Feasibility Study of Developing a Saline-Based Antiviral Nanoformulation Containing Lipid-Soluble EGCG: A Potential Nasal Drug to Treat Long COVID.

A recent estimate indicates that up to 23.7 million Americans suffer from long COVID, and approximately one million workers may be out of the workforce each day due to associated symptoms, leading to a USD 50 billion annual loss of salary. Post-COVID (Long COVID) neurologic symptoms are due to the initial robust replication of SARS-CoV-2 in the nasal neuroepithelial cells, leading to inflammation of the olfactory epithelium (OE) and the central nervous system (CNS), and the OE becoming a persistent infection site. Previously, our group showed that Epigallocatechin-3-gallate-palmitate (EC16) nanoformulations possess strong antiviral activity against human coronavirus, suggesting this green tea-derived compound in nanoparticle formulations could be developed as an intranasally delivered new drug to eliminate the persistent SARS-CoV-2 infection, leading to restored olfactory function and reduced inflammation in the CNS. The objective of the current study was to determine the compatibility of the nanoformulations with human nasal primary epithelial cells (HNpECs). METHODS: Nanoparticle size was measured using the ZetaView Nanoparticle Tracking Analysis (NTA) system; contact antiviral activity was determined by TCID50 assay for cytopathic effect on MRC-5 cells; post-infection inhibition activity was determined in HNpECs; and cytotoxicity for these cells was determined using an MTT assay. The rapid inactivation of OC43 (a ß-coronavirus) and 229E (α-coronavirus) viruses was further characterized by transmission electron microscopy. RESULTS: A saline-based nanoformulation containing 0.1% w/v EC16 was able to inactivate 99.9999% ß-coronavirus OC43 on direct contact within 1 min. After a 10-min incubation of infected HNpECs with a formulation containing drug-grade EC16 (EGCG-4' mono-palmitate or EC16m), OC43 viral replication was inhibited by 99%. In addition, all nanoformulations tested for their effect on cell viability were comparable to normal saline, a regularly used nasal irrigation solution. A 1-min incubation of an EC16 nanoformulation with either OC43 or 229E showed an altered viral structure. CONCLUSION: Nanoformulations containing EC16 showed properties compatible with nasal application to rapidly inactivate SARS-CoV-2 residing in the olfactory mucosa and to reduce inflammation in the CNS, pending additional formulation and safety studies.

Saline nasal irrigation and gargling in COVID-19: a multidisciplinary review of effects on viral load, mucosal dynamics, and patient outcomes.

With unrelenting SARS-CoV-2 variants, additional COVID-19 mitigation strategies are needed. Oral and nasal saline irrigation (SI) is a traditional approach for respiratory infections/diseases. As a multidisciplinary network with expertise/experience with saline, we conducted a narrative review to examine mechanisms of action and clinical outcomes associated with nasal SI, gargling, spray, or nebulization in COVID-19. SI was found to reduce SARS-CoV-2 nasopharyngeal loads and hasten viral clearance. Other mechanisms may involve inhibition of viral replication, bioaerosol reduction, improved mucociliary clearance, modulation of ENaC, and neutrophil responses. Prophylaxis was documented adjunctive to personal protective equipment. COVID-19 patients experienced significant symptom relief, while overall data suggest lower hospitalization risk. We found no harm and hence recommend SI use, as safe, inexpensive, and easy-to-use hygiene measure, complementary to hand washing or mask-wearing. In view of mainly small studies, large well-controlled or surveillance studies can help to further validate the outcomes and to implement its use.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning.

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction.

We construct a polygenic health index as a weighted sum of polygenic risk scores for 20 major disease conditions, including, e.g., coronary artery disease, type 1 and 2 diabetes, schizophrenia, etc. Individual weights are determined by population-level estimates of impact on life expectancy. We validate this index in odds ratios and selection experiments using unrelated individuals and siblings (pairs and trios) from the UK Biobank. Individuals with higher index scores have decreased disease risk across almost all 20 diseases (no significant risk increases), and longer calculated life expectancy. When estimated Disability Adjusted Life Years (DALYs) are used as the performance metric, the gain from selection among ten individuals (highest index score vs average) is found to be roughly 4 DALYs. We find no statistical evidence for antagonistic trade-offs in risk reduction across these diseases. Correlations between genetic disease risks are found to be mostly positive and generally mild. These results have important implications for public health and also for fundamental issues such as pleiotropy and genetic architecture of human disease conditions.

