The Impact of the COVID-19 Pandemic on the Connectedness between Green Industries and Financial Markets in China: Evidence from Time-Frequency Domain with Portfolio Implications

To achieve sustainable economic growth, a significant amount of private capital must be invested in green industries. However, risk management in the green industry stock market has drawn much attention recently due to the uncertainty and high risk present in this market. By applying the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Baruník and Křehlík, and the dynamic conditional correlation (DCC) model, this paper focuses mainly on the heterogeneity of the volatility spillovers among six green industry equities and other financial assets in China, under various market economy situations. Based on the empirical results obtained in this paper, we find that the green industry stock markets have the least impact on the gold and energy futures markets. Additionally, based on asymmetric analyses, it can be concluded that the green bond market has experienced the smallest shocks from the six green industry stock markets. By utilizing frequency-domain analyses, the energy futures market experiences the least amount of volatility from green stocks. Additionally, the COVID-19 pandemic affects the interconnectedness of markets. Prior to the COVID-19 pandemic, energy futures were the most suitable portfolio instrument for green industry stocks. When the COVID-19 pandemic occurred, however, gold proved to be the most advantageous portfolio asset. The research findings of this paper demonstrate the impact of COVID-19 on the selection of the best investment instruments for green industry stocks, which is beneficial for reducing the investment risk of green financial market participants and increasing the demand for green stock markets, while also providing practical advice for environmentally conscious investors and policymakers.

Non-contact screening system based for COVID-19 on XGBoost and logistic regression.

BACKGROUND: The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. OBJECTIVE: We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. METHODS: We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. RESULTS: The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. CONCLUSION: The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

[Effect of noninvasive positive pressure ventilation and high-flow nasal cannula oxygen therapy on the clinical efficacy of coronavirus disease 2019 patients with acute respiratory distress syndrome].

OBJECTIVE: To observe the effect of noninvasive positive pressure ventilation (NIPPV) and high-flow nasal cannula oxygen therapy (HFNC) on the prognosis of patients with coronavirus disease 2019 (COVID-19) accompanied with acute respiratory distress syndrome (ARDS). METHODS: A retrospective study was conducted in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology when authors worked as medical team members for treating COVID-19. COVID-19 patients with pulse oxygen saturation/fraction of inspiration oxygen (SpO2/FiO2, S/F) ratio < 235, managed by medical teams [using S/F ratio instead of oxygenation index (PaO2/FiO2) to diagnose ARDS] from February to April 2020 were included. The patients were divided into NIPPV group and HFNC group according to their oxygen therapy modes. Clinical data of patients were collected, including general characteristics, respiratory rate (RR), fraction of FiO2, SpO2, heart rate (HR), mean arterial pressure (MAP), S/F ratio in the first 72 hours, lymphocyte count (LYM), percentage of lymphocyte (LYM%) and white blood cell count (WBC) at admission and discharge or death, the duration of dyspnea before NIPPV and HFNC, and the length from onset to admission. The differences of intubation rate, all-cause mortality, S/F ratio and RR were analyzed, and single factor analysis and generalized estimation equation (GEE) were used to analyze the risk factors affecting S/F ratio. RESULTS: Among the 41 patients, the proportion of males was high (68.3%, 28 cases), the median age was 68 (58-74) years old, 28 cases had complications (68.3%), and 34 cases had multiple organ dysfunction syndrome (MODS, 82.9%). Compared with HFNC group, the proportion of complications in NIPPV group was higher [87.5% (21/24) vs. 41.2% (7/17), P < 0.05], and the value of LYM% was lower [5.3% (3.4%-7.8%) vs. 10.0% (3.9%-19.7%), P < 0.05], the need of blood purification was also significantly lower [0% (0/24) vs. 29.4% (5/17), P < 0.05]. The S/F ratio of NIPPV group gradually increased after 2 hours treatment and RR gradually decreased with over time, S/F ratio decreased and RR increased in HFNC group compared with baseline, but there was no significant difference in S/F ratio between the two groups at each time point. RR in NIPPV group was significantly higher than that in HFNC group after 2 hours treatment [time/min: 30 (27-33) vs. 24 (21-27), P < 0.05]. There was no significant difference in rate need intubation and hospital mortality between NIPPV group and HFNC group [66.7% (16/24) vs. 70.6% (12/17), 58.3% (14/24) vs. 52.9% (9/17), both P > 0.05]. Analysis of the factors affecting the S/Fratio in the course of oxygen therapy showed that the oxygen therapy mode and the course of illness at admission were the factors affecting the S/F ratio of patients [ßvalues were -15.827, 1.202, 95% confidence interval (95%CI) were -29.102 to -2.552 and 0.247-2.156, P values were 0.019 and 0.014, respectively]. CONCLUSIONS: Compared with HFNC, NIPPV doesn't significantly reduce the intubation rate and mortality of patients with COVID-19 accompanied with ARDS, but it significantly increases the S/F ratio of those patients.

