BackgroundPrevious reports individually identified different factors that predict death after burns. The authors employed the multi-center American Burn Association’s (ABA) National Burn Repository (NBR) to elucidate which parameters have the highest negative impact on burn mortality.MethodsWe audited data from the NBR v8.0 for the years 2002–2011 and included 137,061 patients in our study. The cases were stratified into two cohorts based on the primary outcome of death/survival and then evaluated for demographic data, intraoperative details, and their morbidity after admission. A multivariable regression analysis aimed to identify independent risk factors associated with mortality.ResultsA total of 3.3% of patients in this analysis did not survive their burn injuries. Of those, 52.0% expired within 7 days after admission. Patients in the mortality cohort were of older age (p < 0.001), more frequently female (p < 0.001), and had more pre-existing comorbidities (p < 0.001). Total body surface area (TBSA), inhalation injury, hospitalization time, and occurrence of complications were higher compared to survivors (p < 0.001). Lack of insurance (odds ratio (OR) = 1.84, confidence interval (CI) 1.38–2.46), diabetes (OR = 1.24, CI 1.01–1.53), any complication (OR = 4.09, CI 3.27–5.12), inhalation injury (OR = 3.84, CI 3.38–4.36), and the need for operative procedures (OR = 2.60, CI 2.20–3.08) were the strongest independent contributors to mortality after burns (p < 0.001). Age (OR = 1.07, CI 1.06–1.07) and TBSA (OR = 1.09, CI 1.09–1.09) were significant on a continuous scale (p < 0.001) while overall comorbidities were not a statistical risk factor.ConclusionUninsured status, inhalation injury, in-hospital complications, and operative procedures were the strongest mortality predictors after burns. Since most fatal outcomes (52.0%) occur within 7 days after injury, physicians and medical staff need to be aware of these risk factors upon patient admission to a burn center.

Background: mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on usersettable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. Results: In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene coexpression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56 Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. Conclusions: We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

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Erythrodermic psoriasis is characterized by diffuse erythema and scaling that can affect total body surface area and cause sepsis and death without treatment. 1 Vaccination has been associated with both guttate and plaque psoriasis flare-ups. 2 To date, we have not found a documented case of erythrodermic psoriasis flare due to the SARS-CoV-2 vaccine. Here, we report the observation of an erythrodermic psoriasis eruption in an adult patient who received the first dose of the Pfizer-BioNTech SARS-CoV-2 vaccine. F I G U R E 1 Initial presentation 6 days after onset of rash and 7 days after the initial dose of SARS-CoV-2 vaccine. (A) Confluent plaques on the torso and arms. (B) Confluent plaques on the back. (C) Annular erythematous plaques with some coalescing into confluent plaques with hyperkeratotic scales on the lower extremities with partial sparing of the medial thighs and sparing of the feet

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