This study aimed to assess the cost-effectiveness of COVID-19 vaccines, preferred COVID-19 vaccine profiles, and the preferred vaccination strategies in Thailand.An age-structured transmission dynamic model was developed based on key local data to evaluate economic consequences, including cost and health outcome in terms of life-years (LYs) saved. We considered COVID-19 vaccines with different profiles and different vaccination strategies such as vaccinating elderly age groups (over 65s) or high-incidence groups, i.e. adults between 20 and 39 years old who have contributed to more than 60% of total COVID-19 cases in the country thus far. Analyses employed a societal perspective in a 1-year time horizon using a cost-effectiveness threshold of 160,000 THB per LY saved. Deterministic and probabilistic sensitivity analyses were performed to identify and characterize uncertainty in the model.COVID-19 vaccines that block infection combined with social distancing were cost-saving regardless of the target population compared to social distancing alone (with no vaccination). For vaccines that block infection, the preferred (cost-effective) strategy was to vaccinate the high incidence group. Meanwhile, COVID-19 vaccines that reduces severity  were cost-effective when the elderly were vaccinated, while vaccinating the high-incidence group was not cost-effective with this vaccine type. Regardless of vaccine type, higher vaccination coverage, higher efficacy, and longer protection duration were always preferred. More so, vaccination with social distancing measures was always preferred to strategies without social distancing. Quarantine-related costs were a major cost component affecting the cost-effectiveness of COVID-19 vaccines.COVID-19 vaccines are good value for money even in a relatively low-incidence and low-mortality setting such as Thailand, if the appropriate groups are vaccinated. The preferred vaccination strategies depend on the type of vaccine efficacy. Social distancing measures should accompany a vaccination strategy.The online version contains supplementary material available at 10.1007/s10198-022-01505-2. The COVID-19 pandemic will not end until the global roll-out of vaccines is fully realized and when people from all corners of the world are directly or indirectly protected against the virus. Although national health regulators around the globe have authorized emergency use of COVID-19 vaccines at a very fast pace, having licensed vaccines is not enough to achieve global control of COVID-19 [Currently, there are more than a hundred COVID-19 vaccine candidates in clinical studies, with 15 approved for human use . ApproveEconomic evaluation can be usefully employed to address such above questions, by systematically comparing the relative costs and consequences of different vaccination strategies. This type of analysis is very relevant to high-income and upper-middle-income countries, which will need to pay for the full cost of COVID-19 vaccines. They should be well-prepared for short and long-term procurement plans, including vaccine selection and pricing negotiations, to ensure that the significant investment in a COVID-19 vaccine justifies its opportunity costs from displaced investment in the health sector or across public programs.This paper shares the methodological approach and results from an economic evaluation conducted to inform policy decisions regarding COVID-19 vaccination in Thailand. Thailand implemented Universal Health Coverage Scheme in 2002 . ThailanWhile the interventions at the early stage were effective in containing COVID-19, these interventions also imposed economic losses as well as inconvenience to people’s daily life. COVID-19 vaccine was considered as the solution to concur COVID-19; however, COVID-19 vaccines did not arrive in Thailand until February 2021 . In addiAn age-structured epidemiological model developed by the Covid-19 International Modelling Consortium (CoMo consortium) was adapted by Luangasanatip N et al. , 8 to esAlthough Thailand has methodological guidelines for conducting economic evaluations of healthcare interventions, it was agreed that certain aspects of these guidelines were not appropriate for evaluations of COVID-19 vaccines during stakeholder engagement meetings. This issue was discussed further in a related methodological principles research article for the Thai and Singaporean economic evaluations of COVID-19 vaccination .Direct medical costs for COVID-19 infection treatment were retrieved from electronic individual records of the e-claim database of the National Health Security Office (NHSO) [Direct non-medical costs related to quarantine costs were obtained from the Department of Disease Control (DDC) [Indirect costs from implementing non-pharmaceutical interventions (NPIs) including the impact of social distancing on the economy was estimated using gross domestic product [The cost analysis was done from a societal perspective. Cost parameters included direct medical costs, direct non-medical costs and indirect costs. e (NHSO) . Due to e (NHSO) , and cose (NHSO) . The proe (NHSO) . The nume (NHSO) . Costs fe (NHSO) . Costs fe (NHSO) , 18. Cose (NHSO) . The incol (DDC) , includi product , the sto product , and oth product . All cos product  and presOutcomes were measured in LY saved by using the age-stratified life loss, using the Thailand standard lifetable .The primary results of the analysis were incremental cost-effectiveness ratios (ICERs), in THB per LY saved. We used the ceiling threshold of 160,000 THB per LY saved in our analysis, as recommended by the Subcommittee for the Development of the National List of Essential Medicines (NLEM).Sensitivity analyses were performed to account for parameter uncertainty. A one-way deterministic sensitivity analysis (DSA) was conducted by varying one parameter at a time, while other parameters remain unchanged, to understand the impact of each individual parameter on the net monetary benefit (NMB) using a ceiling threshold of 160,000 THB per LY saved. We focused on the parameters listed in Table Figure If the vaccine reduces severity, under the scenario without social distancing, vaccinating the elderly and the general adults were cost-effective. Vaccinating the elderly was preferred over vaccinating adults with lower incremental cost and more life years saved, e.g. triangle E versus triangle D. Social distancing alone provided better value for money compared to vaccination, i.e. dotted no vaccine versus triangles. When social distancing was implemented, only vaccinating the elderly represented good value for money. A figure using the strategy of no vaccine combining with social distancing as the comparator is presented in Supplementary Information 3 Fig S4 Panel B.Breakdown costs for selected scenarios are presented in Table Of the considered cost components, the leading cost was due to quarantine, which contributed approximately 30–70% of the total policy costs. Vaccine-related costs, including vaccine acquisition, vaccine supply chain, vaccine administration, and medical costs for vaccine-related adverse events, were relatively small compared to, for example, contact tracing and COVID-19 treatment costs.Figure Results exploring the impact of using different ceiling thresholds are presented in Supplementary Information 3 Fig S6. If vaccines block infection, all vaccination strategies yield positive NMBs at any ceiling threshold compared to no-vaccine scenario. Nevertheless, if vaccines reduce severity, offering vaccines to the elderly yields positive