An outbreak of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that started in Wuhan, China, at the end of 2019 has become a global pandemic. Both SARS-CoV-2 and SARS-CoV enter host cells via the angiotensin-converting enzyme 2 (ACE2) receptor, which is expressed in various human organs. We have reviewed previously published studies on SARS and recent studies on SARS-CoV-2 infection, named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO), confirming that many other organs besides the lungs are vulnerable to the virus. ACE2 catalyzes angiotensin II conversion to angiotensin-(1–7), and the ACE2/angiotensin-(1–7)/MAS axis counteracts the negative effects of the renin-angiotensin system (RAS), which plays important roles in maintaining the physiological and pathophysiological balance of the body. In addition to the direct viral effects and inflammatory and immune factors associated with COVID-19 pathogenesis, ACE2 downregulation and the imbalance between the RAS and ACE2/angiotensin-(1–7)/MAS after infection may also contribute to multiple organ injury in COVID-19. The SARS-CoV-2 spike glycoprotein, which binds to ACE2, is a potential target for developing specific drugs, antibodies, and vaccines. Restoring the balance between the RAS and ACE2/angiotensin-(1–7)/MAS may help attenuate organ injuries. Graphical abstract SARS-CoV-2 enters lung cells via the ACE2 receptor. The cell-free and macrophage-phagocytosed virus can spread to other organs and infect ACE2-expressing cells at local sites, causing multi-organ injury.

30Qing Mao (Phone +86 135 9418 0020;Abstract: An excessive immune response contributes to SARS-CoV, MERS-CoV and SARS-CoV-2 pathogenesis and lethality, but the mechanism remains unclear. In this study, the N proteins of SARS-CoV, MERS-CoV and SARS-CoV-2 were found to bind to MASP-2, the key serine protease in the lectin pathway of complement activation, resulting in aberrant complement activation and aggravated inflammatory lung injury. Either blocking the N protein:MASP-2 5 interaction or suppressing complement activation can significantly alleviate N protein-induced complement hyper-activation and lung injury in vitro and in vivo. Complement hyper-activation was also observed in COVID-19 patients, and a promising suppressive effect was observed when the deteriorating patients were treated with anti-C5a monoclonal antibody. Complement suppression may represent a common therapeutic approach for pneumonia induced by these 10 highly pathogenic coronaviruses. Short Title: SARS-CoV N over-activates complement by MASP-2One Sentence Summary: The lectin pathway of complement activation is a promising target for 15 the treatment of highly pathogenic coronavirus induced pneumonia.All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Background Coronavirus infectious disease 2019 (COVID-19) has developed into a global pandemic. It is essential to investigate the clinical characteristics of COVID-19 and uncover potential risk factors for severe disease to reduce the overall mortality rate of COVID-19. Methods Sixty-one critical COVID-19 patients admitted to the intensive care unit (ICU) and 93 severe non-ICU patients at Huoshenshan Hospital (Wuhan, China) were included in this study. Medical records, including demographic, platelet counts, heparin-involved treatments, heparin-induced thrombocytopenia-(HIT) related laboratory tests, and fatal outcomes of COVID-19 patients were analyzed and compared between survivors and nonsurvivors. Findings Sixty-one critical COVID-19 patients treated in ICU included 15 survivors and 46 nonsurvivors. Forty-one percent of them (25/61) had severe thrombocytopenia, with a platelet count (PLT) less than 50x109/L, of whom 76% (19/25) had a platelet decrease of >50% compared to baseline; 96% of these patients (24/25) had a fatal outcome. Among the 46 nonsurvivors, 52.2% (24/46) had severe thrombocytopenia, compared to 6.7% (1/15) among survivors. Moreover, continuous renal replacement therapy (CRRT) could induce a significant decrease in PLT in 81.3% of critical CRRT patients (13/16), resulting in a fatal outcome. In addition, a high level of anti-heparin-PF4 antibodies, a marker of HIT, was observed in most ICU patients. Surprisingly, HIT occurred not only in patients with heparin exposure, such as CRRT, but also in heparin-naive patients, suggesting that spontaneous HIT may occur in COVID-19. Interpretation Anti-heparin-PF4 antibodies are induced in critical COVID-19 patients, resulting in a progressive platelet decrease. Exposure to a high dose of heparin may trigger further severe thrombocytopenia with a fatal outcome. An alternative anticoagulant other than heparin should be used to treat COVID-19 patients in critical condition.

Type 2 diabetes mellitus (T2DM) is associated with dementia. Mild cognitive impairment (MCI) is a key determinant in this association. It is not clear whether T2DM increases the risk of conversion from MCI to dementia. We plan to explore the relationship between T2DM-MCI and dementia and identify its potential risk factors. A prospective community-based cohort study was conducted from March 2010 to March 2014, including 634 participants with T2DM-MCI, 261 T2DM participants who were cognitively intact, and 585 MCI participants without diabetes. All cohort members received detailed annual evaluations to detect dementia onset during the 5 years of follow-up. The three cohorts were compared to assess differences in dementia onset. Furthermore, Cox proportional hazards regression was used to identify risk factors for dementia onset in the T2DM-MCI cohort. During follow-up, 152 and 49 subjects developed dementia in the MCI and cognitively-intact cohorts, amounting to an adjusted hazard ratio (HR) of 1.66 (95% CI 1.07-2.26). In a survival analysis of the cohorts, MCI accelerated the median progression to dementia by 2.74 years. In a multivariable analysis of the T2DM-MCI cohort, major risk factors for dementia were age >75 years and longer durations of diabetes, while significantly reduced risks of dementia were associated with oral hypoglycemic agents and HMG-CoA reductase inhibitors. Insulin was not associated with significantly changed risk. T2DM-MCI may aggravate the clinical picture as a concomitant factor. To minimize progression to dementia, it may be worthwhile to target several modifiable diabetes-specific features, such as the duration of disease, glycemic control, and antidiabetic agents.

