concentration of 10 µg/mL the percentage of inhibition varied from 49.8 ± 0.3% to 69.3 ± 1.0% for β-glucosidase inhibition. The highest inhibition was shown by compounds 7 and 8 on α-amylase and β-glucosidase assays, respectively. The results show that introduction of benzoyl ester groups at C-3 of lupeol and β-amyrin considerably improves their antibiofilm and antidiabetic potentials.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".