Influence of Boiling, Steaming and Frying of Selected Leafy Vegetables on the In Vitro Anti-inflammation Associated Biological Activities
Bibliographic record
Abstract
The aim of the present study was to evaluate the effect of cooking (boiling, steaming, and frying) on anti-inflammation associated properties in vitro of six popularly consumed green leafy vegetables in Sri Lanka, namely: Centella asiatica, Cassia auriculata, Gymnema lactiferum, Olax zeylanica, Sesbania grnadiflora, and Passiflora edulis. The anti-inflammation associated properties of methanolic extracts of cooked leaves were evaluated using four in vitro biological assays, namely, hemolysis inhibition, proteinase inhibition, protein denaturation inhibition, and lipoxygenase inhibition. Results revealed that the frying of all the tested leafy vegetables had reduced the inhibition abilities of protein denaturation, hemolysis, proteinase, and lipoxygenase activities when compared with other food preparation methods. Steaming significantly increased the protein denaturation and hemolysis inhibition in O. zeylanica and P. edulis. Steaming of leaves increased inhibition activity of protein denaturation in G. lactiferum (by 44.8%) and P. edulis (by 44%); hemolysis in C. asiatica, C. auriculata, and S. grandiflora; lipoxygenase inhibition ability in P. edulis (by 50%), C. asiatica (by 400%), and C. auriculata leaves (by 250%); proteinase inhibition in C. auriculata (100%) when compared with that of raw leaves. In general, steaming and boiling in contrast to frying protect the health-promoting properties of the leafy vegetables.
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) | 0.001 | 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".