R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction  Polygenic scores (PGS) have high potential for clinical use but are currently underpowered for many applications. Here, the authors develop an approach that leverages an agnostic library of hundreds of PGS to increase prediction of complex diseases and other traits. This multi-PGS framework is ideal for emerging biobank data. The predictive performance of PGS is largely determined by four factors: the sample size of the GWAS used for training the score, the proportion of causal variants and the heritability of the phenotype, as well as heterogeneity between GWAS and target samples, including differences in genetic ancestry6. Increasing the number of samples for a phenotype is costly and takes time, but a possible alternative is to use genetically correlated phenotypes to increase the effective sample size at no cost14. Previously proposed prediction methods using multiple PGS have either required individual-level genotype and phenotype validation data for each PGS in the model11, or the inclusion of multiple PGS for the same GWAS summary statistics file corresponding to different p-value thresholds or proportions of causal variants15 (i.e. the PGS model hyper-parameters). For the latter, the number of included PGS can become very large (e.g. 10 for each GWAS summary statistic number), which increases the number of validation samples required to fit the model and limits the number of PGS that can be included in practice.Although polygenic scores (PGS) have high potential for clinical use20. This development has two major implications in the context of prediction using multiple PGS. First, individual-level genotype validation data for each of the correlated phenotypes included in the prediction model is no longer necessary because selecting the best-performing hyper-parameters is no longer needed. Second, PGS for any genetically correlated phenotype, even those not available in the target data, can now be included easily in the same prediction model, which significantly expands the set of phenotypes one can study. Therefore, as only one PGS per phenotype needs to be included in the prediction model, the only practical limitation is effectively the number of individual-level samples available for the desired phenotype, which is constantly growing for biobank data.Recent advances in PGS methods allow us to generate scores for a phenotype without requiring validation data to tune the hyper-parameters21) into a multi-PGS model. This model is then evaluated in an independent dataset in terms of the prediction accuracy of the multi-PGS. We apply our multi-PGS framework to the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH)23, one of the largest datasets on the genetics of major psychiatric disorders. These disorders are genetically correlated with many other psychiatric and neurological disorders as well as other behavioral phenotypes25, which are precisely the circumstances under which the proposed multi-PGS might boost the polygenic prediction accuracy. We benchmark the multi-PGS against each phenotype’s respective single PGS prediction and compare it with an existing PGS method that meta-analyzes multiple PGSs using GWAS summary statistics, wMT-SBLUP8. Although the iPSYCH cohort has been designed around psychiatric disorders, the study individuals can be linked to the National Danish Registers23, making it possible to generate multi-PGS for any phenotype captured in these registers. We demonstrate that multi-PGS improves prediction accuracy results for a range of different diseases, subtypes and phenotypes for which no GWAS summary statistics currently exist . Our goal is to showcase our multi-PGS framework and its potential advantage to be applied to new emerging biobank data.In this work, we propose and evaluate a multi-PGS framework that leverages these key PGS developments to construct more powerful and generalisable prediction models. These multi-PGS models can be trained on thousands of different PGS such as for health outcomes, body measurements, and behavioral phenotypes which are not necessarily genetically correlated with the outcome. Multiple PGS and covariates can be combined using either a linear model  or a nonlinear model . In Step 2 “Train Multi-PGS Models”, the PGS library is standardized  and used to develop multi-PGS prediction models for a target outcome using both a linear model  and a non-linear model . These models include sex, age and 20 first PCs as covariates for training. Finally in Step 3 “Evaluate models”, the multi-PGS models are projected into the test data, evaluated in terms of prediction accuracy and benchmarked against single PGS and another multivariate PGS method (wMT-SBLUP8). We used 5-fold cross-validation to alternate between Step 2 and Step 3 to get out-of-sample prediction accuracy estimates.Here we summarize the framework used for generating the proposed multi-PGS. This framework consists of three steps Fig. . In StepUsing the proposed multi-PGS framework, we generated a library of 937 PGS models (described in detail in Supplementary Methods) and projected it into the genotypes of individuals in iPSYCH. We then trained multi-PGS models for 6 major psychiatric disorders: attention-deficit/hyperactivity disorder (ADHD), affective disorder (AFF), anorexia nervosa (AN), autism spectrum disorder (ASD), bipolar disorder (BD) and schizophrenia (SCZ). We focus the first part of the results section on these 6 psychiatric disorders and extend the multi-PGS application to other 62 ICD10 code disease definitions, continuous phenotypes and case-case classification in the last result section.R2 4-fold on average over the single GWAS PGS for all disorders, with up to 9-fold improvement for ADHD and ASD  and the 937 PGS using linear models  and non-linear models (boosted gradient trees: multiPGS_XGBoost) to predict 6 major psychiatric disorders: ADHD, AFF, AN, ASD, BD and SCZ. We used a model including a single PGS for the largest available GWAS for each psychiatric disorder as the standard reference (ST3). In terms of variance explained, the multi-PGS increased the mean ASD Fig. , from 0.ASD Fig. .Fig. 2PeR2 over lasso multi-PGS. For SCZ, there was no difference in variance explained by the two PGS.In terms of which multi-PGS performed better at combining the variables, the results appear to be disorder specific. For ADHD, ASD and BD the lasso multi-PGS increased the mean prediction R2 over the XGBoost multi-PGS. On the contrary, for AFF and AN the XGBoost multi-PGS increased the mean prediction We further investigated if the increase in prediction of the XGBoost multi-PGS over lasso multi-PGS was driven by the nonlinear combination of covariates alone, and if it was independent from the PGS combination. In practice, we obtained an XGBoost risk score for the covariates and fitted it as an additional variable in the lasso model, together with the 937 PGS. The mean variance explained by this mixed multi-PGS was comparable to the lasso multi-PGS for ADHD, ASD, BD and SCZ, while it was comparable to the mean variance explained by the XGBoost multi-PGS for AFF and AN. This demonstrated that the non-linear combination was only beneficial at the covariate level and not at the PGS-level.Less pronounced differences were observed in terms of the mean area under the curve (AUC) prediction (SF5), indicating that both models are similar in terms of classification. In terms of quintile odds ratio, the lasso multi-PGS was generally the best at separating the top 20% to the middle quintile, even for the models where the maximum mean variance was explained by the XGBoost multi-PGS Fig. . Since tNext, we investigated which of the 937 PGS in the multi-PGS model were the ones contributing the most to increasing prediction accuracy. First, we identified the number of non-zero PGS selected by the lasso model, which ranged from 10 to 154 for the 6 psychiatric disorders  and one from a UK Biobank GWAS on depressive symptoms31 (tagged Depressive symptoms). These results confirm that non-overlapping signals from multiple GWAS of similar phenotypes can be combined to increase prediction accuracy. Interestingly, the PGS from the PGC GWAS excluding iPSYCH samples ranked 5th for ADHD. This study had only 4225 cases, similarly to the ASD study, with 5305 cases. The iPSYCH individuals were excluded from the PGC ADHD and ASD studies.For affective disorder, three depression PGS were selected, two from the PGCR2. Prediction estimates for the top 5 PGSs in each model ranked by their lasso weights are also shown in Fig. We compared the prediction accuracy of a multi-PGS with all non-zero weights to a simplified lasso multi-PGS that included only PGS with an absolute lasso weight larger than 0.01, multi-PGS_lasso_s . The simplified multi-PGS showed consistently higher mean R2 than wMT-SBLUP, even though they both contained the same number of PGS.By using the lasso regression as a feature selection algorithm (only selecting the ones with large weights), it was feasible to compare the prediction accuracy to another prediction method using multiple PGSs, wMT-SBLUPR2 are out-of-sample estimates. The resulting comparison showed that the prediction accuracy of multi-PGS outperformed both single-PGS and BLUP PGS predictions, indicating that the accuracy gained from multi-PGS was not only based on sample size but on the use of a large and diverse set of 937 PGS . Here, we compared the prediction accuracy of single-PGS (based on GWAS summary statistics) and multi-PGS  to a best linear unbiased predictor (BLUP) PGS . We used fivefold cross validation for deriving both the multi-PGS and BLUP PGS, so that the reported adjusted PGS Fig. . As showple Fig. .Fig. 4PeNext, we further explored the capacity of our multi-PGS to predict outcomes for which there are no available external GWAS summary statistics. This question is inspired by a scenario where the studied outcome could benefit from PGS analyses, but there is still no GWAS for that outcome. We tested this by simulating a scenario where the six analyzed psychiatric disorders did not have external PGC GWAS. In practice this meant removing each time the disorder’s PGS from the PGS library, resulting in a PGS library of 936 scores. For affective disorder (AFF), we removed the two depression PGS from the library. The difference in mean prediction R2 was not significantly different between both multi-PGS (using either 937 or 936 PGS) for any psychiatric disorders Fig. . Moreove33 with at least 500 diagnosed cases and compared the prediction performance of the multi-PGS lasso to the multi-PGS XGBoost. Similarly to the results for the main 6 psychiatric disorders, the mean prediction R2 for both multi-PGS was on average the same, but ultimately depended on the disorder (SF13). We emphasize that both these phenotypes all nested within the psychiatric disorder cases and population cohort of iPSYCH case-cohort design (ST4).Finally, we extended the multi-PGS results to other phenotypes defined in the Danish National Registers in the overlapping