COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine

External validation of two clinical prediction models for mortality in COVID-19 patients (4C and NEWS2), in three centers in Medellín, Colombia: Assessing the impact of vaccination over time

OBJECTIVES

External validation of the 4C and NEWS2 scores for the prediction of in-hospital mortality in COVID-19 patients, and evaluation of its operational performance in two time periods: before and after the start of the vaccination program in Colombia.

METHODS

Retrospective cohort in three high complexity hospitals in the city of Medellín, Colombia, between June 2020 and April 2022.

RESULTS

The areas under the ROC curve (AUC) for the 4C mortality risk score and the NEWS2 were 0.75 (95% CI 0.73-0.78) and 0.68 (95% CI 0.66-0.71), respectively. For the 4C score, the AUC for the first and second periods was 0.77 (95% CI 0.74-0.80) and 0.75 (95% CI 0.71-0.78); whilst for the NEWS2 score, it was 0.68 (95% CI 0.65-0.71) and 0.69 (95% CI 0.64-0.73). The calibration for both scores was adequate, albeit with reduced performance during the second period.

CONCLUSIONS

The 4C mortality risk score proved to be the more adequate predictor of in-hospital mortality in COVID-19 patients in this Latin American population. The operational performance during both time periods remained similar, which shows its utility notwithstanding major changes, including vaccination, as the pandemic evolved.

Improved discrimination of COVID-19 based on data enhancement technology and an information balance feature selection (INB) method

The coronavirus disease (COVID-19) ravaged the world in late 2019 and posed a serious threat to human life and property destruction on a global scale. In this paper, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) method was selected for balancing the data sample, and an information balance feature selection (INB) method was first proposed to realize the accurate discrimination of COVID-19 saliva samples based on the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The results of the experiment showed that the INB method obtained higher classification accuracy than the traditional feature selection methods in both the original spectrum and the second-order derivative spectrum, especially in the second-order derivative spectrum where all the indexes reached about 85 %. In addition, the combination of WGAN_GP data augmentation and the INB method resulted in an accuracy of 88.7 % for the original spectrum and even 90.6 % for the second-order derivative spectrum. According to these findings, classification research using the WGAN_GP data enhancement model may increase classification accuracy. Additionally, the ability to successfully separate COVID-19 indicates that the INB method to identify spectral data features is a workable method, which also offers a fresh viewpoint on feature selection.

Aryl hydrocarbon receptor activation alters immune cell populations in the lung and bone marrow during coronavirus infection

Respiratory viral infections are one of the major causes of illness and death worldwide. Symptoms associated with respiratory infections can range from mild to severe, and there is limited understanding of why there is large variation in severity. Environmental exposures are a potential causative factor. The aryl hydrocarbon receptor (AHR) is an environment-sensing molecule expressed in all immune cells. Although there is considerable evidence that AHR signaling influences immune responses to other immune challenges, including respiratory pathogens, less is known about the impact of AHR signaling on immune responses during coronavirus (CoV) infection. In this study, we report that AHR activation significantly altered immune cells in the lungs and bone marrow of mice infected with a mouse CoV. AHR activation transiently reduced the frequency of multiple cells in the mononuclear phagocyte system, including monocytes, interstitial macrophages, and dendritic cells in the lung. In the bone marrow, AHR activation altered myelopoiesis, as evidenced by a reduction in granulocyte-monocyte progenitor cells and an increased frequency of myeloid-biased progenitor cells. Moreover, AHR activation significantly affected multiple stages of the megakaryocyte lineage. Overall, these findings indicate that AHR activation modulates multiple aspects of the immune response to a CoV infection. Given the significant burden of respiratory viruses on human health, understanding how environmental exposures shape immune responses to infection advances our knowledge of factors that contribute to variability in disease severity and provides insight into novel approaches to prevent or treat disease.NEW & NOTEWORTHY Our study reveals a multifaceted role for aryl hydrocarbon receptor (AHR) signaling in the immune response to coronavirus (CoV) infection. Sustained AHR activation during in vivo mouse CoV infection altered the frequency of mature immune cells in the lung and modulated emergency hematopoiesis, specifically myelopoiesis and megakaryopoiesis, in bone marrow. This provides new insight into immunoregulation by the AHR and extends our understanding of how environmental exposures can impact host responses to respiratory viral infections.

Quaternary ammonium salts for water treatment with balanced rate of sterilization and degradation

The growing number of infections caused by drug-resistant bacteria which arise from the overuse of antibiotics has severely affected the normal operation of human society. The high antibacterial activity of QAS makes it promising as an alternative to antibiotics, but it suffers from secondary pollution due to its non-degradation. Here we have synthesized a class of gemini quaternary ammonium salts (GQAS) with different carbon chain lengths containing ester groups by using facile methylation reaction. Quaternary ammonium groups contribute to insert negatively charged bacterial membranes, resulting in membrane damage and bacteria death. Compared with conventional single-chain QAS, except for the more efficient antibacterial efficiency attribute to the presence of the second carbon chain, GQAS with alterable antibacterial properties can minimize the possibility of bacterial resistance and reduce the accumulation of GQAS in the environment through the introduction of degradable ester groups. GQAS is completely superior to the commercial bactericide benzalkonium chloride (BAC) in both antibacterial activity and degrade performance, which can be used as a more environmentally friendly bactericide.

People with Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Exhibit Similarly Impaired Vascular Function

BACKGROUND

This study aimed to compare flow-mediated dilation values between individuals with long COVID, individuals with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and healthy age-matched controls to assess the potential implications for clinical management and long-term health outcomes.

METHODS

A case-case-control approach was employed, and flow-mediated dilation measurements were obtained from 51 participants (17 long COVID patients, 17 ME/CFS patients, and 17 healthy age-matched controls). Flow-mediated dilation values were analyzed using 1-way analysis of variance for between-group comparisons.

RESULTS

Results revealed significantly impaired endothelial function in both long COVID and ME/CFS groups compared with healthy age-matched controls as determined by maximum % brachial artery diameter post-occlusion compared with pre-occlusion resting diameter (6.99 ± 4.33% and 6.60 ± 3.48% vs 11.30 ± 4.44%, respectively, both P < .05). Notably, there was no difference in flow-mediated dilation between long COVID and ME/CFS groups (P = .949), despite significantly longer illness duration in the ME/CFS group (ME/CFS: 16 ± 11.15 years vs long COVID: 1.36 ± 0.51 years, P < .0001).

CONCLUSION

The study demonstrates that both long COVID and ME/CFS patients exhibit similarly impaired endothelial function, indicating potential vascular involvement in the pathogenesis of these post-viral illnesses. The significant reduction in flow-mediated dilation values suggests an increased cardiovascular risk in these populations, warranting careful monitoring and the development of targeted interventions to improve endothelial function and mitigate long-term health implications.

