A high-resolution typical pollution source emission inventory and pollution source changes during the COVID-19 lockdown in a megacity, China.

To control the spread of COVID-19, China has imposed national lockdown policies to restrict the movement of its population since the Chinese New Year of January 2020. In this study, we quantitatively analyzed the changes of pollution sources in Shanghai during the COVID-19 lockdown; a high-resolution emission inventory of typical pollution sources including stationary source, mobile source, and oil and gas storage and transportation source was established based on pollution source data from January to February 2020. The results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs) were 9520.2, 37,978.6, 2796.7, and 7236.9 tons, respectively, during the study period. Affected by the COVID-19 lockdown, the mobile source experienced the largest decline. The car mileage and oil sales decreased by about 80% during the COVID-19 lockdown (P3) when compared with those during the pre-Spring Festival (P1). The number of aircraft activity decreased by approximately 50%. The impact of the COVID-19 epidemic on industries such as iron and steel and petrochemicals was less significant, while the greater impact was on coatings, chemicals, rubber, and plastic. The emissions of SO2, NOx, PM2.5, and VOCs decreased by 11%, 39%, 37%, and 47%, respectively, during P3 when compared with those during P1. The results show that the measures to control the spread of the COVID-19 epidemic made a significant contribution to emission reductions. This study may provide a reference for other countries to assess the impact of the COVID-19 epidemic on emissions and help establish regulatory actions to improve air quality.

COVID-2019 Associated with Acquired Monocular Blindness.

OBJECTIVE: We documented an older female with Coronavirus(CoV) Disease 2019 (COVID-19) and concomitant acquired monocular blindness. We examined this phenomenon in order to understand COVID-19 better. METHODS: We observed an older female with COVID-19 and concomitant acquired monocular blindness. The following indicators were monitored during the course of the disease: ocular examinations, flash visual evoked potential examination, a blood test for COVID-19 IgM antibodies, as well as nasopharyngeal swab and tear sample tests for COVID-19 nucleic acid. RESULTS: The patient's visual acuity for the left eye was NLP and the intraocular pressure was 51 mmHg. Keratic precipitates similar to mutton-fat were spread over the corneal endothelium of the left eye. The funduscopic examination of the patient's left eye revealed severe retinal arterial ischemia, and the color of the retina was off-white. Compared to the right eye, the flash visual evoked potential examination revealed a moderate decrease in P2 wave amplitude for the left eye. A blood test was positive for COVID-19 IgM antibodies, and a nasopharyngeal swab test taken for COVID-19 nucleic acid was positive on May 4, 2020. A sample of the patient's tears was taken, and the nucleic acid test for COVID-19 was still positive two weeks later. CONCLUSIONS: Our study was the first to find that acute viral retinitis could occur in patients with COVID-19 and severe blindness could be associated with SARS-CoV-2 infection. Therefore, physicians should consider the possibility of coronavirus infection in patients with an abnormal fundus or suddenly vision loss.

Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model.

BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.

Factors associated with acute cardiac injury and their effects on mortality in patients with COVID-19.

To determine the incidence of acute cardiac injury (ACI), the factors associated with ACI and the in-hospital mortality in patients with COVID-19, especially in severe patients. All consecutive in-patients with laboratory-confirmed COVID-19 from Tongji Hospital in Wuhan during February 1 and March 29, 2020 were included. The demographic, clinical characteristics, laboratory, radiological and treatment data were collected. Univariate and Firth logistic regression analyses were used to identify factors associated with ACI and in-hospital mortality, and Kaplan-Meier method was used to estimate cumulative in-hospital mortality. Among 1031 patients included, 215 (20.7%) had ACI and 501 (48.6%) were severe cases. Overall, 165 patients died; all were from the severe group, and 131 (79.39%) had ACI. ACI (OR = 2.34, P = 0.009), male gender (OR = 2.58, P = 0.001), oximeter oxygen saturation (OR = 0.90, P < 0.001), lactate dehydrogenase (OR = 3.26, P < 0.001), interleukin-6 (IL-6) (OR = 8.59, P < 0.001), high sensitivity C-reactive protein (hs-CRP) (OR = 3.29, P = 0.016), N-terminal pro brain natriuretic peptide (NT-proBNP) (OR = 2.94, P = 0.001) were independent risk factors for the in-hospital mortality in severe patients. The mortality was significantly increased among severe patients with elevated hs-CRP, IL-6, hs-cTnI, and/or NT-proBNP. Moreover, the mortality was significantly higher in patients with elevation of both hs-cTnI and NT proBNP than in those with elevation of either of them. ACI develops in a substantial proportion of patients with COVID-19, and is associated with the disease severity and in-hospital mortality. A combination of hs-cTnI and NT-proBNP is valuable in predicting the mortality.

Clinical manifestations and evidence of neurological involvement in 2019 novel coronavirus SARS-CoV-2: a systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a global pandemic, affecting millions of people. However, clinical research on its neurological manifestations is thus far limited. In this study, we aimed to systematically collect and investigate the clinical manifestations and evidence of neurological involvement in COVID-19. METHODS: Three medical (Medline, Embase, and Scopus) and two preprints (BioRxiv and MedRxiv) databases were systematically searched for all published articles on neurological involvement in COVID-19 since the outbreak. All included studies were systematically reviewed, and selected clinical data were collected for meta-analysis via random-effects. RESULTS: A total of 41 articles were eligible and included in this review, showing a wide spectrum of neurological manifestations in COVID-19. The meta-analysis for unspecific neurological symptoms revealed that the most common manifestations were fatigue (33.2% [23.1-43.3]), anorexia (30.0% [23.2-36.9]), dyspnea/shortness of breath (26.9% [19.2-34.6]), and malaise (26.7% [13.3-40.1]). The common specific neurological symptoms included olfactory (35.7-85.6%) and gustatory (33.3-88.8%) disorders, especially in mild cases. Guillain-Barré syndrome and acute inflammation of the brain, spinal cord, and meninges were repeatedly reported after COVID-19. Laboratory, electrophysiological, radiological, and pathological evidence supported neurologic involvement of COVID-19. CONCLUSIONS: Neurological manifestations are various and prevalent in COVID-19. Emerging clinical evidence suggests neurological involvement is an important aspect of the disease. The underlying mechanisms can include both direct invasion and maladaptive inflammatory responses. More studies should be conducted to explore the role of neurological manifestations in COVID-19 progression and to verify their underlying mechanisms.

Diabetes is a risk factor for the progression and prognosis of COVID-19.

BACKGOUND: To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19). METHODS: A total of 174 consecutive patients confirmed with COVID-19 were studied. Demographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed. RESULTS: We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism. Furthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P < .01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19. CONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19. More intensive attention should be paid to patients with diabetes, in case of rapid deterioration.