Virucidal activities of novel hand hygiene and surface disinfectant formulations containing EGCG-palmitates (EC16).

BACKGROUND: Non-toxic hand hygiene and surface disinfectant products with virucidal activity against alcohol-resistant nonenveloped norovirus are in urgent need. METHOD: Alcohol-based formulations were made with epigallocatechin-3-gallate-palmitate (EC16), an FDA accepted food additive. Based on in-house testing of formulations, 3 prototypes, PTV80 hand gel, PST70 surface disinfectant spray and PST70 surface disinfectant wipe, were selected from in-house tests for independent testing at GLP (good laboratory practice) laboratories according to EN 14476:2019 (hand gel), ASTM test method E1053-20 (spray), and ASTM E2362-15, E1053, and ASTM E2896-12 (wipe). RESULTS: The PTV80 hand gel prototype demonstrated a >99.999% reduction of murine norovirus S99 infectivity in 60 seconds. Carrier testing of the PST70 surface spray and surface wipe demonstrated reduction of feline calicivirus infectivity by >99.99% in 60 seconds. In addition, testing with human coronavirus and human herpes simplex virus demonstrated >99.99% efficacy in 60 seconds, consistent with broad spectrum virucidal activity. CONCLUSIONS: The novel non-toxic prototypes containing EC16 were found to be suitable for use in future hand sanitizer gel, surface disinfectant spray and wipe products against norovirus. Products based on these formulations could be used safely to help prevent and control norovirus and other emerging virus outbreaks, pending future studies.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits.

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

Quantum Hair from Gravity.

We explore the relationship between the quantum state of a compact matter source and of its asymptotic graviton field. For a matter source in an energy eigenstate, the graviton state is determined at leading order by the energy eigenvalue. Insofar as there are no accidental energy degeneracies there is a one to one map between graviton states on the boundary of spacetime and the matter source states. Effective field theory allows us to compute a purely quantum gravitational effect which causes the subleading asymptotic behavior of the graviton state to depend on the internal structure of the source. This establishes the existence of ubiquitous quantum hair due to gravitational effects.

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations.

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank.

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Lithography-defect-driven source-mask optimization solution for full-chip optical proximity correction.

In the domain of computational lithography, the performance of an optimized imaging solution is usually qualified with a full-chip posted-optical-proximity-correction lithography printing check to ensure the printing is defect free before committed for mask writing. It is thus highly preferable for the optimization process itself to be driven by the same defect detection mechanism towards a defect-free solution. On the other hand, the huge data size of chip layout poses great challenge to such optimization process, in terms of runtime and data storage. A gradient-based optimization scheme thus becomes necessary. To date, no successful engineering tool is capable of accommodating these two requirements at the same time. We demonstrate the technology of defect-driven gradient-based optimization to achieve a defect-free solution within practical runtime specification, using ASML's computational lithography product Tachyon SMO.

Epigallocatechin-3-Gallate (EGCG) Inhibits SARS-CoV-2 Infection in Primate Epithelial Cells: (A Short Communication).