Effectiveness of nirmatrelvir-ritonavir for the treatment of patients with mild to moderate COVID-19 and at high risk of hospitalization: Systematic review and meta-analyses of observational studies

OBJECTIVE

To assess the effectiveness of nirmatrelvir-ritonavir in the treatment of outpatients with mild to moderate COVID-19 who are at higher risk of developing severe illness, through a systematic review with meta-analyses of observational studies.

METHODS

A systematic search was performed, in accordance with the Cochrane search methods, to identify observational studies that met the inclusion criteria. The outcomes of mortality and hospitalization were analyzed. Search was conducted on PubMed, EMBASE, and The Cochrane Library. Two reviewers independently screened references, selected the studies, extracted the data, assessed the risk of bias using ROBINS-I tool and evaluated the quality of evidence using the GRADE tool. This study followed the PRISMA reporting guideline.

RESULTS

A total of 16 observational studies were finally included. The results of the meta-analysis showed that in comparison to standard treatment without antivirals, nirmatrelvir-ritonavir reduced the risk of death by 59% (OR = 0.41; 95% CI: 0.35-0.52; moderate certainty of evidence). In addition, a 53% reduction in the risk of hospital admission was observed (OR = 0.47; 95% CI: 0.36-0.60, with very low certainty of evidence). For the composite outcome of hospitalization and/or mortality, there was a 56% risk reduction (OR = 0.44; 95% CI: 0.31-0.64, moderate certainty of evidence).

CONCLUSION

The results suggest that nirmatrelvir-ritonavir could be effective in reducing mortality and hospitalization. The results were valid in vaccinated or unvaccinated high-risk individuals with COVID-19. Data from ongoing and future trials may further advance our understanding of the effectiveness and safety of nirmatrelvir-ritonavir and help improve treatment guidelines for COVID-19.

Gene Polymorphisms of Epithelial Cell-Derived Alarmins and Their Effects on Protein Levels and Disease Severity in Patients with COVID-19

BACKGROUND

The immune response in COVID-19 is characterized by the release of alarmin cytokines, which play crucial roles in immune activation and inflammation. The interplay between these cytokines and genetic variations may influence disease severity and outcomes, while sex differences might further contribute to variations in the immune response.

METHODS

We measured the levels of alarmin cytokines in a cohort of COVID-19 and non-COVID-19 patients using a sensitive Meso Scale Discovery system. Additionally, we conducted an SNP analysis to identify genetic variations within the IL-33 and TSLP genes. The association between these genetic variations, cytokine production, and COVID-19 severity was examined.

RESULTS

Our findings revealed elevated levels of IL-33 and IL-25 in COVID-19-positive patients compared to COVID-19-negative patients (p < 0.05), indicating their potential as therapeutic targets for disease modulation. Moreover, a minor allele within the IL-33 gene (rs3939286) was found to be associated with a protective effect against severe COVID-19 (p < 0.05), and minor alleles of the TSLP gene (rs2289276 and rs13806933) were found to significantly reduce TSLP protein levels in serum (p < 0.05). Sex-specific effects of TSLP and IL-33 SNPs were observed, suggesting a potential influence of sex hormones and genetic variations on the regulation of cytokine production.

CONCLUSION

The present study highlights the importance of alarmin cytokines and genetic variations in COVID-19 severity, providing valuable insights into personalized treatment approaches. Our results suggest that targeting alarmin cytokines may offer potential therapeutic benefits in managing COVID-19. Furthermore, the sex-specific effects of genetic variations emphasize the need to consider individual genetic profiles and sex differences when designing targeted interventions.

Sequencing the SARS-CoV-2 Genome from Stool Samples of Post-acute Cases Implicates a Novel Mutation Associated with Reduced Antibody Neutralization

Whole-genome SARS-CoV-2 sequencing tools are crucial for tracking the COVID-19 pandemic. However, current techniques require sampling of actively infectious patients following COVID-19 testing to recover enough SARS-CoV-2 RNA from the nasopharyngeal passage, which rapidly clears during the first few weeks of infection. A prospective assessment of the viral genome sourced from recovered non-infectious patients would greatly facilitate epidemiological tracking. Thus, we developed a protocol to isolate and sequence the genome of SARS-CoV-2 from stool samples of post-acute SARS-CoV-2 patients, at timepoints ranging from 10-120 days after onset of symptoms. Stool samples were collected from patients at varying timepoints post-convalescence, and viral DNA was isolated and sequenced using the QIAamp Viral RNA Mini Kit (Qiagen Inc.) and Ion Ampliseq^™ Library Kit Plus (Life Technologies Corporation). Capacity of neutralizing antibodies in patient plasma was tested using a Luminex panel (Coronavirus Ig Total Human 11-Plex ProcartaPlex^™ Panel, ThermoFisher). Of 64 samples obtained from post-acute patients, 21 (32.8%) yielded sufficient material for whole-genome sequencing. This allowed us to identify widely divergent phylogenetic relativity of the SARS-CoV-2 genome from post-acute patients living in the same households and infected around the same time. Additionally, we observed that individuals who recovered from infection expressed varying degrees of antibodies against SARS-CoV-2 structural proteins that corresponded to distinct variants. Interestingly, we identified a novel point mutation in the viral genome where infected patients expressed antibodies with a significantly reduced capacity to neutralize the virus in vitro relative to that of those infected with the wild-type strain. Altogether, we demonstrate a protocol to successfully sequence the SARS-CoV-2 genome from stool samples from patients up to 4 months post-infection, which can be applied to studies that assess the relationship between variants and immune response post-hoc and safe monitoring of the SARS-CoV-2 genome during the pandemic.

Relationship between anthropometric and body composition parameters and anti-SARS-CoV-2 specific IgG titers in females vaccinated against COVID-19 according to the heterologous vaccination course: A cohort study

INTRODUCTION

The aim of this cohort study was to evaluate the relationship between anthropometric and body composition parameters and anti-SARS-CoV-2 IgG titers in a group of females who were vaccinated against COVID-19 with two doses of ChAdOx1 vaccine and then boosted with the BNT162b2 vaccine.

MATERIALS AND METHODS

The study group consisted of 63 women. Basic demographic and clinical data were collected. To assess the anti-SARS-CoV-2 immunoglobulin G titers following the vaccination, five blood draws were performed: 1) before the first dose, 2) before the second dose, 3) 14-21 days after the primary vaccination, 4) before the booster, and 5) 21 days after the booster. Blood samples were analyzed using a two-step enzymatic chemiluminescent assay. Body mass index and body composition were evaluated using bioelectrical impedance analysis. To select the most distinguishing parameters and correlations between anthropometric and body composition parameters and anti-SARS-CoV-2 IgG titers, factor analysis using the Principal Component Analysis was conducted.

