Forecast for peak infections in the second wave of the Omicron after the adjustment of zero-COVID policy in the mainland of China.

On December 7, 2022, the Chinese government optimized the current epidemic prevention and control policy, and no longer adopted the zero-COVID policy and mandatory quarantine measures. Based on the above policy changes, this paper establishes a compartment dynamics model considering age distribution, home isolation and vaccinations. Parameter estimation was performed using improved least squares and Nelder-Mead simplex algorithms combined with modified case data. Then, using the estimated parameter values to predict a second wave of the outbreak, the peak of severe cases will reach on 8 May 2023, the number of severe cases will reach 206,000. Next, it is proposed that with the extension of the effective time of antibodies obtained after infection, the peak of severe cases in the second wave of the epidemic will be delayed, and the final scale of the disease will be reduced. When the effectiveness of antibodies is 6 months, the severe cases of the second wave will peak on July 5, 2023, the number of severe cases is 194,000. Finally, the importance of vaccination rates is demonstrated, when the vaccination rate of susceptible people under 60 years old reaches 98%, and the vaccination rate of susceptible people over 60 years old reaches 96%, the peak of severe cases in the second wave of the epidemic will be reached on 13 July 2023, when the number of severe cases is 166,000.

Clinical and Cytokine Profile of Children With COVID-19: A Report From Turkey.

Background We aimed to analyze the expression of infection-related biomarkers and inflammatory cytokines in laboratory-confirmed cases and compare the differences between clinically severe and non-severe ones. Method We randomly selected 35 patients who were hospitalized with the diagnosis of coronavirus disease 2019 (COVID-19). Blood serum was obtained at the time of admission to the hospital, on the third to the fifth day, and at the time of discharge. Result The median age of our patients was 56.5±69.7 months (range: 1-205 months). The mean pro-B-type natriuretic peptide (pro-BNP) was significantly higher at the time of admission than on the third to the fifth day of illness. The mean pro-B-type natriuretic peptide levels at three time points were significantly higher in patients with severe cases than in mild-moderate cases. However, there was no significant difference between the clinical severity with regard to the cytokine levels at disease onset and recovery. Conclusion In the study, it was shown that cytokines play an important role in the pathogenesis of COVID-19. Therefore, it may be beneficial to use agents such as tocilizumab in the treatment.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Mediastinal Lymphadenopathy As A Predictor Of Worse Outcome In Severe Covid-19 Cases.

BACKGROUND: This cross-sectional study is aimed at evaluating the association of mediastinal lymphadenopathy with COVID-19 prognosis in severe cases. Place and Duration of Study: Department of Medicine, Pak Emirates Military Hospital, Pakistan, from June to July 2020. METHODS: One hundred and fifty (150) laboratory-confirmed SARS CoV-2 infected, severe cases in Intensive Care Unit/ High Dependency Unit were included. These cases were divided into two categories, i.e., with and without mediastinal lymphadenopathy on High Resolution Computed Tomography chest. The two categories were compared on the basis of data obtained including age, gender, comorbid, White Blood Cell count, lymphocyte count, median days of hospitalization, need for invasive ventilation, Intensive Care Unit admission, clinical outcome and High-Resolution Computed Tomography chest findings. The data was compiled on a questionnaire and analysed on SPSS 24. RESULTS: Total 155 severe COVID-19 patients were reviewed, out of which 36 (23.2%) had mediastinal lymphadenopathy (category 1) and 119 (76.8%) had no mediastinal lymphadenopathy (category 2). Laboratory findings including median of white blood cells and lymphocyte percentage had no significant change in both categories. Intensive care unit admissions were 12 (33.3%) and 56 (47.1%) in category 1 and 2 respectively. Median days of hospitalization (8 days) and mortality rate (16%) were almost the same in both categories. CONCLUSIONS: Our study concludes that presence of mediastinal lymphadenopathy in severe COVID-19 cases is not associated with worse outcome. However, overall prevalence of mediastinal lymphadenopathy in severe cases is high (23.2%).

Hyperglycemia and blood glucose deterioration are risk factors for severe COVID-19 with diabetes: A two-center cohort study.