SARS-CoV-2, the novel coronavirus responsible for the COVID-19 pandemic, caused >26 million cases in the United States and >437,000 deaths as of Jan 30, 2020. Worldwide by that date, there had been 102 million cases of infections, and deaths had climbed to 2.21 million. Mutated variants of SARS-CoV-2 that have emerged from the United Kingdom, Brazil, and South Africa are associated with higher transmission rates and associated deaths. Therefore, novel therapeutic and prophylactic methods against SARS-CoV-2 are in urgent need. While some antiviral drugs, such as Remdesivir, provide relief to certain patient populations, other existing antiviral drugs or combinations of FDA approved pharmaceuticals have yet to show clinical efficacy against COVID-19. Compounds that possess strong and broad antiviral properties with different mechanisms of action against respiratory viruses may provide novel approaches to combat SARS-CoV-2 and its variants, especially if the compounds are classified as generally recognized as safe (GRAS). A large body of evidence indicates a promising potential for the use of epigallocatechin-3-gallate (EGCG) and its derivatives as effective agents against infections from a wide range of pathogenic viruses. However, EGCG or its derivatives have not been tested directly against SARS-CoV-2. The current study was designed to evaluate the potential antiviral activity of EGCG against SARS-CoV-2 infection in primate epithelial cells. Methods applied in the study include cytopathic effect (CPE) assay and virus yield reduction (VYR) assays using Vero 76 (green monkey epithelial cells) and Caco-2 (human epithelial cells) cell lines, respectively. The results demonstrated that EGCG at 0.27 µg/ml (0.59 µM) inhibited SARS-CoV-2 infection in Vero 76 cells by 50% (i.e., EC50=0.27 µg/ml). EGCG also inhibited SARS-CoV-2 infection in Caco-2 cells with EC90=28 µg/ml (61 µM). These results, to the best of our knowledge, are the first observations on the antiviral activities of EGCG against SARS-CoV-2, and suggest that EGCG and its derivatives could be used to combat COVID-19 and other respiratory viral infection-induced illness, pending in vivo and clinical studies.

Effects of Epigallocatechin-3-Gallate-Palmitate (EC16) on In Vitro Norovirus Infection.

Background: Norovirus is the world-leading cause of acute gastroenteritis associated with severe symptoms and deaths. However, vaccines against norovirus are currently not available, and medications that specifically target human norovirus infection are still under development. The current study evaluated the virucidal and antiviral activities of epigallocatechin-3-gallate-palmitate (EC16), a compound derived from green tea polyphenols, against murine norovirus (MNV S99, a surrogate for human norovirus). Method: Initially, formulation suitability tests were conducted to compare EGCG (epigallocatechin-3-gallate), EC16 and tea polyphenol-palmitate in alcohol solution and hand hygiene formulations. The virucidal activity of EC16 was then tested in hand sanitizer gel and hand sanitizer foam formulations using a TCID50 time-kill suspension assay. In vitro treatment and prevention tests were performed using a 1-hour incubation of EC16 or EGCG with RAW264.7 cells, either post-infection or pre-infection with MNV. Statistical analysis employed two-tailed student t test (alpha=0.05). Results: Unlike EC16, both EGCG and tea polyphenol-palmitate showed auto-oxidation (color change) and precipitation in alcohol solution and hand hygiene formulations, and thus less suitable for potential hand hygiene product or new drug development. The time-kill suspension test results demonstrated that EC16 in both sanitizer gel and foam formulations reduced MNV by >99.99% (>log10 4) after 60 sec direct contact. One-hour incubation of EC16 with RAW264.7 cells either before or after MNV infection (i.e., without direct contact with MNV), resulted in >99% (>log10 2) reduction of MNV infectivity. Conclusion: EC16 is a candidate for use as a virucidal and antiviral compound to prevent and treat norovirus infection, with potential to be developed as a new drug against norovirus, pending in vivo and clinical tests.

Bactericidal and fungicidal activities of novel ProtecTeaV formulations - alcohol-based hand hygiene and surface disinfectant prototypes containing epigallocatechin-3-gallate-palmitate (EC16).

Objective: Currently used alcohol-based hand sanitizers and surgical hand rubs are not effective against alcohol-resistant microorganisms. We reported previously that nontoxic antioxidant food additive compounds derived from green tea polyphenols, particularly epigallocatechin-3-gallate-palmitate (EC16), are suitable in alcohol formulations to effectively inactivate nonenveloped viruses and bacterial spores. However, whether EC16 influences the bactericidal and fungicidal activity of alcohol is not clear. The objective of the current study was to determine the bactericidal and fungicidal activities of ProtecTeaV hand sanitizer and surface spray prototypes containing EC16. Methods: The prototypes were tested according to the guidelines from the Food and Drug Administration (FDA) and the Environmental Protection Agency (EPA). Results: As expected, EC16 did not reduce the bactericidal and fungicidal activities of ethanol. The hand sanitizer gel formulation was equally effective as 70% ethanol and met the tested standard, and the surface spray prototype met the EPA performance standard. Conclusions: EC16 can be combined with ethanol without reducing antibacterial or antifungal activity, and the ProtecTeaV prototypes could be further developed into novel hand hygiene and surface disinfectant products with virucidal, bactericidal, fungicidal and sporicidal activities.

