Mapping the Minnesota Living with Heart Failure Questionnaire (MLHFQ) to SF-6Dv2 in Chinese patients with heart failure.

PURPOSE: Mapping the Minnesota Living with Heart Failure Questionnaire (MLHFQ) to SF-6Dv2 in Chinese patients with chronic heart failure, and to obtain the health utility value for health economic assessment. METHODS: Four statistical algorithms, including ordinary least square method (OLS), Tobit model, robust MM estimator (MM) and censored least absolute deviations (CLAD), were used to establish the alternative model. Models were validated by using a tenfold cross-validation technique. The mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the prediction performance of the model. The Spearman correlation coefficient and Intraclass Correlation Coefficients (ICC) were used to examine the relationship between the predicted and observed SF-6Dv2 values. RESULTS: A total of 195 patients with chronic heart failure were recruited from 3 general hospitals in Beijing. The MLHFQ summary score and domain scores of the study sample were negatively correlated with SF-6Dv2 health utility value. The OLS regression model established based on the MLHFQ domain scores was the optimal fitting model and the predicted value was highly positively correlated with the observed value. CONCLUSION: The MLHFQ can be mapped to SF-6Dv2 by OLS, which can be used for health economic assessment of cardiovascular diseases such as chronic heart failure.

Mitochondrial genome complexity in Stemona sessilifolia: nanopore sequencing reveals chloroplast gene transfer and DNA rearrangements.

Mitochondria are semi-autonomous organelles in eukaryotic cells with their own genome. Plant mitogenomes differ from animal mitogenomes in size, structure, and repetitive DNA sequences. Despite larger sizes, plant mitogenomes do not have significantly more genes. They exhibit diverse structures due to variations in size, repetitive DNA, recombination frequencies, low gene densities, and reduced nucleotide substitution rates. In this study, we analyzed the mitochondrial genome of Stemona sessilifolia using Nanopore and Illumina sequencing. De-novo assembly and annotation were conducted using Unicycler, Geseq, tRNAscan-SE and BLASTN, followed by codon usage, repeat sequence, RNA-editing, synteny, and phylogenetic analyses. S. sessilifolia's mitogenome consisted of one linear contig and six circular contigs totaling 724,751 bp. It had 39 protein-coding genes, 27 tRNA genes, and 3 rRNA genes. Transfer of chloroplast sequences accounted for 13.14% of the mitogenome. Various analyses provided insights into genetic characteristics, evolutionary dynamics, and phylogenetic placement. Further investigations can explore transferred genes' functions and RNA-editing's role in mitochondrial gene expression in S. sessilifolia.

Search for key genes, key signaling pathways, and immune cell infiltration in uterine fibroids by bioinformatics analysis.

Uterine fibroids grow in the myometrium and are benign tumors. The etiology and molecular mechanism are not fully understood. Here, we hope to study the potential pathogenesis of uterine fibroids by bioinformatics. Our aim is to search for the key genes, signaling pathways and immune infiltration about the development of uterine fibroids. The GSE593 expression profile was downloaded from the Gene Expression Omnibus database, which contains 10 samples, including 5 uterine fibroids samples and 5 normal controls. Bioinformatics methods were used to find differentially expressed genes (DEGs) in tissues and further analyze the DEGs. R (version 4.2.1) software was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis of DEGs in uterine leiomyoma tissues and normal control. STRING database was used to generate protein-protein interaction (PPI) networks of key genes. Then, CIBERSORT was used to assess the infiltration of immune cells in uterine fibroids. A total of 834 DEGs were identified, of which 465 were up-regulated and 369 were down-regulated. GO andKEGG pathway analysis showed that the DEGs were mainly concentrated in extracellular matrix and cytokine related signaling pathways. We identified 30 key genes in DEGs from the PPI network. There were some differences in infiltration immunity between the 2 tissues. This study indicated that screening key genes, signaling pathways and immune infiltration by comprehensive bioinformatics analysis is helpful to understand the molecular mechanism of uterine fibroids and provide new insights into understanding the molecular mechanism.