NMBs at ceiling thresholds higher than 20,000–100,000 THB per LY depending on levels of vaccine efficacy and protection duration.Figure Focusing on the strategies with social distancing, three strategies were compared in the PSA, including no vaccine, vaccinating elderly, and vaccinating high-incidence group. The PSA results, in Fig. p) and probability of reductions in severity (1 – p) at a fixed level of overall efficacy. We assumed that the vaccine will either block infection for the vaccinated population or reduce severity for the vaccinated population. The expected incremental NMB was calculated as a linear combination of the incremental NMB of the scenario with the vaccine blocking infection and incremental NMB of the scenario with the vaccine reducing severity. Specifically, as shown in Fig. As the actual profile of the vaccine, e.g. vaccine type, efficacy, protection duration, were unknown to the policymakers at the time point when they need to make decisions, we explored the expected incremental NMB of vaccinating high-incidence groups and vaccinating elderly groups with vaccines with different probability in blocking infection  by considering a range of probabilities that vaccines can block infection using a ceiling threshold of 160,000 THB per LY, as shown in Fig. This study is the first economic evaluation of COVID-19 vaccines to inform policy decisions in an upper middle-income country with relatively low incidence and low mortality. From a societal perspective, if vaccines block infection, then vaccines are cost-saving for all population groups compared to no vaccination. If vaccines would only reduce severity, then vaccinating elderly groups is the preferred strategy. Although these findings indicate that cost-effectiveness of vaccination strategies depends on vaccine type; all vaccine efficacy trials, except AZD1222 (Oxford/AstraZeneca) trials , were noThis local study applied a framework for "collaborative modelling for effective policy implementation and evaluation" through iterative stakeholder participation and discussion in order to enhance trust, accountability, and public ownership to decisions . Series Our results are in line with the previous findings in the United Kingdom (UK) , the UniThe results of this study must be interpreted with caution. Notably, the mathematical model was matched to the projected data of Thai population’s contact patterns  and natiMoreover, the vaccines considered in the analyses were hypothetical, based on the WHO’s TPP characteristics. The results of this study cannot directly correspond to individual vaccines currently available in the global market. We selected this approach because it allows decision-makers assessing the value for money of new vaccines, or existing vaccines as new information becomes available. Although this study adopted a societal perspective, we did not include the productivity loss due to COVID-19 infected patients. This will not alter our findings and conclusion given the small size of people infected in the country. Also, this study considered relatively low costs of social distancing, when compared to the UK study which applied a much higher societal cost for NPIs (1% compared to 2% of GDP) . We alsoCOVID-19 vaccines are likely to be cost-saving or cost-effective in resource-limited settings with limited or low-level community transmission. Regarding the target population, the preferred vaccination strategy depended on the type of vaccine efficacy realized. High-incidence groups should be vaccinated if the probability of vaccines blocking infection is high. Elderly groups should be prioritized over high-incidence groups if the only effect of vaccines is to prevent severe infections among COVID-19 cases.Supplementary file1 (DOCX 21 KB)Supplementary file2 (DOCX 1847 KB)Below is the link to the electronic supplementary material.


We combined OPLS-DA with machine learning modelling to identify metabolites discrimination of the treatment groups. Both phenol-enriched OO interventions decreased the levels of glutamine, creatinine, creatine, dimethylamine, and histidine in comparison to VOO one. In addition, FOOT decreased the plasma levels of glycine and DMSO2 compared to VOO, while FOO decreased the circulating alanine concentrations but increased the plasma levels of acetone and 3-HB compared to VOO. Based on these findings, phenol-enriched OOs were shown to result in a favorable shift in the circulating metabolic phenotype, inducing a reduction in metabolites associated with cardiometabolic diseases.Phenol-rich foods consumption such as virgin olive oil (VOO) has been shown to have beneficial effects on cardiovascular diseases. The broader biochemical impact of VOO and phenol-enriched OOs remains, however, unclear. A randomized, double-blind, cross-over, controlled trial was performed with thirty-three hypercholesterolemic individuals who ingested for 3-weeks (25 mL/day): (1) an OO enriched with its own olive oil phenolic compounds (PCs) ; (2) an OO enriched with its own olive oil PCs (250 ppm) plus thyme PCs ; and (3) a VOO with low phenolic content (80 ppm). Serum lipid and glycemic profiles, serum  Virgin olive oil (VOO) phenolic compounds (PCs) have been reported to possess antioxidant and anti-inflammatory properties, and exert chemoprotective effects in experimental studies ,2. MoreoPhenol-enriched foods can display a dual action since antioxidants may revert to pro-oxidant actors ,9,10. FuNutritional metabolomics, or nutrimetabolomics, is being increasingly employed to study molecular interactions between diet and the global metabolic system. It allows the study of metabolic responses to dietary modulations, and permits the inter-individual variation in responses to the intake of specific nutrients and diets to be established. In addition, the metabolic phenotype contains signals derived from dietary inputs, as well as information related to the metabolic activity of the intestinal microbiota and its interactions with the diet and host. We have previously described interactions between phenol-enriched OOs and the human microbiota .Food enriched with complementary antioxidants, according to their structure/activity relationship, may be a possibility to achieve healthy effects avoiding thes harmful ones. The aim of this study was to measure the serum metabolic phenotypes of hypercholesterolemic patients, and characterize the biochemical modulations induced by the intake of functional OOs enriched with their own PCs or with their own PCs plus complementary ones from thyme.Thymus vulgaris) . The FOOT thus contained 50% OOPCs  and 50% thyme PCs, which have been shown to possess a range of anti-inflammatory and anti-oxidative properties [A VOO with a low-phenolic content (80 ppm) was used as a control condition and as a matrix of enrichment to prepare two functional OOs . The first was a functional VOO  enriched with its own PCs by the addition of a phenolic extract obtained from freeze-dried olive cake. The phenolic profile of the olive cake extract was comparable to the control VOO, as they were obtained from the same olive variety (Arbequina cv) and the same olive-growing area. The second FOO was enriched with its own PCs and complemented with thyme PCs using a phenolic extract obtained from a mixture of freeze-dried olive cake and dried thyme  , diabetes, multiple allergies, intestinal or any other diseases/conditions that would worsen adherence to the measurements or treatments. The study was conducted at IMIM-Hospital del Mar Medical Research Institute  from April 2012 to