A diaphragmatic retroperitoneal cyst

Diaphragmatic lesions are usually congenital bronchogenic cysts. A patient with a known diaphragmatic cyst presented with new onset right upper quadrant pain. Repeat imaging showed enlargement of the cyst, the CA19–9 cancer marker was raised at 312iu/ml (normal: <27iu/ml) and positron emission tomography combined with computed tomography showed focally increased uptake in the cystic wall. In view of symptoms and risk of neoplasia, the lesion was excised. Histology showed a benign epidermoid cyst. Features falsely suggesting neoplasia have been reported previously with benign splenic cysts but not with a benign diaphragmatic epidermoid cyst.     

Successful Treatment with Cyclosporine and High-Dose Gamma Immunoglobulin for Persistent Parvovirus b19 Infection in a Patient with Refractory Autoimmune Hemolytic Anemia

We describe a patient with persistent pure red cell aplasia due to human parvovirus B19 (HPVB19) infection during immunosuppressive therapy for refractory autoimmune hemolytic anemia (AIHA). The patient had been given corticosteroid (CS) and/or azathioprine for AIHA. During the course of treatment, reticulocyte count and hemoglobin levels decreased suddenly. Bone marrow aspirate showed erythroid lineage-specific aplasia with a few giant proerythroblasts, suggesting the presence of HPVB19 infection. The diagnosis of aplastic crisis due to HPVB19 infection was based on positive test results by polymerase chain reaction for HPVB19 immunoglobulin M (IgM) antibody and B19 DNA. Although splenectomy followed by administration of high-dose gamma globulin (HDIG) and plasma exchange were performed, the crisis and hemolysis recurred. Aplastic crises occurred several times when the B19 IgG result became negative and the CD4+ lymphocyte count was less than 300/microL. The patient showed complete recovery from anemia after CS was switched to cyclosporin A (CsA) and intermittent administration of HDIG. The result for B19 IgG antibody was continuously positive, and the DNA result became negative after these treatments. The results in this case indicated that concomitant administration of CsA and intermittent administration of HDIG can lead to cure of chronic anemia due to HPVB19 infection in patients with refractory AIHA.     

Marcadores de daño miocárdico en la predicción del pronóstico a corto plazo de los pacientes con COVID-19

Introduction and objectivesCOVID-19 is currently causing high mortality and morbidity worldwide. Information on cardiac injury is scarce. We aimed to evaluate cardiovascular damage in patients with COVID-19 and determine the correlation of high-sensitivity cardiac-specific troponin T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) with the severity of COVID-19.MethodsWe included 872 consecutive patients with confirmed COVID-19 from February to April 2020. We tested 651 patients for high-sensitivity troponin T (hs-TnT) and 506 for NT-proBNP on admission. Cardiac injury was defined as hs-TnT > 14 ng/L, the upper 99th percentile. Levels of NT-proBNP > 300 pg/mL were considered related to some extent of cardiac injury. The primary composite endpoint was 30-day mortality or mechanical ventilation (MV).ResultsCardiac injury by hs-TnT was observed in 34.6% of our COVID-19 patients. Mortality or MV were higher in cardiac injury than noncardiac injury patients (39.1% vs 9.1%). Hs-TnT and NT-proBNP levels were independent predictors of death or MV (HR, 2.18; 95%CI, 1.23-3.83 and 1.87 (95%CI, 1.05-3.36), respectively) and of mortality alone (HR, 2.91; 95%CI, 1.211-7.04 and 5.47; 95%CI, 2.10-14.26, respectively). NT-ProBNP significantly improved the troponin model discrimination of mortality or MV (C-index 0.83 to 0.84), and of mortality alone (C-index 0.85 to 0.87).ConclusionsMyocardial injury measured at admission was a common finding in patients with COVID-19. It reliably predicted the occurrence of mortality and need of MV, the most severe complications of the disease. NT-proBNP improved the prognostic accuracy of hs-TnT.     

