Pharmacognostical Standardization, Formulation And Evaluation Of Carica Papaya L. Sparkling Water For Dengue Fever And Its Management
Bibliographic record
Abstract
The incidence of Dengue fever, a mosquito-borne viral illness, has escalated in recent years across various countries including Bangladesh, Philippines, Thailand, Brazil, and India, leading to substantial morbidity and mortality. Traditional remedies utilizing Carica papaya leaves in fresh form have shown promise in alleviating Dengue fever symptoms, particularly thrombocytopenia, a common complication. To address issues of stability and palatability of fresh Carica papaya leaves a novel dosage form utilizing carbonated fresh juice of Carica papaya leaves is proposed. Carbonation not only preserves the bioavailability of the active constituents but also enhances patient acceptability. This innovative approach offers a convenient and effective means of delivering Carica papaya leaf extract for Dengue fever management, potentially improving patient outcomes worldwide. The described DNA isolation and subsequent sequencing and BLAST analysis conclusively identified the sample as Carica papaya, affirming the reliability and efficacy of the employed methodology for genetic analysis and species identification. Different compositions of the mobile phase for HPTLC analysis were tested in order to obtain high resolution and reproducible peaks. Authentic markers of flavonol (quercetin) obtained commercially was cochromatographed. Blue brown colour zone was detected in UV derivatisation in the chromatogram which confirms the presence of flavonoid quercetin in the formulation. This novel formulation was standardized using modern sophisticated instuments like TLC, HPTLC, DNA barcoding and UV analysis. This formulation would be definitely helpful for the future mankind for better dengue management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".