September 2012. Subjects were allocated, by generating random numbers, to one of 3 sequences of administration of 25 mL/day of raw: (a) VOO; (b) functional OO enriched with its own PC; and (c) functional OO enriched with its own PC plus complementary phenols from thyme. The statistician was who generated the random allocation sequence while the researcher was who enrolled participants and the doctor who assigned participants to interventions according to the random sequence. Because all participants received each one of the three OOs, restrictions such as blocking were not necessary. The flow-chart of the study was previously described by Pedret et al. [The VOO and HDL Functionality (VOHF) study was a randomized, double-blind, crossover, controlled trial including 33 volunteers with cholesterol levels higher than recommended  (19 men/14 women), aged 35 to 80. Exclusion criteria were body mass index (BMI) > 35 Kg/mwww.controlled-trials.com; ISRCTN77500181) and followed CONSORT-guidelines . To register the amount of OO consumed, the volunteers had to return the OO containers to the center after each OO intervention. Participants with less than 80% of treatment adherence (≥5 full OO containers returned) were considered non-compliant subjects for this treatment. PA was evaluated by a questionnaire at baseline and cessation of the study. The trial was conducted in accordance with the Helsinki Declaration and the Good Clinical Practice for Trials on Medical Products in the European Community. All participants provided written informed consent, and the local institutional ethics committees approved the protocol (CEIC-IMAS 2009/3347/I). The protocol was registered with the International Standard Randomized Controlled Trial register .Blood pressure (BP), BMI, endothelial function (determined by ischemic reactive hyperaemia (IRH) as previously described ), and caBlood samples were collected following fasting for at least 10 h. Plasma samples were obtained by centrifugation of EDTA whole blood directly after being drawn and were preserved at −80 °C until use. Plasma glucose, total-cholesterol (TC), and triglyceride (TG) levels were measured using standard enzymatic automated methods and apolipoprotein A1 (ApoA1) and apolipoprotein B-100 (APOB100) by immunoturbidimetry in a PENTRA-400 autoanalyzer . HDL-C was measured using an accelerator selective detergent method . LDL-C was calculated by the Friedewald equation whenever TGs were inferior to 300 mg/dL.g for 10 min. The supernatant (550 μL) was transferred to a 5 mm internal diameter NMR tube. The metabolic profiles of the serum samples were then characterized by 1H-NMR spectroscopy using a 500 MHz Bruker NMR spectrometer operating at 310 K. For each serum sample a water-suppressed Carr-Purcell-Meiboom-Gill (CPMG) spin-echo spectrum was acquired using 8 dummy scans followed by 128 scans collected into 64K data points.Serum samples (200 μL) were combined with 400 μL of saline solution (100% D2O), mixed by vortexing, and centrifugated at 13,000× 1H-NMR spectra were manually corrected for phase and baseline distortions. Chemical shifts in the spectra were referenced to the anomeric proton of α-glucose at 5.223 ppm. Spectra were digitized using an in-house Matlab  script. Resonances arising from imperfect water saturation were removed to minimize distortions to the spectral baseline.Metabolomics data from human studies are characterized by large variations between the subjects. Therefore, subtle treatment effects can be easily overlooked due to the considerable variation among participants due to age, disease state, genetics and the like. In addition, the impact of the treatment effect may differ between the subjects. Cross-over designed experiments are particularly useful to tackle these problems, since each subject in the study act as their own controls, making the data paired. A specific limitation of using typical techniques for metabolomics studies such as PCA, PLS or OPLS in a crossover experiments is that the net treatment effect is not separated from the biological variation between the subjects. Therefore, to identify metabolic differences between treatment groups we used a multilevel orthogonal partial least squares discriminant analysis (M-OPLS-DA) modelling ,23. It cL is the total number of spectra, Xc contains the mean-centered data, 1L contains ones, and m is defined as the offset term.MSCA decomposes the data into an offset term, a between-subjects part and a within-subjects part ,25. Decoi over the K interventions:ic contains the mean-centered data for subject i, 1K contains ones, i, and Xiw contains the mean-centered data per subject i. Concatenating the matrices for each individual, we can write the mean-centered data matrix as:b contains the between-subjects variation and Xw contains the within-subjects variation. Therefore, the metabolomic data matrix can be written as three terms containing the offset, the between-subjects variation , and the within-subjects variation (treatment effect):Then, a second mean-centering is performed per subject http://www.bdagroup.nl/content/Downloads/software/software.php (accessed on 1 June 2018).Variation splitting was performed using Matlab with the routines available from van Velzen et al. : http://p) and orthogonal (To) variation:After variation splitting, OPLS-DA is performed on the within-subject data to find systematic differences among the treatment groups. The within-subject variation contains both variation that is equal for all subjects as well as variation different between subjects. An OPLS-DA on the within-subjects matrix is used to focus on the similarity in the treatment effect between subjects. The OPLS-DA was introduced as modification of the PLS-DA method that incorporates orthogonal signal correction (OSC) filters to discriminate between two or more groups using multivariate data ,27. PLS The predictive block contains the correlated variation between X and Y, whereas the orthogonal block contains the uncorrelated variation between X and Y. The advantage of OPLS compared to PLS is that a single component (the predictive component) is used as a predictor of the response Y, while the other components describe the other variation that is orthogonal to Y. Therefore, the main advantage of OPLS lies on enhancing model interpretation by forcing all Y-related information into a single component. However, it is worth noting that in terms of predictive power, both PLS and OPLS give similar results .2) between each metabolite and the Y-response variable, with red indicating strong significance and blue indicating weak significance. The predictive performance (Q2Y) of the model was calculated using a 7-fold cross-validation approach and model validity was established by permutation testing (1000 permutations).OPLS-DA Loading coefficient plots were generated by back-scaling transformation where covariance is plotted between the Y-response matrix and the signal intensity of the metabolites in the NMR data (X). These plots are coloured based on the correlation coefficient  models based on trees: random forest (RF). Unlike classical linear model-based statistical methods, RF are fully non-parametric model-free methods that capture complex interaction dependency patterns within predictor features affecting the phenotype. In addition, they provide several variable importance measures (VIMs) that can be used to identify relevant features. RF is an ensemble machine learning method based on “growing” hundreds or thousands of decision trees that uses the average output of all