Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03–4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55–5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14–1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37–0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17–1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20–1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45–1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80–13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10–1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32–1.67) and 365 days (RR 1.54, 95%CI 1.21–1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section

Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data. Author Summary The ability of cells to survive and grow depends on their ability to metabolize nutrients and create products vital for cell function. This is done through a complex network of reactions controlled by many genes. Changes in cellular metabolism play a role in a wide variety of diseases. However, despite the availability of genome sequences and of genome-scale expression data, which give information about which genes are present and how active they are, our ability to use these data to understand changes in cellular metabolism has been limited. We present a new approach to this problem, linking gene expression data with models of cellular metabolism. We apply the method to predict the effects of drugs and agents on Mycobacterium tuberculosis (M. tb). Virulence, growth in human hosts, and drug resistance are all related to changes in M. tb's metabolism. We predict the effects of a variety of conditions on the production of mycolic acids, essential cell wall components. Our method successfully identifies seven of the eight known mycolic acid inhibitors in a compendium of 235 conditions, and identifies the top anti-TB drugs in this dataset. We anticipate that the method will have a range of applications in metabolic engineering, the characterization of disease states, and drug discovery.: National Institute of Allergy and Infectious Disease; National Institutes of Health (HHSN 26620040000IC); Department of Health and Human Services (HHSN266200400001C) National Institue of Allergy and Infectious Diseases (1U19AI076217, R01 071155, 014334-001); the Bill & Melinda Gates Foundation Dedicated Tuberculosis Gene Expression Database; the Ellison Medical Foundation (ID-SS-0693-04); Burroughs Wellcome Fun

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

ICAR: endoscopic skull‐base surgery

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Effect of Milling on DSC Thermogram of Excipient Adipic Acid

The purpose of this research was to investigate why and how mechanical milling results in an unexpected shift in differential scanning calorimetry (DSC) measured fusion enthalpy (∆fusH) and melting point (Tm) of adipic acid, a pharmaceutical excipient. Hyper differential scanning calorimetry (hyper-DSC) was used to characterize adipic acid before and after ball-milling. An experimental study was conducted to evaluate previous postulations such as electrostatic charging using the Faraday cage method, crystallinity loss using powder X-ray diffraction (PXRD), thermal annealing using DSC, impurities removal using thermal gravimetric analysis (TGA) and Karl Fischer titration. DSC thermograms showed that after milling, the values of ∆fusH and Tm were increased by approximately 9% and 5 K, respectively. Previous suggestions of increased electrostatic attraction, change in particle size distribution, and thermal annealing during measurements did not explain the differences. Instead, theoretical analysis and experimental findings suggested that the residual solvent (water) plays a key role. Water entrapped as inclusions inside adipic acid during solution crystallization was partially evaporated by localized heating at the cleaved surfaces during milling. The correlation between the removal of water and melting properties measured was shown via drying and crystallization experiments. These findings show that milling can reduce residual solvent content and causes a shift in DSC results

Differences in osteocyte density and bone histomorphometry between men and women and between healthy and osteoporotic subjects

Bone defects related to osteoporosis develop with increasing age and differ between males and females. It is currently thought that the bone remodeling process is supervised by osteocytes in a strain-dependent manner. We have shown an altered response of osteocytes from osteoporotic patients to mechanical loading, and osteocyte density is reduced in osteoporotic patients, which might relate to imperfect bone remodeling, leading to lack of bone mass and strength. Hence, information on osteocyte density will contribute to a better understanding of bone biology in males and females and to the assessment of osteoporosis. Osteocyte density as well as conventional histomorphometric parameters of trabecular bone were determined in cancellous iliac crest bone of healthy postmenopausal women and men and of osteoporotic women and men. Osteocyte density was higher in healthy females than in healthy males and lower in osteoporotic females than in healthy females. Bone mass was reduced in osteoporotic patients, both male and female. In females, trabecular number was reduced, whereas in males, trabecular thickness was reduced and eroded surface was increased. There were no correlations between the parameter groups bone architecture, bone formation, bone resorption, and osteocyte density. These results are consistent with impaired osteoblast function in osteoporotic patients and with a different mechanism of bone loss between men and women, in which osteocyte density might play a role. The reduced osteocyte numbers in female osteoporotic patients might relate to imperfect bone remodeling leading to lack of bone mass and strength. © 2005 Springer Science+Business Media, Inc