samples with iPSYCH to showcase its potential to generate polygenic scores in a biobank framework. First, we selected 62 ICD10 codes from the Danish Psychiatric Central Research Register34 and (d) case−case predictions. For the last category, we explored two example pairs of disorders with a high degree of comorbidity. First, we excluded the cases with both disorders and re-codified each single disorder case as 0 or 1.To expand the comparison to external data, we selected 15 phenotypes from four different categories; (a) other ICD10 codes with available GWAS summary statistics, (b) other ICD10 codes with not known available GWAS summary statistics / disorder sub-phenotypes, (c) continuous phenotypes from the Medical Birth RegisterIn this case we compared the prediction accuracy of the multi-PGS against a single-PGS, the top weighted PGS as outputted from the multi-PGS lasso (ST5).30 is selected as the top lasso weighted PGS for multiple outcomes . This is mostly due to the inclusion of various behavioral PGS in our library  with very large sample sizes and large genetic correlation with ADHD.Here we have proposed a multi-PGS framework derived from nearly one thousand GWAS summary statistics for different phenotypes, and showed that it increased the accuracy of PGS for psychiatric disorders. Multi-PGS explained a larger proportion of the SNP-heritability (a fourfold increase on average for the main psychiatric disorders over a PGS trained on the target outcome only) and stratified the population in more distinct risk groups than single-phenotype PGS. The disorder with the largest increase in prediction accuracy from multi-PGS was ADHD, with a ninefold increased prediction R2over the single PGS (corresponding to ~40% of the SNP linear multi-PGS (lasso) and non-linear multi-PGS (XGBoost) resulted in similar prediction accuracies, the improved performance of the non-linear models for affective disorder and anorexia nervosa was due to the presence of non-linear covariate interactions between sex and age, as previously reported36. We therefore focused the analyses on the lasso multi-PGS, as those results have easier interpretability.While the compared 8, another PGS method trained on GWAS summary statistics for multiple phenotypes, the multi-PGS also resulted in a more accurate prediction. The improvement in prediction accuracy is likely due to the fact that while wMT-SBLUP bases its weights on the genetic correlation to the external GWAS summary statistics for each phenotype, multi-PGS is trained directly on the individual-level samples, allowing multi-PGS to better tailor the weights to the cohort. Interestingly, the multi-PGS still obtained much more accurate predictions by combining hundreds of external PGS. This result suggests that as both the number of genome-wide association studies and their sample size grows, training multi-PGS will become more feasible.We benchmarked our multi-PGS prediction results against a PGS trained on external GWAS summary statistics for each of the six major psychiatric disorders respectively. The multi-PGS always resulted in a more accurate prediction, even when the phenotype itself was not included in the PGS library. When benchmarked against wMT-SBLUPsufficient individual-level genetic data available with the phenotype of interest. The multiPGS predictors do not require PGS for the target phenotype of interest to be available in the PGS library used. This application is particularly interesting for sub-phenotype analyses within diseases, where GWAS summary statistics are not generally available for the sub-phenotypes. We demonstrated in practice how these multi-PGS could be generated for various psychiatric sub-diagnoses e.g. different ICD10 subcodes within Autism Spectrum Disorder (ICD10 F8). Similar multi-PGS method could also be applied to other sub-phenotypes of psychiatric disorders like psychosis within bipolar disorder, as defined in Hasseris et al.37. Another exciting application we explored is the case-case prediction, where multi-PGS models can be trained for highly comorbid disorders. In this last category, we highlight the relatively high prediction accuracy of our predictor of ADHD cases from a pool of ADHD-ASD cases.Finally, we showcased how multi-PGS can be used to train predictors for any phenotype of interest, as long as one has 38, becomes both difficult and important when considering thousands of GWAS summary statistics. We addressed this manually by checking the GWAS summary statistics, but an automatization of this step (e.g. using bivariate LDSC39) could help streamline this procedure. Third, the resulting multi-PGS is potentially predicting a subset of individuals in the case group enriched or comorbid with a phenotype with large genetic correlation to the PGS phenotypes, but not the disorder itself. Therefore, although multi-PGS can improve prediction accuracy, it should not be used to estimate genetic correlations or study genetic overlap. Fourth, we have not explored whether including a PRS trained on individual-level data could improve the prediction further, as suggested by a previous study32. Fifth, we have not explored how generalisable the multi-PGS are across different ancestry groups, but we expect the R2 prediction accuracy to decay with genetic distance between training and testing, as previously shown for PGS40. However, as more individuals of non-European ancestry are included in GWAS, the multi-PGS based on the resulting summary statistics may be able to improve cross-ancestry prediction.This study and the multi-PGS approach has several limitations. First, as we performed a fivefold cross-validation in the iPSYCH  data when training and testing the multi-PGS, it is possible that some of the prediction accuracy gain is due to overfitting. However, the multi-PGS also resulted in multiple null predictions despite having large training sample sizes  suggesting that overfitting is small. To further avoid overfitting we restricted the analyses to a set of unrelated individuals of European ancestry to control for population structure. Second, controlling for sample overlap between external and internal data, which can lead to overfitting42, gene expression43, and/or protein levels44. Combining different types of data into a multi-PGS could potentially improve or quantify the importance for prediction of the different data types. The framework can also make use of published PGS variant weights  instead of deriving the PGS from the GWAS summary statistics.The same multi-PGS framework could be applied to other types of biological data summary statistics, like GWAS for brain or cardiac imagesIn this study, by leveraging nearly a thousand external PGS, we show how we can increase the polygenic prediction further without the need to genotype more individuals of a specific phenotype. We think this multi-PGS framework has a lot of potential for new emerging biobanks or register-based genetic cohorts to generate PGS for every available phecode or defined phenotype in their system, both because of the ever growing set of publicly available GWAS summary statistics and, as they are new, these biobanks do not have the issue of sample overlap with the external GWAS summary statistics used.The study was approved by the local scientific ethics committees and institutional review boards. The iPSYCH study was approved by the Scientific Ethics Committee in the Central Denmark Region (case no. 1-10-72-287-12) and the Danish Data Protection Agency. In accordance with Danish legislation, the Danish Scientific Ethics Committee has, for this study, waived the need for specific informed consent in biomedical research based on existing biobanks.26, GWAS Atlas27, the Psychiatric Genomics Consortium Website (https://www.med.unc.edu/pgc). For the specific PGC GWAS summary statistics where iPSYCH was used in the discovery dataset, we used in-house GWAS results where these samples were excluded from the calculation. The GWAS files were specifically selected to be based on European ancestry individuals, to not be overly redundant (only the latest GWAS for each same phenotype) and to not contain iPSYCH samples. From an original list of 6206 files (ST1), this filtering resulted in 1377 files to download.A detailed description of the PGS library construction and file filtering process is provided as Supplementary Methods and all code used will be available. A number of resources were used to obtain an initial list of GWAS summary statistics for generating the PGS library. The majority of the GWAS summary statistics were downloaded from publicly available databases . The set of 952 GWAS summary statistics that passed QC and kept over 200,000 SNPs were used to derive PGS with LDpred2-auto16.We developed a pipeline for downloading, parsing, reformatting and doing a quality control filter on the list of files. We created a GWAS summary statistic column-name library (SF1) to ease file parsing, and after re-formatting and removing corrupted files, this step resulted in 1005 files. We restricted the number of SNPs to the overlap of the iPSYCH imputed variants with the HapMap3 variants and the LD reference provided by LDpred2, resulting in a maximum of 1,053,299 SNPs per GWAS summary statistics file. Filtering SNPs with a large discrepancy in standard deviation between the genotyped/imputed data and the GWAS summary statistics can increase the robustness and prediction accuracy of PGS16 that does not require a validation dataset to fit the hyperparameters , but these are fitted as part of the Gibbs sampler instead. We used the provided European-ancestry independent LD blocks as reference panel46. For each GWAS summary statistics file, LDpred2-auto was run with 30 Gibbs sampler chains, 800 burn-in iterations and 400 iterations. The SNP-h2 initial value was set to the LD score regression estimate47 from the GWAS summary statistics after. Each of the chains was initialized with a different prior for the proportion of causal variants:  in log scale (example plot for a chain in SF3). Chains were filtered according to the recommendation in the LDpred2 tutorial, and effect sizes of chains kept were averaged. After running LDpred2-auto on the QC’d file set and post-processing, we were left with 937 PGS. This constitutes the final PGS library and all information on its GWAS summary statistics meta-data, number of SNPs per file on each step, number of chains on the final PGS and estimates of SNP-h2 and p can be found in SD1. A plot comparing the LDSC and LDpred2-auto SNP-h2 estimates can be found at SF4.Polygenic score weights were derived using LDpred2-auto, a method within the LDpred2 framework23. After genotyping and sample quality control, it includes 92,765 individuals diagnosed with a major psychiatric disorder i.e. attention-deficit/hyperactivity disorder (ADHD), affective disorder (AFF), autism (ASD), schizophrenia (SCZ) and bipolar disorder (BD). We also included the anorexia nervosa  samples from the Anorexia Nervosa Genetics Initiative (ANGI)48, as they were samples within the same framework as iPSYCH 2015. The