Carica papaya Leaf Extract modulates mRNA expression of Aquaporins in Mouse Model of Allergic Airway Inflammation
Bibliographic record
Abstract
Background: Asthma is a chronic inflammatory disease affecting smaller airways. Airflow obstruction leading to airway hyper-responsiveness and increased mucus production are salient features of asthma pathophysiology. Generally, Th2 cytokines are increased in allergic asthma. Aim: To propose the molecular mechanisms by which Carica Papaya Leaves Extract (CPLE) alleviates pulmonary edema in animal model of allergic airway inflammation comparable to methylprednisolone. Place and duration of study: Pharmacology Department, University of Health Sciences Lahore for 1 year. Methods: We took twenty four male BALB/c mice and divided them equally into four groups. The control group was given PBS only, while Group II served as diseased group and induced airway inflammation by ovalbumin. Group III and IV were first induced with airway inflammation and side by side treated with Carica papaya leaf extract (CPLE) 100mg/kg body weight orally and methylprednisolone 15 mg/kg body weight intraperitoneally for seven consecutive days respectively. At the end of the experimental protocol, mice were euthanized and lung wet/dry ratio was measured. mRNA expression of AQP1 and AQP5 in lung tissue were also determined using RT-PCR. Results: Ethanolic extract of Carica Papaya leaves decreased all markers of pulmonary edema in mouse model of allergic airway inflammation comparable to methylprednisolone by decreasing lung wet/dry ratio and enhancing AQP1 and AQP5 mRNA expression. Conclusion: Carica Papaya leaves extract may diminish pulmonary edema in mice associated with allergic asthma. Keywords: AQP1, AQP5 (Aquaporins), Carica Papaya Leaves Extract (CPLE), Pulmonary Edema, Th2 cytokines.
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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 | 0.000 | 0.000 |
| 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".