Detection and analysis of clinical features of patients with different types of coronavirus disease 2019

Prevention, diagnostic evaluation, management and prognostic implications of liver disease in critically ill patients with COVID-19

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, broke out in December 2019 in Wuhan city of China and spread rapidly worldwide. Therefore, by March 2020, the World Health Organization declared the disease a global pandemic. Apart from the respiratory system, various other organs of the human body are also seriously affected by the virus. Liver injury in patients with a severe form of COVID-19 is estimated to be 14.8%-53.0%. Elevated levels of total bilirubin, aspartate aminotransferase and alanine aminotransferase and low levels of serum albumin and prealbumin are the main laboratory findings. Patients with pre-existing chronic liver disease and cirrhosis are much more prone to develop severe liver injury. This literature review presented the recent scientific findings regarding the pathophysiological mechanisms responsible for liver injury in critically ill patients with COVID-19, the various interactions between drugs used to treat the disease and the function of the liver and the specific tests providing the possibility of early diagnosis of severe liver injury in these patients. Moreover, it highlighted the burden that COVID-19 put on health systems worldwide and its effect on transplant programs and the care provided to critically ill patients in general and particularly to those with chronic liver disease.

A Review of COVID-19 in Relation to Metabolic Syndrome: Obesity, Hypertension, Diabetes, and Dyslipidemia

Although severe cases and mortality of coronavirus disease 2019 (COVID-19) are proportionally infrequent, these cases are strongly linked to patients with conditions of metabolic syndrome (obesity, hypertension, diabetes, and dyslipidemia). However, the pathophysiology of COVID-19 in relation to metabolic syndrome is not well understood. Thus, the goal of this secondary literature review was to examine the relationship between severe acute respiratory syndrome (SARS-CoV-2) infection and the individual conditions of metabolic syndrome.

The objective of this secondary literature review was achieved by examining primary studies, case studies, and other secondary studies, to obtain a comprehensive perspective of theories and observations of COVID-19 etiology with metabolic syndrome. The most extensive research was available on the topics of diabetes, hypertension, and obesity, which yielded multiple (and sometimes conflicting) hypothetical pathophysiology. The sources on dyslipidemia and COVID-19 were scarcer and failed to provide an equally comprehensive image, highlighting the need for further research.

It was concluded that hypertension had the strongest correlation with COVID-19 incidence (followed by obesity), yet the causative pathophysiology was ambiguous; most likely related to cardiovascular, angiotensin-converting enzyme 2 (ACE-2)-related complications from renin-angiotensin-aldosterone system (RAAS) imbalance. Obesity was also positively correlated to the severity of COVID-19 cases and was believed to contribute to mechanical difficulties with respiration, in addition to hypothetical connections with the expression of ACE-2 on abundant adipose tissue. Diabetes was believed to contribute to COVID-19 severity by producing a chronic inflammatory state and interfering with neutrophil and T-cell function. Furthermore, there were indications that COVID-19 may induce acute-onset diabetes and diabetic ketoacidosis. Lastly, dyslipidemia was concluded to potentially facilitate SARS-CoV-2 infection by enhancing lipid rafts and immunosuppressive functions. There were also indications that cholesterol levels may have prognostic indications and that statins may have therapeutic benefits.

COVID-19 combined with liver injury: Current challenges and management

Coronavirus disease 2019 (COVID-19) combined with liver injury has become a very prominent clinical problem. Due to the lack of a clear definition of liver injury in patients with COVID-19, the different selection of evaluation parameters and statistical time points, there are the conflicting conclusions about the incidence rate in different studies. The mechanism of COVID-19 combined with liver injury is complicated, including the direct injury of liver cells caused by severe acute respiratory syndrome coronavirus 2 replication and liver injury caused by cytokines, ischemia and hypoxia, and drugs. In addition, underlying diseases, especially chronic liver disease, can aggravate COVID-19 liver injury. In the treatment of COVID-19 combined with liver injury, the primary and basic treatment is to treat the etiology and pathogenesis, followed by support, liver protection, and symptomatic treatment according to the clinical classification and severity of liver injury. This article evaluates the incidence, pathogenesis and prevention and treatment of COVID-19 combined with liver injury, and aims to provide countermeasures for the prevention and treatment of COVID-19 combined with liver injury.