RESULTS

Sixty-three females (mean age: 46.52 years) who met the inclusion criteria were enrolled. 40 of them (63.50%) participated in the post-booster follow-up. After receiving two doses of the ChAdOx1 vaccine, the study group's anti-SARS-CoV-2 IgG titers were 67.19 ± 77.44 AU/mL (mean ± SD), whereas after receiving a heterologous mRNA booster, the level of anti-SARS-CoV-2 IgG titers was about three-times higher and amounted to 212.64 ± 146.40 AU/mL (mean ± SD). Our data shows that seropositivity, obesity, non-fat-related, and fat-related body composition parameters all had a significant effect on the level of IgG titer after a two-dose vaccination of ChAdOx1. However, only non-fat-related and fat-related body composition parameters had a significant effect on the IgG titer after booster vaccination.

CONCLUSION

COVID-19 infection before the first dose of vaccination is not related to IgG titer after booster administration. Body composition has a significant effect on the production of anti-SARS-CoV-2 IgG after booster vaccination in females.

In vitro high-content tissue models to address precision medicine challenges

The field of precision medicine allows for tailor-made treatments specific to a patient and thereby improve the efficiency and accuracy of disease prevention, diagnosis, and treatment and at the same time would reduce the cost, redundant treatment, and side effects of current treatments. Here, the combination of organ-on-a-chip and bioprinting into engineering high-content in vitro tissue models is envisioned to address some precision medicine challenges. This strategy could be employed to tackle the current coronavirus disease 2019 (COVID-19), which has made a significant impact and paradigm shift in our society. Nevertheless, despite that vaccines against COVID-19 have been successfully developed and vaccination programs are already being deployed worldwide, it will likely require some time before it is available to everyone. Furthermore, there are still some uncertainties and lack of a full understanding of the virus as demonstrated in the high number new mutations arising worldwide and reinfections of already vaccinated individuals. To this end, efficient diagnostic tools and treatments are still urgently needed. In this context, the convergence of bioprinting and organ-on-a-chip technologies, either used alone or in combination, could possibly function as a prominent tool in addressing the current pandemic. This could enable facile advances of important tools, diagnostics, and better physiologically representative in vitro models specific to individuals allowing for faster and more accurate screening of therapeutics evaluating their efficacy and toxicity. This review will cover such technological advances and highlight what is needed for the field to mature for tackling the various needs for current and future pandemics as well as their relevancy towards precision medicine.

Raman and fourier transform infrared spectroscopy techniques for detection of coronavirus (COVID-19): a mini review

Coronavirus pandemic has been a huge jeopardy to human health in various systems since it outbroke, early detection and prevention of further escalation has become a priority. The current popular approach is to collect samples using the nasopharyngeal swab method and then test for RNA using the real-time polymerase chain reaction, which suffers from false-positive results and a longer diagnostic time scale. Alternatively, various optical techniques, namely, optical sensing, spectroscopy, and imaging shows a great promise in virus detection. In this mini review, we briefly summarize the development progress of vibrational spectroscopy techniques and its applications in the detection of SARS-CoV family. Vibrational spectroscopy techniques such as Raman spectroscopy and infrared spectroscopy received increasing appreciation in bio-analysis for their speediness, accuracy and cost-effectiveness in detection of SARS-CoV. Further, an account of emerging photonics technologies of SARS-CoV-2 detection and future possibilities is also explained. The progress in the field of vibrational spectroscopy techniques for virus detection unambiguously show a great promise in the development of rapid photonics-based devices for COVID-19 detection.

Investigating the association of measures of epigenetic age with COVID-19 severity: evidence from secondary analyses of open access data

BACKGROUND

Epigenetic modifications may contribute to inter-individual variation that is unexplainable by presently known risk factors for COVID-19 severity (e.g., age, excess weight, or other health conditions). Estimates of youth capital (YC) reflect the difference between an individual's epigenetic - or biological - age and chronological age, and may quantify abnormal aging due to lifestyle or other environmental exposures, providing insights that could inform risk-stratification for severe COVID-19 outcomes. This study aims to thereby a) assess the association between YC and epigenetic signatures of lifestyle exposures with COVID-19 severity, and b) to assess whether the inclusion of these signatures in addition to a signature of COVID-19 severity (EPICOVID) improved the prediction of COVID-19 severity.

METHODS

This study uses data from two publicly-available studies accessed via the Gene Expression Omnibus (GEO) platform (accession references: GSE168739 and GSE174818). The GSE168739 is a retrospective, cross-sectional study of 407 individuals with confirmed COVID-19 across 14 hospitals in Spain, while the GSE174818 sample is a single-center observational study of individuals admitted to the hospital for COVID-19 symptoms (n = 102). YC was estimated using the (a) Gonseth-Nusslé, (b) Horvath, (c) Hannum, and (d) PhenoAge estimates of epigenetic age. Study-specific definitions of COVID-19 severity were used, including hospitalization status (yes/no) (GSE168739) or vital status at the end of follow-up (alive/dead) (GSE174818). Logistic regression models were used to assess the association between YC, lifestyle exposures, and COVID-19 severity.

RESULTS

Higher YC as estimated using the Gonseth-Nusslé, Hannum and PhenoAge measures was associated with reduced odds of severe symptoms (OR = 0.95, 95% CI = 0.91-1.00; OR = 0.81, 95% CI = 0.75 - 0.86; and OR = 0.85, 95% CI = 0.81-0.88, respectively) (adjusting for chronological age and sex). In contrast, a one-unit increase in the epigenetic signature for alcohol consumption was associated with 13% increased odds of severe symptoms (OR = 1.13, 95% CI = 1.05-1.23). Compared to the model including only age, sex and the EPICOVID signature, the additional inclusion of PhenoAge and the epigenetic signature for alcohol consumption improved the prediction of COVID-19 severity (AUC = 0.94, 95% CI = 0.91-0.96 versus AUC = 0.95, 95% CI = 0.93-0.97; p = 0.01). In the GSE174818 sample, only PhenoAge was associated with COVID-related mortality (OR = 0.93, 95% CI = 0.87-1.00) (adjusting for age, sex, BMI and Charlson comorbidity index).

CONCLUSIONS

Epigenetic age is a potentially useful tool in primary prevention, particularly as an incentive towards lifestyle changes that target reducing the risk of severe COVID-19 symptoms. However, additional research is needed to establish potential causal pathways and the directionality of this effect.

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach

BACKGROUND

The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19.

