Cancer is the leading cause of death and has remained a big challenge for the scientific community. Because of the growing concerns, new therapeutic regimens are highly demanded to decrease the global burden. Despite advancements in chemotherapy, drug resistance is still a major hurdle to successful treatment. The primary challenge should be identifying and developing appropriate therapeutics for cancer patients to improve their survival. Multiple pathways are dysregulated in cancers, including disturbance in cellular metabolism, cell cycle, apoptosis, or epigenetic alterations. Over the last two decades, natural products have been a major research interest due to their therapeutic potential in various ailments. Natural compounds seem to be an alternative option for cancer management. Natural substances derived from plants and marine sources have been shown to have anti-cancer activity in preclinical settings. They might be proved as a sword to kill cancerous cells. The present review attempted to consolidate the available information on natural compounds derived from plants and marine sources and their anti-cancer potential underlying EMT mechanisms.

Enterococci are troublesome nosocomial, opportunistic Gram-positive cocci bacteria showing enhanced resistance to many commonly used antibiotics. This study aims to investigate the prevalence and genetic basis of antibiotic resistance to macrolides, lincosamides, and streptogramins (MLS) in Enterococci, as well as the correlation between MLS resistance and biocide resistance. From 913 clinical isolates collected from King Khalid Hospital, Hail, Saudi Arabia, 131 isolates were identified as Enterococci spp. The susceptibility of the clinical enterococcal isolates to several MLS antibiotics was determined, and the resistance phenotype was detected by the triple disk method. The MLS-involved resistance genes were screened in the resistant isolates. The current results showed high resistance rates to MLS antibiotics, and the constitutive resistance to all MLS (cMLS) was the most prevalent phenotype, observed in 76.8% of resistant isolates. By screening the MLS resistance-encoding genes in the resistant isolates, the erythromycin ribosome methylase (erm) genes that are responsible for methylation of bacterial 23S rRNA were the most detected genes, in particular, ermB. The ereA esterase-encoding gene was the most detected MLS modifying-encoding genes, more than lnuA (adenylation) and mphC (phosphorylation). The minimum inhibitory concentrations (MICs) of commonly used biocides were detected in resistant isolates and correlated with the MICs of MLS antibiotics. The present findings showed a significant correlation between MLS resistance and reduced susceptibility to biocides. In compliance with the high incidence of the efflux-encoding genes, especially mefA and mefE genes in the tolerant isolates with higher MICs to both MLS antibiotics and biocides, the efflux of resistant isolates was quantified, and there was a significant increase in the efflux of resistant isolates with higher MICs as compared to those with lower MICs. This could explain the crucial role of efflux in developing cross-resistance to both MLS antibiotics and biocides.

Farnesiferol C (Far-C) is a coumarin commonly extracted from Ferula asafetida and is popularly used as a traditional source of natural remedy. Liver cancer or hepatocellular carcinoma (HCC) has emerged as a major cause behind cancer burden, and limited therapeutic interventions have further aggravated the clinical management of HCC. In the present study, the authors tested the hypothesis that Far-C-instigated oxidative stress resulted in anti-proliferation and apoptosis instigation within human liver cancer HepG2 cells. The observations reported herewith indicated that Far-C exerted considerable cytotoxic effects on HepG2 cells by reducing the cell viability (p < 0.001) in a dose-dependent manner. Far-C exposure also resulted in enhanced ROS production (p < 0.01) which subsequently led to loss of mitochondrial membrane potential. Far-C-instigated oxidative stress also led to enhanced nuclear fragmentation and condensation as revealed through Hoechst-33342. These molecular changes post-Far-C exposure also incited apoptotic cell death which concomitantly led to significant activation of caspase-3 (p < 0.001). Furthermore, Far-C exhibited its competence in altering the expression of genes involved in apoptosis regulation (Bax, Bad, and Bcl2) along with genes exerting regulatory effects on cell cycle (cyclinD1) and its progression (p21^Cip1 and CDK4). The evidence thus clearly shows the preclinical efficacy of Far-C against HepG2 cells. However, further mechanistic investigations deciphering the alteration of different pathways post-Far-C exposure will be highly beneficial.

