COVID-19 and Acute Cardiac Injury: Clinical Manifestations, Biomarkers, Mechanisms, Diagnosis, and Treatment.

PURPOSE OF REVIEW: This review aims to comprehensively explore the clinical characteristics of COVID-19-related cardiac injury and examine the potential mechanisms underlying cardiac injury in patients affected by COVID-19. RECENT FINDINGS: The COVID-19 pandemic has primarily been associated with severe respiratory symptoms. However, emerging evidence has indicated that a significant number of COVID-19 patients also experience myocardial injury, leading to conditions such as acute myocarditis, heart failure, acute coronary syndrome, and arrhythmias. The incidence of myocardial injury is notably higher in patients with preexisting cardiovascular diseases. Myocardial injury often manifests with elevated levels of inflammation biomarkers, as well as abnormalities observed on electrocardiograms and echocardiograms. COVID-19 infection has been found to be associated with myocardial injury, which can be attributed to several pathophysiological mechanisms. These mechanisms include injury caused by hypoxia, resulting from respiratory compromise, a systemic inflammatory response triggered by the infection, and direct attack on the myocardium by the virus itself. Furthermore, the angiotensin-converting enzyme 2 (ACE2) receptor plays a crucial role in this process. Early recognition, prompt diagnosis, and a comprehensive understanding of the underlying mechanisms are essential for effectively managing and reducing the mortality associated with myocardial injury in COVID-19 patients.

ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve O ( log N log ( log N ) ) improvements compared to O N 2 in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.

Influencing factors of proteinuria in patients with hypertension in Qinghai-Tibet Plateau

Objective: To investigate the risk factors of proteinuria in patients with hypertension in Qinghai-Tibet Plateau.

Viral RNA Load in Symptomatic and Asymptomatic COVID-19 Omicron Variant-Positive Patients.

Objectives: Viral load is important when evaluating viral transmission potential, involving the use of a polymerase chain reaction (PCR) cycle threshold (Ct) value. We aimed to analyze the PCR Ct values of respiratory tract samples taken from patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant strains to evaluate these strains' viral dynamics. Methods: This study comprised 361 patients. The Ct values of SARS-CoV-2-related respiratory samples were compared between symptomatic and asymptomatic patients. Results: The median (25th percentile and 75th percentile) nasopharynx and oropharynx SARS-CoV-2 Ct values were 30.5 (24.5-35.0) and 34.5 (30.0-37.0) in the symptomatic group, respectively, and 27.8 (23.4-34.5) and 33.5 (26.0-35.0) in the asymptomatic group, respectively, without significance. In the symptomatic group, subgroup analyses according to age showed the mean nasal Ct value for patients aged >18 years was 29.0 (23.5-34.5), which was significantly lower than that of patients aged 0-4 years and 5-13 years (36.0 (30.5-38.0) and 34.5 (31.0-39.0), respectively). The nasal Ct value for asymptomatic patients aged >18 years was 25.5 (20.9-28.4), which was significantly lower than of patients aged 5-13 years (34.5 (25.6-36.4)). Conclusion: Our findings suggest that the viral loads of asymptomatic and symptomatic patients did not differ significantly. However, adults infected with SARS-CoV-2 had higher nasal viral loads that those of young children.

Dynamic Classification of Imageless Bioelectrical Impedance Tomography Features with Attention-Driven Spatial Transformer Neural Network.

Point-of-Care monitoring devices have proven to be pivotal in the timely screening and intervention of critical care patients. The urgent demands for their deployment in the COVID-19 pandemic era has translated into the escalation of rapid, reliable, and low-cost monitoring systems research and development. Electrical Impedance Tomography (EIT) is a highly promising modality in providing deep tissue imaging that aids in patient bedside diagnosis and treatment. Motivated to bring forth an accurate and intelligent EIT screening system, we bypassed the complexity and challenges typically associated with its image reconstruction and feature identification processes by solely focusing on the raw data output to extract the embedded knowledge. We developed a novel machine learning architecture based on an attention-driven spatial transformer neural network to specifically accommodate for the patterns and dependencies within EIT raw data. Through elaborate precision-mapped phantom experiments, we validated the reproduction and recognition of features with systemically controlled changes. We demonstrated over 95% accuracy via state-of-the-art machine learning models, and an enhanced performance using our adapted transformer pipeline with shorter training time and greater computational efficiency. Our approach of using imageless EIT driven by a novel attention-focused feature learning algorithm is highly promising in revolutionizing conventional EIT operations and augmenting its practical usage in medicine and beyond.

The effect of the systemic immune-inflammation index on conversion time of virus nucleic acid turning negative in COVID-19 patients

Objective: To investigate the effect of the systemic immune-inflammation index(SII) on the conversion time of virus nucleic acid turning negative in patients with coronavirus disease 2019(COVID-19).

