Natural Hemp-Ginger Extract and Its Biological and Therapeutic Efficacy.

The prevention and treatment of skin diseases remains a major challenge in medicine. The search for natural active ingredients that can be used to prevent the development of the disease and complement treatment is on the rise. Natural extracts of ginger and hemp offer a wide range of bioactive compounds with potential health benefits. This study evaluates the effectiveness of hemp and ginger extract as a supportive treatment for skin diseases. It reports a synergistic effect of hemp and ginger extract. The contents of cannabinoids and components of ginger are determined, with the highest being CBD (587.17 ± 8.32 µg/g) and 6-gingerol (60.07 ± 0.40 µg/g). The minimum inhibitory concentration for Staphylococcus aureus (156.5 µg/mL), Escherichia coli (625.2 µg/mL) and Candida albicans (78.3 µg/mL) was also analyzed. Analysis of WM-266-4 cells revealed the greatest decrease in metabolic activity in cells exposed to the extract at a concentration of 1.00 µg/mL. Regarding the expression of genes associated with cellular processes, melanoma aggressiveness, resistance and cell survival, a significant difference was found in the expression of ABCB5, CAV1 and S100A9 compared with the control (cells not exposed to the extract).

Meta-Analytic Comparison of Global RNA Transcriptomes of Acute and Chronic Myeloid Leukemia Cells Reveals Novel Gene Candidates Governing Myeloid Malignancies.

BACKGROUND: Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) represent a group of hematological malignancies characterized by the pathogenic clonal expansion of leukemic myeloid cells. The diagnosis and clinical outcome of AML and CML are complicated by genetic heterogeneity of disease; therefore, the identification of novel molecular biomarkers and pharmacological targets is of paramount importance. METHODS: RNA-seq-based transcriptome data from a total of five studies were extracted from NCBI GEO repository and subjected to an in-depth bioinformatics analysis to identify differentially expressed genes (DEGs) between AML and CML. A systemic literature survey and functional gene ontology (GO) enrichment analysis were performed for the top 100 DEGs to identify novel candidate genes and biological processes associated with AML and CML. RESULTS: LINC01554, PTMAP12, LOC644936, RPS27AP20 and FAM133CP were identified as novel risk genes for AML and CML. GO enrichment analysis showed that DEGs were significantly associated with pre-RNA splicing, reactive oxygen species and glycoprotein metabolism, the cellular endomembrane system, neutrophil migration and antimicrobial immune response. CONCLUSIONS: Our study revealed novel biomarkers and specific biological processes associated with AML and CML. Further studies are required to evaluate their value as molecular targets for managing and treating the myeloid malignancies.

Genetic prediction profile for adalimumab response in Slovenian Crohn's disease patients.

INTRODUCTION: Response to anti-TNF therapy is crucial for life expectancy and life quality in patients with severe Crohn's disease. We investigated if a previously reported gene expression profile predictive for infliximab response could be also applied to adalimumab response in an independent cohort. METHODS: Forty-seven Slovene Crohn's disease patients indicated for adalimumab therapy were enrolled in the study. Inflamed and non-inflamed colon biopsy samples were obtained during routine colonoscopy prior to adalimumab treatment. Response to adalimumab was measured with IBDQ. Gene expression in inflamed and non-inflamed colon biopsy samples was measured with RT-qPCR. Genotypes were extracted from previously available genotype data. Statistical analysis was performed with SPSS software. The R package e1071 was used to train bootstrap aggregated support vector machines (SVM). RESULTS: SVM prediction model analysis was used to analyze pooled, non-inflamed, and inflamed colon tissue datasets using IBDQ response after 4, 12, 20 and 30 weeks of adalimumab treatment. The bagging approach was used in an endeavor to obtain 100 % accuracy using 10 × 100 or 100 × 100 iterations. Average adalimumab response prediction accuracy is 75.5 % for pooled samples, 90.5 % for inflamed samples, and 100 % for non-inflamed samples. Moreover, models trained on selected SNPs from analyzed genes had an average accuracy of 92.8 %, confirming the involvement of genetic regions mapping the reported genes. Finally, using combined gene expression and SNP data we observed 100 % adalimumab response prediction accuracy for pooled, inflamed, and non-inflamed datasets. DISCUSSION: Our study supports the reported genetic anti-TNF response profile and extends it for adalimumab prediction.