Lactate dehydrogenase contribution to symptom persistence in long COVID: A pooled analysis

Integrated Analysis of Noncoding RNAs (PVT-1 and miR-200c) and Their Correlation with STAT4/IL-6 Axis as Reliable Biomarkers for COVID-19 Severity

Inefficient control of elevated inflammatory mediators in coronavirus disease 2019 (COVID-19) has led to health complications, prompting the exploration of efficient biomarkers for monitoring this condition. We herein sought to investigate the implications of plasmacytoma variant translocation 1 (PVT-1), microRNA-200c (miR-200c), signal transducer and activator of transcription 4 (STAT-4), and interleukin-6 (IL-6), as well as how they correlated with creatinine, C-reactive protein (CRP), and lactate dehydrogenase (LDH) activity to identify biomarkers able to the early prognosis and diagnosis of COVID-19. Our study included a total of 105 infected COVID-19 patients and 35 healthy subjects as controls. Individuals with COVID-19 showed a significant increase in CRP, creatinine, and LDH activity. In addition, COVID-19 patients exhibited significantly higher levels of IL-6. These patients also demonstrated notably elevated expressions of miR-200c and PVT-1. The expression level of STAT4 decreased in the COVID-19 patients, and this decrease was negatively correlated with creatinine and LDH activity. The levels of miR-200c and PVT-1 expressions, and their connections with IL-6 and STAT4 levels, increased significantly with the severity of COVID-19 cases. In addition, receiver operating characteristic analysis showed that PVT-1 and miR-200c could be reliable biomarkers for determining the severity of COVID-19.

Clinical Features and Vaccination Effects among Children with Post-Acute Sequelae of COVID-19 in Taiwan

BACKGROUND

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects patients after recovering from acute coronavirus disease 2019 (COVID-19). This study investigates the impact of SARS-CoV-2 vaccination on PASC symptoms in children in Taiwan during the Omicron pandemic.

METHODS

We enrolled children under 18 years with PASC symptoms persisting for more than 4 weeks. Data collected included demographics, clinical information, vaccination status, and symptom persistence. We used logistic regression models to compare symptoms in the acute and post-COVID-19 phases and to assess the association between vaccination and these symptoms.

RESULTS

Among 500 PASC children, 292 (58.4%) were vaccinated, 282 (52.8%) were male, and the mean (SD) age was 7.6 (4.6) years. Vaccinated individuals exhibited higher odds of experiencing symptoms in the previous acute phase, such as cough (adjusted odds ratio [AOR] = 1.57; 95% confidence interval [CI]: 1.02-2.42), rhinorrhea/nasal congestion (AOR = 1.74; 95% CI: 1.13-2.67), sneezing (AOR = 1.68; 95% CI: 1.02-2.76), sputum production (AOR = 1.91; 95% CI: 1.15-3.19), headache/dizziness (AOR = 1.73; 95% CI: 1.04-2.87), and muscle soreness (AOR = 2.33; 95% CI: 1.13-4.80). In contrast, there were lower odds of experiencing abdominal pain (AOR = 0.49; 95% CI: 0.25-0.94) and diarrhea (AOR = 0.37; 95% CI: 0.17-0.78) in children who had received vaccination during the post-COVID-19 phase.

CONCLUSIONS

This study revealed clinical features and vaccination effects in PASC children in Taiwan. Vaccination may reduce some gastrointestinal symptoms in the post-COVID-19 phase.

Serological Biomarkers in Individuals with Interstitial Lung Disease after SARS-CoV-2 Infection and Association with Post-COVID-19 Symptoms

Patients with interstitial lung disease (ILD) represent a vulnerable population against an acute SARS-CoV-2 infection. It has been observed that up to 80% of patients with ILD can develop post-COVID-19 symptomatology one year after. This secondary analysis aimed to, 1, compare serological biomarkers before and after surpassing a SARS-CoV-2 infection in individuals with interstitial lung disease (ILD) and, 2, to compare serological biomarkers between ILD patients who develop and those who do not develop post-COVID-19 symptoms. Seventy-six patients with ILD (40.4% women, age: 69, SD: 10.5 years) who survived a SARS-CoV-2 infection participated. High-resolution computerized tomography (CT) of the lungs, two pulmonary function tests (forced vital capacity (FVC) and diffusion value of carbon monoxide (DLCO)) and fourteen serological biomarkers were collected before and after SARS-CoV-2 infection. Participants were asked for the presence of post-COVID-19 symptomatology a mean of twelve (SD: eight) months after infection. Sixty patients (79%) showed post-COVID-19 symptoms (mean: 3.5, SD 1.1), with fatigue (68.4%), dyspnea (31.5%), and concentration loss (27.6%) being the most prevalent. Creatine phosphokinase (CPK) was the only biomarker showing differences in our study. In fact, CPK levels were higher after the acute SARS-CoV-2 infection (mean difference: 41.0, 95%CI 10.1 to 71.8, p = 0.03) when compared to before the infection. Thus, CPK levels were also higher in ILD patients with post-COVID-19 fatigue (mean difference: 69.7, 95%CI 12.7 to 126.7, p = 0.015) or with post-COVID-19 dyspnea (mean difference: 34.8, 95%CI 5.2 to 64.4, p = 0.025) than those patients without these post-COVID-19 symptoms. No significant changes in CT or functional pulmonary tests were observed after COVID-19 in patients with ILD. In conclusion, patients with ILD exhibited an increase in CPK levels after SARS-CoV-2 infection, albeit no changes in other serological biomarkers were identified. Similarly, the presence of post-COVID-19 fatigue or dyspnea was also associated with higher CPK levels in ILD patients. Studies investigating long COVID mechanisms in vulnerable populations such as ILD are needed.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians.

Methods

Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity.

Results

Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively.

Conclusion

Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.