Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Neural-network approach to dynamic optimization of batch distillation: Application to a middle-vessel column

A framework is proposed to optimize the operation of batch columns with substantial reduction of the computational power needed to carry out the optimization calculations. The proposed framework relies on the use of an artificial neural network (ANN) based process model to be employed by the optimizer. To test the viability of the framework, the optimization of a pilot-plant middle-vessel batch column (MVBC) is considered. The maximum-product problem is formulated and solved by optimizing the column operating parameters, such as the reflux and reboil ratios and the batch time. It is shown that the ANN based model is capable of reproducing the actual plant dynamics with good accuracy, and that the proposed framework allows a large number of optimization studies to be carried out with little computational effort

Mathematical modeling of solid oxide fuel cells: A review

This paper presents a review of studies on mathematical modeling of solid oxide fuel cells (SOFCs) with respect to the tubular and planar configurations. In this work, both configurations are divided into five subsystems and the factors such as mass/energy/momentum transfer, diffusion through porous media, electrochemical reactions with and without CO oxidation, shift and reforming reactions, and polarization losses inside the subsystems are discussed. Using variety of fuels fed to SOFCs is issued and their effect on the system is compared briefly. A short review of solid oxide fuel cell configurations and different flow manifolding are also presented in this study. Novel models based on statistical data-driven approach existing in the literatures are considered shortly. Although many studies on solid oxide fuel cells modeling have been done, still more research needs to be done to improve the models in order to predict the fuel cell behaviors more accurately. At the end of this paper the works and studies that can be done for improving the fuel cell models is suggested and pointed by the authors

An Application of Analytic Hierarchy Process (AHP) for Sustainable Procurement of Construction Equipment: Multicriteria-Based Decision Framework for Malaysia

Sustainable procurement is an emerging theme in the construction industry across the globe. However, organizations in the construction industry often encounter impediments in improving environmental performance in construction projects, especially in procurement. Besides its other facets, procurement of construction equipment is inherited to be capital-intensive and vital for managing environmental concerns associated with built environment projects. In this regard, selection criteria in such procurement processes are generally supportive of considering cost and engineering specifications as key parameters. However, sustainability apprehensions in today’s Malaysian construction industry have mounted pressure on industry professionals to rethink their equipment acquisition strategies. The notion of green or sustainable procurement is still infancy for the Malaysian construction industry and facing challenges for embedding it in the current procurement practices. This research aims to address these apprehensions by considering six main criteria, namely, life cycle cost (LCC), performance (P), system capability (SC), operational convenience (OC), environmental impact (EI), and social benefits (SBs), and their 38 subcriteria towards procurement of sustainable construction equipment. A multicriteria-based equipment selection framework on the triple bottom line of sustainability in the context of the Malaysian construction industry has been developed and tested. The application of analytical hierarchy process (AHP) established the sustainable procurement index with a consistent sensitivity analysis results. As such, the proposed procurement index shall help decision-makers in the process of the acquisition of sustainable construction equipment in Malaysia