The In-vitro Antibacterial Effect of Colored Rice Crude Extracts against Staphylococcus aureus Associated with Skin and Soft-Tissue Infection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to increases in the resistance of bacteria to the existing antimicrobial agents, plants are being used as alternative sources for the development of safe, effective, and inexpensive new agents to treat and prevent bacterial infections. Recent studies have shown that rice (Oryza sativa L.), an important source of nutrients, consumed by most of the world’s population, can suppress some bacterial infections. There are many varieties of rice, e.g. white, brown, black, and red; however, the relationship between rice color and antibacterial properties remains unclear. In the present study, we investigated the antibacterial activity of colored-rice crude extracts from four different types of colored rice (Hom Nil, Neaw Dum, Mun Poo, and Sang Yod) against common bacteria causing skin and soft-tissue infections, such as Staphylococcus aureus, Streptococcus pyogenes, Enterococcus spp., Pseudomonas aeruginosa, and Escherichia coli. The results showed that all colored-rice crude extracts had antibacterial effects against S. aureus; and more notably, crude extracts of differently colored rice restricted diverse antibacterial activities. To our knowledge, this is the first report that provides a basic understanding of the antibacterial properties of colored rice against skin and wound pathogens. An understanding of these properties would be invaluable in the development of alternative, natural, and safe methods of controlling bacterial infections.
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.
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