Concurrence of immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura: a case report and review of the literature.

BACKGROUND: Immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura are both causes of thrombocytopenia. Recognizing thrombotic thrombocytopenic purpura is crucial for subsequent treatment and prognosis. In clinical practice, corticosteroids and rituximab can be used to treat both immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura; plasma exchange therapy is the first-line treatment in thrombotic thrombocytopenic purpura, while corticosteroids are strongly recommended as first-line treatment in immune thrombocytopenic purpura. The differential diagnosis of immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura is essential in clinical practice. However, case reports have suggested that immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura can occur concurrently. CASE PRESENTATION: We report the case of a 32-year-old Asian female without previous disease who presented with pancytopenia, concurrent with immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura. The morphology of the megakaryocytes in the bone marrow indicated immune-mediated thrombocytopenia. The patient received glucocorticoid treatment, and her platelet count increased; however, schistocytes remained high during the course of the therapy. Further investigations revealed ADAMTS13 activity deficiency and positive ADAMTS13 antibodies. The high titer of antinuclear antibody and positive anti-U1-ribonucleoprotein/Smith antibody indicated a potential autoimmune disease. However, the patient did not fulfill the current criteria for systemic lupus erythematosus or mixed connective tissue disease. The patient responded well to plasma exchange therapy, and her platelet count remained normal on further follow-up. CONCLUSIONS: Concurrence of immune thrombocytopenic purpura and thrombotic thrombocytopenic purpura is rare, but clinicians should be aware of this entity to ensure prompt medical intervention. Most of the reported cases involve young women. Human immunodeficiency virus infection, pregnancy, and autoimmune disease are the most common underlying conditions.

Management Strategies of Corona Virus Disease 2019 in Neurology Ward of Large General Hospital

Abstract: During the corona virus disease 2019 prevention and control period, based on the conscientiously studying the relevant documents issued by the National Health Protection Committee and the hospital infection department, and combining the characteristics of the neurological diseases and the actual work situation, the neurology ward reasonably set up three districts and two channels, ensure the adequacy and accessibility of the protective products, establish a comprehensive and detailed identification management, Strengthen the standardized training of staff, improve the relevant system, standardize the work process;strengthen the management of patients during admission and hospitalization, pay attention to the symptomatic nursing of patients, and strengthen the predictive nursing of various complications. From three levels of overall management (environment, staffs, patients), the spread of new coronavirus in the department of neurology was curbed effectively.

Clinical characteristics of 1327 patients with coronavirus disease 2019 (COVID-19) in the largest Fangcang shelter hospital in Wuhan

This article aimed to study the clinical characteristics of these patients admitted to Jianghan Fangcang shelter hospital, the largest Fangcang shelter hospital in Wuhan, China. It is worth highlighting that six patients had anosmia without nasal congestion as the initial symptom. At the time of discharge from the Fangcang shelter hospital, the findings on chest CT were alleviated in 95.5% (1241/1300) of the patients. However, only one patient had chest CT findings suggestive of disease progression. Symptomatic patients have higher CRP level and lower lymphocytes counts than asymptomatic patients, which might suggest that higher CRP level and lower lymphocytes counts were related to the severity of symptoms. However, CT characteristics were not statistically different between symptomatic and asymptomatic patients, which might indicate that CT characteristics were not associated with the severity of symptoms in non-critical patients. In conclusion, patients with fever and anosmia but without nasal congestion are more likely to be suffering from COVID-19. Higher CRP level and lower lymphocytes counts might relate to the severity of symptoms, while CT abnormalities were not associated with the severity of symptoms in non-critical patients.

Chinese Computing and Computing China as Global Knowledge Production [Special Section]

As data-driven technologies and business models pervade on a global scale, China’s enormous digital economy often signals its dominating power by dint of data extraction. Complicating this view, this critical commentary focuses on knowledge production, an important dimension for examining the ways in which postsocialist China transpires in global political economy in the age of Big Data analytics. First, I show how Chinese commercial surveillance analytics profits from legitimation lent by the West-centric hierarchical academe. Then, I move to transnational academic repurposing of Big Data from China, which becomes increasingly common. Such social research tends to yield specters of China that are untethered to the lived realities of those whose data are taken. Drawing on decolonial thinking and feminist care ethics, this commentary concludes by urging social scientists to “stay with the trouble,” making China “legible” in their computing of Chinese Big Data.

Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.

Twelve-month specific IgG response to SARS-CoV-2 receptor-binding domain among COVID-19 convalescent plasma donors in Wuhan (preprint)

To investigate the duration of humoral immune response in convalescent coronavirus disease 2019 (COVID-19) patients, we conducted a 12-month longitudinal study through collecting a total of 1,782 plasma samples from 869 convalescent plasma donors in Wuhan, China and tested specific antibody response. The results show that positive rate of IgG antibody against receptor-binding domain of spike protein (RBD-IgG) to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the COVID-19 convalescent plasma donors exceeded 70% for 12 months post diagnosis. RBD-IgG kinetics displayed a gradually downward trend, the titer started to stabilize after 9 months and decreased by 68.1% compared with the 1st month. Moreover, male plasma donors produced more RBD-IgG than female plasma donors and patient age positively correlated with the RBD-IgG titer. A strong positive correlation between RBD-IgG and neutralizing antibody titers was also identified. This study is essential for understanding SARS-CoV-2-induced immune memory to develop vaccine and therapeutics.

