Computational biology and chemistry最新文献

Background: Pediatric septic arthritis, driven by Staphylococcus aureus, leads to substantial morbidity due to the host's complex inflammatory response. This study integrates bioinformatics analyses to map the genomic and immune profiles of pediatric septic arthritis, aiming to identify key biomarkers and therapeutic targets.

Methods: An integrative bioinformatics approach was adopted to analyze gene expression datasets from the GEO database, focusing on pediatric septic arthritis. DEGs were identified using GEO2R, and gene co-expression networks were generated via GeneMANIA. STRING database and Cytoscape software facilitated PPI network construction. DAVID enabled functional enrichment analysis to elucidate biological processes and pathways, while iRegulon predicted transcription factor regulation. CIBERSORT provided a detailed profile of immune cell alterations in the condition.

Results: From the datasets analyzed, 576 DEGs were extracted, with 35 shared between the two datasets, revealing an innate immunity signature with notable hub genes such as MPO and ELANE, indicative of a pronounced neutrophilic response. Functional enrichment analysis highlighted pathways pertinent to antimicrobial defense and NET formation. Key transcription factors, including PBX1, POLR2A, and STAT3, were identified as potential modulators of these pathways. Immune profiling demonstrated significant shifts in cell populations, with increased plasma cells and reduced CD4+ naïve T cells.

Conclusions: This study elucidates the complex genomic and immunological milieu of pediatric septic arthritis, uncovering potential biomarkers and signaling pathways for targeted therapeutic intervention. These findings underscore the preeminence of innate immune mechanisms in the disease's pathology and offer a foundation for future research to explore diagnostic and treatment innovations. Translation of these bioinformatics discoveries into clinical applications requires further validation and consideration of the limitations inherent to gene expression data and its interpretation.

The rapid growth of abnormal cells in the brain presents a serious risk to the health of humans as it can result in death. Since these tumors have a varied range of shapes, sizes, and positions, identifying Brain Tumors (BTs) is challenging. Magnetic Resonance Images (MRI) are most utilized for identifying malignant tumors. This paper develops a new approach, named EfficientNet-Deep batch normalized eLUAlexnet (EfficientNet-DbneAlexnet) for detecting BTs. Firstly, the input MRI image is transmitted for image enhancement. Here, the image is enhanced by the Piecewise Linear Transformation (PLT). After this, skull stripping is carried out, which is performed by the Fuzzy Local Information C Means (FLICM). Following this, the tumor area in the image is segmented with the help of a Projective Adversarial Network (PAN). The segmented image is later applied to the feature extraction module, wherein features like textural and statistical features are extracted. Finally, the BT detection is accomplished using the developed EfficientNet-DbneAlexnet, which is created by assimilating EfficientNet and Deep batch normalized eLUAlexnet (DbneAlexnet). The results demonstrate that EfficientNet-DbneAlexnet obtained a sensitivity of 90.36 %, accuracy of 92.77 %, and specificity of 91.82 %.