Plasma proteomic and metabolomic characterization of COVID-19 survivors 6 months after discharge.

Coronavirus disease 2019 (COVID-19) has gained prominence as a global pandemic. Studies have suggested that systemic alterations persist in a considerable proportion of COVID-19 patients after hospital discharge. We used proteomic and metabolomic approaches to analyze plasma samples obtained from 30 healthy subjects and 54 COVID-19 survivors 6 months after discharge from the hospital, including 30 non-severe and 24 severe patients. Through this analysis, we identified 1019 proteins and 1091 metabolites. The differentially expressed proteins and metabolites were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Among the patients evaluated, 41% of COVID-19 survivors reported at least one clinical symptom and 26.5% showed lung imaging abnormalities at 6 months after discharge. Plasma proteomics and metabolomics analysis showed that COVID-19 survivors differed from healthy control subjects in terms of the extracellular matrix, immune response, and hemostasis pathways. COVID-19 survivors also exhibited abnormal lipid metabolism, disordered immune response, and changes in pulmonary fibrosis-related proteins. COVID-19 survivors show persistent proteomic and metabolomic abnormalities 6 months after discharge from the hospital. Hence, the recovery period for COVID-19 survivors may be longer.

Serum levels of laminin and von Willebrand factor in COVID-19 survivors 6 months after discharge.

OBJECTIVES: The aim of this study was to evaluate the clinical characteristics, pulmonary diffusion function, chest computed tomography (CT), and serum lung cell damage indicators of coronavirus disease 2019 (COVID-19) survivors 6 months after discharge. METHODS: Data of COVID-19 survivors discharged from hospital between January 21, 2020 and January 11, 2021 and healthy controls were collected. Serum levels of surfactant protein D (SP-D)1, the receptor for advanced glycation end products (RAGE)2, laminin, and von Willebrand factor (vWF) were measured in the healthy controls and COVID-19 survivors 6 months after discharge. The relationships between serum lung cell damage indicator levels and various parameters were explored. RESULTS: Fifty-two COVID-19 survivors (31 with non-severe disease and 21 with severe disease) and 30 controls were included. Serum levels of laminin in COVID-19 survivors 6 months after discharge were significantly higher than those in the controls. The increase was more significant in elderly and female patients. Serum levels of RAGE and vWF were not statistically different from those of the controls. However, 6 months after discharge, COVID-19 survivors with abnormal chest CT and those in the severe group had higher vWF levels. CONCLUSIONS: COVID-19 patients had abnormal lung injury indicators 6 months after discharge. The recovery time after infection is currently unknown, and long-term observation is required.

Impact of COVID-19 pandemic on STEMI undergoing primary PCI treatment in Beijing, China.

OBJECTIVE: Strict control measures under the COVID epidemic have brought an inevitable impact on ST-segment elevation myocardial infarction (STEMI)'s emergency treatment. We investigated the impact of the COVID on the treatment of patients with STEMI undergoing primary PCI. METHODS: In this single center cohort study, we selected a time frame of 6 month after declaration of COVID-19 infection (Jan 24-July 24, 2020); a group of STEMI patients in the same period of 2019 was used as control. Finally, a total of 246 STEMI patients, who were underwent primary PCI, were enrolled into the study (136 non COVID-19 outbreak periods and 110 COVID-19 outbreak periods). The impact of COVID on the time of symptom onset to the first medical contact (symptom-to-FMC) and door to balloon (D-to-B) was investigated. Moreover, the primary outcome was in-hospital major adverse cardiac events (MACE), defined as a composite of cardiac death, heart failure and malignant arrhythmia. RESULTS: Compared with the same period in 2019, there was a 19% decrease in the total number of STEMI patients undergoing primary PCI at the peak of the pandemic in 2020. The delay in symptom-to-FMC was significantly longer in COVID Outbreak period (180 [68.75, 342] vs 120 [60,240] min, P = 0.003), and the D-to-B times increased significantly (148 [115-190] vs 84 [70-120] min, P < 0.001). However, among patients with STEMI, MACE was similar in both time periods (18.3% vs 25.7%, p = 0.168). On multivariable analysis, COVID was not independently associated with MACE; the history of diabetes, left main disease and age>65 years were the strongest predictors of MACE in the overall population. CONCLUSIONS: The COVID pandemic was not independently associated with MACE; suggesting that active primary PCI treatment preserved high-quality standards even when challenged by a severe epidemic. CLINICAL TRIAL REGISTRATION: URL: https://ClinicalTrials.gov Unique identifier: NCT04427735.

Detection and predictors of anti-SARS-CoV-2 antibody levels in COVID-19 patients at 8 months after symptom onset.

