Virological characteristics of the SARS-CoV-2 Omicron BA.2 spike.

Soon after the emergence and global spread of the SARS-CoV-2 Omicron lineage BA.1, another Omicron lineage, BA.2, began outcompeting BA.1. The results of statistical analysis showed that the effective reproduction number of BA.2 is 1.4-fold higher than that of BA.1. Neutralization experiments revealed that immunity induced by COVID vaccines widely administered to human populations is not effective against BA.2, similar to BA.1, and that the antigenicity of BA.2 is notably different from that of BA.1. Cell culture experiments showed that the BA.2 spike confers higher replication efficacy in human nasal epithelial cells and is more efficient in mediating syncytia formation than the BA.1 spike. Furthermore, infection experiments using hamsters indicated that the BA.2 spike-bearing virus is more pathogenic than the BA.1 spike-bearing virus. Altogether, the results of our multiscale investigations suggest that the risk of BA.2 to global health is potentially higher than that of BA.1.

Elevated Myl9 reflects the Myl9-containing microthrombi in SARS-CoV-2-induced lung exudative vasculitis and predicts COVID-19 severity.

The mortality of coronavirus disease 2019 (COVID-19) is strongly correlated with pulmonary vascular pathology accompanied by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-triggered immune dysregulation and aberrant activation of platelets. We combined histological analyses using field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy analyses of the lungs from autopsy samples and single-cell RNA sequencing of peripheral blood mononuclear cells to investigate the pathogenesis of vasculitis and immunothrombosis in COVID-19. We found that SARS-CoV-2 accumulated in the pulmonary vessels, causing exudative vasculitis accompanied by the emergence of thrombospondin-1-expressing noncanonical monocytes and the formation of myosin light chain 9 (Myl9)-containing microthrombi in the lung of COVID-19 patients with fatal disease. The amount of plasma Myl9 in COVID-19 was correlated with the clinical severity, and measuring plasma Myl9 together with other markers allowed us to predict the severity of the disease more accurately. This study provides detailed insight into the pathogenesis of vasculitis and immunothrombosis, which may lead to optimal medical treatment for COVID-19.

Suppression of Type I Interferon Signaling in Myeloid Cells by Autoantibodies in Severe COVID-19 Patients.

PURPOSE: Auto-antibodies (auto-abs) to type I interferons (IFNs) have been identified in patients with life-threatening coronavirus disease 2019 (COVID-19), suggesting that the presence of auto-abs may be a risk factor for disease severity. We therefore investigated the mechanism underlying COVID-19 exacerbation induced by auto-abs to type I IFNs. METHODS: We evaluated plasma from 123 patients with COVID-19 to measure auto-abs to type I IFNs. We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from the patients with auto-abs and conducted epitope mapping of the auto-abs. RESULTS: Three of 19 severe and 4 of 42 critical COVID-19 patients had neutralizing auto-abs to type I IFNs. Patients with auto-abs to type I IFNs showed no characteristic clinical features. scRNA-seq from 38 patients with COVID-19 revealed that IFN signaling in conventional dendritic cells and canonical monocytes was attenuated, and SARS-CoV-2-specific BCR repertoires were decreased in patients with auto-abs. Furthermore, auto-abs to IFN-α2 from COVID-19 patients with auto-abs recognized characteristic epitopes of IFN-α2, which binds to the receptor. CONCLUSION: Auto-abs to type I IFN found in COVID-19 patients inhibited IFN signaling in dendritic cells and monocytes by blocking the binding of type I IFN to its receptor. The failure to properly induce production of an antibody to SARS-CoV-2 may be a causative factor of COVID-19 severity.

Early Elevation of Cell-Free DNA After Acute Mesenteric Ischemia in Rats.

BACKGROUND: Acute mesenteric ischemia (AMI) is challenging to diagnose in the early phase. We tested the hypothesis that blood levels of cell-free DNA would increase early after AMI. In addition, proteome analysis was conducted as an exploratory analysis to identify other potential diagnostic biomarkers. METHODS: Mesenteric ischemia, abdominal sepsis, and sham model were compared in Sprague-Dawley rats. The abdominal sepsis model was induced by cecum puncture and mesenteric ischemia model by ligation of the superior mesenteric artery. Blood levels of cell-free DNA were measured 2 h and 6 h after wound closure. Shotgun proteome analysis was performed using plasma samples obtained at the 2 h timepoint; quantitative analysis was conducted for proteins detected exclusively in the AMI models. RESULTS: Blood cell-free DNA levels at 2 h after wound closure were significantly higher in the AMI model than in the sham and the abdominal sepsis models (P < 0.05). Cell-free DNA was positively correlated with the pathologic ischemia severity score (correlation coefficient 0.793-0.834, P < 0.001). Derivative proteome analysis in blood at 2-h time point revealed higher intensity of paraoxonase-1 in the AMI models than in the abdominal sepsis models; the significantly high blood paraoxonase-1 levels in the AMI models were confirmed in a separate quantitative analysis (P = 0.015). CONCLUSIONS: Cell-free DNA was demonstrated to be a promising biomarker for the early diagnosis of mesenteric ischemia in a rat model of AMI. Paraoxonase-1 may also play a role in the differential diagnosis of mesenteric ischemia from abdominal sepsis. The current results warrant further investigation in human studies.

