IntroductionAbnormal body temperatures (Tb) are frequently seen in patients with severe sepsis. However, the relationship between Tb abnormalities and the severity of disease is not clear. This study investigated the impact of Tb on disease severity and outcomes in patients with severe sepsis.MethodsWe enrolled 624 patients with severe sepsis and grouped them into 6 categories according to their Tb at the time of enrollment. The temperature categories (≤35.5°C, 35.6–36.5°C, 36.6–37.5°C, 37.6–38.5°C, 38.6–39.5°C, ≥39.6°C) were based on the temperature data of the Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring. We compared patient characteristics, physiological data, and mortality between groups.ResultsPatients with Tb of ≤36.5°C had significantly worse sequential organ failure assessment (SOFA) scores when compared with patients with Tb >37.5°C on the day of enrollment. Scores for APACHE II were also higher in patients with Tb ≤35.5°C when compared with patients with Tb >36.5°C. The 28-day and hospital mortality was significantly higher in patients with Tb ≤36.5°C. The difference in mortality rate was especially noticeable when patients with Tb ≤35.5°C were compared with patients who had Tb of >36.5°C. Although mortality did not relate to Tb ranges of ≥37.6°C as compared to reference range of 36.6–37.5°C, relative risk for 28-day mortality was significantly greater in patients with 35.6–36.5°C and ≤35.5°C (odds ratio; 2.032, 3.096, respectively). When patients were divided into groups based on the presence (≤36.5°C, n = 160) or absence (>36.5°C, n = 464) of hypothermia, disseminated intravascular coagulation (DIC) as well as SOFA and APACHE II scores were significantly higher in patients with hypothermia. Patients with hypothermia had significantly higher 28-day and hospital mortality rates than those without hypothermia (38.1% vs. 17.9% and 49.4% vs. 22.6%, respectively). The presence of hypothermia was an independent predictor of 28-day mortality, and the differences between patients with and without hypothermia were observed irrespective of the presence of septic shock.ConclusionsIn patients with severe sepsis, hypothermia (Tb ≤36.5°C) was associated with increased mortality and organ failure, irrespective of the presence of septic shock.Trial registrationUMIN-CTR ID UMIN000008195

IntroductionTo validate the Japanese Association for Acute Medicine (JAAM) disseminated intravascular coagulation (DIC) scoring system in patients with severe sepsis, we conducted a multicenter, prospective study at 15 critical care centers in tertiary care hospitals.MethodsThis study included 624 severe sepsis patients. JAAM DIC was scored on the day of diagnosis of severe sepsis (day 1) and day 4. Scores for disease severity and organ dysfunction were also evaluated.ResultsThe prevalence of JAAM DIC was 46.8% (292/624), and 21% of the DIC patients were scored according to the reduction rate of platelets. The JAAM DIC patients were more seriously ill and exhibited more severe systemic inflammation, a higher prevalence of multiple organ dysfunction syndrome (MODS) and worse outcomes than the non-DIC patients. Disease severity, systemic inflammation, MODS and the mortality rate worsened in accordance with an increased JAAM DIC score on day 1. The Kaplan-Meier curves demonstrated lower 1-year survival in the JAAM DIC patients than in those without DIC (log-rank test P <0.001). The JAAM DIC score on day 1 (odds ratio = 1.282, P <0.001) and the Delta JAAM DIC score (odds ratio = 0.770, P <0.001) were independent predictors of 28-day death. Dynamic changes in the JAAM DIC score from days 1 to 4 also affected prognoses. The JAAM DIC scoring system included all patients who met the International Society on Thrombosis and Haemostasis overt DIC criteria on day 1. The International Society on Thrombosis and Haemostasis scoring system missed a large number of nonsurvivors recognized by the JAAM scoring system.ConclusionsThe JAAM DIC scoring system exhibits good prognostic value in predicting MODS and poor prognosis in patients with severe sepsis and can detect more patients requiring treatment. Conducting repeated daily JAAM scoring increases the ability to predict the patient's prognosis.

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

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To identify the proteins induced by Fe deficiency, we have compared the proteins of Fe-sufficient and Fe-deficient barley (Hordeum vulgare L.) roots by two-dimensional polyacrylamide gel electrophoresis. Peptide sequence analysis of induced proteins revealed that formate dehydrogenase (FDH), adenine phosphoribosyltransferase, and the Ids3 gene product (for Fe deficiency-specific) increased in Fe-deficient roots. FDH enzyme activity was detected in Fe-deficient roots but not in Fe-sufficient roots. A cDNA encoding FDH (Fdh) was cloned and sequenced. Fdh expression was induced by Fe deficiency. Fdh was also expressed under anaerobic stress and its expression was more rapid than that induced by Fe deficiency. Thus, the expression of Fdh observed in Fe-deficient barley roots appeared to be a secondary effect caused by oxygen deficiency in Fe-deficient plants.In Fe-deficient calcareous soils graminaceous plants secrete mugineic acid family phytosiderophores, which are natural Fe chelators, from the roots (Takagi, 1976) to solubilize Fe required for plant growth. This Fe-acquisition mechanism in graminaceous plants is called strategy II and in nongraminaceous plants it is called strategy I (Takagi et al., 1984;Marschner et al., 1986). The pathway of the biosynthesis of mugineic acid family phytosiderophores has been established Nishizawa, 1987, 1989;Shojima et al., 1989Shojima et al., , 1990Mori et al., 1990;Ma and Nomoto, 1993). Among the enzymes involved in this biosynthetic pathway, Higuchi et al. (1994Higuchi et al. ( , 1996 purified nicotianamine synthase and Kanazawa et al. (1994) purified nicotianamine aminotransferase. Comparison of 2D profiles of proteins in barley (Hordeum vulgare L.) roots under Fesufficient and Fe-deficient conditions (Suzuki et al., 1995(Suzuki et al., , 1997 allowed us to identify a 36-kD protein that was specifically induced by Fe deficiency. In addition, several genes related to the Fe-deficiency response have been reported: Ids1 (Okumura et al., 1991), Ids2 (Okumura et al., 1994), and Ids3 (Nakanishi et al., 1993. In this study, we characterized several other proteins induced by Fedeficiency stress in barley roots, one of which was identified as FDH. FDH was induced not only by Fe deficiency but also by anaerobic stress. The relationship between Fe deficiency and anaerobic stress in barley roots is discussed. MATERIALS AND METHODS Plant Material and Growth ConditionsSeeds of barley (Hordeum vulgare L. cv Ehimehadaka no. 1) were germinated at room temperature on paper towels soaked with distilled water. Plants were transferred 4 d after germination to a plastic net floating on tap water at pH 5.5 in a greenhouse under natural light. On d 10, plants were transferred to a continuously aerated nutrient solution of the following composition: 0.7 mm K 2 SO 4 , 0.1 mm KCl, 0.1 mm KH 2 PO 4 , 2.0 mm Ca(NO 3 ) 2 , 0.5 mm MgSO 4 , 10 m H 3 BO 3 , 0.5 m MnSO 4 , 0.2 m CuSO 4 , 0.5 m ZnSO 4 , 0.01 m (NH 4 ) 6 Mo 7 O 24 , and 0.1 mm Fe-EDTA. The pH of the culture solution was adjusted to 5.5 da...