METHODS

Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.

RESULTS

Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes.

CONCLUSIONS

This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model's components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies.

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

BACKGROUND

Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.

METHODS

1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.

RESULTS

We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

CONCLUSIONS

We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.

Theranostics for COVID-19 Antiviral Drugs: Prospects and Challenges for Worldwide Precision/Personalized Medicine

Coronavirus disease 2019 (COVID-19) is a systemic disease that impacts multiple organ systems with a complex clinical presentation and outcomes that can vary from person to person and between populations. To optimize COVID-19 treatment outcomes, and in light of the availability of antiviral drugs, there is a need for greater attention to the field of theranostics, the fusion of therapeutics and diagnostics. Theranostics tests would be invaluable, we suggest in this expert review, so as to optimize the efficacy and safety of current and future antiviral drugs against COVID-19. Theranostics would also assist in the design and implementation of clinical trials with antiviral drug candidates. We discuss here theranostics considering drugs such as remdesivir, Paxlovid™, and molnupiravir. All in all, we underscore that theranostics as a concept and practice is essential for efficient and safe health interventions against COVID-19 and other ecological crises in the 21st century.

Insight into the mechanisms of olfactory dysfunction by COVID-19

One of the unique symptoms of COVID-19 is chemosensory dysfunction. Almost three years since the beginning of the pandemic of COVID-19, there have been many studies on the symptoms, progress, and possible causes, and also studies on methods that may facilitate recovery of the senses. Studies have shown that some people recover their senses even within a couple of weeks whereas there are other patients that fail to recover chemosensory functions fully for several months and some never fully recover. Here we summarize the symptoms and the progress, and then review the papers on the causation as well as the treatments that may help facilitate the recovery of the symptoms. Depending on the differences in the levels of severity and the locations where the main pathological venues are, what is most effective in facilitating recovery can vary largely across patients and thus may require individualized strategies for each patient. The goal of this paper is to provide some thoughts on these choices depending on the differences in the causes and severity.

Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes that reduce COVID-19 host susceptibility is a critical next step. Using a translational genomics approach that integrates COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX), and perturbagen signatures, we identified IL10RB as the top candidate gene target for COVID-19 host susceptibility. In a series of validation steps, we show that predicted GReX upregulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes and that in vitro IL10RB overexpression is associated with increased viral load and activation of disease-relevant molecular pathways.

Characterization of ACE2 naturally occurring missense variants: impact on subcellular localization and trafficking

Background

Human angiotensin-converting enzyme 2 (ACE2), a type I transmembrane receptor physiologically acting as a carboxypeptidase enzyme within the renin-angiotensin system (RAS), is a critical mediator of infection by several severe acute respiratory syndrome (SARS) corona viruses. For instance, it has been demonstrated that ACE2 is the primary receptor for the SARS-CoV-2 entry to many human cells through binding to the viral spike S protein. Consequently, genetic variability in ACE2 gene has been suggested to contribute to the variable clinical manifestations in COVID-19. Many of those genetic variations result in missense variants within the amino acid sequence of ACE2. The potential effects of those variations on binding to the spike protein have been speculated and, in some cases, demonstrated experimentally. However, their effects on ACE2 protein folding, trafficking and subcellular targeting have not been established.

Results

In this study we aimed to examine the potential effects of 28 missense variants (V801G, D785N, R768W, I753T, L731F, L731I, I727V, N720D, R710H, R708W, S692P, E668K, V658I, N638S, A627V, F592L, G575V, A501T, I468V, M383I, G173S, N159S, N149S, D38E, N33D, K26R, I21T, and S19P) distributed across the ACE2 receptor domains on its subcellular trafficking and targeting through combinatorial approach involving in silico analysis and experimental subcellular localization analysis. Our data show that none of the studied missense variants (including 3 variants predicted to be deleterious R768W, G575V, and G173S) has a significant effect on ACE2 intracellular trafficking and subcellular targeting to the plasma membrane.

Conclusion

Although the selected missense variants display no significant change in ACE2 trafficking and subcellular localization, this does not rule out their effect on viral susceptibility and severity. Further studies are required to investigate the effect of ACE2 variants on its expression, binding, and internalization which might explain the variable clinical manifestations associated with the infection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00411-1.

Serum microRNAs targeting ACE2 and RAB14 genes distinguish asymptomatic from critical COVID-19 patients

Despite the extraordinary advances achieved to beat COVID-19 disease, many questions remain unsolved, including the mechanisms of action of SARS-CoV-2 and which factors determine why individuals respond so differently to the viral infection. Herein, we performed an in silico analysis to identify host microRNA targeting ACE2, TMPRSS2, and/or RAB14, all genes known to participate in viral entry and replication. Next, the levels of six microRNA candidates previously linked to viral and respiratory-related pathologies were measured in the serum of COVID-19-negative controls (n = 16), IgG-positive COVID-19 asymptomatic individuals (n = 16), and critical COVID-19 patients (n = 17). Four of the peripheral microRNAs analyzed (hsa-miR-32-5p, hsa-miR-98-3p, hsa-miR-423-3p, and hsa-miR-1246) were upregulated in COVID-19 critical patients compared with COVID-19-negative controls. Moreover, hsa-miR-32-5p and hsa-miR-1246 levels were also altered in critical versus asymptomatic individuals. Furthermore, these microRNA target genes were related to viral infection, inflammatory response, and coagulation-related processes. In conclusion, SARS-CoV-2 promotes the alteration of microRNAs targeting the expression of key proteins for viral entry and replication, and these changes are associated with disease severity. The microRNAs identified could be taken as potential biomarkers of COVID-19 progression as well as candidates for future therapeutic approaches against this disease.

Plasma metabolome and cytokine profile reveal glycylproline modulating antibody fading in convalescent COVID-19 patients

The COVID-19 pandemic has incurred tremendous costs worldwide and is still threatening public health in the "new normal." The association between neutralizing antibody levels and metabolic alterations in convalescent patients with COVID-19 is still poorly understood. In the present work, we conducted absolutely quantitative profiling to compare the plasma cytokines and metabolome of ordinary convalescent patients with antibodies (CA), convalescents with rapidly faded antibodies (CO), and healthy subjects. As a result, we identified that cytokines such as M-CSF and IL-12p40 and plasma metabolites such as glycylproline (gly-pro) and long-chain acylcarnitines could be associated with antibody fading in COVID-19 convalescent patients. Following feature selection, we built machine-learning-based classification models using 17 features (six cytokines and 11 metabolites). Overall accuracies of more than 90% were attained in at least six machine-learning models. Of note, the dipeptide gly-pro, a product of enzymatic peptide cleavage catalyzed by dipeptidyl peptidase 4 (DPP4), strongly accumulated in CO individuals compared with the CA group. Furthermore, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination experiments in healthy mice demonstrated that supplementation of gly-pro down-regulates SARS-CoV-2-specific receptor-binding domain antibody levels and suppresses immune responses, whereas the DPP4 inhibitor sitagliptin can counteract the inhibitory effects of gly-pro upon SARS-CoV-2 vaccination. Our findings not only reveal the important role of gly-pro in the immune responses to SARS-CoV-2 infection but also indicate a possible mechanism underlying the beneficial outcomes of treatment with DPP4 inhibitors in convalescent COVID-19 patients, shedding light on therapeutic and vaccination strategies against COVID-19.