dataset also includes 42,912 individuals randomly sampled from the same birth cohort, making it representative of the general Danish population. The genotype data was imputed using the Haplotype Reference Consortium (HRC)49 as the reference panel and following the RICOPILI pipeline50. After removing SNPs with minor allele frequency (MAF) < 0.01 and Hardy-Weinberg p value < 10-6, we restricted to the HapMap3 variants in the LDpred2 LD reference panel, resulting in 1,053,299 SNPs.The iPSYCH 2015 case-cohort sample is a genotyped dataset from neonatal dried blood spots (DBS) nested within the entire Danish population born between 1981 and 2008, including 1,657,449 persons51 and obtained 20 PCs. The process has already been described in Albiñana et al.32. Using the set of 20 PCs, we defined genetically homogeneous individuals as having <4.5 log distance units to the multidimensional center of the 20 PCs . We also computed the KING-relatedness robust coefficient of the sample and excluded the second of each pair with >3rd degree relatedness. We identified a set of 108,031 unrelated genetically homogeneous individuals (Danish-European ancestry), which we used for all subsequent analyses.We performed principal component analysis (PCA) following Privé et al.33 with register data available until December 2016. Except for the category of all psychiatric disorders , all categories of diagnosis are given a variable name starting with a letter and followed by four digits. The first letter is the chapter of disease in the ICD10 system and the first number is the ICD10 diagnosis. The other 3 numbers are not informative of ICD10 diagnosis. We also used 3 continuous phenotypes from the Danish Medical Birth Register34: apgar5 , fvagt (birth weight in grams) and gest_age . All used phenotypes, sample sizes and metadata are available at SD3-SD4.We used phenotypes from the Danish Psychiatric Central Research Register53 and (2) tree gradient boosting as implemented in the XGBoost (eXtreme Gradient Boosting) algorithm in the R package xgboost21. For both models, we trained a base model using only the covariates  and a full model using the covariates plus the 937 standarized LDpred2-auto PGS. For the base model, we used the glm function with the option family = “logistic”. For lasso, we used the function cv.glmnet from the glmnet R package with the options alpha = 0 and family = “binomial” for binary phenotypes. The covariates were not regularized by giving them a penalty factor of 0 with the option penalty.factor, while the rest of the PGS were given a penalty factor of 1. For XGBoost, we used the xgboost function from the xgboost R package with options eta = 0.01 and nrounds = 10. We used objective = “binary:logistic” for binary phenotypes and objective = “reg:squarederror” for continuous variables.We used the library of 937 PGS to train multi-phenotype predictors (multi-PGS) using two different algorithms (1) L1 penalized regression (lasso) as implemented in the R package glmnet55 (using the flag –predBetasFile) on the set of 1,118,443 HM3 SNPs on iPSYCH. Depending on the polygenicity of the phenotype, BOLT-LMM computes a mixture-of-Gaussians prior or a single-Gaussian BOLT-LMM-inf model, equivalent to best linear unbiased prediction (BLUP). In the case of psychiatric disorders, our results show that BOLT-LMM-inf is always the model selected and therefore we refer to the BOLT-LMM PGS as BLUP PGS.We computed an internal best linear unbiased prediction (BLUP) PGS trained on the individual-level data. We obtained the per-SNP prediction betas with BOLT-LMM56. The adjusted R2 was defined as the variance explained by the full model after accounting for the variance explained by the base model as R2_adjusted = (R2_full - R2_cov)/(1 − R2_cov). (2) Odds ratio (OR) of the 5th quintile to the middle quintile. All OR were calculated from a logistic regression model based on the PGS percentiles, sex, birth year and first 20PCs.For each phenotype, we used a fivefold cross-validation scheme to obtain out-of-sample prediction accuracy estimates. The prediction was evaluated by (1) adjusted variance explained in the liability scale. We used population prevalences specified in SD2 to convert the variance explained in a linear regression to the liability scaleFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Herpes simplex virus type 1 (HSV-1) is a contagious pathogen with a large global footprint, due to its ability to cause lifelong infection in patients. Current antiviral therapies are effective in limiting viral replication in the epithelial cells to alleviate clinical symptoms, but ineffective in eliminating latent viral reservoirs in neurons. Much of HSV-1 pathogenesis is dependent on its ability to manipulate oxidative stress responses to craft a cellular environment that favors HSV-1 replication. However, to maintain redox homeostasis and to promote antiviral immune responses, the infected cell can upregulate reactive oxygen and nitrogen species (RONS) while having a tight control on antioxidant concentrations to prevent cellular damage. Non-thermal plasma (NTP), which we propose as a potential therapy alternative directed against HSV-1 infection, is a means to deliver RONS that affect redox homeostasis in the infected cell. This review emphasizes how NTP can be an effective therapy for HSV-1 infections through the direct antiviral activity of RONS and via immunomodulatory changes in the infected cells that will stimulate anti-HSV-1 adaptive immune responses. Overall, NTP application can control HSV-1 replication and address the challenges of latency by decreasing the size of the viral reservoir in the nervous system. As a prevalent pathogen and global health concern, herpes simplex virus type 1 (HSV-1) is among one of the most studied human herpesviruses. HSV-1 has long served as a useful research tool in providing information that has enhanced the understanding of viruses. It also has a large global footprint in which more than 70% of the world’s population under the age of 50 harbors an HSV-1 infection ,3. In raFor patients who experience severe clinical symptoms of HSV-1 infection, treatment options, while available, are limited and sometimes ineffective. Antiviral therapies are typically administered upon the appearance of clinical symptoms after the initial acquisition of the virus or when the virus is reactivated from latency. The first line of defense against these symptoms is nucleoside analogs, the most common being acyclovir. As a prodrug, acyclovir becomes activated through phosphorylation by virus-encoded thymidine kinases (TK) to the monophosphate form and further phosphorylation by other thymidylate kinases, converting it to acyclovir trisphosphate ,10,11. TDue to their mechanism of action, nucleoside analogs are effective during acute infection but do not affect the latent virus residing in neurons, allowing HSV-1 to persist in its host. This persistence can lead to the development of viral mutants upon reactivation, which give rise to drug-resistant strains ,11,12. AReduction-oxidation (redox) homeostasis, which involves the maintenance of intracellular reactive oxygen and nitrogen species (RONS), is one of these processes that is significantly disrupted during HSV-1 infection in both epithelial cells (host cells for lytic infection) and neurons (latently infected cells). Disruption of redox homeostasis by HSV-1 in both cell types allows the increase in RONS generation, leading to greater RONS concentrations in spite of secondary reactions and enzymatic pathways. This increase in RONS, as a result, contributes to their activity in modulating cellular signaling pathways, the downregulation of cellular antiviral responses, and the promotion of an environment favorable for HSV-1 infection . This reRONS are potent intracellular signaling molecules with abilities to stimulate various cell signaling pathways, including those that regulate cell proliferation and apoptosis ,17,18,19To maintain redox homeostasis, RONS are constantly produced and destroyed in a healthy cell. The functions of RONS are attributed to their abilities to react with macromolecular structures. RONS are reactive species derived from oxygen and/or nitrogen-containing compounds, some containing an unpaired electron in the outer shell ,22. TherOxidative stress is often induced by viruses to overwhelm the infected cell and create an environment that favors its replication. For example, respiratory syncytial virus (RSV) upregulates RONS in airways, while simultaneously diminishing the host’s antioxidant system ,43. SimiLike RSV and HIV-1, HSV-1 induces intracellular oxidative stress conditions during infection ,48. HSV-HSV-1 is an enveloped virus, composed of an encapsulated genome surrounded by a host cell-derived lipid bilayer decorated with glycoproteins ,50. In tIn establishing a redox environment that favors HSV-1 replication, two envelope proteins have secondary roles in recalibrating oxidative stress responses in the cell. Glycoprotein B (gB), which mediates attachment to HS, also induces ER stress by interacting with the ER lumen domain protein kinase R-like ER kinase (PERK), a signaling transducer involved in the ER stress response to misfolded proteins during viral infections. To prevent ER-mediated oxidative stress and subsequent apoptosis, gB interacts with PERK to maintain homeostasis in the ER . HSV-1 gFollowing fusion of the viral and cellular membranes, compartmentalization of the encapsulated viral genome into host endosomes allows trafficking of the viral contents through the cytoplasm toward the nucleus in a pH-dependent process. The lower pH of the endosome facilitates the release of the capsid into the cytosol . HSV-1 tUpon entry into the nucleus, the HSV-1 genome is still at risk of detection and subsequent clearance by the cell. Therefore, HSV-1 continues to employ tactics to dodge these attempts at cellular immune detection by manipulating redox homeostasis. By upregulating the production of RONS in the cell, HSV-1 can modify key cell signaling proteins to dampen and negate innate detection mechanisms. For example, HSV-1 interferes with the cyclic GMP-AMP synthase/stimulator of interferon genes (cGas/STING), a nuclear DNA sensing pathway that induces potent type 1 interferon (IFN) responses. By directly modifying the STING protein via RONS production, HSV-1 can dampen the IFN response . HSV-1 aAs a DNA virus, HSV-1 diverts the cellular machinery in the nucleus to replicate its own viral genome and produce new progeny viruses. Once the cell replication machinery is taken over by HSV-1, the virus begins to express its genes in three distinct but overlapping phases: immediate early (IE), early (E), and late (L). All phases of viral gene expression involve viral and cellular factors ,72. FirsDuring the later events in viral replication, HSV-1 must evade cell detection mechanisms and interfere with signaling to negate anti-HSV-1 immune responses . InterfeCellular mechanisms are also manipulated by HSV-1 to promote virus assembly. Cellular proteins are preferentially