Interphase FISH to detect PBX1/E2A fusion resulting from the der(19)t(1;19)(q23;p13.3) or t(1;19)(q23;p13.3) in paediatric patients with acute lymphoblastic leukaemia

Approximately 6% of paediatric patients with precursor B-cell acute lymphoblastic leukaemia (B-ALL) harbour a rearrangement involving the gene regions of PBX1 (1q23) and E2A (19p13.3) which is visualized cytogenetically either as a der(19)t(1;19)(q23;p13.3) or the less common balanced t(1;19)(q23;p13.3). Unfortunately, no commercial dual-colour, double fusion fluorescence in situ hybridization (D-FISH) strategies are available to detect this recurrent anomaly. Therefore, we have created a D-FISH assay to detect these translocations and monitor minimal residual disease. This probe set was created using four bacterial artificial chromosomes (BACs) corresponding to the PBX1 gene region at 1q23 and four BACs corresponding to the E2A gene region at 19p13.3. We analysed 30 negative bone marrow controls and 20 diagnostic and post-treatment specimens from 13 paediatric B-ALL patients with a cytogenetically defined 1;19 translocation. Once unblinded, the results demonstrated that our D-FISH method effectively identified all diagnostic samples as abnormal and identified disease in four post-treatment samples that were previously considered to be normal by conventional cytogenetic analysis. The development of this FISH strategy for the detection of der(19)t(1;19)(q23;p13.3) and t(1;19)(q23;p13.3) proved to be an effective technique, allowing both the detection of disease in diagnostic samples and in post-treatment samples.     

In silico dynamics of COVID-19 phenotypes for optimizing clinical management

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin−angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8⁺ T cells and sufficient control of the innate immune response. Furthermore, the best treatment—or combination of treatments—depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.

COVID-19 has created unprecedented challenges for the health care system, and, until an effective vaccine is developed and made widely available, treatment options are limited. A challenge to the development of optimal treatment strategies is the extreme heterogeneity of presentation. Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in a syndrome that ranges in severity from asymptomatic to multiorgan failure and death. In addition to local complications in the lung, the virus can cause systemic inflammation and disseminated microthrombosis, which can cause stroke, myocardial infarction, or pulmonary emboli (1–4). Risk factors for poor COVID-19 outcome include advanced age, obesity, diabetes, and hypertension (5–13).Computational analyses can provide insights into the transmission, control, progression, and underlying mechanisms of infectious diseases. Indeed, epidemiological and statistical modeling has been used for COVID-19, providing powerful insights into comorbidities, transmission dynamics, and control of the disease (14–17). However, to date, these analyses have been population dynamics models of SARS-CoV-2 infection and transmission or correlative analyses of COVID-19 comorbidities and treatment response. Simple viral dynamics models have been also developed and used to predict the SARS-CoV-2 response to antiviral drugs (18, 19). These models, however, do not explicitly consider the biological or physiological mechanisms underlying disease progression or the time course of response to various therapeutic interventions, and only a few more-sophisticated models have been developed toward this direction (20, 21).Several therapies targeting various aspects of COVID-19 pathogenesis have been proposed and have either completed—or are currently being tested in—clinical trials (22). Despite strong biologic rationale, these treatments have generally produced conflicting results in the clinic. For example, trials of antiviral therapies (e.g., remdesivir) have been mixed: The original trial from China failed (23), a subsequent trial in the United States led to approval of remdesivir in the United States and other countries (24), and the recent results of the World Health Organization Solidarity trial again show no benefit (25). Other antiviral drugs alone or in combination are also showing promise (26).Other potential treatments include antiinflammatory drugs and antithrombotic agents. Because of the systemic inflammation seen in many patients, antiinflammatory drugs have been tested, including anti-IL6/IL6R therapy (e.g., tocilizumab, siltuximab) and anti-JAK1/2 drugs (e.g., barcitinib). It is not clear whether these drugs will be effective as stand-alone treatments, particularly after the recent failure of tocilizumab in a phase III trial (1, 27–29). In addition, given that a common complication of COVID-19 is the development of coagulopathies with microvascular thrombi potentially leading to the dysfunction of multiple organ systems (2, 3), antithrombotic drugs (e.g., low molecular weight heparin) are being tested. Recognizing the interactions of COVID-19 with the immune system (30), the corticosteroid dexamethasone has been tested, showing some promising results. Given the large range of patient comorbidities, disease severities, and variety of complications such as thrombosis, it is likely that patients will have heterogeneous responses to any given therapy, and such heterogeneity will continue to be a challenge for clinical trials of unselected COVID-19 patients (31).Here, we developed a systems biology-based mathematical model to address this urgent need. Our model incorporates the known mechanisms of SARS-CoV-2 pathogenesis and the potential mechanisms of action of various therapeutic interventions that have been tested in COVID-19 patients. In previous work, we have exploited angiotensin receptor blockers (ARBs) and angiotensin converting enzyme inhibitors (ACEis) for the improvement of cancer therapies and developed mathematical models of the renin−angiotensin system in the context of cancer desmoplasia (32–35). Using a similar approach, we developed a detailed model that includes lung infection by the SARS-CoV-2 virus and a pharmacokinetic/pharmacodynamic (PK/PD) model of infection and thrombosis to simulate events that take place throughout the body during COVID-19 progression (Fig. 1 and SI Appendix, Fig. S1). The model is first validated against clinical data of healthy people and COVID-19 patients and then used to simulate disease progression in patients with specific comorbidities. Subsequently, we present model predictions for various therapies currently employed for treatment of COVID-19 alone or in combination, and we identify protocols for optimal clinical management for each of the clinically observed COVID-19 phenotypes.Open in a separate windowFig. 1.Schematic of the detailed lung model. The model incorporates the virus infection of epithelial and endothelial cells, the RAS, T cells activation and immune checkpoints, the known IL6 pathways, neutrophils, and macrophages, as well as the formation of NETs, and the coagulation cascade. The lung model is coupled with a PK/PD model for the virus and thrombi dissemination through the body.     