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a “non-severe” and 80 had a “severe” outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a “severe” outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

BACKGROUND

We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.

OBJECTIVES

The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS).

METHODS

We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays.

RESULTS

Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location.

CONCLUSION

An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04570488.

Approach to the management of COVID-19 patients: When home care can represent the best practice

BACKGROUND

The pandemic that began around February 2020, caused by the viral pathogen SARS-CoV-2 (COVID-19), has still not completed its course at present in June 2022.

OBJECTIVE

The open research to date highlights just how varied and complex the outcome of the contagion can be.

METHOD

The clinical pictures observed following the contagion present variabilities that cannot be explained completely by the patient's age (which, with the new variants, is rapidly changing, increasingly affecting younger patients) nor by symptoms and concomitant pathologies (which are no longer proving to be decisive in recent cases) in relation to medium-to-long term sequelae. In particular, the functions of the vascular endothelium and vascular lesions at the pre-capillary level represent the source of tissue hypoxia and other damage, resulting in the clinical evolution of COVID-19.

RESULTS

Keeping the patient at home with targeted therapeutic support, aimed at not worsening vascular endothelium damage with early and appropriate stimulation of endothelial cells, ameliorates the glycocalyx function and improves the prognosis and, in some circumstances, could be the best practice suitable for certain patients.

CONCLUSION

Clinical information thus far collected may be of immense value in developing a better understanding of the present pandemic and future occurrences regarding patient safety, pharmaceutical care and therapy liability.

Marking the Profile of the Microflora of the Endometrium and Uterine Cervix in Women as a Potential Factor Determining the Effectiveness of In Vitro Fertilization

One promising research trend involves evaluating the influence of microbiota in the reproductive system of women on becoming pregnant and maintaining pregnancy. The goal of this study was to define the microflora profile of the endometrium and uterine cervix in women qualified for an in vitro fertilization (IVF) procedure, which is expected to contribute to increasing the percentage of successful IVF implantations. Based on the conducted molecular analysis in the collected swabs, 22 bacterial strains were identified. Eleven strains (57%) that were isolated belong to the physiological microflora, the most common strain of which was Lactobacillus. Eight of the isolated strains (33%) were pathological microflora, among which the most common bacteria were from the Enterobacteriaceae family (which includes E. coli, Shigella, and Salmonella). Finally, three of the bacterial strains (10%) may be a component of both physiological or pathological microflora of the vagina: Bifidobacterium breve, Bifidobacterium longum group, and Alloscardovia omnicolens. The presence of Escherichia coli was detected in six women, Staphylococcus aureus also in six patients, Atopobium parvulum in three, Streptococcus salivarius group in three, Enterococcus faecalis in four, and Aerococcus christensenii in two patients. We found statistically significant relationships (p < 0.05) between Lactobacillus fermentum and Enterococcus faecalis, Lactobacillus delbrueckii and Escherichia coli groups, Lactobacillus FN667084_s and Staphylococcus aureus groups, as well as Lactobacillus fermentum and Streptococcus agalactiae. Based on the conducted study, it may be confirmed that the endometrium is, to a large extent, colonized by lactic acid bacilli. Apart from that, endometrial dysbiosis was not noted in patients qualified for the IVF procedure.

SARS-CoV-2 Mutations and COVID-19 Clinical Outcome: Mutation Global Frequency Dynamics and Structural Modulation Hold the Key

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an enormous burden on the healthcare system worldwide as a consequence of its new emerging variants of concern (VOCs) since late 2019. Elucidating viral genome characteristics and its influence on disease severity and clinical outcome has been one of the crucial aspects toward pandemic management. Genomic surveillance holds the key to identify the spectrum of mutations vis-à-vis disease outcome. Here, in our study, we performed a comprehensive analysis of the mutation distribution among the coronavirus disease 2019 (COVID-19) recovered and mortality patients. In addition to the clinical data analysis, the significant mutations within the two groups were analyzed for their global presence in an effort to understand the temporal dynamics of the mutations globally in comparison with our cohort. Interestingly, we found that all the mutations within the recovered patients showed significantly low global presence, indicating the possibility of regional pool of mutations and the absence of preferential selection by the virus during the course of the pandemic. In addition, we found the mutation S194L to have the most significant occurrence in the mortality group, suggesting its role toward a severe disease progression. Also, we discovered three mutations within the mortality patients with a high cohort and global distribution, which later became a part of variants of interest (VOIs)/VOCs, suggesting its significant role in enhancing viral characteristics. To understand the possible mechanism, we performed molecular dynamics (MD) simulations of nucleocapsid mutations, S194L and S194*, from the mortality and recovered patients, respectively, to examine its impacts on protein structure and stability. Importantly, we observed the mutation S194* within the recovered to be comparatively unstable, hence showing a low global frequency, as we observed. Thus, our study provides integrative insights about the clinical features, mutations significantly associated with the two different clinical outcomes, its global presence, and its possible effects at the structural level to understand the role of mutations in driving the COVID-19 pandemic.

Evaluation of the genetic risk for COVID-19 outcomes in COPD and differences among worldwide populations

Background

Populations seem to respond differently to the global pandemic of severe acute respiratory syndrome coronavirus 2. Recent studies show individual variability in both susceptibility and clinical response to COVID-19 infection. People with chronic obstructive pulmonary disease (COPD) constitute one of COVID-19 risk groups, being already associated with a poor prognosis upon infection. This study aims contributing to unveil the underlying reasons for such prognosis in people with COPD and the variability in the response observed across worldwide populations, by looking at the genetic background as a possible answer to COVID-19 infection response heterogeneity.

Methods

SNPs already associated with susceptibility to COVID-19 infection (rs286914 and rs12329760) and severe COVID-19 with respiratory failure (rs657152 and rs11385942) were assessed and their allelic frequencies used to calculate the probability of having multiple risk alleles. This was performed on a Portuguese case-control COPD cohort, previously clinically characterized and genotyped from saliva samples, and also on worldwide populations (European, Spanish, Italian, African, American and Asian), using publicly available frequencies data. A polygenic risk analysis was also conducted on the Portuguese COPD cohort for the two mentioned phenotypes, and also for hospitalization and survival to COVID-19 infection.