degraded over viral proteins by HSV-1 induced expression of virus-induced chaperone-enriched (VICE) domains in the nucleus. In addition, heat shock protein 27 (Hsp27), a molecular chaperone that aids in protein folding, was found to be associated with VICE domains in the nucleus during HSV-1 infection. It is likely that the increased presence of Hsp27 is correlated with promoting viral protein folding in preparation for assembly ,85.Following HSV-1 genome replication, the virus begins the transcription of L genes that encode structural viral components required to assemble progeny virions. This assembly process is complex, requiring several viral proteins to form the capsid structure and initiate insertion of the newly replicated genome into the nascent virus particle ,87,88,89The alternative outcome of HSV-1 replication is a latent infection. Latency is a state in which virus gene expression and replication are silenced to escape cellular immune detection and clearance, and to allow for long-term persistence of the viral genome in the infected cell. During acute HSV-1 infection, progeny viruses produced by lytic infection of epithelial cells leave the mucosal epithelium, enter the trigeminal nerve, and latently infect the cell bodies of sensory neurons. Since genes associated with replication and viral assembly are expressed to a much lesser extent, patients exhibit no clinical symptoms of HSV-1 infection . The abiLatent infection of neurons begins with mechanisms that parallel those involved in lytic infection but quickly diverges as the virus enters the neuron. Like lytic infection of epithelial cells, initial attachment of the HSV-1 virion to neurons involves interactions between the envelope glycoproteins and cell surface proteoglycans, including HS and nectin-1 ,94. FollWhen HSV-1 reaches the cell body of the neuron, its genome rapidly condenses into heterochromatin via epigenetic mechanisms, making genes associated with lytic infection inaccessible to the host transcription machinery. Instead, the condensation of the genome facilitates the expression of a single transcript, the latency-associated transcript (LAT), expressed under a neuron-specific promoter ,99,100. The nervous system, where HSV-1 resides during latency, is highly susceptible to oxidative stress. In particular, the trigeminal ganglia at the base of the brain are characterized by high oxygen uptake and generally low levels of antioxidants ,103. ThePersistence of HSV-1 in the neuron and the associated chronic oxidative stress can result in damage to infected neural tissues. Although latency allows the evasion of HSV-1 from immune clearance, the host still tries to protect itself. Dissemination of HSV-1 to neural tissues can lead to oxidative stress from microglia cells, a common cell type in the nervous system involved in protective immune responses. They are cited as being major cytokine producers and influencing excessive inflammatory responses to HSV-1 infection. Over time, these stressful conditions, in some cases, can lead to serious disease such as encephalitis in the brain, resulting in neuronal death and irreversible brain tissue damage ,107,108.Reactivation from latency is a hallmark of HSV-1 infection and occurs periodically throughout the lifetime of an infected patient. This process involves the escape of HSV-1 virions from neurons and re-infection of the innervated epithelial cells within the mucosal epithelium via anterograde transport. Upon re-infection of epithelial cells, the ensuing productive infection of epithelial cells results in the re-appearance of clinical symptoms ,65,105.The process of reactivation has long been associated with an individual’s response to external and internal stress stimuli that trigger the escape of HSV-1 from neurons. Regardless of the stimulus, HSV-1 takes advantage of the stress stimuli to escape immune detection in order to re-infect epithelial cells within the mucosal epithelium, resulting in the re-emergence of clinical symptoms . While sBoth in vitro and in vivo models of HSV-1 infection have been used to demonstrate the effects of external stimuli in HSV-1 reactivation from latency. Many of these external stimuli are tied to the induction of oxidative stress responses that push the potential of reactivation of latent HSV-1. Early studies of HSV-1 reactivation focused on the delivery of electrical stimulation of the trigeminal nerve . A commoHerpesviridae family known to induce oxidative stress) can activate HSV-1 infection from latently infected cells [The reactivation of HSV-1 replication can also be affected by the onset of oxidative stress caused by intracellular stimuli. Reductions in the temperature or superinfection with cytomegalovirus -stimulated pathways that can activate or inhibit cellular responses to a pathogen . TLRs arPRRs play active roles in sensing and activation of the host cell immune response, including the generation of RONS. HSV-1 envelope glycoproteins are recognized by PRRs such as TLR2 and αvβ3 integrin on the plasma membrane. Specifically, gH/gL glycoprotein heterodimer was sufficient for this activation and subsequent immune response ,127. IntRONS produced during viral infection can lead to oxidative damage to cellular macromolecules, which can prompt immune responses toward the invading virus. Mitochondrial DNA (mtDNA) is a nucleic acid susceptible to RONS-mediated damage from virus-induced oxidative stress due to the lack of DNA repair mechanisms present in the mitochondria. DNA packing proteins that bind the mtDNA to maintain its structure and control the accessibility of genes can also be subjected to oxidative damage by RONS. Overall, damage to mtDNA serves as an antiviral sensor during infection and promotes the stimulation of innate immune responses  Figure .Following recognition of HSV-1 by innate sensors, the cell is prompted to create an antiviral environment to prevent efficient viral replication and assembly. Activation of the NF-κB pathway, known to be influenced by RONS, results in the production of proinflammatory mediators, primarily type 1 IFN, which promote inflammation and T cell responses. These molecules are important mediators in antiviral defenses ,130,131.As HSV-1 infection progresses, accumulation of RONS can result in the activation of inflammasomes  and autoImmune-associated RONS also have roles in the induction of adaptive immune responses. As a novel sensor for HSV-1, TLR3 is linked to the induction of antigen presentation by dendritic cells, which are involved in the induction of a robust CD8+ T cell response against HSV-1. TLR3-deficient mice demonstrate impaired HSV-1-specific CD8+ T cell responses and loss of control over HSV-1 infection in epithelial cells . TLR3 isThe potent antiviral capabilities of RONS have garnered interest in the development of RONS cocktails as antiviral therapies. RONS have demonstrated roles in the cellular antiviral response, and some species are currently utilized as the basis of disinfectant agents for the decontamination of surfaces.By itself, ozone inactivates a multitude of viruses. For example, ozone application to cell-free poliovirus-1, which is a single-stranded RNA virus, was shown to modify the protein sequences within the viral capsid, resulting in the impairment of viral adsorption. Furthermore, ozone damaged the viral RNA genome, leading to its inactivation . SimilarAs a common disinfectant, hydrogen peroxide is known to be a powerful antimicrobial agent against viruses and microorganisms. Hydrogen peroxide has antiviral effects against both cell-free and intracellular viruses. Specifically, hydrogen peroxide is virucidal against many viruses spread through contaminated surfaces in healthcare, veterinary, and public facilities. These include feline calicivirus (FCV), transmissible gastroenteritis coronavirus (TGEV), avian influenza virus (AIV), and swine influenza virus (SwIV) . AdditioSinglet oxygen is a short-lived RONS known to directly oxidize lipids containing carbon double bonds. Singlet oxygen is most effective against enveloped viruses (viruses enclosed by lipid envelopes). Singlet oxygen inactivates viruses by disrupting the viral envelope, which compromises its capacity to mediate entry into a target cell. In a singlet oxygen inactivated pseudorabies virus (PRV) vaccine, oxidized lipids within the virion structure also elicited a strong antibody-mediated response, while reducing PRV infectivity . SingletEndogenously, nitric oxide is a primary RONS involved in cellular antiviral defense. Following viral sensing by cellular PRRs, immune signaling pathways  are activated to promote the expression of proinflammatory cytokines that enhance the recruitment of innate immune cells to the site of viral infection. Additionally, immune signaling pathways involved in antiviral defense can further upregulate the expression of iNOS to promote nitric oxide generation . Nitric Although RONS are produced by and used by the cell in its antiviral defense, these species are often not produced in quantities sufficient to effectively control viral infections. Therefore, strategies that boost RONS and their effects have been explored as stand-alone antiviral therapies against viral infections. Many of the potentially therapeutic RONS cannot be produced artificially due to their stringent generation requirements and short half-lives. Furthermore, clinical approaches that use chemistry-based methods to generate RONS are often limited by the short half-lives of chemically active products and the identification of effective means to deliver them. These limitations can be addressed in therapeutic approaches involving the application of non-thermal plasma (NTP).The field of plasma medicine is defined by explorations of NTP as a tool for the controllable delivery of RONS to biological targets. Also referred to as cold atmospheric plasma (CAP), gas plasma, or low temperature plasma (LTP), NTP has emerged as a relatively safe therapeutic tool with applications in wound healing, cancer, and infectious disease . As the Redox homeostasis is an important determinant in the survival of cells. During viral infections, this homeostasis is challenged by oxidative stress induced by the cell to protect itself and by the virus to promote its replication and pathogenesis. Therefore, the redox state of the cell during a viral infection determines the likelihood of the cell being able to control the viral infection. Given the effective hijacking mechanisms of viruses, infections usually overwhelm the cell and allow the virus to utilize its machinery for its replication, which downregulates cellular DNA replication. With NTP and its controllable delivery of antiviral RONS, this redox state can be shifted back in the cell’s favor to promote interference with virus replication.While RONS are important mediators of antiviral activity, other components of NTP also contribute to its antiviral activity. By themselves, electric fields and UV radiation can kill microorganisms and can act as stand-alone therapies for treating infections ,166,167.NTP