Sibling validation of polygenic risk scores and complex trait prediction.

We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, i.e. > + 2 SD), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55-65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.

Genetic architecture of complex traits and disease risk predictors.

Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Using data from the UK Biobank, predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome-sequencing data. This suggests that exome data alone will miss much of the heritability for these traits-i.e., existing PRS cannot be computed from exome data alone. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

PREIMPLANTATION GENETIC TESTING: Preimplantation genetic testing for polygenic disease risk.

Since its introduction to clinical practice, preimplantation genetic testing (PGT) has become a standard of care for couples at risk of having children with monogenic disease and for chromosomal aneuploidy to improve outcomes for patients with infertility. The primary objective of PGT is to reduce the risk of miscarriage and genetic disease and to improve the success of infertility treatment with the delivery of a healthy child. Until recently, the application of PGT to more common but complex polygenic disease was not possible, as the genetic contribution to polygenic disease has been difficult to determine, and the concept of embryo selection across multiple genetic loci has been difficult to comprehend. Several achievements, including the ability to obtain accurate, genome-wide genotypes of the human embryo and the development of population-level biobanks, have now made PGT for polygenic disease risk applicable in clinical practice. With the rapid advances in embryonic polygenic risk scoring, diverse considerations beyond technical capability have been introduced.

Endophthalmitis after Cataract Surgery in the United States: A Report from the Intelligent Research in Sight Registry, 2013-2017.

PURPOSE: To determine recent incidence and visual outcomes for acute-onset endophthalmitis after cataract surgery performed in the United States. DESIGN: Retrospective cohort study. PARTICIPANTS: United States cataract surgery patients, 2013-2017 (5 401 686 patients). METHODS: Cases of acute-onset postoperative endophthalmitis occurring within 30 days after cataract surgery were identified using diagnosis codes in the American Academy of Ophthalmology IRIS (Intelligent Research in Sight) Registry database, drawn from electronic health records in ophthalmology practices across the nation. Annual and aggregate 5-year incidences were determined for all cataract surgeries and specifically for standalone procedures versus those combined with other ophthalmic surgeries. Patient characteristics were compared. Mean and median visual acuity was determined at 1 month preoperative as well as 1 week, 1 month, and 3 months postoperative among patients with and without endophthalmitis. MAIN OUTCOME MEASURES: Incidence of acute-onset postoperative endophthalmitis after cataract surgery. RESULTS: A total of 8 542 838 eyes underwent cataract surgery, 3629 of which developed acute-onset endophthalmitis (0.04%; 95% confidence interval, 0.04%-0.04%). Endophthalmitis incidence was highest among patients aged 0 to 17 years (0.37% over 5 years), followed by patients aged 18 to 44 years (0.18% over 5 years; P < 0.0001). Endophthalmitis occurred 4 times more often after combined cases (cataract with other ophthalmic procedures) than after standalone cataract surgeries (0.20% vs. 0.04% of cases), and occurred in 0.35% of patients receiving anterior vitrectomy. Mean 3-month postoperative visual acuity was 20/100 (median, 20/50) among endophthalmitis patients, versus a mean of approximately 20/40 (median, 20/30) among patients without endophthalmitis. However, 4% of endophthalmitis patients still achieved 20/20 or better visual acuity, and 44% achieved 20/40 or better visual acuity at 3 months. CONCLUSIONS: Acute-onset endophthalmitis occurred in 0.04% of 8 542 838 cataract surgeries performed in the United States between 2013 and 2017. Risk factors may include younger age, cataract surgery combined with other ophthalmic surgeries, and anterior vitrectomy. Visual acuity outcomes vary; however, patients can recover excellent vision after surgery. Big data from clinical registries like the IRIS Registry has great potential for evaluating rare conditions such as endophthalmitis, including developing benchmarks, longer-term time trend investigation, and comprehensive analysis of risk factors and prophylaxis.

Author Correction: Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.