Findings

No differences in genetic risk for COVID-19 susceptibility, hospitalization, severity or survival were found between people with COPD and the control group (all p-values > 0.01), either considering risk alleles individually, allelic combinations or polygenic risk scores. All populations, even those with European ancestry (Portuguese, Spanish and Italian), showed significant differences from the European population in genetic risk for both COVID-19 susceptibility and severity (all p-values < 0.0001).

Conclusion

Our results indicate a low genetic contribution for COVID-19 infection predisposition or worse outcomes observed in people with COPD. Also, our study unveiled a high genetic heterogeneity across major world populations for the same alleles, even within European sub-populations, demonstrating the need to build a higher resolution European genetic map, so that differences in the distribution of relevant alleles can be easily accessed and used to better manage diseases, ultimately, safeguarding populations with higher genetic predisposition to such diseases.

Iron dysregulation in COVID-19 and reciprocal evolution of SARS-CoV-2: Natura nihil frustra facit

After more than a year of the COVID-19 pandemic, SARS-CoV-2 infection rates with newer variants continue to devastate much of the world. Global healthcare systems are overwhelmed with high positive patient numbers. Silent hypoxia accompanied by rapid deterioration and some cases with septic shock is responsible for COVID-19 mortality in many hospitalized patients. There is an urgent need to further understand the relationships and interplay with human host components during pathogenesis and immune evasion strategies. Currently, acquired immunity through vaccination or prior infection usually provides sufficient protection against the emerging variants of SARS-CoV-2 except Omicron variant requiring recent booster. New strains have shown higher viral loads and greater transmissibility with more severe disease presentations. Notably, COVID-19 has a peculiar prognosis in severe patients with iron dysregulation and hypoxia which is still poorly understood. Studies have shown abnormally low serum iron levels in severe infection but a high iron overload in lung fibrotic tissue. Data from our in-silico structural analysis of the spike protein sequence along with host proteolysis processing suggests that the viral spike protein fragment mimics Hepcidin and is resistant to the major human proteases. This functional spike-derived peptide dubbed "Covidin" thus may be intricately involved with host ferroportin binding and internalization leading to dysregulated host iron metabolism. Here, we propose the possible role of this potentially allogenic mimetic hormone corresponding to severe COVID-19 immunopathology and illustrate that this molecular mimicry is responsible for a major pathway associated with severe disease status. Furthermore, through 3D molecular modeling and docking followed by MD simulation validation, we have unraveled the likely role of Covidin in iron dysregulation in COVID-19 patients. Our meta-analysis suggests the Hepcidin mimetic mechanism is highly conserved among its host range as well as among all new variants to date including Omicron. Extensive analysis of current mutations revealed that new variants are becoming alarmingly more resistant to selective human proteases associated with host defense.

Polymorphisms and mutations of ACE2 and TMPRSS2 genes are associated with COVID-19: a systematic review

Objective

To determine the effect of polymorphisms and mutations in angiotensin-converting enzyme 2 (ACE2) and Type 2 transmembrane serine proteases (TMPRSS2) genes on susceptibility to corona virus disease 2019 (COVID-19) and patient prognosis.

Introduction

From December 2019 to the current time, an outbreak of epidemic of COVID-19, characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has occurred around the world. It is now clear that SARS-CoV-2 binds to human ACE2 receptors, with expression of these receptors correlated with the rate of SARS-CoV-2 infection and mortality. Polymorphisms in individual patient factors, such as ACE2 and TMPRSS2 genes have been linked with an increase in negative outcomes, although evidence to affirm remains debatable.

Methods

Here, we performed a systematic review, based on guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, with the aim of assessing whether polymorphisms in ACE2 and TMPRSS2 genes affect the COVID-19 condition. We extensively searched PubMed, MEDLINE, Embase, the Cochrane Library, and Web of Science databases, for relevant articles and reports published in English between December 2019 and December 2021.

Results

A total of 495 full-text articles were downloaded, of which 185 were excluded after preliminary examination as they were duplicates. Finally, 310 articles were evaluated, by reading their titles and abstracts, and 208 of them eliminated based on our selection criteria. Finally, 33 articles met our inclusion criteria and were included in the final assessment. Genetic data from 33,923 patients with COVID-19 drawn from the general population and deriving from over 160 regions and 50 countries, as well as approximately 560,000 samples from global-public genetic databases, were included in our analysis. Ultimately, we identified 10 SNPs and 21 mutations in the ACE2 gene, along with 13 SNPs and 12 variants in the TMPRSS2 gene, which may be associated with COVID-19.

Conclusions

ACE2 and TMPRSS2 play vital roles in the onset, development, and prognosis of SARS-CoV-2 infection, and have both been strongly associated with vulnerability, intensity, and the clinical result of COVID-19. Overall, these genetic factors may have potential for future development of personalized drugs and vaccines against COVID-19.

Trial registration: CRD42021239400 in PROSPERO 2021.

[Development of COVID-19 drugs using human iPS cell technology]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which results in a rapid increase in the number of patients and deaths. In addition, various mutant strains have emerged and to be considered to accelerate the number of infected persons. To overcome this situation, effective strategies against COVID-19 include the development of vaccines to prevent SARS-CoV-2 infection and therapeutic agents that suppress the severity after infection. The drug repositioning approach, which search existing drugs that are effective against COVID-19, are expected to develop anti-COVID-19 drugs. In addition, various methods using human iPSC-derived differentiated cells has been developed to evaluate the efficacy and safety of drugs, and are also used for searching for therapeutic drugs for COVID-19. Here, we would like to describe the recent research and future perspectives for COVID-19 therapeutic drugs from the viewpoint of human iPS cell technology.

Methylene blue, Mycophenolic acid, Posaconazole, and Niclosamide inhibit SARS-CoV-2 Omicron variant BA.1 infection of human airway epithelial organoids

Sublineages of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Omicron variants continue to amass mutations in the spike (S) glycoprotein, which leads to immune evasion and rapid spread of the virus across the human population. Here we demonstrate the susceptibility of the Omicron variant BA.1 (B.1.1.529.1) to four repurposable drugs, Methylene blue (MB), Mycophenolic acid (MPA), Posaconazole (POS), and Niclosamide (Niclo) in post-exposure treatments of primary human airway cell cultures. MB, MPA, POS, and Niclo are known to block infection of human nasal and bronchial airway epithelial explant cultures (HAEEC) with the Wuhan strain, and four variants of concern (VoC), Alpha (B.1.1.7), Beta (B.1.351), Gamma (B.1.1.28), Delta (B.1.617.2) (Weiss et al., 2021, Murer et al., 2022). Our results here not only reinforce the broad anti-coronavirus effects of MB, MPA, POS and Niclo, but also demonstrate that the Omicron variant BA.1 (B.1.1.529.1) sheds infectious virus from HAEEC over at least 15 d, and maintains both intracellular and extracellular viral genomic RNA without overt toxicity, suggesting viral persistence. The data emphasize the potential of repurposable drugs against COVID-19.

