Ganoderma lucidum Methanolic Extraction as a Potent Phytoconstituent: Characterization, in-vitro Antimicrobial and Cytotoxic Activity
Bibliographic record
Abstract
Abstract Background: Ganoderma lucidum has attracted tremendous attention due to its exceptional antimicrobial and anticancer properties that can be delicately tuned by controlling the initial extraction content and concentration. In the present experiment, we detailed the characterization, antimicrobial, and cytotoxic performance of Ganoderma lucidum as a potential multi-functional therapeutic agent.Methods: In this study, we used FTIR, XRD, FESEM, EDX, and HPLC techniques to evaluate the samples, which were then followed by disc diffusion and microdilution broth methods to test its antibacterial effects against four Gram-positive and Gram-negative bacterial strains, Enterococcus faecalis, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. MTT assay was applied to determine the cytotoxic activity of this natural product against MCF-7 and K562 cancer cell lines.Results: The results revealed that the inhibitory effects of this product had higher antibacterial activity against E. coli and Pseudomonas aeruginosa. The IC50 values of 0.5 and 0.75 mg/mL were obtained for MCF-7 and K-562 cancer cell lines, which confirmed the higher anticancer activity of the GLME against breast cancer cells compared to blood cancer cells.Conclusions: Hence, these data provide pioneer insights into the therapeutic usage of Ganoderma lucidum for treating breast and blood cancers. This work is motivated by research studies looking for pharmacological products to address chronic and acute diseases, where further resources and studies are required to explore such products' adverse effects and toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".