Honeydew melon (Cucumis melo L.) is an oval-shaped delicious fruit of one cultivar group of the muskmelon with immense nutritional importance and is extensively consumed by many tropical countries. The effect of various organic solvents on the recovery of phytochemicals from honeydew melon plant fruits and seeds was assessed. Further, High-Performance Liquid Chromatography (HPLC) was used to examine and assess the contents of phenolic acid (gallic acid) and flavonoid (rutin) compounds. The use of gas chromatography–mass spectrometry (GC-MS) analysis explained the presence of volatile phytocompounds in the extracts. The use of organic solvents had a substantial impact on the total dry weight and extract yield. In general, the solvent-extracted constituents remained in the order of methanol>chloroform>distilled water for both honeydew melon seeds and whole fruit. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) was used to assess the cytotoxicity effect against PC3, HCT116, HeLa, and Jurkat cell lines. The chloroform extract exhibited a good cytotoxic activity against all cell lines as compared to other solvent extracts. HPLC analysis revealed the occurrence of gallic acid content of 0.102±0.23 mg/10 mg of dry whole fruit extract, while 10 mg of dry seed extract contained only 0.022±0.12 mg of gallic acid content. Likewise, rutin content was observed to be 0.224±0.31 mg and 0.1916±0.82 mg/10 mg of dry whole fruit and seed extract, respectively. Further, GC-MS analysis revealed the presence of a total of 37 compounds in chloroform extract of whole fruit, while only 14 compounds were found in seed extract. Nevertheless, more examinations are needed to identify and characterize other metabolites from honeydew melon and evaluate their pharmacological importance.
The current investigation reports the structural and biological evaluation of silver nanoparticles (AgNPs) biosynthesized from the pericarp extract of Cucumis melo L. (muskmelon). The AgNPs were characterized by ultraviolet-visible (UV-Vis) spectrophotometry, XRD (X-ray diffraction),
SEM (scanning electron microscopy) and EDAX (energy-dispersive X-ray spectroscopy). The XRD analysis showed that biosynthesized AgNPs were having FCC (face centered cubic) crystalline structures. Further, the SEM and EDAX showed spherically shaped AgNPs having an average size of 25 nm. The
AgNPs effectively inhibited the growth of Bacillus subtilis and Escherichia coli. Moreover, the cytotoxic assay of AgNPs revealed effective cytotoxicity against different cancer cells, such as HeLa, HCT-116, PC-3 and Jurkat in a dose reliant way. The cell viability was noticed
to range from 50% to 60% with IC50 values ranging from 150 μg/mL to 224 μg/mL. The lower cell viability indicates the toxic effects of biosynthesized AgNPs against these malignant cells. Thus, the current study shows that these biosynthesized AgNPs could be utilized
in various medical applications in near future.
Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale autolabeled data. However, its evaluation has long been a problem: previous works either take costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled datawhich, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. This problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models more credibly, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. The experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared with previous methods. We hope that both our manual test sets and observations can help advance future DS-RE research.
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