Torbangun (Coleus amboinicus Lour) Extracts Affect Microbial and Fungus Activities
Bibliographic record
Abstract
Coleus also known as Torbangun or Ati Ati plants leaves i.e. Coleus amboinicus Lour from Indonesia (CAL-I) and Coleus aromaticus (CAT-M), Pogostemon cablin (PC-M), Coleus blumei–red leaves (CBR-M), Coleus amboinicus– (CAL-M) Coleus blumei –purple leaves (CBP-M) from Malaysia were collected, freeze dried and extracted with aqueous methanol. The effect of the extract was assessed on microbial and fungal activities in relation to their phytochemicals and antioxidants concentrations. The total phenolic content was determined according to the Folin-Ciocalteu method whilst antioxidant activity was assessed using 2, 2-diphenyl-1-picrylhydrazyl (DPPH) method. The anti-microbial and anti-fungal activities were assessed by minimum inhibitory concentrations (MIC) and disc diffusion methods. The result indicates that the extracts are rich sources of phytochemicals and antioxidants from the listed plants. When the effect of the extracts was assessed on microbial and fungal activities it was observed that the effect was more pronounced on the gram-positive bacteria compared to gram-negative bacteria. Furthermore, there was strong association between phytochemicals and antioxidants concentration and with the microbial and fungal activities. However, it was not consistent for all types of strains. This study shows that Torbangun plants extracts are rich in phenolic contents therefore; it can be used as free radical scavengers and antimicrobial agent apart from other traditional uses
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 | 0.000 | 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) | 0.002 | 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".