The Anticancer Potential of Psidium guajava (Guava) Extracts
Bibliographic record
Abstract
The fruits, leaves, and bark of the guava (Psidium guajava) tree have traditionally been used to treat a myriad of ailments, especially in the tropical and subtropical regions. The various parts of the plant have been shown to exhibit medicinal properties, such as antimicrobial, antioxidant, anti-inflammatory, and antidiabetic activities. Recent studies have shown that the bioactive phytochemicals of several parts of the P. guajava plant exhibit anticancer activity. This review aims to present a concise summary of the in vitro and in vivo studies investigating the anticancer activity of the plant against various human cancer cell lines and animal models, including the identified phytochemicals that contributes to their activity via the different mechanisms. In vitro growth and cell viability studies, such as the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, the sulforhodamine B (SRB) assay, and the trypan blue exclusion test, were conducted using P. guajava extracts and their biomolecules to assess their effects on human cancer cell lines. Numerous studies have showcased that the P. guajava plant and its bioactive molecules, especially those extracted from its leaves, selectively suppress the growth of human cancer cells without cytotoxicity against the normal cells. This review presents the potential of the extracts of P. guajava and the bioactive molecules derived from it, to be utilized as a feasible alternative or adjuvant treatment for human cancers. The availability of the plant also contributes towards its viability as a cancer treatment in developing countries.
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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 | 0.001 |
| 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".