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images

COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification.

Longitudinal changes of laboratory measurements after discharged from hospital in 268 COVID-19 pneumonia patients

BACKGROUND AND OBJECTIVE

Monitoring recovery process of coronavirus disease 2019 (COVID-19) patients released from hospital is crucial for exploring residual effects of COVID-19 and beneficial for clinical care. In this study, a comprehensive analysis was carried out to clarify residual effects of COVID-19 on hospital discharged patients.

METHODS

Two hundred sixty-eight cases with laboratory measured data at hospital discharge record and five follow-up visits were retrospectively collected to carry out statistical data analysis comprehensively, which includes multiple statistical methods (e.g., chi-square, T-test and regression) used in this study.

RESULTS

Study found that 13 of 21 hematologic parameters in laboratory measured dataset and volume ratio of right lung lesions on CT images highly associated with COVID-19. Moderate patients had statistically significant lower neutrophils than mild and severe patients after hospital discharge, which is probably caused by more efforts on severe patients and slightly neglection of moderate patients. COVID-19 has residual effects on neutrophil-to-lymphocyte ratio (NLR) of patients who have hypertension or chronic obstructive pulmonary disease (COPD). After released from hospital, female showed better performance in T lymphocytes subset cells, especially T helper lymphocyte% (16% higher than male). According to this sex-based differentiation of COVID-19, male should be recommended to take clinical test more frequently to monitor recovery of immune system. Patients over 60 years old showed unstable recovery process of immune cells (e.g., CD45 + lymphocyte) within 75 days after discharge requiring longer clinical care. Additionally, right lung was vulnerable to COVID-19 and required more time to recover than left lung.

CONCLUSIONS

Criterion of hospital discharge and strategy of clinical care should be flexible in different cases due to residual effects of COVID-19, which depend on several impact factors. Revealing remaining effects of COVID-19 is an effective way to eliminate disorder of mental health caused by COVID-19 infection.

Correlation between ground-glass opacity on pulmonary CT and the levels of inflammatory cytokines in patients with moderate-to-severe COVID-19 pneumonia

Objectives: Comparative analysis of laboratory data in moderate-to-severe COVID-19 patients presenting with or without ground-glass opacities (GGOs). Methods: This retrospective study examined 61 patients with moderate-to-severe COVID-19, as defined by the report of the WHO-China Joint Mission on COVID-19. All patients were admitted to the Department of Infectious Diseases, Wuhan Union Hospital from Dec 28, 2019 to Feb 22, 2020 and classified into a GGO group or a non-GGO group based on CT results. The clinical characteristics and laboratory data of the two groups were compared. Data were analyzed using univariate and multivariate analysis, and using receiver operating characteristic (ROC) analysis. Results: Forty-five patients were in the GGO group (73.8%, 21 females, 24 males, mean age 54.8±17.8 years) and 16 were in the non-GGO group (26.2%, 11 females, 5 males, mean age 53±14.9 years). The levels of IL-2, IL-4, and IFN-γ were greater in the GGO group (all P<0.05). ROC analysis indicated that an elevated level of IL-2 was a good predictor of GGO (area under the curve: 0.716, optimal cutoff: 3.205 pg/mL, 53.8% sensitivity, 87.5% specificity, p<0.05). Multivariate analysis showed that IL-2 level was a significant and independent risk factor for lung GGO (OR: 8.167; 95% CI: 1.63, 40.8; P<0.05). Conclusions: There were correlations between GGO in the lungs of patients with moderate-to-severe COVID-19 and the levels of IL-2, IL-4, and INF-γ. IL-2 was a significant and independent risk factor for GGO. These findings provide a basis for studying the mechanism of pulmonary lesions in COVID-19 patients.