the trees to predict the response . RandomnThe advantage of the RF is that the observations not used for the construction of a specific tree (termed out-of-bag (OOB) observations) may be used to estimate the VIM. In particular, we used the mean minimal depth, which is calculated based on the position of the features in the decision tree. Thus, unlike other VIM such as the permutation variable importance, it is only based on the structure of the forest and independent of prediction errors . The RF However, VIMs do not provide the sign of the association with the response variable. Therefore, to facilitate model interpretation, the contribution and effect of each selected feature  in predicting the treatment group was determined by the exact computation of SHapley Additive exPlanations (SHAP) scores by leveraging the internal structure of RF models . The exaBonferroni). Predictor features with significantly higher, significantly lower, or non-significantly different VIM Z-scores than expected at random compared to the MZSF are deemed important (selected), unimportant (rejected), or tentative, respectively; and (d) Iteration: Unimportant and shadow features are removed and the previous steps are repeated until the status of all features is decided or a predefined number of iterations has been performed. All RF models were calculated using 5000 trees and a number of features (ntree) randomly sampled at each split given by the rounded down square root of the number of features (the mtry recommended for classification problems). In addition, in the Boruta algorithm we used 500 iterations and a confidence level cut-off of 0.005 for the Bonferroni adjusted p values.A drawback of VIMs in RF is that they are not directly related to the statistical significance. Therefore, we further applied an all-relevant ML feature selection strategy based on applying RF iteratively as implemented in the Boruta algorithm . The Borp-value < 0.05 was considered significant. Spearman correlations were performed and a p < 0.002 was considered significant due to the assessment of 25 variables . A PLS model was also used to represent the associations between metabolites and clinical cardiovascular measures. Statistical analyses were performed by SPSS 13.0 software  and Matlab .Normality of continuous variables was assessed by normal probability plots. Non-normally distributed variables were log transformed when necessary. Non-compliant volunteers were excluded from the analysis. To compare means  or medians  among groups, ANOVA and Kruskal-Wallis test were performed, respectively. The χ2 and exact F-test, as appropriate, were used to compare proportions. A general linear model for repeated measurements was employed to assess the comparisons of changes in intra- (pre-treatment vs. post-treatment) and inter-interventions (FOOT vs. FOO vs. VOO). A value of Participants’ baseline characteristics are shown in 2Y = 0.86; p < 0.001) was obtained comparing the serum metabolic phenotypes of individuals after receiving the VOO and FOOT interventions   (A significant MSCA model (Qventions a. Compar< 0.001) b. Partic< 0.001) c. Serum The OPLS-DA results were further validated by ML analyses . Glutamip < 0.002). A number of non-branched chain amino acids were strong negatively correlated with APOB100 (glutamine), glucose , triglycerides , cardiovascular risk , strong positively correlated with HDL-C (glutamine), and APOA1 (glutamine) (p < 0.002). In addition, a moderate postitive correlation between APOA1 and glycine was observed (p < 0.002) (The branched chain amino acids (BCAAs) were moderate positively correlated with APOB100 (isoleucine) and triglycerides (isoleucine), and moderate negatively correlated with HDL-C (isoleucine) and APOA1  (< 0.002) .In this work we show that the consumption of an OO enriched with either autologous PCs (FOO) or a combination of autologous plus complementary thyme PCs (FOOT) altered the circulating metabolomes of hypercholesterolemic volunteers compared to the consumption of a standard VOO. In particular, reductions were seen in circulating glutamine, histidine, DMA, creatine, and creatinine after both PC-enriched OOs.Glutamine has been previously associated with cardiovascular diseases with Shah et al. reporting higher peripheral blood glutamine concentrations in cardiovascular disease patients compared to their healthy equivalents . It has Alanine was found decreased only after the FOO intervention in comparison with VOO. The precursors of glutamine and alanine are the BCAA. The initial site of BCAA catabolism is the skeletal muscle and there is a release of glutamine and alanine to the blood during this process . In thisSeveral studies have observed a relationship between ketone bodies and cardiovascular risk as they are involved in diabetic ketoacidosis . ConversCreatinine is a degradation product from creatine, and is a marker of muscle mass, adverse lipid profiles, and kidney dysfunction. Both creatinine and creatine were decreased in serum after the two phenol-enriched OOs in comparison to VOO. Creatinine has been associated with cardiovascular diseases ,49 and eInterestingly, the microbial metabolite DMA was reduced in the circulation after consumption of both phenol-enriched OOs. DMA arises from the bacterial metabolism of trimethylamine and trimethylamine-N-oxide (TMAO), derived from the microbial breakdown of dietary choline and L-carnitine. TMAO promotes atherosclerosis in animal models and is associated with CVD and adverse cardiac events in humans ,53,54. TThe similar metabolomic effects observed after both phenol-enriched olive oils intake suggest that the complementarity of the antioxidants does not produce additional beneficial effects in the outcome analyzed in this paper, the metabolome. Nevertheless, our group reported before that this functional olive oil enriched with complementary antioxidants showed a capacity to improve HDL-subclass distribution and composition, and metabolism/antioxidant enzyme activities versus VOO, and the functional olive oil enriched with only its own antioxidants did not show this capacity . In thisOne strength of our nutritional intervention trial is its randomized and cross-over design, which permitted the volunteers to consume all OO types and therefore deleted the inter-individual variability. In addition, the laboratory analyses were centralized and all the time-series samples from the same participant were measured in the same run. A limitation of the study was its sample size, which could be responsible for reduced statistical power in some metabolites with high inter-individual variability. A synergistic effect on serum metabolome between PC and other OO components is as yet unknown. Another limitation is the inability to assess potential interactions among the OOs and other diet components and medication, although the controlled diet and medication followed during all clinical trial should have limited the aim of these interactions.In conclusion, dietary supplementation of a regular daily dose (25 mL/day) of phenol-enriched OOs for three weeks reduced several serum metabolites which were associated with cardiovascular-risk, in hypercholesterolemic patients. In particular, two different phenol-enriched OOs reduce the levels of glutamine, histidine, DMA, creatine, and creatinine. Such results highlight the potential of this cost-effective nutritional modification to reduce the global burden of cardiovascular diseases, and lead to its development as a nutritional tool for the treatment of cardiometabolic diseases.