Human CST (CTC1-STN1-TEN1) is an RPA-like complex that associates with G-rich single-strand DNA and helps resolve replication problems both at telomeres and genome-wide. We previously showed that CST binds and disrupts G-quadruplex (G4) DNA in vitro , suggesting that CST may prevent in vivo blocks to replication by resolving G4 structures. Here, we demonstrate that CST binds and unfolds G4 with similar efficiency to RPA. In cells, CST is recruited to telomeric and non-telomeric chromatin upon G4 stabilization, even when ATR/ATM pathways were inhibited. STN1 depletion increases G4 accumulation and slows bulk genomic DNA replication. At telomeres, combined STN1 depletion and G4 stabilization causes multi-telomere FISH signals and telomere loss, hallmarks of deficient telomere duplex replication. Strand-specific telomere FISH indicates preferential loss of C-strand DNA while analysis of BrdU uptake during leading and lagging-strand telomere replication shows preferential under-replication of lagging telomeres. Together these results indicate a block to Okazaki fragment synthesis. Overall, our findings indicate a novel role for CST in maintaining genome integrity through resolution of G4 structures both ahead of the replication fork and on the lagging strand template.

Chromosomal aberrations are a hallmark of human cancers, with complex cytogenetic rearrangements leading to genetic changes permissive for cancer initiation and progression. Protection of Telomere 1 (POT1) is an essential component of the shelterin complex and functions to maintain chromosome stability by repressing the activation of aberrant DNA damage and repair responses at telomeres. Sporadic and familial mutations in the oligosaccharide-oligonucleotide (OB) folds of POT1 have been identified in many human cancers, but the mechanism underlying how hPOT1 mutations initiate tumorigenesis has remained unclear. Here we show that the human POT1’s OB-folds are essential for the protection of newly replicated telomeres. Oncogenic mutations in hPOT1 OB-fold fail to bind to ss telomeric DNA, eliciting a DNA damage response at telomeres that promote inappropriate chromosome fusions via the mutagenic alternative non-homologous end joining (A-NHEJ) pathway. hPOT1 mutations also result in telomere elongation and the formation of transplantable hematopoietic malignancies. Strikingly, conditional deletion of both mPot1a and p53 in mouse mammary epithelium resulted in development of highly invasive breast carcinomas and the formation of whole chromosomes containing massive arrays of telomeric fusions reminiscent of chromothripsis. Our results reveal that hPOT1 OB-folds are required to protect and prevent newly replicated telomeres from engaging in A-NHEJ mediated fusions that would otherwise promote genome instability to fuel tumorigenesis.

IntroductionWe explored the diagnostic value of a urine soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) for early sepsis identification, severity and prognosis assessment, and for secondary acute kidney injury (AKI). We compared this with white blood cell (WBC) counts, serum C-reactive protein (CRP), serum procalcitonin (PCT), urine output, creatinine clearance (CCr), serum creatinine (SCr), and blood urea nitrogen (BUN).MethodsWe enrolled 104 subjects admitted to the ICU: 16 cases with systemic inflammatory response syndrome (SIRS); 35 with sepsis and 53 with severe sepsis. Results for urine sTREM-1, WBC, serum CRP and serum PCT were recorded on days 1, 3, 5, 7, 10, and 14. For 17 sepsis cases diagnosed with secondary AKI, comparisons between their urine sTREM-1, urine output, CCr, SCr and BUN at diagnosis and 48 h before diagnosis were made.ResultsOn the day of admission to the ICU, and compared with the SIRS group, the sepsis group exhibited higher levels of urine sTREM-1 and Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) scores (P < 0.05). Areas under the curve (AUC) shaped by the scores were 0.797 (95% CI 0.711 to 0.884) and 0.722 (95% CI 0.586 to 0.858), respectively. On days 1, 3, 5, 7, 10, and 14, urine sTREM-1, serum PCT and WBC levels registered higher in the severe sepsis group in contrast to the sepsis group (P < 0.05). Urine sTREM-1 and serum PCT levels continuously increased among non-survivors, while WBC and serum CRP levels in both groups declined. For 17 patients with AKI, urine sTREM-1, SCr and BUN levels at 48 h before AKI diagnosis were higher, and CCr level was lower than those for non-AKI subjects (P < 0.05). AUC for urine sTREM-1 was 0.922 (95% CI 0.850 to 0.995), the sensitivity was 0.941, and the specificity was 0.76 (based on a cut-off point of 69.04 pg/ml). Logistic regression analysis showed that urine sTREM-1 and severity were risk factors related to AKI occurrence.ConclusionsBesides being non-invasive, urine sTREM-1 testing is more sensitive than testing WBC, serum CRP, and serum PCT for the early diagnosis of sepsis, as well as for dynamic assessments of severity and prognosis. It can also provide an early warning of possible secondary AKI in sepsis patients.Trial RegistrationClinicalTrial.gov identifier NCT01333657