is generated and delivered to a biological sample through specialized devices. In research and clinical settings, two main types of devices are used. Dielectric barrier discharges (DBD) devices allow the direct application of NTP to samples. These devices consist of at least one electrode encased in a dielectric material to which a high voltage is applied. The counter electrode could be the biological substrate as shown in 3), singlet oxygen (1O2), hydroxy radicals (OH), hydrogen peroxide (H2O2), nitric oxide (NO), nitrogen dioxide (NO2−), peroxynitrite (ONOO−), and dinitrogen pentoxide (N2O5) have been implicated in pathogen inactivation [The composition of the RONS generated is also highly dependent on the power supply utilized to generate the NTP, as well as the sample being exposed. In the gas phase, the working gas and the parameters that result in NTP generation can influence the composition of RONS produced. As these species are delivered to a target, the interactions with the liquid-phase  can result in the formation of secondary species . Among ttivation ,163. TheThe antiviral activity of NTP-associated RONS has been clearly documented and summarized by Mohamed et al. . Due to The animal virus FCV, a surrogate for human NoV, has also been the focus of research to test the antiviral efficacy of NTP. Using an argon plasma jet, FCV inactivation was more apparent with shorter exposure distances and increasing power. A correlation between NTP exposure and the oxidation of FCV viral capsid mediated by the presence of NTP-generated RONS was suggested. Specifically, modifications of the capsid and viral inactivation were proposed to be influenced by the presence of RNS, ozone, singlet oxygen, and the formation of peroxynitrite . The antFew studies have investigated NTP as a potential therapeutic alternative for viruses that infect humans and cause chronic disease. Hepatitis B virus (HBV), a viral pathogen associated with a chronic liver disease, is spread through contact with bodily fluids. NTP, generated by a DBD device, was applied to blood samples infected with HBV. Key HBV antigens were susceptible to RONS-mediated damage following NTP exposure, leading to inactivation of the virus that increased with longer durations of exposure to NTP . The antThe antiviral effect of NTP on HSV-1 was investigated in a model for herpes keratitis, in which explanted HSV-1-infected human cornea cells were indirectly exposed to NTP. In these experiments, cell culture medium was exposed to NTP and then applied to the HSV-1-infected cells, resulting in an 80% reduction in viral infectivity and increased antiviral activity with longer durations of NTP exposure. Importantly, minimal host cell cytotoxicity was observed . An incrOral, ocular, and genital lesions are considered hallmarks for HSV-1 infection and appear when the virus is actively replicating in mucosal epithelial cells ,3. In anStudies to date have focused on the short-term antiviral effects of NTP using cell-free HSV-1 and HSV-1 infected cells, with changes in viral infectivity as the measure of the antiviral effect of NTP. The in vitro antiviral effect of NTP on HSV-1 infection was demonstrated through the application of NTP-treated cell culture medium to HSV-1-infected cells (indirect NTP) . There aNTP is proposed to act as an antiviral agent through the controllable delivery of RONS, which are known to interact and modify macromolecular structures. Proteins, lipids, and nucleic acids in enveloped viruses such as HSV-1 are likely susceptible to RONS-mediated damage. For example, cysteine, a sulfur-containing amino acid, is prone to disulfide bond modification by RONS . AdditioGiven the complexity of the HSV-1 structure, many viral components can be targets for damage caused by NTP RONS. As previously described, this damage will take the form of modifications to viral macromolecular components, leading to impairment of their functions. Due to the location of the envelope and envelope proteins on the exterior of the virus particle, reductions in virus infectivity attributable to NTP exposure are most likely to result from oxidative damage to the viral glycoproteins that mediate viral entry and modifications of lipids within the viral envelope that protect the encapsulated genome. Due to its chemical stability and hydrophilicity, hydrogen peroxide is an example of a RONS that can penetrate membranes . As a reRedox control in a cell is important for cellular homeostasis and preventing damaging oxidative stress conditions. In the context of HSV-1, there is a clear correlation between infection and the induction of oxidative stress . Part ofInduction of oxidative stress by HSV-1 can be observed through the recruitment of VICE domains, the localization of heat shock proteins, and the activation of the unfolded protein response (UPR) in the ER. To promote the assembly of proteins for viral replication, HSV-1 induces ER stress to target cellular proteins for degradation to promote the proper assembly of virions ,85. ThisAlthough NTP has a demonstrated antiviral effect on HSV-1, the mechanism by which RONS generated by the application of NTP to infected cells contributes to the antiviral effect of NTP is unclear. Based on reports cited above, it may be speculated that NTP exposure of HSV-1-infected cells could potentially stabilize the redox balance in a cell, diminishing oxidative stress mechanisms that favor replication.In addition to its antiviral activity, NTP has potential as an immunotherapy for HSV-1 infection. RONS, as one of the main effectors of NTP, have demonstrated immunomodulatory effects on cancer cells, inducing immunogenic cell death (ICD), characterized by cell death that elicits an immune response ,195. TheLiterature regarding immunomodulatory changes, or SAMPs, caused by NTP in virus-infected cells is sparse. Our work in this area demonstrated the immunomodulatory effect of NTP in the context of HIV-1 infection using J-Lat cells. These cells, which contain an integrated latent HIV-1 genome that is not capable of supporting multiple rounds of replication, serve as a model for latently-infected T lymphocytes. NTP exposure of J-Lat cells resulted in emission of molecules, characteristic of SAMPs that are typically associated with ICD in tumor cells. Furthermore, there was upregulated phagocytosis by antigen presenting cells, suggesting an adjuvant effect of NTP. There was also evidence of neoepitope generation that may increase the breadth of adaptive immune responses . SimilarInnate and adaptive immune responses play integral roles in the host defense against HSV-1 infection. As previously mentioned, TLRs recognize viral components within the HSV-1 virion on the plasma membrane and within the cytoplasm ,128,202.HSV-1 has developed many ways to overcome the cellular sensing and immune clearance tactics to persist in its host. As previously mentioned, this includes the manipulation of the cell’s redox state which can influence immune signaling and responses toward HSV-1. Application of NTP may be effective in overcoming viral evasion strategies through the generation of NTP-associated RONS that will enhance and stimulate more robust antiviral responses in the cell. Signaling pathways, such as NF-κB, which induce type 1 IFN responses, are upregulated by NTP exposure ,127,128.The use of NTP as a treatment for HSV-1 infection will require an integrated understanding of the roles played by oxidative stress during infection and treatment. Oxidative stress has multiple functions and effects in immune responses mounted by cells against infection, in HSV-1 replication in host cells, and in cellular and viral responses to NTP application . Some ofOnce considerations of NTP dose are satisfied, the potential net effects of immunomodulation induced by NTP application to an HSV-1-associated lesion are the promotion of more effective innate antiviral responses in infected cells, induction of innate protective responses in nearby uninfected cells, and recruitment of immune cells that will participate in a more effective adaptive response to infection . By contHSV-1 continues to be a global health concern, with high infection rates worldwide and its ability to cause lifelong infection. Although the current antiviral therapies are effective in moderately reducing the severity of symptoms associated with acute infection, they are ineffective with respect to curing a patient after primary HSV-1 infectious due to their inability to address viral latency. Therefore, HSV-1 persists in its host, risking dissemination of the virus to other organs and causing other acute and chronic diseases.For HSV-1, the manipulation of redox homeostasis is an evasion strategy to overcome immune clearance from the cell and to craft a cellular environment favorable for viral replication. On the other hand, maintenance of redox homeostasis by the cell is crucial in controlling the immune response directed toward HSV-1 and preventing damaging oxidative damage by RONS. Therefore, control over the redox balance within an infected cell is one of the key factors involved in determining the outcome of HSV-1 infection. Given the antiviral properties of RONS in the cell and in commercial disinfectant agents, RONS have the potential to be harnessed as antiviral therapies.NTP technology is an inexpensive, innovative method of producing RONS responsible for inducing desired biological effects. It has the potential to act as a multi-faceted therapy effective against HSV-1 disease. Decreases in HSV-1 infectivity attributed to the direct antiviral effect of NTP on cell-free virus will decrease the overall viral burden in the lesion, thereby decreasing the clinical symptoms of infection and accelerate resolution of the lesion. An added benefit of reduced titers in the lesion will be a smaller pool of latently infected neurons that serve as reservoirs of long-term infection. The immunomodulatory effects of NTP exposure will augment local innate antiviral effects and boost adaptive immune responses involving HSV-1-specific CD8+ T lymphocytes. The combined antiviral and immunomodulatory activities of NTP are hypothesized to provide short-term relief for acute infection as well as long-term immunological control over reactivation from latently infected neurons, as indicated by reductions in or full control of recurrent lesions in HSV-1-infected individuals.Of course, the potential of an NTP-based therapy must be fully explored along a developmental path that leads to clinical use. Future studies of the use of NTP as a therapy for HSV-1 infection will include examinations of how NTP can be used to treat lesions, a common manifestation of human HSV-1 infection. Using a preclinical murine model of HSV-1 infection, the antiviral and immunomodulatory effects of NTP on HSV-1 infection will be examined in the context of innate and adaptive, HSV-1-specific immune responses. These models will provide the insights necessary to develop this novel therapeutic as a non-invasive, pain-free, and effective clinical approach against HSV-1 infection.