Orthocoronavirinae related to 2 other clinically relevant coronaviruses, SARS-CoV and MERS-CoV. Like other coronaviruses and several other viruses, SARS-CoV-2 originated in bats. However, unlike other coronaviruses, SARS-CoV-2 resulted in a devastating pandemic. The SARS-CoV-2 pandemic rages on due to viral evolution that leads to more transmissible and immune evasive variants. Technology such as genomic sequencing has driven the shift from syndromic to molecular epidemiology and promises better understanding of variants. The COVID-19 pandemic has exposed critical impediments that must be addressed to develop the science of pandemics. Much of the progress is being applied in the developed world. However, barriers to the use of molecular epidemiology in low- and middle-income countries (LMICs) remain, including lack of logistics for equipment and reagents and lack of training in analysis. We review the molecular epidemiology literature to understand its origins from the SARS epidemic (2002–2003) through influenza events and the current COVID-19 pandemic. We advocate for improved genomic surveillance of SARS-CoV and understanding the pathogen diversity in potential zoonotic hosts. This work will require training in phylogenetic and high-performance computing to improve analyses of the origin and spread of pathogens. The overarching goals are to understand and abate zoonosis risk through interdisciplinary collaboration and lowering logistical barriers.The coronavirus disease 2019 (COVID-19) pandemic was one of the significant causes of death worldwide in 2020. The disease is caused by severe acute coronavirus syndrome (SARS) coronavirus 2 (SARS-CoV-2), an RNA virus of the subfamily  Here, we focus on recent advances toward genomic epidemiology  GenBank website, which was customary. Meanwhile, dashboards, graphs, and maps emerged to track cases over time and space. Janies et al combined genomic and geographic data for SARS-CoV and H5N1 influenza, respectively, being the first to project phylogenies onto a virtual globe. Janies et al used Keyhole Markup Language (KML) to develop Supramap, which facilitates geographic mapping of phylogenies. Supramap allowed hypothesis testing ranging from the host and geographic origins of pathogens to tracing mutations that conferred drug resistance or host switching. Limitations of computing large data sets, coupled with a preference for sharing data after publication, resulted in a greater turnaround between data acquisition and results than occurs today. However, these conditions did not impede a hypothesis-driven field with value to decision makers, as demonstrated in a 2007 congressional hearing.The first SARS-CoV genome was shared after publication This changed due to factors such as new DNA sequencing technologies.In the 2000s, some genomes were sequenced for respiratory pathogens such as H1N1-2009. However, even SARS-CoV genomes were not always sequenced completely, and sequences were released gradually. Sequencing the SARS-CoV genome was instrumental in recognizing it as a novel coronavirus associated with HCoV-OC43 and HCoV-229E. Researchers combined genomic and epidemiological data to trace the genotypic variation of the viral transmission paths between 2002 and 2003. However, today’s genomic surveillance evolved with the advance of high-throughput sequencing (HTS)  Fig. . summarized HTS history until 2015 and Pérez-Losada reviewed recent HTS advances. We focus on the sequence cost variation per raw megabase between 2001 and 2020). Considering raw nucleotide sequencing cost, US$100 was not sufficient to sequence one coronavirus genome in 2020, but $100 it would cover >400,000 genomes in 2020.Reuter et alOrthocoronavirinae. Gammacoronavirus (GammaCoVs) and Deltacoronavirus (DeltaCoVs) mainly infect birds and rarely infect mammals.Alphacoronavirus (AlphaCoVs) and Betacoronavirus (BetaCoVs) originated from Chiroptera (bats) and are often found in other mammals, including humans.Coronaviruses correspond to the four genera of the subfamily  which has complex gene expression and variable gene content among genera ,2 kb, wh However, the episodic emergence of human coronaviruses (HCoVs) is a pressing concern because they cause infections in all age groups, often leading to respiratory or enteric diseases. Neurological illness or hepatitis is less frequent. The US Centers for Disease Control (CDC) website lists 7 HCoVs: 2 AlphaCoVs (HCoV-229E and HCoV-NL63) and 5 BetaCoVs . We added the human enteric coronavirus 4408 (HECV-4408) to the list because it was isolated from a child with acute gastroenteritis.Coronavirus infections in domestic animals are economically significant. The dangers of HCoVs were made more evident by the 2012 outbreak of Middle East respiratory syndrome (MERS) coronavirus (MERS-CoV). Nevertheless, coronaviruses did not receive the current level of attention until the pandemic coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was first reported in humans in Wuhan, China, in December 2019. However, Pekar et al inferred that the virus was present in Hubei approximately a month before. On March 11, 2020, the World Health Organization (WHO) declared a pandemic due to the spread of SARS-CoV-2.Coronaviruses were not deemed highly pathogenic to humans until the 2002 SARS-CoV outbreak. The question can be divided into 3 components. First, was the virus purposefully manipulated? Several peer-reviewed publications have concluded that SARS-CoV-2 emerged naturally via zoonosis . Moreover, previous serology data indicate natural human infections by bat-hosted, SARS-like viruses.Understanding the emergence and evolution of SARS-CoV-2 is vital to preventing future pandemics. However, no evidence indicates that SARS-CoV-2 was known to scientists before December 2019.Second, was SARS-CoV-2 an accidental release? If a naturally occurring virus was transported to a laboratory and humans were infected shortly thereafter, the virus may not have accumulated sufficient mutations to record its passage through controlled environments.  first identified in horseshoe bats (Rhinophulus spp). Bat-hosted viruses similar to SARS-CoV-2 were collected in the Yunnan province, >1,500 km away from Wuhan, but the hosts have a wide geographic range.Third, what is the natural source of SARS-CoV-2? The most comprehensive phylogenomic analysis of coronavirus Fig. b address Fig. c and sho and denying the origin of SARS-CoV-2 from pangolin (Manis javanica) hosts, pangolins are not involved in the lineage of SARS-CoV-2 that infected humans. This finding is similar to the emergence of SARS-CoV, which also infected humans from bat-hosted viruses without any need for intermediate hosts, including Himalayan palm civets (Parguma larvata) and raccoon dogs (Nyctereutes procyonoides).Despite a confusing array of reports confirming EpiCoV database, before the first peer-reviewed article was published in February 2020.SARS-CoV-2 was identified on January 7, 2020. Three days later, its genome and metadata were shared via the Global Initiative on Sharing Avian Influenza Data (GISAID) The speed at which such data are released was changed by several forces, illustrated by Janies et al. In brief, the reasons include the increased feasibility of genome sequencing, the