We aimed to assess whether blood glucose control can be used as predictors for the severity of 2019 coronavirus disease (COVID-19) and to improve the management of diabetic patients with COVID-19. A two-center cohort with a total of 241 confirmed cases of COVID-19 with definite outcomes was studied. After the diagnosis of COVID-19, the clinical data and laboratory results were collected, the fasting blood glucose levels were followed up at initial, middle stage of admission and discharge, the severity of the COVID-19 was assessed at any time from admission to discharge. Hyperglycemia patients with COVID-19 were divided into three groups: good blood glucose control, fair blood glucose control, and blood glucose deterioration. The relationship of blood glucose levels, blood glucose control status, and severe COVID-19 were analyzed by univariate and multivariable regression analysis. In our cohort, 21.16% were severe cases and 78.84% were nonsevere cases. Admission hyperglycemia (adjusted odds ratio [aOR], 1.938; 95% confidence interval [95% CI], 1.387-2.707), mid-term hyperglycemia (aOR, 1.758; 95% CI, 1.325-2.332), and blood glucose deterioration (aOR, 22.783; 95% CI, 2.661-195.071) were identified as the risk factors of severe COVID-19. Receiver operating characteristic (ROC) curve analysis, reaching an area under ROC curve of 0.806, and a sensitivity and specificity of 80.40% and 68.40%, respectively, revealed that hyperglycemia on admission and blood glucose deterioration of diabetic patients are potential predictive factors for severe COVID-19. Our results indicated that admission hyperglycemia and blood glucose deterioration were positively correlated with the risk factor for severe COVID-19, and deterioration of blood glucose may be more likely to the occurrence of severe illness in COVID-19.

COVID-19: risk factors for severe cases of the Delta variant.

BACKGROUND: Since April 2021, the SARS-CoV-2 (B.1.167) Delta variant has been rampant worldwide. Recently, this variant has spread in Guangzhou, China. Our objective was to characterize the clinical features and risk factors of severe cases of the Delta variant in Guangzhou. METHODS: A total of 144 patients with the Delta variant were enrolled, and the data between the severe and non-severe groups were compared. Logistic regression methods and Cox multivariate regression analysis were used to investigate the risk factors of severe cases. RESULTS: The severity of the Delta variant was 11.1%. Each 1-year increase in age (OR, 1.089; 95% CI, 1.035-1.147; P = 0.001) and each 1-µmol/L increase in total bilirubin (OR, 1.198; 95% CI, 1.021-1.406; P = 0.039) were risk factors for severe cases. Moreover, the risk of progression to severe cases increased 13.444-fold and 3.922-fold when the age was greater than 58.5 years (HR, 13.444; 95% CI, 2.989-60.480; P = 0.001) or the total bilirubin level was greater than 7.23 µmol/L (HR, 3.922; 95% CI, 1.260-12.207; P = 0.018), respectively. CONCLUSION: Older age and elevated total bilirubin were independent risk factors for severe cases of the Delta variant in Guangzhou, especially if the age was greater than 58.5 years or the total bilirubin level was greater than 7.23 µmol/L.

[Relevant immunologic markers in COVID-19]. / COVID-19 et marqueurs immunologiques pertinents.

In its severe form, corona virus disease-19 (COVID-19) is characterized by various immunological abnormalities, dominated by massive pro-inflammatory cytokine and chemokine release, such as IL-6, TNF-α, IL-1b, IFN-y and monocyte chemoattractant protein-1 (MCP-1), associated with T CD3, T CD4 and T CD8 lymphopenia. These two abnormalities are significantly associated with COVID-19 acute severe respiratory syndrome. Conversely, these markers decrease during the favorable course of the disease. Coupled with other biological parameters such as leukopenia, increased level of CRP (C Reactive Protein), ferritin and D-dimers, high levels of IL-6 with CD4 and CD8 T cell lymphopenia may be considered as criteria of disease severity, justifying a rapid admission to the intensive care unit, and are also useful for patient monitoring.

Early prediction keys for COVID-19 cases progression: A meta-analysis.