Breast cancer represents the most frequently occurring cancer globally among women. As per the recent report of the World Health Organization (WHO), it was documented that by the end of the year 2020, approximately 7.8 million females were positively diagnosed with breast cancer and in 2020 alone, 685,000 casualties were documented due to breast cancer. The use of standard chemotherapeutics includes the frontline treatment option for patients; however, the concomitant side effects represent a major obstacle for their usage. Carbazole alkaloids are one such group of naturally-occurring bioactive compounds belonging to the Rutaceae family. Among the various carbazole alkaloids, 3-Methoxy carbazole or C₁₃H₁₁NO (MHC) is obtained from Clausena heptaphylla as well as from Clausena indica. In this study, MHC was investigated for its anti-breast cancer activity based on molecular interactions with specific proteins related to breast cancer, where the MHC had predicted binding affinities for NF-κB with −8.3 kcal/mol. Furthermore, to evaluate the biological activity of MHC, we studied its in vitro cytotoxic effects on MCF-7 cells. This alkaloid showed significant inhibitory effects and induced apoptosis, as evidenced by enhanced caspase activities and the cellular generation of ROS. It was observed that a treatment with MHC inhibited the gene expression of NF-kB in MCF-7 breast cancer cells. These results suggest that MHC could be a promising medical plant for breast cancer treatment. Further studies are needed to understand the molecular mechanisms behind the anticancer action of MHC.

Herein, we reported an HPLC method for the simultaneous determination of tibezonium iodide (TBN) and lignocaine hydrochloride (LGN). The method was developed according to the International Conference for Harmonization guidelines (ICH) Q2R1 using Agilent^® 1260 with a mobile phase consisting of acetonitrile and phosphate buffer (pH 4.5) in a volumetric ratio of 70:30 and flowing through a C₈ Agilent^® column at 1 mL/min. The results revealed that TBN and LGN peaks were isolated at 4.20 and 2.33 min, respectively, with a resolution of 2.59. The accuracy of TBN and LGN was calculated to be 100.01 ± 1.72% and 99.05 ± 0.65% at 100% concentration, respectively. Similarly, the respective precision was 100.03 ± 1.61% and 99.05 ± 0.48%. The repeatability for TBN and LGN was found to be 99.05 ± 0.48% and 99.19 ± 1.72%, respectively, indicating that the method was precise. The respective regression co-efficient (r²) for TBN and LGN was found to be 0.9995 and 0.9992. Moreover, the LOD and LOQ values for TBN were 0.012 and 0.037 µg/mL, respectively, while for LGN, they were 0.115 and 0.384 µg/mL, respectively. The calculated greenness of the method for ecological safety was found to be 0.83, depicting a green contour on the AGREE scale. No interfering peaks were found when the analyte was estimated in dosage form and in volunteers’ saliva, depicting the specificity of the method. Conclusively, a robust, fast, accurate, precise and specific method was successfully validated to estimate TBN and LGN.

Membrane-based purification of therapeutic agents has recently attracted global attention as a promising replacement for conventional techniques like distillation and pervaporation. Despite the conduction of different investigations, development of more research about the operational feasibility of using polymeric membranes to separate the detrimental impurities of molecular entities is of great importance. The focus of this paper is to develop a numerical strategy based on multiple machine learning methods to predict the concentration distribution of solute through a membrane-based separation process. Two inputs are being analyzed in this study, specifically r and z. Furthermore, the single target output is C, and the number of data points exceeds 8000. To analyze and model the data for this study, we used the Adaboost (Adaptive Boosting) model over three different base learners (K-Nearest Neighbors (KNN), Linear Regression (LR), and Gaussian Process Regression (GPR)). In the process of hyper-parameter optimization for models, the BA optimization algorithm applied on the adaptive boosted models. Finally, Boosted KNN, Boosted LR, and Boosted GPR have scores of 0.9853, 0.8751, and 0.9793 in terms of R² metric. Based on the recent fact and other analyses, boosted KNN model is introduced as the most appropriate model of this research. The error rates for this model are 2.073 × 10¹ and 1.06 × 10^-2 in terms of MAE and MAPE metrics.