Long COVID: the next public health crisis is already on its way

Interactions of SARS-CoV-2 with Human Target Cells-A Metabolic View

Viruses are obligate intracellular parasites, and they exploit the cellular pathways and resources of their respective host cells to survive and successfully multiply. The strategies of viruses concerning how to take advantage of the metabolic capabilities of host cells for their own replication can vary considerably. The most common metabolic alterations triggered by viruses affect the central carbon metabolism of infected host cells, in particular glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle. The upregulation of these processes is aimed to increase the supply of nucleotides, amino acids, and lipids since these metabolic products are crucial for efficient viral proliferation. In detail, however, this manipulation may affect multiple sites and regulatory mechanisms of host-cell metabolism, depending not only on the specific viruses but also on the type of infected host cells. In this review, we report metabolic situations and reprogramming in different human host cells, tissues, and organs that are favorable for acute and persistent SARS-CoV-2 infection. This knowledge may be fundamental for the development of host-directed therapies.

Photobiomodulation Treatment with a Home-Use Device for COVID-19: A Randomized Controlled Trial for Efficacy and Safety

Background: Photobiomodulation therapy (PBMT) using devices to deliver red and/or near-infrared light to tissues has shown promising effects in clinical settings for respiratory diseases, including potential benefits in managing symptoms associated with COVID-19. Objective: To determine if at-home self-administered PBMT for patients with COVID-19 is safe and effective. Methods: This was a randomized controlled trial (RCT) carried out at home during the COVID-19 pandemic (September 2020 to August 2021). The treatment group self-administered the Vielight RX Plus PBMT device (635 nm intranasal and 810 nm chest LEDs) and were monitored remotely. Eligible patients scored 4-7 (out of 7) for severity on the Wisconsin Upper Respiratory Symptom Survey (WURSS-44). Patients were randomized equally to Control group receiving standard-of-care (SOC) only or Treatment group receiving SOC plus PBMT. The device was used for 20 min 2X/day for 5 days and, subsequently, once daily for 30 days. The primary end-point was time-to-recovery (days) based on WURSS-44 question 1, "How sick do you feel today?". Subgroup analysis was performed, and Kaplan-Meier and Cox Proportional Hazards analysis were employed. Results: One hundred and ninety-nine eligible patients (18-65 years old) were divided into two subgroups as follows: 136 patients with 0-7 days of symptoms at baseline and 63 patients with 8-12 days of symptoms. Those with 0-7 days of symptoms at baseline recovered significantly faster with PBMT. The median for Treatment group was 18 days [95% confidence interval (CI), 13-20] versus the Control group 21 days (95% CI, 15-28), p = 0.050. The treatment:control hazard ratio was 1.495 (95% CI, 0.996-2.243), p = 0.054. Patients with symptom duration ≥7 days did not show any significant improvement. No deaths or severe adverse events (SAEs) occurred in the Treatment group, whereas there was 1 death and 3 SAEs requiring hospitalization in the Control group. Conclusions: Patients with ≤7 days of COVID-19 symptoms recovered significantly faster with PBMT compared to SOC. Beyond 7 days, PBMT showed no superiority over SOC. Trial Registration: ClinicalTrials.gov NCT04418505.

Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID

Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.

Association between COVID-19 and sensorineural hearing loss: Evidence from a Mendelian randomization study in European and East Asian population

BACKGROUND

Long coronavirus disease (COVID), characterized by persistent and sometimes debilitating symptoms following a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has garnered increasing attention as a potential public health crisis. Emerging evidence indicates a higher incidence of hearing loss in individuals who have had COVID 2019 (COVID-19) compared to the general population. However, the conclusions were inconsistent, and the causal relationship between COVID-19 and sensorineural hearing loss remains unknown.

METHODS

To addresses this outstanding issue, we performed Mendelian randomization analysis to detect the causal association between COVID-19 and hearing loss using the largest genome-wide association study data to date in the European population and confirmed the results in the East Asian population. Comprehensively sensitive analyses were followed, including Cochran's Q test, Mendelian randomization (MR)-Egger intercept test, MR-pleiotropy residual sum and outlier, and leave-one-out analysis, to validate the robustness of our results.

RESULTS

Our results suggested that there is no causal association between COVID-19 and the risk of hearing loss in the European population. Neither the susceptibility, hospitalization, and severity of COVID-19 on hearing loss (inverse variance weighted method: odds ratio (OR) = 1.046, 95% confidence interval (CI) = 0.907-1.205, p = .537; OR = 0.995, 95% CI = 0.956-1.036, p = .823; OR = 0.995, 95% CI = 0.967-1.025, p = .76). Replicated analyses in the East Asian population yielded consistent results. No pleiotropy and heterogeneity were found in our results.