Impact of sewer overflow on public health: A comprehensive scientometric analysis and systematic review

Sewer overflow (SO), which has attracted global attention, poses serious threat to public health and ecosystem. SO impacts public health via consumption of contaminated drinking water, aerosolization of pathogens, food-chain transmission, and direct contact with fecally-polluted rivers and beach sediments during recreation. However, no study has attempted to map the linkage between SO and public health including Covid-19 using scientometric analysis and systematic review of literature. Results showed that only few countries were actively involved in SO research in relation to public health. Furthermore, there are renewed calls to scale up environmental surveillance to safeguard public health. To safeguard public health, it is important for public health authorities to optimize water and wastewater treatment plants and improve building ventilation and plumbing systems to minimize pathogen transmission within buildings and transportation systems. In addition, health authorities should formulate appropriate policies that can enhance environmental surveillance and facilitate real-time monitoring of sewer overflow. Increased public awareness on strict personal hygiene and point-of-use-water-treatment such as boiling drinking water will go a long way to safeguard public health. Ecotoxicological studies and health risk assessment of exposure to pathogens via different transmission routes is also required to appropriately inform the use of lockdowns, minimize their socio-economic impact and guide evidence-based welfare/social policy interventions. Soft infrastructures, optimized sewer maintenance and prescreening of sewer overflow are recommended to reduce stormwater burden on wastewater treatment plant, curtail pathogen transmission and marine plastic pollution. Comprehensive, integrated surveillance and global collaborative efforts are important to curtail on-going Covid-19 pandemic and improve resilience against future pandemics.

Differential expression in humans of the viral entry receptor ACE2 compared with the short deltaACE2 isoform lacking SARS-CoV-2 binding sites

ACE2 is a membrane protein that regulates the cardiovascular system. Additionally, ACE2 acts as a receptor for host cell infection by human coronaviruses, including SARS-CoV-2 that emerged as the cause of the on-going COVID-19 pandemic and has brought unprecedented burden to economy and health. ACE2 binds the spike protein of SARS-CoV-2 with high affinity and shows little variation in amino acid sequence meaning natural resistance is rare. The discovery of a novel short ACE2 isoform (deltaACE2) provides evidence for inter-individual differences in SARS-CoV-2 susceptibility and severity, and likelihood of developing subsequent 'Long COVID'. Critically, deltaACE2 loses SARS-CoV-2 spike protein binding sites in the extracellular domain, and is predicted to confer reduced susceptibility to viral infection. We aimed to assess the differential expression of full-length ACE2 versus deltaACE2 in a panel of human tissues (kidney, heart, lung, and liver) that are implicated in COVID-19, and confirm ACE2 protein in these tissues. Using dual antibody staining, we show that deltaACE2 localises, and is enriched, in lung airway epithelia and bile duct epithelia in the liver. Finally, we also confirm that a fluorescently tagged SARS-CoV-2 spike protein monomer shows low binding at lung and bile duct epithelia where dACE2 is enriched.

Nasal and Salivary Mucosal Humoral Immune Response Elicited by mRNA BNT162b2 COVID-19 Vaccine Compared to SARS-CoV-2 Natural Infection

SARS-CoV-2 antibody assays are crucial in managing the COVID-19 pandemic. Approved mRNA COVID-19 vaccines are well known to induce a serum antibody responses against the spike protein and its RBD. Mucosal immunity plays a major role in the fight against COVID-19 directly at the site of virus entry; however, vaccine abilities to elicit mucosal immune responses have not been reported. We detected anti-SARS-CoV-2 IgA-S1 and IgG-RBD in three study populations (healthy controls, vaccinated subjects, and subjects recovered from COVID-19 infection) on serum, saliva, and nasal secretions using two commercial immunoassays (ELISA for IgA-S1 and chemiluminescent assay for IgG-RBD). Our results show that the mRNA BNT162b2 vaccine Comirnaty (Pfizer/BioNTech, New York, NY, USA) determines the production of nasal and salivary IgA-S1 and IgG-RBD against SARS-CoV-2. This mucosal humoral immune response is stronger after the injection of the second vaccine dose compared to subjects recovered from COVID-19. Since there is a lack of validated assays on saliva and nasal secretions, this study shows that our pre-analytical and analytical procedures are consistent with the data. Our findings indicate that the mRNA COVID-19 vaccine elicits antigen-specific nasal and salivary immune responses, and that mucosal antibody assays could be used as candidates for non-invasive monitoring of vaccine-induced protection against viral infection.

The Immunomodulatory Function of Vitamin D, with Particular Reference to SARS-CoV-2

Vaccines are the only way to reduce the morbidity associated to SARS-CoV-2 infection. The appearance of new mutations urges us to increase the effectiveness of vaccines as a complementary alternative. In this context, the use of adjuvant strategies has improved the effectiveness of different vaccines against virus infections such as dengue, influenza, and common cold. Recent reports on patients infected by COVID-19 reveal that low levels of circulating vitamin D correlate with a severe respiratory insufficiency. The immunomodulatory activity of this micronutrient attenuates the synthesis of pro-inflammatory cytokines and at the same time, increases antibody production. Therefore, the present review proposes the use of vitamin D as adjuvant micronutrient to increase the efficacy of vaccines against SARS-CoV-2 infection.

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19

BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

Allotypic variation in antigen processing controls antigenic peptide generation from SARS-CoV-2 S1 spike glycoprotein

Population genetic variability in immune system genes can often underlie variability in immune responses to pathogens. Cytotoxic T-lymphocytes are emerging as critical determinants of both severe acute respiratory syndrome coronavirus 2 infection severity and long-term immunity, after either recovery or vaccination. A hallmark of coronavirus disease 2019 is its highly variable severity and breadth of immune responses between individuals. To address the underlying mechanisms behind this phenomenon, we analyzed the proteolytic processing of S1 spike glycoprotein precursor antigenic peptides across ten common allotypes of endoplasmic reticulum aminopeptidase 1 (ERAP1), a polymorphic intracellular enzyme that can regulate cytotoxic T-lymphocyte responses by generating or destroying antigenic peptides. We utilized a systematic proteomic approach that allows the concurrent analysis of hundreds of trimming reactions in parallel, thus better emulating antigen processing in the cell. While all ERAP1 allotypes were capable of producing optimal ligands for major histocompatibility complex class I molecules, including known severe acute respiratory syndrome coronavirus 2 epitopes, they presented significant differences in peptide sequences produced, suggesting allotype-dependent sequence biases. Allotype 10, previously suggested to be enzymatically deficient, was rather found to be functionally distinct from other allotypes. Our findings suggest that common ERAP1 allotypes can be a major source of heterogeneity in antigen processing and through this mechanism contribute to variable immune responses in coronavirus disease 2019.