Diabetes, even newly defined by HbA1c testing, is associated with an increased risk of in-hospital death in adults with COVID-19.

BACKGROUND: Diabetes is associated with poor coronavirus disease 2019 (COVID-19) outcomes. However, little is known on the impact of undiagnosed diabetes in the COVID-19 population. We investigated whether diabetes, particularly undiagnosed diabetes, was associated with an increased risk of death from COVID-19. METHODS: This retrospective study identified adult patients with COVID-19 admitted to Tongji Hospital (Wuhan) from January 28 to April 4, 2020. Diabetes was determined using patients' past history (diagnosed) or was newly defined if the hemoglobin A1c (HbA1c) level at admission was ≥6.5% (48 mmol/mol) (undiagnosed). The in-hospital mortality rate and survival probability were compared between the non-diabetes and diabetes (overall, diagnosed, and undiagnosed diabetes) groups. Risk factors of mortality were explored using Cox regression analysis. RESULTS: Of 373 patients, 233 were included in the final analysis, among whom 80 (34.3%) had diabetes: 44 (55.0%) reported a diabetes history, and 36 (45.0%) were newly defined as having undiagnosed diabetes by HbA1c testing at admission. Compared with the non-diabetes group, the overall diabetes group had a significantly increased mortality rate (22.5% vs. 5.9%, p <  0.001). Moreover, the overall, diagnosed, and undiagnosed diabetes groups displayed lower survival probability in the Kaplan-Meier survival analysis (all p <  0.01). Using multivariate Cox regression, diabetes, age, quick sequential organ failure assessment score, and D-dimer ≥1.0 µg/mL were identified as independent risk factors for in-hospital death in patients with COVID-19. CONCLUSIONS: The prevalence of undiagnosed pre-existing diabetes among patients with COVID-19 is high in China. Diabetes, even newly defined by HbA1c testing at admission, is associated with increased mortality in patients with COVID-19. Screening for undiagnosed diabetes by HbA1c measurement should be considered in adult Chinese inpatients with COVID-19.

Novel coronavirus infection and acute kidney injury in two renal transplant recipients: a case report.

BACKGROUND: The causative virus of coronavirus disease 2019 (COVID-19) may cause severe fatal pneumonia. The clinical presentation includes asymptomatic infection, severe pneumonia, and acute respiratory failure. Data pertaining to acute renal injury due to COVID-19 in patients who have undergone renal transplantation are scarce. We herein report two cases of COVID-19 along with acute kidney injury following kidney transplantation.Case presentation: Two patients with COVID-19 underwent renal transplantation and were subsequently diagnosed with acute kidney injury. The first patient presented with progressive respiratory symptoms and acute renal injury. He was treated with diuretics and suspension of immunosuppressive therapy; however, the patient died. The second patient presented with respiratory tract symptoms, hypoxemia, and progressive deterioration of renal function followed by improvement. Her mycophenolate mofetil was stopped after admission, and tacrolimus was discontinued 10 days later. Moxifloxacin and methylprednisolone were continued in combination with albumin and gamma globulin infusion. A diuretic was administered, and prednisone was gradually reduced along with tacrolimus. The patient exhibited a satisfactory clinical recovery. CONCLUSION: Patients who develop COVID-19 after kidney transplantation are at risk of acute kidney injury, and their prednisone, immunosuppressant, and gamma globulin treatment must be adjusted according to their condition.

