Classification of clinically actionable genetic mutations in cancer patients.

Personalized medicine in cancer treatment aims to treat each individual's cancer tumor uniquely based on the genetic sequence of the cancer patient and is a much more effective approach compared to traditional methods which involve treating each type of cancer in the same, generic manner. However, personalized treatment requires the classification of cancer-related genes once profiled, which is a highly labor-intensive and time-consuming task for pathologists making the adoption of personalized medicine a slow progress worldwide. In this paper, we propose an intelligent multi-class classifier system that uses a combination of Natural Language Processing (NLP) techniques and Machine Learning algorithms to automatically classify clinically actionable genetic mutations using evidence from text-based medical literature. The training data set for the classifier was obtained from the Memorial Sloan Kettering Cancer Center and the Random Forest algorithm was applied with TF-IDF for feature extraction and truncated SVD for dimensionality reduction. The results show that the proposed model outperforms the previous research in terms of accuracy and precision scores, giving an accuracy score of approximately 82%. The system has the potential to revolutionize cancer treatment and lead to significant improvements in cancer therapy.

Antimicrobial activity of bacteria associated with the rhizosphere and phyllosphere of Avena fatua and Brachiaria reptans.

Environmental pollution especially heavy metal-contaminated soils adversely affects the microbial communities associated with the rhizosphere and phyllosphere of plants growing in these areas. In the current study, we identified and characterized the rhizospheric and phyllospheric bacterial strains from Avena fatua and Brachiaria reptans with the potential for antimicrobial activity and heavy metal resistance. A total of 18 bacterial strains from the rhizosphere and phyllosphere of A. fatua and 19 bacterial strains from the rhizosphere and phyllosphere of B. reptans were identified based on 16S rRNA sequence analysis. Bacterial genera, including Bacillus, Staphylococcus, Pseudomonas, and Enterobacter were dominant in the rhizosphere and phyllosphere of A. fatua and Bacillus, Marinobacter, Pseudomonas, Enterobacter, and Kocuria, were the dominating bacterial genera from the rhizosphere and phyllosphere of B. reptans. Most of the bacterial strains were resistant to heavy metals (Cd, Pb, and Cr) and showed antimicrobial activity against different pathogenic bacterial strains. The whole-genome sequence analysis of Pseudomonas putida BR-PH17, a strain isolated from the phyllosphere of B. reptans, was performed by using the Illumina sequencing approach. The BR-PH17 genome contained a chromosome with a size of 5774330 bp and a plasmid DNA with 80360 bp. In this genome, about 5368 predicted protein-coding sequences with 5539 total genes, 22 rRNAs, and 75 tRNA genes were identified. Functional analysis of chromosomal and plasmid DNA revealed a variety of enzymes and proteins involved in antibiotic resistance and biodegradation of complex organic pollutants. These results indicated that bacterial strains identified in this study could be utilized for bioremediation of heavy metal-contaminated soils and as a novel source of antimicrobial drugs.