Very Low Density Lipoprotein Receptor Sequesters Lipopolysaccharide Into Adipose Tissue During Sepsis.

OBJECTIVES: Obese patients have lower sepsis mortality termed the "obesity paradox." We hypothesized that lipopolysaccharide, known to be carried within lipoproteins such as very low density lipoprotein, could be sequestered in adipose tissue during sepsis; potentially contributing a survival benefit. DESIGN: Retrospective analysis. SETTING: University research laboratory. SUBJECTS AND PATIENTS: Vldlr knockout mice to decrease very low density lipoprotein receptors, Pcsk9 knockout mice to increase very low density lipoprotein receptor, and Ldlr knockout mice to decrease low density lipoprotein receptors. Differentiated 3T3-L1 adipocytes. Caucasian septic shock patients. INTERVENTIONS: We measured lipopolysaccharide uptake into adipose tissue 6 hours after injection of fluorescent lipopolysaccharide into mice. Lipopolysaccharide uptake and very low density lipoprotein receptor protein expression were measured in adipocytes. To determine relevance to humans, we genotyped the VLDLR rs7852409 G/C single-nucleotide polymorphism in 519 patients and examined the association of 28-day survival with genotype. MEASUREMENTS AND MAIN RESULTS: Lipopolysaccharide injected into mice was found in adipose tissue within 6 hours and was dependent on very low density lipoprotein receptor but not low density lipoprotein receptors. In an adipocyte cell line decreased very low density lipoprotein receptor expression resulted in decreased lipopolysaccharide uptake. In septic shock patients, the minor C allele of VLDLR rs7852409 was associated with increased survival (p = 0.010). Previously published data indicate that the C allele is a gain-of-function variant of VLDLR which may increase sequestration of very low density lipoprotein (and lipopolysaccharide within very low density lipoprotein) into adipose tissue. When body mass index less than 25 this survival effect was accentuated and when body mass index greater than or equal to 25 this effect was diminished suggesting that the effect of variation in very low density lipoprotein receptor function is overwhelmed when copious adipose tissue is present. CONCLUSIONS: Lipopolysaccharide may be sequestered in adipose tissue via the very low density lipoprotein receptor and this sequestration may contribute to improved sepsis survival.

Associations between fluid overload and outcomes in critically ill patients with acute kidney injury: a retrospective observational study.

Increased fluid overload (FO) is associated with poor outcomes in critically ill patients, especially in acute kidney injury (AKI). However, the exact timing from when FO influences outcomes remains unclear. We retrospectively screened intensive care unit (ICU) admitted patients with AKI between January 2011 and December 2015. Logistic or linear regression analyses were performed to determine when hourly %FO was significant on 90-day in-hospital mortality (primary outcome) or ventilator-free days (VFDs). In total, 1120 patients were enrolled in this study. Univariate analysis showed that a higher %FO was significantly associated with higher mortality from the first hour of ICU admission (odds ratio 1.34, 95% confidence interval 1.15-1.56, P < 0.001), whereas multivariate analysis adjusted with age, sex, APACHE II score, and sepsis etiology showed the association was significant from the 27th hour. Both univariate and multivariate analyses showed that a higher %FO was significantly associated with shorter VFDs from the 1st hour. The significant associations were retained during all following observation periods after they showed significance. In patients with AKI, a higher %FO was associated with higher mortality and shorter VFDs from the early phase after ICU admission. FO should be administered with a physiological target or goal in place from the initial phase of critical illness.

Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest.

Predicting out-of-hospital cardiac arrest (OHCA) events might improve outcomes of OHCA patients. We hypothesized that machine learning algorithms using meteorological information would predict OHCA incidences. We used the Japanese population-based repository database of OHCA and weather information. The Tokyo data (2005-2012) was used as the training cohort and datasets of the top six populated prefectures (2013-2015) as the test. Eight various algorithms were evaluated to predict the high-incidence OHCA days, defined as the daily events exceeding 75% tile of our dataset, using meteorological and chronological values: temperature, humidity, air pressure, months, days, national holidays, the day before the holidays, the day after the holidays, and New Year's holidays. Additionally, we evaluated the contribution of each feature by Shapley Additive exPlanations (SHAP) values. The training cohort included 96,597 OHCA patients. The eXtreme Gradient Boosting (XGBoost) had the highest area under the receiver operating curve (AUROC) of 0.906 (95% confidence interval; 0.868-0.944). In the test cohorts, the XGBoost algorithms also had high AUROC (0.862-0.923). The SHAP values indicated that the "mean temperature on the previous day" impacted the most on the model. Algorithms using machine learning with meteorological and chronological information could predict OHCA events accurately.

Prediction algorithm for ICU mortality and length of stay using machine learning.

Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922-0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876-0.908) and 0.889 (95% CI 0.849-0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.