The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling

BACKGROUND: COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response. METHODS: We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time. FINDINGS: The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19. INTERPRETATION: Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID. FUNDING: This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods

Internationalisation speed and MNE performance: A study of the market-seeking expansion of retail MNEs

Existing research is divided on whether firms that rapidly expand their overseas operations perform better than firms that internationalize slowly. Drawing on Penrose’s theory of the growth of the firm we argue that the positive effects of rapid internationalization give way to negative effects with increasing internationalization speed, leading to an inverted U-shaped association between internationalization speed and firm performance. We analyse the market-seeking expansion of 110 retailers over a 10-year period (2003–2012) and find support for a curvilinear relationship between internationalization speed and firm performance that is moderated by the geographic scope of firms’ internationalization path and firms’ international experience. Our study contributes to resolving conflicting views on the link between internationalization speed and firm performance

Secretion of Genome-Free Hepatitis B Virus – Single Strand Blocking Model for Virion Morphogenesis of Para-retrovirus

As a para-retrovirus, hepatitis B virus (HBV) is an enveloped virus with a double-stranded (DS) DNA genome that is replicated by reverse transcription of an RNA intermediate, the pregenomic RNA or pgRNA. HBV assembly begins with the formation of an “immature” nucleocapsid (NC) incorporating pgRNA, which is converted via reverse transcription within the maturing NC to the DS DNA genome. Only the mature, DS DNA-containing NCs are enveloped and secreted as virions whereas immature NCs containing RNA or single-stranded (SS) DNA are not enveloped. The current model for selective virion morphogenesis postulates that accumulation of DS DNA within the NC induces a “maturation signal” that, in turn, triggers its envelopment and secretion. However, we have found, by careful quantification of viral DNA and NCs in HBV virions secreted in vitro and in vivo, that the vast majority of HBV virions (over 90%) contained no DNA at all, indicating that NCs with no genome were enveloped and secreted as empty virions (i.e., enveloped NCs with no DNA). Furthermore, viral mutants bearing mutations precluding any DNA synthesis secreted exclusively empty virions. Thus, viral DNA synthesis is not required for HBV virion morphogenesis. On the other hand, NCs containing RNA or SS DNA were excluded from virion formation. The secretion of DS DNA-containing as well as empty virions on one hand, and the lack of secretion of virions containing single-stranded (SS) DNA or RNA on the other, prompted us to propose an alternative, “Single Strand Blocking” model to explain selective HBV morphogenesis whereby SS nucleic acid within the NC negatively regulates NC envelopment, which is relieved upon second strand DNA synthesis

Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods

Determination of the number of wounded nucleons in Pb+Pb collisions at 158 A GeV/c

The charged particle multiplicity distributions measured by two experiments, WA97 and NA57, in Pb+Pb collisions at 158 A GeV/c have been analyzed in the framework of the wounded nucleon model (WNM). We obtain a good description of the data within the centrality range of our samples. This allows us to make use of the measured multiplicities to estimate the number of wounded nucleons of the collision

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

Data Availability Statement: The individual-level data from these studies is not publicly available to main confidentiality. Data generated by the ISARIC4C consortium is available for collaborative analysis projects through an independent data and materials access committee at isaric4c.net/sample_access. Data and samples from the COVID-Clinical Neuroscience Study are available through collaborative research by application through the NIHR bioresource at https://bioresource.nihr.ac.uk/using-our-bioresource/apply-for-bioresource-data-access/. Brain injury marker and immune mediator data are present in the paper and in the source data file. Source data are provided with this paper.To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely.National Institute for Health and Care Research (NIHR) (CO-CIN-01) and jointly by NIHR and UK Research and Innovation (CV220-169, MC_PC_19059). B.D.M. is supported by the UKRI/MRC (MR/V03605X/1), the MRC/UKRI (MR/V007181/1), MRC (MR/T028750/1) and Wellcome (ISSF201902/3). C.D. is supported by MRC (MC_PC_19044). We would like to thank the University of Liverpool GCP laboratory facility team for Luminex assistance and the Liverpool University Biobank team for all their help, especially Dr. Victoria Shaw, Lara Lavelle-Langham, and Sue Holden. We would like to acknowledge the Liverpool Experimental Cancer Medicine Centre for providing infrastructure support for this research (Grant Reference: C18616/A25153). We acknowledge the Liverpool Centre for Cell Imaging (CCI) for provision of imaging equipment (Dragonfly confocal microscope) and excellent technical assistance (BBSRC grant number BB/R01390X/1). Tom Solomon is supported by The Pandemic Institute and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool. D.K.M. and E.N. are supported by the NIHR Cambridge Biomedical Centre and by NIHR funding to the NIHR BioResource (RG94028 and RG85445), and by funding from Brain Research UK 201819-20. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. The authors would like to acknowledge the eDRIS team (Public Health Scotland) for their support in obtaining approvals, the provisioning and linking of data and facilitating access to the National Safe Haven. The views expressed are those of the author(s) and not necessarily those of the UKRI, NHS, the NIHR or the Department of Health and Social Care