Characterization of a novel exopolysaccharide from Acinetobacter rhizosphaerae with ability to enhance the salt stress resistance of rice seedlings.

We here describe the isolation of a novel exopolysaccharide from Acinetobacter rhizosphaerae, named ArEPS. The structure of ArEPS was characterized by analysis of the monosaccharide composition, molecular weight, infrared spectrum, methylation, and nuclear magnetic resonance spectrum. ArEPS was found to be an acidic heteropolysaccharide composed of glucose, galactose, galacturonic acid, glucuronic acid, mannose, and glucosamine; the molecular weight was 1533 kDa. Structural analysis showed that the main-chain structure of ArEPS predominantly comprised 1,3,6-ß-Glcp, 1,3,4-α-Galp, 1,2-ß-Glcp, 1,4-ß-GlcpA, 1,4-ß-GalpA, and the side-chain structure comprised 1,6-ß-Glcp, 1,3-ß-Galp, 1-α-Glcp, 1-ß-Galp, 1-α-Manp, 1,4,6-α-Glcp, 1,2,4-ß-Glcp, 1,2,3-ß-Glcp, and 1,3-ß-GlcpN. ArEPS significantly enhanced the tolerance of rice seedlings to salt stress. Specifically, plant height, fresh weight, chlorophyll content, and the K+/Na+ ratio increased by 51 %, 63 %, 29 %, and 162 %, respectively, and the malondialdehyde content was reduced by 45 % after treatment with 100 mg/kg ArEPS compared to treatment with 100 mM NaCl. Finally, based on the quadratic regression between fresh weight and ArEPS addition, the optimal ArEPS addition level was estimated to be 135.12 mg/kg. These results indicate the prospects of ArEPS application in agriculture.

Development of a nomogram to estimate the risk of community-acquired pneumonia in adults with acute asthma exacerbations.

OBJECTIVE: The aim of this study is to investigate the clinical characteristics of acute asthma exacerbations (AEs) with community-acquired pneumonia (CAP) in adults and establish a CAP prediction model for hospitalized patients with AEs. METHODS: We retrospectively collected clinical data from 308 patients admitted to Beijing Luhe Hospital, Capital Medical University, for AEs from December 2017 to August 2021. The patients were divided into CAP and non-CAP groups based on whether they had CAP. We used the Lasso regression technique and multivariate logistic regression analysis to select optimal predictors. We then developed a predictive nomogram based on the optimal predictors. The bootstrap method was used for internal validation. We used the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) to assess the nomogram's discrimination, accuracy, and clinical practicability. RESULTS: The prevalence of CAP was 21% (65/308) among 308 patients hospitalized for AEs. Independent predictors of CAP in patients hospitalized with an AE (P < 0.05) were C-reactive protein > 10 mg/L, fibrinogen > 4 g/L, leukocytes > 10 × 109 /L, fever, use of systemic corticosteroids before admission, and early-onset asthma. The AUC of the nomogram was 0.813 (95% CI: 0.753-0.872). The concordance index of internal validation was 0.794. The calibration curve was satisfactorily consistent with the diagonal line. The DCA indicated that the nomogram provided a higher clinical net benefit when the threshold probability of patients was 3% to 89%. CONCLUSIONS: The nomogram performed well in predicting the risk of CAP in hospitalized patients with AEs, thereby providing rapid guidance for clinical decision-making.

Development of a Diagnostic Nomogram to Predict CAP in Hospitalized Patients with AECOPD.

The purpose of this study was to establish a nomogram for predicting community-acquired pneumonia (CAP) in hospitalized patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). The retrospective cohort study included 1249 hospitalized patients with AECOPD between January 2012 and December 2019. The patients were divided into pneumonia-complicating AECOPD (pAECOPD) and non-pneumonic AECOPD (npAECOPD) groups. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to identify prognostic factors. A prognostic nomogram model was established, and the bootstrap method was used for internal validation. Discrimination and calibration of the nomogram model were evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Logistic and LASSO regression analysis showed that C-reactive protein (CRP) >10 mg/L, albumin (Alb) <40 g/L, alanine transferase (ALT) >50 U/L, fever, bronchiectasis, asthma, previous hospitalization for pAECOPD in the past year (Pre-H for pAECOPD), and age-adjusted Charlson score (aCCI) ≥6 were independent predictors of pAECOPD. The area under the ROC curve (AUC) of the nomogram model was 0.712 (95% CI: 0.682-0.741). The corrected AUC of internal validation was 0.700. The model had well-fitted calibration curves and good clinical usability DCA curve. A nomogram model was developed to assist clinicians in predicting the risk of pAECOPD.China Clinical Trials Registry: ChiCTR2000039959.