Background: The prognostic value of right ventricular (RV) function in chronic heart failure (HF) has lately been well established. However, research on its role in acute heart failure (AHF) is sparse. Results: This study comprised 195 patients, aged between 18 and 80 years, with acute left-sided heart failure (HF) and a left ventricular ejection fraction (LVEF) < 50%. Patients with LVEF ≥ 50%, mechanical ventilatory or circulatory support, poor echocardiographic windows, prosthetic valves, congenital heart diseases, infective endocarditis, and/or life expectancy < 1 year due to non-cardiac causes were excluded. The study participants’ mean age was 57.7 ± 10.9 years, and 74.9% were males. Coronary artery disease was present in 80.5% of patients. The mean LVEF was 31% ± 8.7. RV dysfunction (RVD), defined as tricuspid annular plane systolic excursion (TAPSE) < 17 mm, RV S' < 9.5 cm/s and/or RV fractional area change (FAC) < 35%, was identified in 48.7% of patients. The RV was dilated in 67.7% of the patients. RVD was significantly associated with a longer HF duration, atrial fibrillation, and idiopathic dilated cardiomyopathy. The primary outcome, a 6-month composite of cardiovascular death or hospitalization for worsening HF (HHF), occurred in 42% of the participants. Cardiovascular mortality and HHF occurred in 30.5% and 23.9% of the patients, respectively. The primary endpoint and longer CCU stays were significantly more common in patients with RVD than in those with normal RV function. RV dilatation was significantly associated with the primary outcome, whether alone or in combination with RVD. Multivariate regression analysis showed that only RV global longitudinal strain (GLS) independently predicted poor outcomes. Conclusions: RVD and RV dilatation strongly predict CV death and HHF in patients with AHF and LVEF < 50%. Multivariate analysis showed that RV GLS was the only predictor of a composite of CV death and HHF.

This study presents an innovative methodology for Highway Route Selection (HRS), specifically tailored for mountainous terrains. The approach focuses on selecting the most suitable route for road alignment while prioritizing road safety and hydrological and geological considerations. Through systematic analysis, the methodology evaluates alternative road alignments by examining various risk factors related to geometric design, hydrological, and geological impacts. Utilizing Fault-Tree Analysis (FTA), 14 key design factors related to geometric design and environmental factors are identified. The results demonstrate the effectiveness of the methodology in selecting road alignments that enhance safety and mitigate environmental risks. A case study is presented where a 90-km segment of a road in Egypt’s Golden Triangle Project, characterized by challenging terrain and diverse geological features, is examined. Through detailed analysis, the study identifies critical design factors to enhance road safety and minimize environmental impact. The methodology’s comprehensive approach offers insights into road design, providing a quantitative framework for decision-making and mitigation strategies.

The ability of wound dressing materials to tackle skin pathogens colonization that is associated with open wound infections is limited. Recently, green-synthesized metal oxide nanoparticles has received a lot of attention to overcome this limitation. However, titanium dioxide nanoparticles (TiO2-NPs) exhibit exceptional antibacterial properties. In this work, several concentrations (0, 1, 3, and 5 wt.%) of TiO2 NPs prepared using Aloe vera leaf extract were added to a blend of polyvinyl alcohol and sodium alginate (PVA:SA). This nanocomposite was designed to enhance the healing process of wounds. The interaction between the PVA:SA composite and the TiO2 NPs was confirmed by FTIR. The thermal behavior of the nanocomposite films was investigated using DSC and TGA. The experimental results indicate that the glass transition temperatures of the nanocomposites increased by increasing the added amount of TiO2 NPs to be 53.7 °C (1 wt.%), 55.8 °C (3 wt.%), and 60.6 °C (5 wt.%), which were consistently lower than the glass transition temperature of the matrix material (69.6 °C). The Dynamic Mechanical Analysis was examined. The nanocomposite doped with 5 wt.% of TiO2 NPs detected a high storage modulus (21.6 × 108). Based on swelling and degradation studies, the prepared PVA:SA:TiO2 nanocomposite films have an excellent swelling rate, and the inclusion of TiO2 NPs increases the stability of the polymeric matrix. The PVA:SA:TiO2 nanocomposite films exhibited a superior antibacterial efficacy against Gram-positive bacteria such as Bacillus cereus and Staphylococcus aureus, compared to their effectiveness against Gram-negative bacteria like Escherichia coli. Moreover, the nanocomposite films were biocompatible with Human Skin Fibroblast. Therefore, the developed PVA:SA:TiO2 nanocomposite films suit wound dressing applications.

