Effectiveness of regdanvimab on mortality in COVID-19 infected patients on hemodialysis.

BACKGROUND: Although several therapeutic agents have been evaluated for the treatment of coronavirus disease 2019 (COVID-19), there are lack of effective and proven treatments for end-stage renal disease (ESRD). The present study aims to evaluate the effectiveness of regdanvimab on mortality in COVID-19-infected patients on hemodialysis (HD). METHODS: We conducted an observational retrospective study in 230 COVID-19-infected patients on HD, of whom 77 (33.5%) were administered regdanvimab alone or in combination with dexamethasone or remdesivir during hospitalization (regdanvimab group) and 153 patients (66.5%) were not (no regdanvimab group). The primary outcome was in-hospital mortality. We compared mortality rates according to the use of regdanvimab and investigated the factors associated with mortality. RESULTS: Fifty-nine deaths occurred during hospitalization, 49 in the no regdanvimab group (32.0%) and 10 in the regdanvimab group (13.0%), and the mortality rate was significantly higher in the no regdanvimab group than that in the regdanvimab group (p = 0.001). Multivariate Cox regression analysis showed that malignancy (p = 0.001), SPO2 of <95% at admission (p = 0.003), and administration of antibiotics and regdanvimab (p = 0.007 and p = 0.002, respectively) were significantly associated factors with mortality. CONCLUSION: Regdanvimab administration is beneficial in improving prognosis in hospitalized COVID-19 patients on HD. Considering the vulnerability to infection and high mortality of ESRD patients, regdanvimab may be considered as a therapeutic option in COVID-19 patients on HD.

Metabolic versatility of toluene-degrading, iron-reducing bacteria in tidal flat sediment, characterized by stable isotope probing-based metagenomic analysis.

DNA stable isotope probing and metagenomic sequencing were used to assess the metabolic potential of iron-reducing bacteria involved in anaerobic aromatic hydrocarbon degradation in oil spill-affected tidal flats. In a microcosm experiment, (13) C-toluene was degraded with the simultaneous reduction of Fe(III)-NTA, which was also verified by quasi-stoichiometric (13) C-CO2 release. The metabolic potential of the dominant member affiliated with the genus Desulfuromonas in the heavy DNA fraction was inferred using assembled scaffolds (designated TF genome, 4.40 Mbp with 58.8 GC mol%), which were obtained by Illumina sequencing. The gene clusters with peripheral pathways for toluene and benzoate conversion possessed the features of strict and facultative anaerobes. In addition to the class II-type benzoyl-CoA reductase (Bam) of strict anaerobes, the class I-type (Bcr) of facultative anaerobes was encoded. Genes related to the utilization of various anaerobic electron acceptors, including iron, nitrate (to ammonia), sulfur and fumarate, were identified. Furthermore, genes encoding terminal oxidases (caa3 , cbb3 and bd) and a diverse array of genes for oxidative stress responses were detected in the TF genome. This metabolic versatility may be an adaptation to the fluctuating availability of electron acceptors and donors in tidal flats.

Prediction of a time-to-event trait using genome wide SNP data.

BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. CONCLUSIONS: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data.

Gradient lasso for Cox proportional hazards model.

MOTIVATION: There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data. RESULTS: Results from simulation studies showed that the prediction model by gradient lasso recovers the prognostic genes. Also results from diffuse large B-cell lymphoma datasets and Norway/Stanford breast cancer dataset indicate that our method is very competitive compared with popular existing methods by Park and Hastie and Goeman in its computational time, prediction and selectivity. AVAILABILITY: R package glcoxph is available at http://datamining.dongguk.ac.kr/R/glcoxph.

Complete sequence and organization of the cucumber (Cucumis sativus L. cv. Baekmibaekdadagi) chloroplast genome.

The nucleotide sequence of the cucumber (Cucumis sativus L. cv. Baekmibaekdadagi) chloroplast genome was completed. The circular double-stranded DNA, consisting of 155,527 bp, contained a pair of inverted repeat regions (IRa and IRb) of 25,187 bp each, which were separated by small and large single copy regions of 86,879 and 18,274 bp, respectively. The presence and relative positions of 113 genes (76 peptide-encoding genes, 30 tRNA genes, four rRNA genes, and three conserved open reading frames) were identified. The major portion (55.76%) of the C. sativus chloroplast genome consisted of gene-coding regions (49.13% protein coding and 6.63% RNA regions; 27.81% LSC, 9.46% SSC and 18.49% IR regions), while intergenic spacers (including 20 introns) made up 44.24%. The overall G-C content of C. sativus chloroplast genome was 36.95%. Sixteen genes contained one intron, while two genes had two introns. The expansion/contraction manner of IR at IRb/LSC and IR/SSC border in Cucumis was similar to that of Lotus and Arabidopsis, and the manner at IRa/LSC was similar to Lotus and Nicotiana. In total, 56 simple sequence repeats (more than 10 bases) were identified in the C. sativus chloroplast genome.