Aim: To determine SARS-CoV-2 specific IgM and IgG levels of patients with COVID-19 at 8 months after symptom onset and to explore the predictors of antibody levels. Materials & methods: The magnetic chemiluminescence method was used to measure the antibody levels. Clinical data were collected and analyzed retrospectively. Results: A total of 54 patients were enrolled in this study, of whom 59.3% were IgM positive and 96.4% were IgG positive. The multiple linear regression analysis revealed that the duration of RNA shedding, C-reactive protein level and disease severity were independent predictors of IgG levels. Conclusion: COVID-19 patients retained long-term viral-specific protective immunity. Disease severity, C-reactive protein level and duration of RNA shedding were related to antibody levels 8 months after symptom onset.

Kinetics of specific IgM and IgG antibodies in patients with COVID-19 in Tianjin

Objective: To dynamically detect and evaluate the serum specific levels of IgM and IgG and their variation patterns in critical and non-critical patients with COVID-19.

Assessment of ICESat-2 ice surface elevations over the Chinese Antarctic Research Expedition (CHINARE) route, East Antarctica, based on coordinated multi-sensor observations

We present the results of an assessment of ice surface elevation measurements from NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) along the CHINARE (CHINese Antarctic Research Expedition) route near the Amery Ice Shelf in East Antarctica. The validation campaign was designed and implemented in cooperation with the 36th CHINARE Antarctic expedition from December 2019 to February 2020. The assessment of the ICESat-2 geolocated photon product (ATL03) and land ice elevation product (ATL06) was performed based on coordinated multi-sensor observations using two roof-mounted kinematic global navigation satellite system (GNSS) receivers, two line arrays of corner cube retroreflectors (CCRs), two sets of retroreflective target sheets (RTSs), and two unmanned aerial vehicles (UAVs) with cameras. This systematic validation of the ICESat-2 data covered a variety of Antarctic ice surface conditions along the 520 km traverse from the coastal Zhongshan Station to the inland Taishan Station. This comprehensive investigation is complementary to the 750 km traverse validation of flat inland Antarctica containing a 300 km latitude traverse of 88∘ S by the mission team (Brunt et al., 2021). Overall, the validation results show that the elevation of the ATL06 ice surface points is accurate to 1.5 cm with a precision of 9.1 cm along the 520 km CHINARE route. The elevation of the ATL03 photons has an offset of 2.1 cm from a GNSS-surveyed CCR and is accurate to 2.5 cm with a precision of 2.7 cm as estimated by using RTSs. The validation results demonstrate that the estimated ICESat-2 elevations are accurate to 1.5–2.5 cm in this East Antarctic region, which shows the potential of the data products for eliminating mission biases by overcoming the uncertainties in the estimation of mass balance in East Antarctica. It should be emphasized that the results based on the CCR and RTS techniques can be improved by further aggregation of observation opportunities for a more robust assessment. The developed validation methodology and sensor system can be applied for continuous assessment of ICESat-2 data, especially for calibration against potential degradation of the elevation measurements during the later operation period.

A follow-up study of respiratory and physical function after discharge in patients with redetectable positive SARS-CoV-2 nucleic acid results following recovery from COVID-19.

OBJECTIVE: The aim of this study was to evaluate the respiratory and physical function of patients who retested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA during post-coronavirus disease 2019 (COVID-19) rehabilitation. METHODS: A total of 302 discharged COVID-19 patients were included. Discharged patients were followed up for 14 days to 6 months. The modified Medical Research Council (mMRC) dyspnea scale, Borg rating of perceived exertion, and manual muscle testing (MMT) scores on day 14 and at 6 months after discharge were compared between the redetectable positive (RP) and non-RP (NRP) groups. Prognoses of respiratory and physical function were compared between patients who recovered from moderate and severe COVID-19. RESULTS: Of the study patients, 7.6% were RP. The proportion of patients who used antiviral drugs was significantly lower in the RP group than in the NRP group. There were no differences in mMRC, Borg, or MMT scores within the RP and NRP groups. The mMRC, Borg, and MMT scores were worse for patients with severe disease when compared to those with moderate disease at both follow-up time points. CONCLUSIONS: COVID-19 patients who did not take antiviral drugs were more likely to be RP after discharge. The recovery of respiratory and physical function was not related to re-positivity during rehabilitation, but was related to disease severity during hospitalization.

A Follow-Up Study of Lung Function and Chest Computed Tomography at 6 Months after Discharge in Patients with Coronavirus Disease 2019.