County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States (preprint)

Background: COVID-19 is an infectious disease that has killed more than 175,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. Objectives: We evaluated whether greenness is related to COVID-19 incidence and mortality in the United States. Methods: We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order. Results: An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and high median home values, and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with high percentages of Black residents, high median home value, and higher population density. Discussion: Exposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

Calibrating Sales Forecast in a Pandemic Using Competitive Online Non-Parametric Regression (preprint)

Motivated by our collaboration with Anheuser-Busch InBev (AB InBev), a consumer packaged goods (CPG) company, we consider the problem of forecasting sales under the coronavirus disease 2019 (COVID-19) pandemic. Our approach combines non-parametric regression, game theory, and pandemic modeling to develop a data-driven competitive online non-parametric regression method. Specifically, the method takes the future COVID-19 cases estimates, which can be simulated via the SIR (i.e., Susceptible-Infectious-Removed) epidemic model, as an input, and outputs the level of calibration for the baseline sales forecast generated by AB InBev's offline learning algorithm. In generating the calibration level, we focus on an online learning setting, where our algorithm sequentially predicts the label (i.e., the level of calibration) of a random covariate (i.e., the current number of active cases) given past observations and the generative process (i.e., the SIR epidemic model) of future covariates. To provide robust performance guarantee for every sequence of data (each corresponds to a different market of AB InBev's), we derive our algorithm by minimizing regret, which is the difference between the squared l_2-norm associated with labels generated by the algorithm and labels generated by an adversary and the squared l_2-norm associated with labels generated by the best isotonic (non-decreasing) function in hindsight and the adversarial labels. We develop a computationally-efficient algorithm that attains the minimax-optimal regret over all possible choices of the labels. We demonstrate the performances of our algorithm on both synthetic and AB InBev’s datasets (from March 2020 to March 2021) of three different geographical regions . The AB InBev’s numerical experiments show that our method is capable of reducing the forecasting error in terms of WMAPE (i.e., weighted mean absolute percentage error) and MSE (i.e., mean squared error) by more than 49\% for the company.

A Method to evaluate analytical sensitivity of qRT-PCR kits for SARS-CoV-2 detection (preprint)

Background: The ongoing Coronavirus disease 2019 (COVID-19) pandemic has spread across the globe and is representing a huge challenge for all human population. Many commercial qRT-PCR assays have been developed to detect SARS-CoV-2, but related method validation especially the sensitivity evaluation has been insufficient, resulting in some false-negative cases have been reported. Methods: The analytical sensitivity of nine brands of qRT-PCR kits for detecting SARS-CoV-2 was evaluated in parallel based on a newly developed certified reference material, which was derived from genomic RNA of SARS-CoV-2 from clinical positive specimens. After validation of the the reference material by digital PCR, the detection sensitivity of these kits was preliminarily tested using the serially diluted reference material, resulting in three kits with two significantly different sensitivity levels were selected for further evaluation. We sequenced the qRT-PCR products for assay specificity evaluation, and used serial dilutions of the reference material to calculate amplification efficiency and estimate the limit of quantification as well as 95% limit of detection..Results: The results indicated that the analytical sensitivity varied markedly among these kits. For the three types of qRT-PCR kits (Kit-1, Kit-2 and Kit-7), specificity of the PCR products was confirmed by sequence alignment, in which the target amplicons completely matched the corresponding parts of the genome of SARS-CoV-2. The resulting limit of detection from replicate tests for the Kit-1 and Kit-2 was 5.6 copies (N), 3.5 copies (ORF 1ab), and 6.4 copies (N), 4.6 copies (ORF 1ab), respectively, at 95% probability. Compared with Kit-7, the limit of detection as well as limit of quantification of Kit-1 and Kit-2 were significantly lower, further supporting that the both kits worked well to detect low abundance of SARS-CoV-2.Conclusions: Considering that most of the tested kits have been approved for in vitro diagnostics (IVD) in China, the established method here provides a reliable tool to evaluate the sensitivity performance of various qRT-PCR kits for SARS-CoV-2 detection and thus enhance quality control of qRT-PCR assays, improving the laboratory diagnostic capability for fighting the COVID-19 pandemic.

Exposure to air pollution and COVID-19 mortality in the United States (preprint)

Objectives: United States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States. Design: A nationwide, cross-sectional study using county-level data. Data sources: COVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. Main outcome measures: We fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state issuance of the stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses. Results: We found that an increase of only 1 g/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses. Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.

The feasibility of convalescent plasma therapy in severe COVID-19 patients: a pilot study (preprint)

Currently, there are no approved specific antiviral agents for 2019 novel coronavirus disease (COVID-19). In this study, ten severe patients confirmed by real-time viral RNA test were enrolled prospectively. One dose of 200 mL convalescent plasma (CP) derived from recently recovered donors with the neutralizing antibody titers above 1:640 was transfused to the patients as an addition to maximal supportive care and antiviral agents. The primary endpoint was the safety of CP transfusion. The second endpoints were the improvement of clinical symptoms and laboratory parameters within 3 days after CP transfusion. The median time from onset of illness to CP transfusion was 16.5 days. After CP transfusion, the level of neutralizing antibody increased rapidly up to 1:640 in five cases, while that of the other four cases maintained at a high level (1:640). The clinical symptoms were significantly improved along with increase of oxyhemoglobin saturation within 3 days. Several parameters tended to improve as compared to pre-transfusion, including increased lymphocyte counts (0.65*109/L vs. 0.76*109/L) and decreased C-reactive protein (55.98 mg/L vs. 18.13 mg/L). Radiological examinations showed varying degrees of absorption of lung lesionswithin 7 days. The viral load was undetectable after transfusion in seven patients who had previous viremia. No severe adverse effects were observed. This study showed CP therapy was welltolerated and could potentially improve the clinical outcomes through neutralizing viremia in severe COVID-19 cases. The optimal dose and time point, as well as the clinical benefit of CP therapy, needs further investigation in larger well-controlled trials.