From the Cover: COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions.

During the first phase of the coronavirus disease 2019 (COVID-19) pandemic, countries around the world implemented a host of containment policies aimed at mitigating the spread of the disease (1–4). Many policies restricted human mobility, intending to reduce close-proximity contacts, the major driver of the disease’s spread (5). In Germany, these policies included border closures, travel bans, and restrictions of public activity (school and business closures), paired with appeals by the government to avoid trips voluntarily whenever possible (6). We refer to these policies as “lockdown” measures for brevity.Based on various digital data sources such as mobile phone data or social media data, several studies show that mobility significantly changed during lockdowns (7). Most studies focused on general mobility trends and confirmed an overall reduction in mobility in various countries (8–12). Other research focused on the relation between mobility and disease transmission: For instance, it has been argued that mobility reduction is likely instrumental in reducing the effective reproduction number in many countries (13–17), in agreement with theoretical models and simulations, which have shown that containment can effectively slow down disease transmission (18–20).However, it remains an open question whether the mobility restrictions promoted deeper structural changes in mobility networks and how these changes impact epidemic spreading mediated by these networks. Recently, Galeazzi et al. (21) found increased geographical fragmentation of the mobility network. A thorough understanding of how structural mobility network changes impact epidemic spreading is needed to correctly assess the consequences of mobility restrictions not only for the current COVID-19 pandemic, but also for similar scenarios in the future.Here, we analyze structural changes in mobility patterns in Germany during the COVID-19 pandemic. We analyze movements recorded from mobile phones of 43.6 million individuals in Germany. Beyond a general reduction in mobility, we find considerable structural changes in the mobility network. Due to the reduction of long-distance travel, the network becomes more local and lattice-like. Most importantly, we find a changed scaling relation between path lengths and geographic distance: During lockdown, the effective distance (and arrival time in spreading processes) to a destination continually grows with geographic distance. This shows a marked reduction of the “small-world” characteristic, where geographic distance is usually of lesser importance in determining path lengths (22, 23). Using simulations of a commuter-based susceptible-infected-removed (SIR) model, we demonstrate that these changes have considerable practical implications as they suppress (or “flatten”) the curve of an epidemic remarkably and delay the disease’s arrival between distant regions.     

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19

BackgroundThe evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic.AimsTo develop and validate a score to predict outcomes in patients hospitalized with COVID-19.MethodsAll consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort.ResultsAmong 2873 patients analysed (57.9% men; 66.6 ± 17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n = 2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75–0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores.ConclusionsThe CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.     

肾移植术后人类微小病毒B19感染致纯红细胞再生障碍性贫血2例并文献复习

目的探讨肾移植术后人类微小病毒(HPV)B19感染致纯红细胞再生障碍性贫血(纯红再障)的诊断和治疗特点。方法总结南方医科大学南方医院器官移植科收治的2例肾移植术后HPV B19感染致纯红再障的病例,结合文献复习讨论该病的临床特点、诊断方法、治疗过程及预后。结果两例肾移植受者术后早发严重贫血且进行性加重,输血治疗无效。排除导致贫血的其他原因,综合骨髓穿刺活检、荧光聚合酶链反应(PCR)检测HPV DNA等方法诊断为HPV B19感染致纯红再障。经调整免疫抑制方案、静脉注射用免疫球蛋白(IVIG)等治疗后2例患者贫血症状明显改善。结论对于肾移植术后早期不明原因、进行性加重的贫血患者,特别是伴随网织红细胞缺乏者,应考虑HPV B19感染致纯红再障的可能性。骨髓穿刺及荧光PCR检测结果是诊断纯红再障的主要依据,免疫抑制剂减量和应用IVIG治疗是主要治疗措施。经治疗后,患者预后较好,但易复发。