We aimed to investigate changes in pulmonary function and computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) during the recovery period. COVID-19 patients underwent symptom assessment, pulmonary function tests, and high-resolution chest CT 6 months after discharge from the hospital. Of the 54 patients enrolled, 31 and 23 were in the moderate and severe group, respectively. The main symptoms 6 months after discharge were fatigue and exertional dyspnea, experienced by 24.1% and 18.5% of patients, respectively, followed by smell and taste dysfunction (9.3%) and cough (5.6%). One patient dropped out of the pulmonary function tests. Of the remaining 54 patients, 41.5% had pulmonary dysfunction. Specifically, 7.5% presented with restrictive ventilatory dysfunction (forced vital capacity <80% of the predicted value), 18.9% presented with small airway dysfunction, and 32.1% presented with pulmonary diffusion impairment (diffusing capacity for carbon monoxide <80% of the predicted value). Of the 54 patients enrolled, six patients dropped out of the chest CT tests. Eleven of the remaining 48 patients presented with abnormal lung CT findings 6 months after discharge. Patients with residual lung lesions were more common in the severe group (52.6%) than in the moderate group (3.4%); a higher proportion of patients had involvement of both lungs (42.1% vs. 3.4%) in the severe group. The residual lung lesions were mainly ground-glass opacities (20.8%) and linear opacities (14.6%). Semiquantitative visual scoring of the CT findings revealed significantly higher scores in the left, right, and both lungs in the severe group than in the moderate group. COVID-19 patients 6 months after discharge mostly presented with fatigue and exertional dyspnea, and their pulmonary dysfunction was mostly characterized by pulmonary diffusion impairment. As revealed by chest CT, the severe group had a higher prevalence of residual lesions than the moderate group, and the residual lesions mostly manifested as ground-glass opacities and linear opacities.

The potential effects of clinical antidiabetic agents on SARS-CoV-2.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is currently posing significant threats to public health worldwide. It is notable that a substantial proportion of patients with sever COVID-19 have coexisting diabetic conditions, indicating the progression and outcome of COVID-19 may relate to diabetes. However, it is still unclear whether diabetic treatment principles can be used for the treatment of COVID-19. METHODS: We conducted a computational approach to screen all commonly used clinical oral hypoglycemic drugs to identify the potential inhibitors for the main protease (Mpro ) of SARS-CoV-2, which is one of the key drug targets for anti-COVID-19 drug discovery. RESULTS: Six antidiabetic drugs with docking scores higher than 8.0 (cutoff value), including repaglinide, canagliflozin, glipizide, gliquidone, glimepiride, and linagliptin, were predicted as the promising inhibitors of Mpro . Interestingly, repaglinide, one of the six antidiabetic drugs with the highest docking score for Mpro , was similar to a previously predicted active molecule nelfinavir, which is a potential anti-HIV and anti-COVID-19 drug. Moreover, we found repaglinide shared similar docking pose and pharmacophores with a reported ligand (N3 inhibitor) and nelfinavir, demonstrating that repaglinide would interact with Mpro in a similar way. CONCLUSION: These results indicated that these six antidiabetic drugs may have an extra effect on the treatment of COVID-19, although further studies are necessary to confirm these findings.

High resolution computed tomography for the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia: a study from multiple medical centers in western China.

BACKGROUND: To evaluate the role of high-resolution computed tomography (HRCT) in the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia and to provide experience in the early detection and diagnosis of 2019-nCoV pneumonia. METHODS: Seventy-two patients confirmed to be infected with 2019-nCoV from multiple medical centers in western China were retrospectively analyzed, including epidemiologic characteristics, clinical manifestations, laboratory findings and HRCT chest features. RESULTS: All patients had lung parenchymal abnormalities on HRCT scans, which were mostly multifocal in both lungs and asymmetric in all patients, and were mostly in the peripheral or subpleural lung regions in 52 patients (72.22%), in the central lung regions in 16 patients (22.22%), and in both lungs with "white lung" manifestations in 4 patients (5.56%). Subpleural multifocal consolidation was a predominant abnormality in 38 patients (52.78%). Ground-glass opacity was seen in 34 patients (47.22%). Interlobular septal thickening was found in 18 patients, 8 of whom had only generally mild thickening with no zonal predominance. Reticulation was seen in 8 patients (11.11%), and was mild and randomly distributed. In addition, both lungs of 28 patients had 2 or 3 CT imaging features. Out of these 72 patients, 36 were diagnosed as early stage, 32 patients as progressive stage, and 4 patient as severe stage pneumonia. Moreover, the diagnostic accuracy of HRCT features combined with epidemiological history was not significantly different from the detection of viral nucleic acid (all P >0.05). CONCLUSIONS: The HRCT features of 2019-nCoV pneumonia are characteristic to a certain degree, which when combined with epidemiological history yield high clinical value in the early detection and diagnosis of 2019-nCoV pneumonia.