Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.

BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)-a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. METHODS: Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. RESULTS: Our analyses revealed that eleven PyMGs-DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2-are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. CONCLUSIONS: This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma's molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.

Inequalities in Violent Death across Income Levels among Young Males and Females in Countries of the Americas.

BACKGROUND: Violent deaths (i.e., those due to road traffic injury, homicide, and suicide) are among the most important causes of premature and preventable mortality in young people. This study aimed at exploring inequalities in violent death across income levels between males and females aged 10 to 24 years from the Americas in 2015, the SDG baseline year. METHODS: In a cross-sectional ecological study design, eleven standard summary measures of health inequality were calculated separately for males and females and for each cause of violent death, using age-adjusted mortality rates and average income per capita for 17 countries, which accounted for 87.9% of the target population. RESULTS: Premature mortality due to road traffic injury and homicide showed a pro-poor inequality pattern, whereas premature mortality due to suicide showed a pro-rich inequality pattern. These inequalities were statistically significant (p < 0.001), particularly concentrated among young males, and dominated by homicide. The ample array of summary measures of health inequality tended to generate convergent results. CONCLUSIONS: Significant inequalities in violent death among young people seems to be in place across countries of the Americas, and they seem to be socially determined by both income and gender. These findings shed light on the epidemiology of violent death in young people and can inform priorities for regional public health action. However, further investigation is needed to confirm inequality patterns and to explore underlying mechanisms, age- and sex-specific vulnerabilities, and gender-based drivers of such inequalities.