Viet Nam experienced four waves of coronavirus disease (COVID-19) and by 30 September 2022 had recorded a total of 10.3 million cases and 43 057 deaths. . Designated COVID-19 treatment hospitals that had ICUs – “third-level” facilities – were reserved for patients with severe and critical COVID-19 disease.Fig. 1). However, on 9 July 2021, the city issued an absolute stay-at-home order to its citizens  from across the country were called upon to support the response in HCMC. . . As part of the reconfiguration, home care and ICFs were combined to deliver first-level care. Case numbers started decreasing in September 2021. During the 5 months from June to October 2021, HCMC reported a total of 430 209 cases and 16 551 deaths . , the following evidence-based measures and support systems were introduced in HCMC to address the surge in COVID-19 cases during the fourth wave and minimize the impact on the health system: (In addition to the timely reconfigurations of the care pathway described above (The government mobilized 133 000 military personnel and 126 000 police officers.HCMC established 536 mobile health stations to provide rapid tests, vaccinations, first aid and referrals.A “network of physician companions” consisting of more than 10 000 medical staff and volunteers across the country provided counselling, health education and psychological support to COVID-19 patients and their families.By providing staff and equipment, central hospitals across the country helped to establish 12 ICU centres in HCMC, creating 7900 beds including 3000 intensive care beds.A warehouse was set up to supply medicines, equipment and other medical consumables to localities across HCMC.An online dashboard for real-time bed occupancy was established and made available to the public by the HCMC health department. This allowed the public to make more informed decisions about which hospital to go to for treatment.All hospitals, both public and private, were instructed to make up to 40% of their beds available for COVID-19 patients during the fourth wave.Interim guidance was issued on home care and the organization of mobile health stations, including the proper use of medication in home care.Political party authorities and other political leaders enhanced community engagement on COVID-19 response measures.Furthermore, government authorities issued updated guidance to support adaptations to the care pathway in response to the evolving pandemic. (In August 2021, at the peak of the fourth wave, HCMC recorded almost 8500 cases of COVID-19 in one day. Throughout the outbreak, both HCMC and the Viet Nam Ministry of Health demonstrated a strong commitment to combating COVID-19 and acted in a timely manner to address the increased demand for COVID-19 case management and referrals using a multisectoral approach. In HCMC, the COVID-19 care pathway was reconfigured several times, while thousands of HCWs and volunteers were mobilized to staff new mobile health units, the expanded ICU capacity and the many thousands of COVID-19 beds that were created as part of the COVID-19 response.During the fourth wave of the pandemic, HCMC shifted away from a centralized, hospital-based case management model towards an integrated care pathway that used multiple levels of health care, including home care and ICFs. Based on the overall framework issued by the Ministry of Health, HCMC continually adapted its care pathway to respond to the real-time ongoing situation. This flexible approach to the care pathway ensured that the health-care delivery system was able to treat the “right patient at the right time,” while avoiding being overwhelmed. High-level commitment and leadership ensured that both the government and society did their part to respond to the pandemic. This was key to HCMC’s successful response to the fourth wave of COVID-19. (In conclusion, the ability to make timely adjustments to the care pathway in response to rapidly changing local contexts through multisectoral engagement and high-level commitment helped Viet Nam to mount a successful COVID-19 pandemic response without overwhelming the health-care system.


Enterococcus faecalis is one of the major threats to global health at present. While aminoglycosides are often used to combat these infections, their effectiveness is reduced by various resistance mechanisms, including aminoglycoside modifying enzymes, and there are currently no drugs to inhibit these enzymes. To address this issue, this study was conducted to identify potential aminoglycoside adjuvants from a database of 462 flavones. The affinity of these molecules with the nucleotide-binding site (NBS) of aminoglycoside phosphotransferase type IIIa of E. faecalis (EfAPH(3’)-IIIa) was evaluated, and the five molecules with the highest binding energies were identified. Of these, four were naphthoflavones, suggesting that their backbone could be useful in designing potential inhibitors. The highest-ranked naphthoflavone, 2-phenyl-4H-benzo[h]chromen-4-one, was modified to generate two new derivatives (ANF2OHC and ANF2OHCC) to interact with the NBS similarly to adenine in ATP. These derivatives showed higher binding free energies, better stability in molecular dynamics analysis and superior pharmacokinetic and toxicological profiles compared to the parent molecule. These findings suggest that these alpha-naphthoflavone derivatives are potential inhibitors of EfAPH(3’)-IIIa and that this core may be a promising scaffold for developing adjuvants that restore the sensitivity of aminoglycosides.The spread of nosocomial infections caused by antibiotic-resistant  Enterococcus faecalis, are a major concern in the hospital environment chromen-4-one, ANF, an alpha-naphthoflavone, showed the best score over the whole flavone dataset. The polar groups added on the backbone of ANF resulted in the derivatives, ANF2OHC and ANF2OHCC, with higher binding free energy, improved stability in MD simulation analyses, and better pharmacokinetic and toxicological profiles. In addition, the alpha-naphthoflavone backbone participates in interactions that are largely responsible for its high affinity for the active site of the enzyme, including interactions with key amino acids for its activity. Taken together, these results present new potentials adjuvants to aminoglycosides and reveal that the alpha-naphthoflavone core may be a promising new scaffold for the development of APH3’-IIIa inhibitors of E. faecalis to restore its sensitivity to aminoglycosides.Due to the limited efficiency of existing antibiotics to circumvent


Consumer Staples and Industrials are the significant risk transmitters, whereas Financials and Real estates are the most significant risk absorbers. Our findings also indicate that Real Estate, Health Care, and Financials record the most significant increase in volatility of more than 300 per cent. Policy implications regarding risk management across Australian sectors have emerged, particularly during extreme events such as the pandemic.While spillover across equity markets has been extensively investigated, volatility spillover across sectors has largely been under-examined in the current literature. This paper estimates the sectoral volatility using the ARMA-GARCH model and its spillover across Australian sectors on the VAR framework during the 2010–2021 period. We then identify breakpoints in market volatility during the Covid-19 pandemic using a wavelet methodology. We find that volatility spillover across Australian sectors is very significant at 60 per cent from 2010 to 2019, reaching 90 per cent during the Covid-19 pandemic in 2020. The spillover then reverts to its pre-pandemic level in 2021.  The Australian economy is ranked 13th globally using nominal GDP and 18th using the PPP-adjusted GDP in 2020 . These GDP rankings are undoubtedly due to the contributions of various economic sectors in Australia. Over the 2010–2020 period, the services sector is a major contributor to the Australian GDP with an average share of approximately 66 per cent, followed by industry and agriculture with an average GDP share of approximately 24 per cent and 2 per cent, respectively. Furthermore, the contribution of these sectors to the Australian economy has remained stable for the past ten years, further highlighting the importance of the services sector in the Australian economy.However, such stability was interrupted by the emergence of the Covid-19 pandemic, causing the Australian economy into a recession for the first time in nearly three decades in September 2020. The country’s GDP dropped by 7 per cent in the June 2020 quarter, which became the worst fall ever to the Australian economy . The decline in Australia’s GDP results from a significant decrease made by the services-related industries within the services sector. The downturn of various industries is usually linked with significant volatility in the financial market. Specifically, Australia’s stock market index (ASX200) and selected sector indices have significantly fluctuated throughout the three Covid-19 waves in Australia, as illustrated in Later, the Australian stock market exhibited signs of recovery in the second and third Covid-19 waves. The second wave occurred from June to late October 2020 as Victoria entered a four-month strict lockdown due to the virus outbreak in Melbourne. Meanwhile, the third wave lasted from June 2021 to the end of the year, witnessing the occurrence of the Delta variant in June 2021 and the Omicron variant in early December 2021. The outbreak of the Delta variant had left nearly half of Australia’s population and major cities in lockdown until the vaccination covered 90 per cent of the population in December 2021. Since then, the Australian government has gradually lifted the lockdown despite the occurrence of the Omicron variant in early December 2021. The end of the lockdown in the latter two waves has caused the volatility, though still significant, to be much less volatile than in the first wave. Notably, the Omicron variant appears to have little effect on the volatility because Australia has effectively opened since November 2021.Unlike previous studies, this study is conducted to achieve the following research objectives. First, we estimate the market volatility across 13 Australian sectors during 2010–2021 and identify the most vulnerable sectors to previous shocks using the ARMA-GARCH model. We then investigate the market volatility spillover across these sectors for the entire research period and then compare it between the pre-Covid-19 pandemic and the during-Covid-19 pandemic. This analysis allows us to identify the sectors considered risk transmitters or receivers among Australian sectors. We use the VAR framework for this analysis. Using the wavelet methodology, our study is then extended to determine the breakpoints in market volatility across the Australian sectors during the Covid-19 