CONCLUSION

In conclusion, our MR results do not support a genetically predicted causal relationship between COVID-19 and sensorineural hearing loss. Thus, the associations observed in prior observational studies may have been influenced by confounding factors rather than a direct cause-and-effect relationship. More clinical and mechanism research are needed to further understand this association in the future.

Molecular Mechanisms of Pathogenesis, Prevention, and Therapy of COVID-19: Summarizing the Results of 2022

The aim of this Special Issue is to analyze the key patterns of the 2019 coronavirus disease pandemic (COVID-19), the biology of SARS-CoV-2 (severe-acute-respiratory-syndrome-related coronavirus 2, formerly 2019-nCoV), and the characteristics of the human body's response to the invasion of this virus [...].

Characterization and predictive risk scoring of long COVID in a south indian cohort after breakthrough COVID infection; a prospective single centre study

BACKGROUND

With the World Health Organization (WHO) declaring an end to the COVID-19 pandemic, the focus has shifted to understanding and managing long-term post-infectious complications. "Long COVID," characterized by persistent or new onset symptoms extending beyond the initial phase of infection, is one such complication. This study aims to describe the incidence, clinical features and risk profile of long COVID among individuals in a South Indian cohort who experienced post-ChAdOx1 n-Cov-2 vaccine breakthrough infections.

METHODS

A single-centre hospital-based prospective observational study was conducted from October to December 2021. The study population comprised adult patients (> 18 years) with a confirmed COVID-19 diagnosis who had received at least a single dose of vaccination. Data was collected using a specially tailored questionnaire at week 2, week 6, and week 12 post-negative COVID-19 test. A propensity score based predictive scoring system was developed to assess the risk of long COVID.

RESULTS

Among the 414 patients followed up in the study, 164 (39.6%) reported long COVID symptoms persisting beyond 6 week's post-infection. The presence of long COVID was significantly higher among patients above 65 years of age, and those with comorbidities such as Type II Diabetes Mellitus, hypertension, dyslipidemia, coronary artery disease, asthma, and cancer. Using backwards selection, a reduced model was developed, identifying age (OR 1.053, 95% CI 0.097-1.07, p < 0.001), hypertension (OR 2.59, 95% CI 1.46-4.59, p = 0.001), and bronchial asthma (OR 3.7176, 95% CI 1.24-11.12, p = 0.018) as significant predictors of long COVID incidence. A significant positive correlation was observed between the symptomatic burden and the number of individual comorbidities.

CONCLUSIONS

The significant presence of long COVID at 12 weeks among non-hospitalised patients underscores the importance of post-recovery follow-up to assess for the presence of long COVID. The predictive risk score proposed in this study may help identify individuals at risk of developing long COVID. Further research is needed to understand the impact of long COVID on patients' quality of life and the potential role of tailored rehabilitation programs in improving patient outcomes.

Persistent Endothelial Lung Damage and Impaired Diffusion Capacity in Long COVID

Since the beginning of the pandemic, both COVID-19-associated coagulopathy biomarkers and a plethora of endothelial biomarkers have been proposed and tested as prognostic tools of severity and mortality prediction. As the pandemic is gradually being controlled, attention is now focusing on the long-term sequelae of COVID-19. In the present study, we investigated the role of endothelial activation/dysfunction in long COVID syndrome. This observational study included 68 consecutive long COVID patients and a healthy age and sex-matched control group. In both groups, we measured 13 endothelial biomarkers. Moreover, in the long COVID patients, we evaluated fatigue and dyspnea severity, lung diffusion capacity (DLCO), and the 6-min walk (6MWT) test as measures of functional capacity. Our results showed that markers of endothelial activation/dysfunction were higher in long COVID patients, and that soluble intracellular adhesion molecule 1 (sICAM-1) and soluble vascular adhesion molecule 1 (sVCAM-1) negatively correlated with lung diffusion and functional capacity (sICAM-1 vs. DLCO, r = -0.306, p = 0.018; vs. 6MWT, r = -0.263, p = 0.044; and sVCAM-1 vs. DLCO, r= -0.346, p = 0.008; vs. 6MWT, r = -0.504, p < 0.0001). In conclusion, evaluating endothelial biomarkers alongside clinical tests might yield more specific insights into the pathophysiological mechanisms of long COVID manifestations.