The Long COVID Symptoms and Severity Score: Development, Validation, and Application.

OBJECTIVES: The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms and severity score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals with long COVID. METHODS: A 3-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against hierarchical clustering and the Gaussian Mixture Model. RESULTS: The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (rs = -0.55), EuroQol visual analog scale (rs = -0.368), Patient Health Questionnaire-9 (rs = 0.538), Beck Anxiety Inventory (rs = 0.689), and Insomnia Severity Index (rs = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies. CONCLUSIONS: The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for patients with long COVID.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection.

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5-2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

The LONELY GUY gene family: from mosses to wheat, the key to the formation of active cytokinins in plants.

LONELY GUY (LOG) was first identified in a screen of rice mutants with defects in meristem maintenance. In plants, LOG codes for cytokinin riboside 5'-monophosphate phosphoribohydrolase, which converts inactive cytokinin nucleotides directly to the active free bases. Many enzymes with the PGGxGTxxE motif have been misannotated as lysine decarboxylases; conversely not all enzymes containing this motif are cytokinin-specific LOGs. As LOG mutants clearly impact yield in rice, we investigated the LOG gene family in bread wheat. By interrogating the wheat (Triticum aestivum) genome database, we show that wheat has multiple LOGs. The close alignment of TaLOG1, TaLOG2 and TaLOG6 with the X-ray structures of two functional Arabidopsis thaliana LOGs allows us to infer that the wheat LOGs 1-11 are functional LOGs. Using RNA-seq data sets, we assessed TaLOG expression across 70 tissue types, their responses to various stressors, the pattern of cis-regulatory elements (CREs) and intron/exon patterns. TaLOG gene family members are expressed variously across tissue types. When the TaLOG CREs are compared with those of the cytokinin dehydrogenases (CKX) and glucosyltransferases (CGT), there is close alignment of CREs between TaLOGs and TaCKXs reflecting the key role of CKX in maintaining cytokinin homeostasis. However, we suggest that the main homeostatic mechanism controlling cytokinin levels in response to biotic and abiotic challenge resides in the CGTs, rather than LOG or CKX. However, LOG transgenics and identified mutants in rice variously impact yield, providing interesting avenues for investigation in wheat.