BACKGROUNDː: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), within few months of being declared as a global pandemic by WHO, the number of confirmed cases has been over 75 million and over 1.6 million deaths since the start of the Pandemic and still counting, there is no consensus on factors that predict COVID-19 case progression despite the diversity of studies that reported sporadic laboratory predictive values predicting severe progression. We review different biomarkers to systematically analyzed these values to evaluate whether are they are correlated with the severity of COVID-19 disease and so their ability to be a predictor for progression. METHODS: The current meta-analysis was carried out to identify relevant articles using eight different databases regarding the values of biomarkers and risk factors of significance that predict progression of mild or moderate cases into severe and critical cases. We defined the eligibility criteria using a PICO model. RESULTS: Twenty-two relevant articles were selected for meta-analysis the following biomarkers C-reactive protein, interleukin-6, LDH, neutrophil, %PD-1 expression, D-dimer, creatinine, AST and Cortisol all recorded high cut-off values linked to severe and critical cases while low lymphocyte count, and low Albumin level were recorded. Also, we meta- analyzed age and comorbidities as a risk factors of progression as hypertension, Diabetes and chronic obstructive lung diseases which significantly correlated with cases progression (p < 0.05). CONCLUSIONS: ː The current meta-analysis is the first step for analysing and getting cut-off references values of significance for prediction COVID-19 case progression. More studies are needed on patients infected with SARS-CoV-2 and on a larger scale to establish clearer threshold values that predict progression from mild to severe cases. In addition, more biomarkers testing also help in building a scoring system for the prediction and guiding for proper timely treatment.

Risk factors for severe cases of COVID-19: a retrospective cohort study.

BACKGROUND: SARS-CoV-2 has raged around the world since March, 2020. We aim to describe the clinical characteristics and risk factors of severe patients with COVID-19 in Guangzhou. RESULTS: The severity and mortality of COVID-19 was 10.4% and 0.3% respectively. And each 1-year increase in age (OR, 1.057; 95% CI, 1.018-1.098; P=0.004), Wuhan exposure history greater than 2 weeks (OR, 2.765; 95% CI, 1.040-7.355; P=0.042), diarrhea (OR, 24.349; 95% CI, 3.580-165.609; P=0.001), chronic kidney disease (OR, 6.966; 95% CI, 1.310-37.058; P = 0.023), myoglobin higher than 106 µg/L (OR, 8.910; 95% CI, 1.225-64.816; P=0.031), white blood cell higher than 10×109/L (OR, 5.776; 95% CI, 1.052-31.722; P=0.044), and C-reactive protein higher than 10 mg/L (OR, 5.362; 95% CI, 1.631-17.626; P=0.006) were risk factors for severe cases. CONCLUSION: Older age, Wuhan exposure history, diarrhea, chronic kidney disease, elevated myoglobin, elevated white blood cell and C-reactive protein were independent risk factors for severe patients with COVID-19 in Guangzhou. METHODS: We included 288 adult patients with COVID-19 and compared the data between severe and non-severe group. We used univariate and multivariate logistic regression methods to explore risk factors of severe cases.

Potential therapeutic effects of dipyridamole in the severely ill patients with COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause acute respiratory distress syndrome, hypercoagulability, hypertension, and multiorgan dysfunction. Effective antivirals with safe clinical profile are urgently needed to improve the overall prognosis. In an analysis of a randomly collected cohort of 124 patients with COVID-19, we found that hypercoagulability as indicated by elevated concentrations of D-dimers was associated with disease severity. By virtual screening of a U.S. FDA approved drug library, we identified an anticoagulation agent dipyridamole (DIP) in silico, which suppressed SARS-CoV-2 replication in vitro. In a proof-of-concept trial involving 31 patients with COVID-19, DIP supplementation was associated with significantly decreased concentrations of D-dimers (P < 0.05), increased lymphocyte and platelet recovery in the circulation, and markedly improved clinical outcomes in comparison to the control patients. In particular, all 8 of the DIP-treated severely ill patients showed remarkable improvement: 7 patients (87.5%) achieved clinical cure and were discharged from the hospitals while the remaining 1 patient (12.5%) was in clinical remission.