willingness to share data before publication, and the rise of the popular GISAID database, which credits submitting laboratories.To put the SARS-CoV-2 genome sequencing speed into context, consider that SARS-CoV was first reported in November 2002, but its genome was publicly released in April 2003. are welcome because these data inform epidemiological forecasts .Figure  Second, genomic sequencing informs the peptide mass fingerprinting (PMF) used to predict novel structures and find inhibitors for viral peptides, although results must be tested in randomized controlled trials to identify effective antivirals. Third, the data are used to model epidemic or pandemic size and severity. Fourth, viral sequences are fundamental for developing mRNA vaccines. For a review on current pitfalls and opportunities in applying HTS to SARS-CoV-2 genomes, see Chiara et al.Genomic sequencing generates a snapshot of a viral lineage in a place and time. When sequences are collected longitudinally, applications in genomic epidemiology and pandemic responses emerge, which we illustrate with 4 examples. First, profiling mutation fingerprints from the viral pangenome to individual infection quasi-species enables molecular contact tracing. sequencing demand will remain high. SARS-CoV-2 infections are decreasing as more people develop immunity through natural infection or vaccination. However, variants may evade infection and vaccine-induced antibodies, especially with infections occurring months after vaccination . Given breakthrough infections, increased transmission of some variants, and the lack of full vaccination among eligible people, we can predict that SARS-CoV-2 will continue to evolve. Whether SARS-CoV-2 is evolving toward more severe or more benign COVID-19 phenotypes is a pressing research question for genomic epidemiology.As SARS-CoV-2 becomes endemic, and addressing sampling bias. For example, if we restrict sequencing viral isolates from hospitalized patients, the relationships between any variables associated with hospitalization will be distorted when compared to the general population. Thus, we would miss mutations associated with asymptomatic and symptomatic cases that did not require hospitalization, which could lead to inducing or misinterpreting the evidence for phenotype-genotype associations.Effective countermeasures depend on understanding SARS-CoV-2 lineages, such as sampling variants for which phenotype is not fully understood analyzed the spatiotemporal heterogeneity in each country’s SARS-CoV-2 genomic surveillance efforts based on metadata submitted to GISAID until May 30, 2021. These researchers estimated that when the prevalence of a rare lineage is 2%, 300 cases would need to be sequenced to detect at least 1 genome of that lineage with 95% probability. Therefore, sequencing capacity should be at least 0.5% of cases per week when incidence is >100 positive cases per 100,000 people.Brito et al observed that countries like Denmark, which have a quick turnaround for sequencing, processing, and sharing SARS-CoV-2 genomic data (<18 days) and a high sequencing rate (>32%), observe greater lineage diversity. Many variants may be missed when sampling rates are low. However, disparities in wealth, investment in research and training, coordination, and supply chain logistics affect the ability of countries to perform genomic surveillance, especially LMICs. Therefore, efforts must be made to provide funds, training, and logistic support for researchers based in LMICs to improve their genomic surveillance capacity and public-health decision making.Brito et al Moreover, convergent evolution among geographically distant variants has been observed . The term “strain” is typically associated with lineages that became sufficiently divergent to exhibit a changed phenotype.Any genome sequence that is genetically distinct from the reference can be called a variant. In practice, the SARS-CoV-2 variants represent clades that share a set of key mutations while still permitting a small amount of other sequence variation.ed Table .85 Alt The emergence of variants that might pose an increased risk to global public health prompted the WHO to characterize specific variants of interest (VOIs) and variants of concern (VOCs) to prioritize global monitoring and research. The US government SARS-CoV-2 interagency group (SIG) developed a separate variant classification scheme, which we compare to the WHO system in Table In late 2020 and throughout 2021, as vaccine availability increased, information on variants began to dominate the COVID-19 response. for simplicity and to avoid association with particular localities. These labels do not replace existing classifications by GISAID (https://gisaid.org/), Nextstrain (https://nexstrain.org/), and Pango lineages (https://cov-lineages.org/). SARS-CoV-2 variants were reviewed by Harvey et al.In March 2021, the WHO assigned letters of the Greek alphabet to categorize VOIs and VOCs, Although some SARS-CoV-2 variants show a degree of escape from protective antibodies induced by natural infection , T-cell responses are retained. Furthermore, first-generation SARS-CoV-2 mRNA-based vaccines induce public antibodies  with robust neutralizing and potentially durable protective activity against variants such as alpha (α), beta (β), and gamma (γ).The speed of development and testing of COVID-19 vaccines development is one of history’s most outstanding public health achievements. Vast vaccination of eligible individuals is the best and safest way to control the pandemic. requiring close international monitoring to determine the need for vaccination boosters and or redesign. As variants emerge in areas of low vaccination, a global COVID-19 vaccination rollout is imperative. Since the vaccine rollout, new questions have arisen regarding vaccine efficacy against the transmission of different variants, the duration of protection, and the efficacy of prime-boost schedules. A demand has also arisen for studies to determine the immunological correlates of protection against COVID-19 as cases decline and prevention of severe disease gains more importance in vaccine efficacy. Meanwhile, nonpharmaceutical interventions to reduce the spread of SARS-CoV-2 and other pathogens are still warranted.SARS-CoV-2 variants will continue to emerge,Genomic epidemiology can be a tool to study emerging infectious diseases (EIDs) in humans, but its effectiveness is maximized when it accounts for animal and environmental components. In the case of zoonosis, there is a knowledge gap between the animal and human components of EID research, and One Health can bridge this gap. One Health proposes placing these realms of research  in the same environmental context. The next steps in pandemic prevention science are to understand factors that create opportunities for zoonosis, such as entering infectious habitats such as bat caves and the use of wildlife as food and medicine.Although most human health researchers have only started focusing on coronaviruses since the emergence of SARS-CoV-2, veterinarians, virologists, and zoologists have been researching animal coronaviruses long before the COVID-19 epidemic. Therefore, another step toward furthering the One Health approach is leveraging