High association of COVID-19 severity with poor gut health score in Lebanese patients

Background

Coronavirus disease 2019 (COVID-19) has affected millions of lives globally. However, the disease has presented more extreme challenges for developing countries that are experiencing economic crises. Studies on COVID-19 symptoms and gut health are scarce and have not fully analyzed possible associations between gut health and disease pathophysiology. Therefore, this study aimed to demonstrate a potential association between gut health and COVID-19 severity in the Lebanese community, which has been experiencing a severe economic crisis.

Methods

This cross-sectional study investigated SARS-CoV-2 PCR-positive Lebanese patients. Participants were interviewed and gut health, COVID-19 symptoms, and different metrics were analyzed using simple and multiple logistic regression models.

Results

Analysis of the data showed that 25% of participants were asymptomatic, while an equal proportion experienced severe symptoms, including dyspnea (22.7%), oxygen need (7.5%), and hospitalization (3.1%). The mean age of the participants was 38.3 ±0.8 years, and the majority were males (63.9%), married (68.2%), and currently employed (66.7%). A negative correlation was found between gut health score and COVID-19 symptoms (Kendall’s tau-b = -0.153, P = 0.004); indicating that low gut health was associated with more severe COVID-19 cases. Additionally, participants who reported unhealthy food intake were more likely to experience severe symptoms (Kendall’s tau-b = 0.118, P = 0.049). When all items were taken into consideration, multiple ordinal logistic regression models showed a significant association between COVID-19 symptoms and each of the following variables: working status, flu-like illness episodes, and gut health score. COVID-19 severe symptoms were more common among patients having poor gut health scores (OR:1.31, 95%CI:1.07–1.61; P = 0.008), experiencing more than one episode of flu-like illness per year (OR:2.85, 95%CI:1.58–5.15; P = 0.001), and owning a job (OR:2.00, 95%CI:1.1–3.65; P = 0.023).

Conclusions

To our knowledge, this is the first study that showed the impact of gut health and exposure to respiratory viruses on COVID-19 severity in Lebanon. These findings can facilitate combating the pandemic in Lebanon.

Artificial Intelligence and Precision Medicine: A Perspective

This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.

IL10RB as a key regulator of COVID-19 host susceptibility and severity

BACKGROUND

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes and readily available compounds that reduce COVID-19 host susceptibility is a critical next step.

METHODS

We integrate COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX) and perturbargen signatures to identify candidate genes and compounds that reverse the predicted gene expression dysregulation associated with COVID-19 susceptibility. The top candidate gene is validated by testing both its GReX and observed blood transcriptome association with COVID-19 severity, as well as by in vitro perturbation to quantify effects on viral load and molecular pathway dysregulation. We validate the in silico drug repositioning analysis by examining whether the top candidate compounds decrease COVID-19 incidence based on epidemiological evidence.

RESULTS

We identify IL10RB as the top key regulator of COVID-19 host susceptibility. Predicted GReX up-regulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. In vitro IL10RB overexpression is associated with increased viral load and activation of immune-related molecular pathways. Azathioprine and retinol are prioritized as candidate compounds to reduce the likelihood of testing positive for COVID-19.

CONCLUSIONS

We establish an integrative data-driven approach for gene target prioritization. We identify and validate IL10RB as a suitable molecular target for modulation of COVID-19 host susceptibility. Finally, we provide evidence for a few readily available medications that would warrant further investigation as drug repositioning candidates.

The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype

COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.

CRISPR-based functional genomics in human dendritic cells

Dendritic cells (DCs) regulate processes ranging from antitumor and antiviral immunity to host-microbe communication at mucosal surfaces. It remains difficult, however, to genetically manipulate human DCs, limiting our ability to probe how DCs elicit specific immune responses. Here, we develop a CRISPR-Cas9 genome editing method for human monocyte-derived DCs (moDCs) that mediates knockouts with a median efficiency of >94% across >300 genes. Using this method, we perform genetic screens in moDCs, identifying mechanisms by which DCs tune responses to lipopolysaccharides from the human microbiome. In addition, we reveal donor-specific responses to lipopolysaccharides, underscoring the importance of assessing immune phenotypes in donor-derived cells, and identify candidate genes that control this specificity, highlighting the potential of our method to pinpoint determinants of inter-individual variation in immunity. Our work sets the stage for a systematic dissection of the immune signaling at the host-microbiome interface and for targeted engineering of DCs for neoantigen vaccination.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

SARS-CoV-2: Pathogenesis, Molecular Targets and Experimental Models

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recent pandemic outbreak threatening human beings worldwide. This novel coronavirus disease-19 (COVID-19) infection causes severe morbidity and mortality and rapidly spreading across the countries. Therefore, there is an urgent need for basic fundamental research to understand the pathogenesis and druggable molecular targets of SARS-CoV-2. Recent sequencing data of the viral genome and X-ray crystallographic data of the viral proteins illustrate potential molecular targets that need to be investigated for structure-based drug design. Further, the SARS-CoV-2 viral pathogen isolated from clinical samples needs to be cultivated and titrated. All of these scenarios demand suitable laboratory experimental models. The experimental models should mimic the viral life cycle as it happens in the human lung epithelial cells. Recently, researchers employing primary human lung epithelial cells, intestinal epithelial cells, experimental cell lines like Vero cells, CaCo-2 cells, HEK-293, H1299, Calu-3 for understanding viral titer values. The human iPSC-derived lung organoids, small intestinal organoids, and blood vessel organoids increase interest among researchers to understand SARS-CoV-2 biology and treatment outcome. The SARS-CoV-2 enters the human lung epithelial cells using viral Spike (S1) protein and human angiotensin-converting enzyme 2 (ACE-2) receptor. The laboratory mouse show poor ACE-2 expression and thereby inefficient SARS-CoV-2 infection. Therefore, there was an urgent need to develop transgenic hACE-2 mouse models to understand antiviral agents' therapeutic outcomes. This review highlighted the viral pathogenesis, potential druggable molecular targets, and suitable experimental models for basic fundamental research.