Treatment of SARS-CoV-2 induced pneumonia with NAD+ in a mouse model (preprint)

The global COVID-19 epidemic has spread rapidly around the world and has already caused the death of more than one million people. As there is yet no vaccine, it is urgent to develop effective strategies to treat COVID-19 patients. Here, we used a mouse-adapted SARS-CoV-2 infection model to explore potential therapeutic targets for SARS-CoV-2 pneumonia. Global gene expression analysis of infected mouse lungs revealed dysregulation of genes associated with NAD+ metabolism, immune response and cell death, similar to that in COVID-19 patients. We therefore investigated the effect of treatment with NAD+ and found that the pneumonia phenotypes, including excessive inflammatory cell infiltration and embolization in SARS-CoV-2 infected lungs were significantly rescued by boosting NAD+ levels. Most notable, cell death was suppressed substantially (>65%) by NAD+ supplementation. Thus, our in vivo mouse study supports trials for treating COVID-19 patients with NAD+ or its precursors.

High Aspartate Aminotransferase to Alanine Aminotransferase Ratio on Admission as Risk Factor for Poor Prognosis in COVID-19 Patients (preprint)

This study aimed to analyze aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio in COVID-19 patients. After exclusion, 567 inpatients were included in this study and separated into two groups according to their AST/ALT ratio on admission. Poor prognosis included death and transfer to other hospitals due to deterioration. Of 567 patients, 56 (9.9%) had AST/ALT ≥ 2. Of the 56 patients, older age (median age 65.5 years), fatigue (29 [51.8%] cases), comorbidities (33 [58.9%] cases) and outcomes were significantly different from patients with AST/ALT < 2. They also had worse chest computed tomography (CT) findings, laboratory results and severity scores. Levels of platelet count (OR = 0.989, 95% CI [0.983-0.996]) were independently associated with AST/ALT ≥ 2 on admission. Furthermore, a high AST/ALT ratio on admission was an independent risk factor for poor prognosis (OR = 22.02, 95% CI [1.84-263.2]), especially in patients with AST levels > 40 U/L. In subsequent monitoring, the AST/ALT ratio was decreased in both patients with AST/ALT < 2 or ≥ 2 on admission. COVID-19 patients who are older, or have fatigue, comorbidities are more likely to have AST/ALT ≥ 2 on admission, which might be the indication of worse status and outcomes.

Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China (preprint)

IMPORTANCE In the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively. OBJECTIVE To develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19). DESIGN Diagnostic model based on retrospective case series. SETTING Two hospitals in Wuhan and Beijing, China. PTRTICIPANTS 584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020. METHODS LASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram. RESULTS Six potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p[≤]0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale. CONCLUSIONS We established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

Recommended prophylactic and management strategies for severe acute respiratory syndrome coronavirus 2 infection in transplant recipients.

Since December 2019, increasing attention has been paid to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Wuhan, China. SARS-CoV-2 primarily invades the respiratory tract and lungs, leading to pneumonia and other systemic disorders. The effect of SARS-CoV-2 in transplant recipients has raised significant concerns, especially because there is a large population of transplant recipients in China. Based on the current epidemic situation, this study reviewed publications on this virus and coronavirus disease 2019 (COVID-19), analyzed common features of respiratory viral pneumonias, and presented the currently reported clinical characteristics of COVID-19 in transplant recipients to improve strategies regarding the diagnosis and treatment of COVID-19 in this special population.