biorepositories in biomedical research. Although the Global Museum initiative already offers a route of international integration among museum biorepositories in a decentralized and geographically dispersed network, the link to EID research is still not fully realized.Deep sequencing the microbiomes and viromes of taxonomically, geographically, and temporally deep biorepository archives of putative host animals will serve as the basis of new approaches to zoonosis, risk assessment, and threat mitigation. The overarching goal of the MEPA is to leverage museum biorepositories in a global, decentralized pathogen surveillance system by expanding biodiversity infrastructure and opening communication channels that foster collaboration among biorepositories and biomedical communities.The recent creation of the Museums and Emerging Pathogens in the Americas network (MEPA) is vital for linking biorepositories and EID research. which has the potential to infect a range of hosts, including tigers, minks, domestic cats, ferrets, raccoon dogs, cynomolgus and rhesus macaques, rabbits, Egyptian fruit bats, Syrian hamsters, and white-tailed deer.The need for this host-based approach to genomic epidemiology is made evident by the transmissible nature of SARS-CoV-2, which are ravaging unvaccinated people. However, the likelihood of new mutations increases as cases rise, possibly leading to enhanced transmission, immune escape, or increased pathogenicity. This process has resulted in more transmissible variants.Vaccines are still effective in preventing severe outcomes against all SARS-CoV-2 variants, The United States is an even more extreme example because it has ranked low in SARS-CoV-2 sequencing despite its capacity and expertise. On the other hand, countries like South Africa have sequencing laboratories struggling with reagent shortages and the scarcity of trained scientists.Researchers face 2 main challenges in keeping pace with SARS-CoV-2 variants: using resources at optimal capacity and lowering barriers to technology and training in genomic epidemiology across the world. On the one hand, countries with a high positivity rate, like India, are not sequencing isolates at full capacity.Global efforts to strengthen pathogen sequencing capacity are still required to respond to technical, logistical, and financial challenges in resource-limited settings despite increased sequencing feasibility. Moreover, good SARS-CoV-2 sequencing performance for some LMICs  further encourages international and domestic collaboration among public health authorities, healthcare facilities, academia, and industries. and data standards to guarantee rapid access to large volumes of raw and processed molecular data at unprecedented scales.Additional challenges include consistent handling of isolates as well as metadata and sequence data curation and deposition in a way that facilitates combining data sets from different laboratories. These challenges require coordinated efforts Although these tools were not designed to manage big data from rapidly evolving pathogens, some have already started to respond to these demands. For example, the ultrafast sample placement on existing trees (UShER) enables the rapid placement of novel genomes into a reference tree using the parsimony optimality criterion. Thus, as phylogenetic principles underpin how we view genetic changes over time, One Health will also include the exchange of knowledge among evolutionary biologists and epidemiologists.We also need to address bioinformatics bottlenecks to respond faster to the threat of emergent diseases and to manage the fast-paced production of genomic information. Most tools are co-opted from evolutionary biology’s arsenal to study the lineages of higher taxa with exemplar approaches. Moreover, a good phylogenetic analysis requires many elements: careful choice of the collected taxa, sequence, and or phenotypic data; method and quality control of sequence data and alignment; evaluation of substitution and indel models; treatment of partitions; tree-search protocol; measures of fit or confidence; and strategies for character coding and optimization. Moreover, results may vary with parameterization. These are only a few of the difficult decisions that go way beyond the level of sophistication of any software manuals and automated systems.Phylogenetic trees are hard to compute and interpret. The need to consult professional phylogeneticists is made plain by the number of prominent papers that did not adhere to the standards of phylogenetics and failed to identify the fundamental hosts of coronaviruses. and drug resistance. However, due to occlusion, Supramaps were not suitable for the visualization of cosmopolitan diseases, such as strains of Salmonella , seasonal influenza , pandemic influenza (H1N1-2009), and SARS-CoV-2. In response, researchers have worked on alternative visualization tools, including pointmaps and route maps and eventually moved beyond the need for mapping trees to globes with Strainhub.In many cases, such as the initial spread of H5N1 influenza, trees and Supramaps were very useful to understand the geographic spread of the pathogen, its multiple geographically and mutationally distinct patterns of zoonosis,Unlike Supramap, Strainhub is less computationally demanding. It can be executed from a web browser; it does not depend on closed source software (Google Earth), and geographical data are optional Fig. . MoreoveThe COVID-19 pandemic has illustrated how unprepared our interconnected global society is for zoonotic disease. For the next pandemic, 2 frontiers of investigation are interesting for genomic epidemiology as a tool to survey microbes of pandemic potential to predict, prevent, or respond faster to the emergence of new disease. Second, we must develop the science of pandemic prevention by moving from tracking pandemics that are occurring to predicting outbreaks. For example, combining artificial intelligence with genomic epidemiology can lead constructing a “viral forecast“ to inform decisions about viruses with pandemic potential. Moreover, we have proposed a novel mathematical modeling framework based on agent-based modeling to predict pathogen patch dynamics underlying zoonosis.First, we must survey the natural diversity of coronaviruses and other microbes of pandemic potential present within animals. and it has surpassed the US death toll from the 1918–1919 H1N1 pandemic, which was ∼675,000. As SARS-CoV-2 becomes endemic, we must remember that it is not as lethal as other pathogens such as H5N1 influenza or Nipah virus. In its last 100 years of existence, smallpox killed 300 million people, and Variola major  killed 30% of these patients.The COVID-19 pandemic, while ongoing, has caused 4,863,818 deaths worldwide as of October 14, 2021, We must heed the warning that pathogens with more severe disease phenotypes than SARS-CoV-2 could resultin a far more devastating pandemic.A novel pathogen at 30% mortality infecting 50% of the US population (166.7 million) would have resulted in 50 million deaths. MERS-CoV, henipaviruses, and hantavirus all have high mortality (>30%) and virulence with no approved vaccines or antivirals available. The 2018 Nipah outbreak had a 91% case-fatality rate, claiming 21 lives.


