The chemical nature of most of the mycotoxins makes them highly liposoluble compounds that can be absorbed from the site of exposure such as from the gastrointestinal and respiratory tract to the blood stream where it can be dissimilated throughout the body and reach different organs such as the liver and kidneys. Mycotoxins have a strong tendency and ability to penetrate the human and animal cells and reach the cellular genome where it causes a major mutagenic change in the nucleotide sequence which leads to strong and permanent defects in the genome. This defect will eventually be transcribed, translated and lead to the development of cancer. In this review, the chemical and physical nature of mycotoxins, the action of mycotoxins on the cellular genome and its effect on humans, mycotoxins and their carcinogenicity and mycotoxins research gaps are discussed, and new research areas are suggested. The research review posed various questions. What are the different mycotoxins that can cause cancer, what is the role of mycotoxins in causing cancer and what types of cancers can be caused by mycotoxins? These questions have been selected due to the significant increase in the mycotoxin contamination and the cancer incidence rate in the contemporary world. By revealing and understanding the role of mycotoxins in developing cancer, measures to reduce the risks and incidents of cancer could be taken.

Background: Emergence of Candida glabrata, which causes potential life-threatening invasive candidiasis, has been widely associated with high morbidity and mortality. In order to cause disease in vivo, a robust and highly efficient metabolic adaptation is crucial for the survival of this fungal pathogen in human host. In fact, reprogramming of the carbon metabolism is believed to be indispensable for phagocytosed C. glabrata within glucose deprivation condition during infection. Methods: In this study, the metabolic responses of C. glabrata under acetate growth condition was explored using high-throughput transcriptomic and proteomic approaches. Results: Collectively, a total of 1482 transcripts (26.96%) and 242 proteins (24.69%) were significantly up- or down-regulated. Both transcriptome and proteome data revealed that the regulation of alternative carbon metabolism in C. glabrata resembled other fungal pathogens such as Candida albicans and Cryptococcus neoformans, with up-regulation of many proteins and transcripts from the glyoxylate cycle and gluconeogenesis, namely isocitrate lyase (ICL1), malate synthase (MLS1), phosphoenolpyruvate carboxykinase (PCK1) and fructose 1,6-biphosphatase (FBP1). In the absence of glucose, C. glabrata shifted its metabolism from glucose catabolism to anabolism of glucose intermediates from the available carbon source. This observation essentially suggests that the glyoxylate cycle and gluconeogenesis are potentially critical for the survival of phagocytosed C. glabrata within the glucose-deficient macrophages. Conclusion: Here, we presented the first global metabolic responses of C. glabrata to alternative carbon source using transcriptomic and proteomic approaches. These findings implicated that reprogramming of the alternative carbon metabolism during glucose deprivation could enhance the survival and persistence of C. glabrata within the host.

Background : Silver nanoparticles (AgNPs) are widely used in food industries, biomedical, dentistry, catalysis, diagnostic biological probes and sensors. The use of plant extract for AgNPs synthesis eliminates the process of maintaining cell culture and the process could be scaled up under a non-aseptic environment. The purpose of this study is to determine the classes of phytochemicals, to biosynthesize and characterize the AgNPs using Clinacanthus nutans leaf and stem extracts. In this study, AgNPs were synthesized from the aqueous extracts of C. nutans leaves and stems through a non-toxic, cost-effective and eco-friendly method. Results : The formation of AgNPs was confirmed by UV-Vis spectroscopy, and the size of AgNP-L (leaf) and AgNP-S (stem) were 114.7 and 129.9 nm, respectively. Transmission electron microscopy (TEM) analysis showed spherical nanoparticles with AgNP-L and AgNP-S ranging from 10 to 300 nm and 10 to 180 nm, with average of 101.18 and 75.38 nm, respectively. The zeta potentials of AgNP-L and AgNP-S were recorded at −42.8 and −43.9 mV. X-ray diffraction analysis matched the face-centred cubic structure of silver and was capped with bioactive compounds. Fourier transform infrared spectrophotometer analysis revealed the presence of few functional groups of phenolic and flavonoid compounds. These functional groups act as reducing agents in AgNPs synthesis. Conclusion : These results showed that the biogenically synthesized nanoparticles reduced silver ions to silver nanoparticles in aqueous condition and the AgNPs formed were stable and less toxic.

Beta-Caryophyllene (BCP), a naturally occurring sesquiterpene abundantly found in cloves, hops, and cannabis, is the active candidate of a relatively new group of vascular-inhibiting compounds that aim to block existing tumor blood vessels. Previously, we have reported the anti-cancer properties of BCP by utilizing a series of in-vitro anti-tumor-related assays using human colorectal carcinoma cells. The present study aimed to investigate the effects of BCP on in-vitro, ex-vivo, and in-vivo models of anti-angiogenic assays and evaluate its anti-cancer activity in xenograft tumor (both ectopic and orthotopic) mice models of human colorectal cancer. Computational structural analysis and an apoptosis antibody array were also performed to understand the molecular players underlying this effect. BCP exhibited strong anti-angiogenic activity by blocking the migration of endothelial cells, tube-like network formation, suppression of vascular endothelial growth factor (VEGF) secretion from human umbilical vein endothelial cells and sprouting of rat aorta microvessels. BCP has a probable binding at Site#0 on the surface of VEGFR2. Moreover, BCP significantly deformed the vascularization architecture compared to the negative control in a chick embryo chorioallantoic membrane assay. BCP showed a remarkable reduction in tumor size and fluorescence molecular tomography signal intensity in all the mice treated with BCP, in a dose-dependent relationship, in ectopic and orthotopic tumor xenograft models, respectively. The histological analysis of the tumor from BCP-treated mice revealed a clear reduction of the density of vascularization. In addition, BCP induced apoptosis through downregulation of HSP60, HTRA, survivin, and XIAP, along with the upregulation of p21 expressions. These results suggest that BCP acts at multiple stages of angiogenesis and could be used as a promising therapeutic candidate to halt the growth of colorectal tumor cells.

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.