Autoimmune hemolytic anemia (AIHA) is a rare disease in children, sometimes associated with acute, life-threatening, rapidly progressive course requiring prompt management. The aim of our study is to describe the role and outcome of plasma exchange in the acute management of pediatric patients with AIHA requiring transfusion and refractory to high doses of corticosteroids.

Glucose-regulated protein 78 (GRP78), also termed HSPA5, was widely studied in cancer. It was recently approved that GRP78 has nuclear localization potential that sheds light on its role in cancer development. The inhibitor of DNA binding and differentiation 2 (ID2) is the nuclear component that associates with GRP78. The interaction between these two proteins is not understood clearly. In the current study, the binding pattern of GRP78/ID2 is predicted using computational methods. Protein-protein docking is used along with molecular dynamics simulation. The substrate binding domain β of GRP78 can stably interact with the loop region (C42-S60) of ID2 as predicted in this study. This paves the way for a possible destabilizer for this association and cancer eradication. © 2024 Elsevier GmbH

SummaryBackground
Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery.
Methods
Data from an international, prospective cohort study were used to develop a novel prognostic risk model for pulmonary complications after elective surgery in adult patients (aged ≥18 years) across all operation and disease types. The primary outcome measure was postoperative pulmonary complications at 30 days after surgery, which was a composite of pneumonia, acute respiratory distress syndrome, and unexpected mechanical ventilation. Model development with candidate predictor variables was done in the GlobalSurg-CovidSurg Week dataset (global; October, 2020). Two structured machine learning techniques were explored (XGBoost and the least absolute shrinkage and selection operator [LASSO]), and the model with the best performance (GSU-Pulmonary Score) underwent internal validation using bootstrap resampling. The discrimination and calibration of the score were externally validated in two further prospective cohorts: CovidSurg-Cancer (worldwide; February to August, 2020, during the COVID-19 pandemic) and RECON (UK and Australasia; January to October, 2019, before the COVID-19 pandemic). The model was deployed as an online web application. The GlobalSurg-CovidSurg Week and CovidSurg-Cancer studies were registered with ClinicalTrials.gov, NCT04509986 and NCT04384926.
Findings
Prognostic models were developed from 13 candidate predictor variables in data from 86 231 patients (1158 hospitals in 114 countries). External validation included 30 492 patients from CovidSurg-Cancer (726 hospitals in 75 countries) and 6789 from RECON (150 hospitals in three countries). The overall rates of pulmonary complications were 2·0% in derivation data, and 3·9% (CovidSurg-Cancer) and 4·7% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0·786, 95% CI 0·774–0·798 vs 0·785, 0·772–0·797), was more explainable, and required fewer covariables. The final GSU-Pulmonary Score included ten predictor variables and showed good discrimination and calibration upon internal validation (AUROC 0·773, 95% CI 0·751–0·795; Brier score 0·020, calibration in the large [CITL] 0·034, slope 0·954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0·746, 95% CI 0·733–0·760; Brier score 0·036, CITL 0·109, slope 1·056), but with some miscalibration in RECON data (AUROC 0·716, 95% CI 0·689–0·744; Brier score 0·045, CITL 1·040, slope 1·009).
Interpretation
This novel prognostic risk score uses simple predictor variables available at the time of a decision for elective surgery that can accurately stratify patients’ risk of postoperative pulmonary complications, including during SARS-CoV-2 outbreaks. It could inform surgical consent, resource allocation, and hospital-level prioritisation as elective surgery is upscaled to address global backlogs.
Funding
National Institute for Health Research.