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Biology subjects

Karami, K.

Publications and source records attributed to Karami, K..

2 recordsLinked to original sources

Molecular responses of chicken embryos to maternal heat stress through DNA methylation and gene expression

Climate change, with its repercussions on agriculture, is one of the most important adaptation challenges for livestock production. Poultry production is a major source of proteins for human consumption all over the world. With a growing human population, improving poultrys adaptation to environmental constraints becomes critical. Extensive evidence highlights the influence of environmental variations on epigenetic modifications. The aim of this paper is therefore to explore chickens molecular response to maternal heat stress. We employed Reduced Representation Bisulfite Sequencing (RRBS) to generate genome-wide single-base resolution DNA methylation profiling and RNA sequencing (RNA-seq) to profile the transcriptome of the brains of embryos hatched from dams reared under either heat stress (32 {degrees}C) or thermoneutrality (22{degrees}C). We detected 289 significant differentially methylated CpG sites (DMCs) and one differentially methylated region (DMR) between heat stressed and control groups. These DMCs were associated with 357 genes involved in processes such as cellular response to stimulus, developmental processes and immune function. In addition, we identified 11 genes differentially expressed between the two groups of embryos, and identified ATP9A as a target gene of maternal heat stress on offspring. This study provides a body of fundamental knowledge on adaptive mechanisms concerning heat tolerance in chickens.

genomics↗

Predictive Power of Machine Learning Models for Relapse Outcomes in Acute Myeloid Leukemia: Unveiling Key Genes and Pathways for Improved Patient Management

Acute Myeloid Leukemia (AML) is a challenging form of blood cancer requiring accurate relapse prediction for effective therapy and patient management. In this study, we applied multiple machine learning techniques to a dataset of AML patients in order to develop a reliable model for predicting relapse and guiding treatment decisions. We utilized various feature selection methods to identify the most relevant features associated with relapse. Additionally, we investigated gene ontology using the Gene Ontology (GO) database to gain insights into the biological processes and KEGG pathways related to the selected features. Our findings revealed key genes and pathways implicated in AML relapse. Among the machine learning models, Decision Tree (DT) showed the highest accuracy in predicting relapse outcomes. Furthermore, we compared the performance of DT models across different feature selections, highlighting the significance of specific factors such as MCL1, WBC, HGB, and BAD.p112 in relapse prediction. The results of our study have important implications for tailoring treatment plans and improving patient outcomes in AML. By accurately identifying patients at high risk of relapse, our model can aid in early interventions and personalized therapies. Ultimately, our research contributes to advancing the field of machine learning in AML and lays the foundation for developing effective strategies to combat relapse in this disease.

cancer biology↗