The novel potential multidrug-resistance biomarkers for Pseudomonas aeruginosa lung infections using transcriptomics data analysis

Assessment of a Novel Photocatalytic TiO2-Zirconia Ultrafiltration Membrane and Combination with Solar Photo-Fenton Tertiary Treatment of Urban Wastewater

The objective of this study was to assess the combination of a photocatalytic TiO2-coated ZrO2 UF membrane with solar photo-Fenton treatment at circumneutral pH for the filtration and treatment of urban wastewater treatment plant (UWWTP) effluents. Photocatalytic self-cleaning properties were tested with a UWWTP effluent under irradiation in a solar simulator. Then, both the permeates and retentates from the membrane process were treated using the solar photo-Fenton treatment. The UWWTP effluent was spiked with caffeine (CAF), imidacloprid (IMI), thiacloprid (THI), carbamazepine (CBZ) and diclofenac (DCF) at an initial concentration of 100 µg/L each. Retention on the membrane of Pseudomonas Aeruginosa (P. Aeruginosa), a Gram-negative bacterial strain, was tested with and without irradiation. It was demonstrated that filtration of a certain volume of UWWTP effluent in the dark is possible, and the original conditions can then be recovered after illumination. The photocatalytic membrane significantly reduces the turbidity of the UWWTP effluent, significantly increasing the degradation efficiency of the subsequent solar photo-Fenton treatment. The results showed that the membrane allowed consistent retention of P. Aeruginosa at an order of magnitude of 1 × 103–1 × 104 CFU/mL.

The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria

Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene-gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA-target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP .