Use of Diaion HP20 Resin to Achieve High Rutin Containing Moringa oleifera Extract: Its Solubility and Anti-Bacterial Properties and Possible Applications in Nebulizer Formulation for COVID Patients
Bibliographic record
Abstract
Rutin with several pharmacological properties has been reported to be an effective inhibitor for SARS-COV-2 viral protease. Due to lack of specific drugs available for treatment of covid infection, hunt is on for possible herbal supplements that will impact multiplication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and also provide immunity to people against such infections. We report a simple and cost-effective method for extraction of rutin from Moringa oleifera, a plant with high nutritive value due to its rich contents in minerals, vitamins and other essential phytochemicals. The solubility of rutin in water was achieved using rutin-arginine mixture (identified as a good water-soluble version of rutin) and tested for its antibacterial activity against Klebsiella pneumoniae and E. coli by MIC studies and compared with rutin solubilized in methanol. Our results demonstrate easy scale of the rutin extraction process and such a process could be applicable to extraction of rutin from other medicinal plants as well. We demonstrate that rutin purified through column chromatography has 20% higher solubility in water and maintains its anti-bacterial properties against Klebsiella pneumoniae and E. coli. Our findings reveal new possibilities of using resin column chromatography for concentrating rutin from plant extracts. Also, potential use of water soluble rutin is envisaged for development of nebulizers for treatment of asthma, hyperglycemia, and pneumonia, seen in COVID-19 affected patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".