pandemic.First, the market volatility across sectors has been under-examined, especially during the Covid-19 pandemic. A paper by Choi et al. [Second, previous studies have examined the volatilities across equity markets and their connectedness. However, these studies have ignored the volatility patterns and their transmission across sectors. Using the wavelets transform methodology. Our study estimates the magnitude to which market volatility changes across sectors at the breakpoints during the Covid-19 pandemic. The wavelet transform approach, widely applied in the image and signal processing area of research, has hardly been used in business studies, particularly in market volatilities and their spillovers. Third, we investigate the change in the risk level for various investment holding periods using the multiresolution analysis.The contributions of this study are threefold. i et al.  focuses i et al.  analysisFollowing this introduction, the remainder of the paper is structured as follows. Section 2 discusses and synthesizes relevant theories and empirical evidence on the issue. Section 3 presents the research methodology and data. Section 4 discusses empirical results, followed by the concluding remarks and policy implications in section 5 of the paper.Several studies have examined the market volatility and the spillover effects across markets, especially the effects of the Covid-19 pandemic on the market risk in the past two years. These analyses have employed the Generalized Autoregressive Conditional Heteroskedasticity (ARCH/GARCH) models and the Vector Autoregression (VAR) models , 6, 8–13Laborda and Olmo  considerScholars have compared the volatility spillover during the Covid-19 pandemic and the global financial crisis (GFC). Choi  reports Ghorbel and Jeribi  observe Many scholars have examined the stock market’s sectoral volatility and spillover effects. For instance, the volatility connectedness across ten sectors in China’s stock market is examined in Su and Liu’s  analysisIn the Australian context, Choi et al.  examinedOur literature review indicates that while spillovers across financial markets and products have been intensively investigated, volatility spillovers across the Australian sectors have been under-examined, particularly under extreme events such as the Covid-19 pandemic. Besides, various techniques should be used to ensure the robustness of the findings, such as the ARMA-GARCH model for estimating volatility, the VAR framework for investigating volatility spillovers and the wavelet methodology for identifying breakpoints in volatility across sectors. These gaps warrant our analysis, as discussed in the following sections, to be conducted.Consumer Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information Technology, Materials, Metals and Mining, Real Estate Investment Trust (REIT), Resources, Telecom Services, and Utilities. In addition, the sub-period of 2020–2021 is also used to examine the impacts of the Covid-19 pandemic on market volatility across Australian sectors. All data are collected from Thomson Reuters.Daily closing prices of thirteen Australian sectors’ indices are used for 12 years, from 2010 to 2021. These sectors include We employ the ARMA-GARCH model to estimate the market volatility and to determine the volatility patterns across 13 Australian sectors. We note that the GARCH model will likely capture both volatility clustering and heteroskedasticity of the variables. The GARCH mode is generally considered more advantageous than the ARCH model in estimating the residuals with unstable magnitude obtained from the AR or ARMA model . We alsoit denotes the closing price of the index i at time t, Xit represents the change in the price of the index i at time t.Where: Dr) and MA (s) are determined using the Bayesian Information Criterion (BIC) maximization.Bi stands for the backshift operator. βi and γi represent the coefficients of the auto-regressive (AR) and moving average (MA) parts of the ARMA model. εt denotes the residual that is expected to be independently and identically distributed.The optimal lag length of AR (Q) test [m stands for the degree of freedom to examine the model stationarity. j. n denotes the number of observations.Next, tests for the heteroskedasticity of the dataset to confirm the ARCH effect are conducted using the Ljung-Box portmanteau (Q) test .Q=n—GARCH models to extract the conditional variance (market volatility). The GARCH is described as follows:The null hypothesis of this test is that the series follows a white noise process, implying no ARCH effects. When the null hypothesis is rejected, the return series have ARCH effects. As such, these findings warrant our next step to use the ARMA and Health Care (β of 0.913). In contrast, volatility in this period from Telecom Services is least affected by volatility from previous periods (the lowest β of 0.774).This section presents and discusses the market volatilities for 13 Australian sectors. Energy has experienced the longest mean reversion (with the highest sum of (α + β) of 0.997). Meanwhile, Telecom Services has the fastest mean reversion with the lowest absolute value of (α + β) of 0.905.Besides, the absolute value of (α + β) presented in the last row of Tables We then estimate volatility across sectors using the same ARMA-GARCH approach for the Covid-19 period of 2020 and 2021 in Australia. As presented in Energy, Resources, and Telecom Services are the most volatile sectors in Australia. Meanwhile, the volatility patterns for the remaining 10 Australian sectors are quite stable. However, with the Covid-19 pandemic at the beginning of 2020, major shocks have been observed in all 13 Australian sectors.Energy is the most severely affected sector, which experiences the highest volatility, represented by its variance of approximately 0.75 per cent per day. REIT follows with the sector’s volatility hitting its peak at approximately 0.6 per cent during the pandemic. In contrast, Consumer Staples and Utilities are the most resilient sectors to the Covid-19 pandemic. These sectors record the lowest market volatility during the 2020–2021 pandemic at the volatility’s peak of approximately 0.15 per cent. Consumer Staples play an indispensable part in daily life. As such, these sectors are least vulnerable to the pandemic shock. In contrast, Energy and REITs are severely affected by quarantine, social distancing, reduction in investment and international trade, and loss in income. As such, these sectors are severely impacted and volatile during the Covid-19 period.Specifically, the volatility patterns across the Australian sectors illustrate the impacts of the Covid-19 pandemic on every Australian. The spike in each sector is observed at around 2,700, which is at the beginning of 2020. After observing the volatility patterns experienced by Australian sectors using the ARMA-GARCH model, we now analyze the changes in the market volatility of all 13 Australian sectors at major breakpoints determined using the wavelet methodology. The wavelet technique, widely used in the image and signal processing area of research, decomposes the entire market returns into smaller components. As a result, many breakpoints are identified during the 2010–2021 period. However, we note that the changes in volatility are not significant between pre- and post-breakpoints compared to the breakpoints identified during the Covid-19 pandemic in 2020 and 2021. As such, this section examines the changes in volatility between the pre-and post-breakpoints during the Covid-19 pandemic across 13 Australian sectors.Australia recorded the first Covid-19 case on 25 January 2020. To date, three Covid-19 waves in Australia have been generally known. The first and second Covid-19 waves cover the period from 1 March 2020 to 1 May 2020 and from 19 June 2020 to 26 October 2020. The third wave covers the period from 18 June 2021 to December 2021. This period occurred in 2021 with the emergence of the Delta variant and, briefly, the Omicron variant in December 2021. In our analysis, the period from the first Covid-19 case recorded (25 January 2020) until the beginning of the first wave in Australia (29 February 2020) is named pre-Wave 1.Utilities, occurred during pre-wave 1 of the pandemic. The market volatility of these 12 sectors substantially increases from 63 per cent to 362 per cent between the pre-and post-breakpoints. The first breakpoints across these sectors identified in February 2020 indicate that all sectors suffered from a drastic increase in volatility after the breakpoint, compared to the pre-breakpoint volatility, leading to a substantial increase in the market volatility for each Australian sector.REIT, Health Care, and Financials record the most significant increase in volatility of more than 300 per cent between the pre-and post-breakpoints. In contrast, Telecom Services and Utilities experience the lowest volatility increase of approximately 60 per cent between the pre-and post- breakpoint. It is also interesting to note that Resources appears to be the first Australian response to the pandemic when its first breakpoint was identified on 28 January 2020, three days after Australia recorded the first Covid-19 case. Besides, Utilities appear to be the last sector responding to the pandemic when its first breakpoint was identified in March 2020 during the Covid-19 wave 1 in Australia.During the pre-wave one period, Consumer Staples and Resources continue experiencing a significant increase in volatility between the pre-and post-breakpoint of 86 per cent and 68 per cent, respectively. The first breakpoint is identified for Utilities, which experiences an increase in volatility of 71 per cent between the pre-and post-breakpoint. We note that four sectors, including Consumer Staples, Metals & Mining, Resources, and Utilities, have experienced two breakpoints during the first Covid-19 wave in Australia during March-April 2020 period. However, the change in volatility of these sectors for the second breakpoint is relatively insignificant, except for Utilities, with a change in volatility of 29 per cent.However, the second breakpoint, which is a coincidence with the Covid-19 wave 1, is only identified in six Australian sectors. The breakpoints in eight Australian sectors are also identified during the second Covid-19 wave in June-October 2020. However, it is noted that the changes in volatility between the pre-and post-breakpoint are small compared to the first Covid-19 wave. Interestingly, no breakpoint was identified during Covid-19 wave 3 in Australia in 2021. After the first few shocks, the Australian stock market seems familiar with the Covid-19 pandemic. The impacts of Covid-19 on the market volatility are reduced over time, as illustrated in In this section, we examine the spillover effects among 13 Australian sectors over the entire period from 2020 to 2021, employing