Efficacy of the Aqueous Extracts of Justicia Galeopsis Leaves on the Improvement of Hematological Parameters in Anemic Rats
Bibliographic record
Abstract
Antianemc potential of aqueous of Justicia galeopsis leaves was studied using Wistar Albino rats after induction of anemia by phenylhydrazine hydrochloride. Forty rats (20 male and 20 female) subdivided into five groups of eight rats were used. There was a group as control (not anemic) and four other anemic groups which had received by gavage respectively 1 ml/kg of distilled water, 1 ml/kg of body weight of Vitafer (reference drug commonly used to treat anemia), 100 mg/kg of body weight of extract of J. galeopsis leaves cooked during 30 min and 30 mg/kg of body weight of extract of J. galeopsis leaves cooked during 45 min. Hematological parameters (red blood cells, hemoglobin and hematocrit) were analyzed as indices of anemia and the weights of specific organs (liver, spleen and kidney) were evaluated. The results of this investigation had showed that aqueous extract of J. galeopsis leaves cooked improved red blood cells, hemoglobin and hematocrit levels. These extracts were not toxic for liver, spleen and kidney. The administration of 100 mg/kg/day of extract of leaves cooked leaves during 30 min promotes a better recovery rate of the number of red blood cells (94.80 %), hemoglobin level (159.53 %) and hematocrit (117.72 %) than Vitafer and the extract of leaves cooked for 45 min. This is suggestive that aqueous extracts of Justicia galeopsis leaves cooked during 30 min may be exploited during 2 weeks in the treatment of anemia.
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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 | 0.000 | 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".