PubMed · 9894499
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E P Turton. 1998. Ruptured aortic aneurysms.. https://doi.org/10.1016/s1078-5884(98)80250-5
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Abdominal aortic aneurysm (AAA) is a prevalent and fatal cardiovascular condition characterized by a high incidence rate and nonspecific clinical manifestations, with no effective preventive or therapeutic measures currently available. Piwi-interacting RNAs (piRNAs) have been identified as significant biomarkers for disease diagnosis due to their essential functions in transposon suppression, maintenance of genomic stability, immune response, and epigenetic modulation. The piRNA is intimately associated with various diseases such as cardiac hypertrophy, tumors, and neurodegeneration, yet its role in AAA is unclear. In this study, we employed gene sequencing to analyze the piRNA expression profiles in AAA vascular tissues and predicted variations in their target genes. Our findings revealed a total of 1368 piRNAs with abnormal expression in the AAA group relative to the control group, including 1240 up-regulated and 128 down-regulated piRNAs (|log2(fold change)| ≥ 1.0), with 82 demonstrating significant differences (P < 0.05). Through bioinformatics analysis, it was determined that the Wnt signaling pathway, calcium signaling, TNF-α and the p53 pathway are crucial mechanisms by which piRNAs contribute to the development of AAA. RT-qPCR confirmed that hsa_piR_011324 was the most significantly up-regulated piRNA in AAA (P < 0.0001), corroborating RNA sequencing results. Further results indicate that hsa_piR_011324 promotes phenotypic transformation of human aortic vascular smooth muscle cells (HAVSMCs), enhances the activity of matrix metalloproteinases (MMPs), increased up-regulation of inflammation-related markers IL-1β and TNF-α, and induces apoptotic processes. In conclusion, the present study emphasizes the important regulatory role of hsa_piR_011324 in AAA, suggesting that it holds promise as a prospective target for diagnostic and therapeutic intervention.
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It is well documented that the commonly used Pearson chi-square and deviance statistics are not adequate for assessing goodness-of-fit in logistic regression models when continuous covariates are modelled. In recent years, several methods have been proposed which address this shortcoming in the binary logistic regression setting or assess model fit differently. However, these techniques have typically not been extended to the ordinal response setting and few techniques exist to assess model fit in that case. We present the modified Pearson chi-square and deviance tests that are appropriate for assessing goodness-of-fit in ordinal response models when both categorical and continuous covariates are present. The methods have good power to detect omitted interaction terms and reasonable power to detect failure of the proportional odds assumption or modelling the wrong functional form of a continuous covariate. These tests also provide immediate information as to where a model may not fit well. In addition, the methods are simple to understand and implement, and are non-specific. That is, they do not require prespecification of a type of lack-of-fit to detect.