the vector autoregression (VAR) methodology. All market indices for 13 Australian sectors are tested for stationarity to confirm that employing the VAR model is appropriate. We use the augmented Dickey-Fuller (ADF) test to test the stationarity. All series employed in the model is stationary. Results are available upon request.to each sector. Meanwhile, the “From Others” column presents the sum of the volatility received from each sector. The “NET" directional connectedness row shows the difference between the volatility transmitted and the volatility received for each sector. The total spillover index (TSI), which is also known as the total connectedness index (TCI), is the number in the lower right-hand corner, which is shaded. The TSI reflects the total average spillover effects across all Australian sectors.The total spillover index (TSI) of all 13 Australian sectors is approximately 64 per cent during 2010–2021. The TSI result indicates that the spillover effects across sectors are substantial and that the inter-sector connectedness among these sectors is strong. This finding implies that risks from the Australian stock market appear to spread quickly across these 13 sectors.Consumer Staples, Industrials, and Telecom Services are the major risk transmitters in the Australian stock market because of their most prominent “NET” values in the volatility transmission network. These figures are 69.37 per cent, 35.19 per cent, and 34.02 per cent, respectively. These sectors are considered the main sources of risk transmission, implying that when significant stock price volatility emerges, the market volatility will spill over to other sectors rapidly. Identifying and considering these characteristics when designing appropriate measures can help policymakers avoid market failure or mitigate the negative impacts on the financial market. In contrast, Financials, REIT, and Consumer Discretionary are respectively the three most significant risk absorbers .Based on the net spillover effect results presented in Consumer Discretionary, and Financials are the significant risk absorbers during the entire period. In contrast, Consumer Staples and Industrials are the main risk transmitter over the research period with consistently positive spillover effects.Consumer Staples is always the risk transmitter because it is the most resilient sector during the pandemic. Health Care switches from the risk transmitter to the largest risk absorber after the emergence of the pandemic. Utilities changes from a relatively neutral sector to a risk absorber during the Covid-19 period.We now shift our attention to the effects of different investment holding periods (investment horizons) on volatility across 13 sectors in Australia using the wavelet methodology. The changes in each sector variances among several holding periods indicate the market risk in terms of volatility that investors must bear when holding the stocks of the relevant sector. We have achieved consistent findings across 13 Australian sectors concerning the relationship between volatility and investment holding periods.Consumer Staples and Utilities, the two most resilient sectors to the Covid-19 pandemic, as analyzed in the ARMA-GARCH estimation. Also, Consumer Staples is the most significant risk transmitter in the spillover network of 13 Australian sectors. The next three selected sectors are REIT, Financials, and Health Care, the most severely affected sectors by the pandemic that record the largest increase in volatility of more than 300 per cent, as presented in REIT and Financials are the two largest risk absorbers in the spillover network, while Health Care switches from the risk transmitter to the largest risk absorber during the Covid-19 period. The final selected sector is Energy, the most volatile sector over the entire period and has the highest volatility spike during the Covid-19 period, as illustrated in In this section, we select six Australian sectors as illustrations. The first two sectors are the increase in the stock’s variance of each sector’s 2-day, 4-day, and 8-day holding periods between pre-and post Covid-19 breakpoints identified in the variance of each sector’s stocks of different investment horizons of 2-to-4, 4-to-8, 8-to-16 days, and the like.Our empirical results indicate that the longer the investment horizon investors hold, the lower the risk in terms of market volatility that the investors must bear. Especially the investment horizons from 2 to 8 days witness the most significant volatility. These findings imply that the short-term investment holding period of 2 days experiences the most extreme risk across the Australian sectors during the Covid-19 pandemic in Australia.Consumer Staples and Utilities, are approximately doubled between pre- and post-breakpoints caused by the Covid-19 pandemic. Meanwhile, the variances increased more than four times in the three highest volatility recorded sectors, including REIT, Financials, and Health Care. The largest variance recorded in these sectors is approximately 0.0005 per day. The variance changes by the level also confirm that the holding period from 2-to-4 days exhibits the largest price volatility, which is three times larger in variances compared to the next holding period of 4-to-8 days. Energy, the most volatile sector over the entire period, recorded the most significant variance for the 2-to-4 days holding period of 0.00012 per day.Specifically, our result from Market volatility and its spillover across equity markets have been extensively investigated. However, market volatility spillovers across various Australian sectors have largely been ignored in the literature. Australia is a small open economy where the economic performance of the sectors and the entire economy is significantly volatile in a major event such as the Covid-19 pandemic. As such, this research estimates the market volatility across 13 Australian sectors from 2010 to 2021 to identify the most vulnerable sectors using the ARMA-GARCH model. The market volatility spillovers across the Australian sectors are then examined using the VAR framework for the pre-Covid-19 pandemic and the during-Covid-19 pandemic. Using the wavelet methodology, we also determine the breakpoints in market volatility across the Australian sectors to understand how significant risks have been changed during the Covid-19 pandemic. Key findings from our analysis can be summarized as follows.Consumer Staples are the most affected sector by the volatility from previous periods. Information Technology and Health Care are also significantly affected. In contrast, our results indicate that Telecom Services are the least affected sector. Energy exhibits the longest mean reversion, whereas Telecom Services exhibits the fastest.First, our empirical analysis from the ARMA-GARCH model indicates that Consumer Staples, Industrials, and Telecom Services are the key risk transmitters. In contrast, Financials, REIT, and Consumer Discretionary are the most significant risk receivers.Second, the total spillover index, indicating the inter-sector connectedness among these 13 Australian sectors, varies between 60 per cent and 70 per cent during the pre-Covid-19 period, from 2010 to 2019. This volatility spillover level indicates that risks from the Australian stock market appear to spread quickly across sectors. The volatility spillover increased to 90 per cent during 2020 when the Covid-19 pandemic emerged. Australia recorded the first Covid-19 case on 25 January 2020. However, the volatility spillover reverted to the pre-pandemic level in 2021 when Australia opened the entire economy without restrictions by the end of 2021. Our analysis also confirms that, among all 13 Australian sectors, REIT, Health Care, and Financials record the most significant increase in volatility of more than 300 per cent between the pre-and post- breakpoints during pre-wave 1. Resources were the first Australian sector to respond to the pandemic when its first breakpoint was identified on 28 January 2020, three days after Australia recorded the first Covid-19 case. Besides, Utilities appear to be the last sector to respond to the pandemic.Third, our wavelet methodology analysis identifies various breakpoints during the Covid-19 pandemic. Three Covid-19 waves have been identified in Australia. The first and second waves cover the March-April 2020 (Wave 1) and June-October 2020 (Wave 2) periods, respectively. The third wave covers July to December 2021 (Wave 3). Market volatility breakdown are identified in 12 Australian sectors for the pre-Wave 1 covering January-February 2020. However, no breakpoint is identified in wave 3. We find that Fourth, our analysis finds that the longer the investment horizon investors hold, the lower the risk in terms of market volatility. Holding investments for only two days or four days shows the largest volatility. These findings imply that the short-term investment holding period of 2 days experiences the most extreme risk across the Australian sectors during the Covid-19 pandemic in Australia.Policy implications have emerged based on the results of our study. The services sector, the most significant contributor to the Australian economy, has been hit hardest among all other Australian sectors. The Australian economy is small and open. As such, external shocks can easily and quickly spread to the economy. Given the Australian economy’s size, albeit ranked 13th globally, Australian sectors are significantly vulnerable to external shocks. The services sector then becomes the target for these external shocks. As such, the Australian economy will need to consider diversification strategies to limit the risks from external shocks to the economy. In addition, our empirical results identify Consumer Staples, Industrials, and Telecom Services as the main risk transmitters and Financials, REIT, and Consumer Discretionary as the most significant risk receivers. Therefore, portfolio managers and investors may need to consider diversification of their investment portfolios when these sectors are considered. Once again, the Australian sectors, particularly REIT, Health Care, and Financials sectors, have experienced very significant volatility in response to an extreme event such as the Covid-19 pandemic. Investors may take notes for their investment strategies.Our study exhibits limitations. The clear mechanism of volatility spillovers across Australian sectors should be established to highlight the most significant factors causing these spillovers. Future studies may focus on a particular industry to understand how risk can transmit or receive from other sectors and to identify the sector as the starting point for volatility spillovers to star. Findings from such studies will provide immense implications for managers and practitioners in understating the nature of risks in the Australian financial market.