PubMed · 10189451
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C F Gonsalves. 1999. The hyperattenuating crescent sign.. https://doi.org/10.1148/radiology.211.1.r99ap1137
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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.
Assessing goodness-of-fit in logistic regression models can be problematic, in that commonly used deviance or Pearson chi-square statistics do not have approximate chi-square distributions, under the null hypothesis of no lack of fit, when continuous covariates are modelled. We present two easy to implement test statistics similar to the deviance and Pearson chi-square tests that are appropriate when continuous covariates are present. The methodology uses an approach similar to that incorporated by the Hosmer and Lemeshow goodness-of-fit test in that observations are classified into distinct groups according to fitted probabilities, allowing sufficient cell sizes for chi-square testing. The major difference is that the proposed tests perform this grouping within the cross-classification of all categorical covariates in the model and, in some situations, allow for a more powerful assessment of where model predicted and observed counts may differ. A variety of simulations are performed comparing the proposed tests to the Hosmer-Lemeshow test.
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