Preliminary evaluation of in vitro anti-urolithiasic effect of six Algerian plant products
Bibliographic record
Abstract
The present paper reports on the preliminary evaluation of in vitro anti-urolithiasic effect (AULE) of six Algerian plant products: lemon (Citrus limon L.), orange (Citrus sinensis L.), grapefruit (Citrus grandis L.), lemon/grapefruit (1/1 ratio, v/v) and tomato (Solanum lycopersicum L.) juices, [and aqueous extract from hairy rupturewort (Herniaria hirsuta L.), the bi-distilled water being taken as reference. The AULE was evaluated, based on the ability of considered plant liquids to prevent the crystallization of calcium oxalate (CaOx) from the mixture of stock solutions of calcium chloride and sodium oxalate. For this, the juices and the aqueous extract to be analyzed were used as solvents to prepare the two stock solutions. The formation of CaOx was assessed by means of microscopic observation and presented in the form of images. A better AULE was observed for citrus juices, compared with bi-distilled water, tomato juice and hairy rupturewort extract. In the case of lemon juice, it was also found that the AULE is more effective when the juice acidity is adjusted to high pH values (6-7). Based on these results, citrus juices may be recommended for the prevention of urinary lithiogenesis. However, in-depth confirmatory studies are always desired, knowing that opinions are not unanimous on this issue.
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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".