Epipericardial fat necrosis (EPFN) is a rare, benign cause of acute chest pain imitating symptoms of life-threatening diseases, such as acute coronary syndrome. Here We report a 37-year-old, healthy male presented to the emergency department (ED) with sudden-onset pleuritic chest pain after an isometric physical training. Initial cardiac workup included ECG, echocardiography was unremarkable, but diagnosis of an inflammatory process that involved the epipericardial fat tissue surrounding the heart was made by showing encapsulated fatty lesion, enhanced adjacent parietal pericardium using of contrast‐enhanced magnetic resonance imaging (MRI). Magnetic resonance imaging would help physicians to differentiate EPFN from severe and life-treating conditions. Fat necrosis can occur at multiple sites in the body: in the breast and subcutaneous fat, in the abdomen, and in the mediastinum. The etiology of fat necrosis is various as trauma, torsion, and inflammation.Epipericardial fat necrosis is an inflammatory process that involves pericardial and adjacent mediastinal fat and leads to acute chest pain CT scan has been suggested as a modality of choice to detect EPFN A 37-year-old man presented to the emergency department (ED) with a sudden-onset pleuritic pain in his right anterior lower chest radiating to the upper abdomen. The symptoms started a day before to presentation, shortly after a powerlifting training session. The pain was graded 6 of 10 on a 1-0 scale by patient. There were no associated signs or symptoms such as fever, chills, palpitations, syncopes, cough, or shortness of breath. His physical exam was unremarkable, with a normal resting blood pressure, normal heart sound without murmurs or friction rub, and normal breath sounds with no wheezing or crackles. The patient did not report any significant past medical history and cardiovascular risk factors. An electrocardiogram (ECG) showed normal sinus rhythm with no ST, T wave changes. The laboratory tests, including the D-dimer: 100 ng/L (Reference range <500 ng/L), troponin I: <0.01 (Reference range < 0.016 ng/L), CRP: 2.0 mg/mL (Reference range < 8.0 mg/l) were normal. A transthoracic echocardiography (TTE) reportedly showed normal global systolic left ventricular function without regional wall motion abnormality. There was no evidence of significant pericardial effusion.A broad range of causes from life-threatening aetiologies to self-limited ones should be considered in the differential diagnosis of acute chest pain. It was including myocardial ischemia, pulmonary embolism, myocarditis, pericarditis, pneumonia, musculoskeletal etiologies, and upper abdominal pathologies such as acute cholecystitis and hepatitis With the suspicion of muscle strain, the emergency physician prescribed analgesic medication for the patient. Due to the poor response to treatment and the radiating nature of the chest pain to the upper abdomen, he was referred for a contrast-enhanced MRI. A 1.5 T MRI revealed an encapsulated, oval-shaped, and fat-containing nodular lesion (18 × 11 mm) with a surrounding thin, low signal intensity rim, that was located in the right cardiophrenic angle  and 2. ABased on the patient history, laboratory test results, clinical and imaging findings, diagnosis of EPFN was made. The course of disease was benign and self-limited. On follow-up evaluations at 1, 3, and 6 months: the patient's pain was completely disappeared.Radiologic follow-up was not performed.EPFN is a rare and self-limited cause of acute chest pain, mimicking life-threatening diseases like myocardial infarction and pulmonary embolism EPFN affects both genders equally, with an average age of 50 EPFN frequently involves the left side The absence of specific signs and symptoms on history and physical exam besides normal laboratory and ECG findings necessitates the utility of imaging modalities to confirm EPFN.CT scan has been suggested as a modality of choice to detect EPFN MRI with fat-suppression techniques is an accurate method to characterize adipose tissue with a high soft-tissue contrast Pineda et al reported using a combination of clinical and CT scan-based criteria for diagnosis of EPFN -To review the clinical and imaging features of EPFN as a rare and underdiagnosed cause of acute chest pain.-To point out the ability of MRI in the differentiation of EPFN from fat-containing tumors, which reduces the need for complementary invasive and non–invasive diagnostic techniques.-To highlight the advantages of MRI compared with CT scan in the diagnostic workup of patients with acute chest pain presentation.The authors of this article confirm that informed consent for publication of this case report was obtained from the patients.


Purpose: To review the demographic and clinical profile of patients developing acute macular neuroretinopathy (AMN) or paracentral acute middle maculopathy (PAMM) after receiving coronavirus disease-2019 (COVID-19) vaccination or infection. Methods: In this review article, the published literature was searched to determine cases developing either AMN or PAMM after COVID-19 vaccinations or infections. Data, including demographic profile, presenting features, symptoms, diagnosis, and clinical outcomes, were extracted from the selected publications. These parameters were compared between the two groups, i.e., patients developing AMN/PAMM either after vaccination or infection. Results: After the literature review, 57 patients developing either AMN (n = 40), PAMM (n = 14), or both (n = 3) after COVID-19 infection (n = 29) or vaccination (n = 28) were included . In 24.6% patients, the diagnosis of COVID-19 infection was preceded by the development of ocular disease. There were no significant differences in the age or gender between the patients developing AMN or PAMM after vaccination or infection (p > 0.13). Among the vaccination group, the highest number of patients developing AMN/PAMM were after the Oxford-AstraZeneca . Patients with vaccination had a significantly early onset of AMN/PAMM compared to those with infection . Conclusions: Both AMN and PAMM are reported to be associated with COVID-19 infections and in persons receiving vaccination against COVID-19. While COVID-19 infections and vaccinations may have a contributory role, other risk factors such as oral contraceptive pills may also play a role in the development of the disease. Novel SARS-CoV-2 infection is attributed to the result of a hypercoagulable state, which can induce thrombus formation leading to local embolism in the small vessels and micro-vessels of the relevant target organs . VariousIn a prospective cross-sectional study by Sim et al., one in nine patients with COVID-19 infections had microvascular alterations on ocular imaging, and these signs were observed even in asymptomatic patients with normal vital signs . On the Acute macular neuroretinopathy (AMN) is a rare, outer retinal, microvascular disorder characterized by acute onset wedge-shaped or petaloid perifoveal lesions and ischemia of the deep retinal capillary plexus, involving the external limiting membrane and photoreceptors. Paracentral acute middle maculopathy (PAMM) is also a manifestation of outer retinal ischemia with intermediate/deep retinal capillary plexus involvement but limited to the inner nuclear layer, outer plexiform/nuclear layers. Various systemic risk factors are reported in association with AMN: flu-like illness, fever, oral contraceptive use, systemic shock, antecedent trauma, and dengue fever, among others. A non-specific flu-like illness or fever was reported in almost half of the patients in a review of 101 cases of AMN . PAMM waThe study was designed as a review of published AMN and PAMM cases in the literature associated with COVID-19 infections or vaccination. Since the study analyzed published manuscripts, institutional ethics clearance was not required for the study, and there were no informed consent documents. The study adhered to the tenets of the declaration of Helsinki. We conducted a detailed review of the literature on Medline  from December 2020 until 30 September 2022. The search was conducted by two ophthalmologists  using terms such as ‘acute macular neuroretinopathy’, ‘paracentral acute middle maculopathy’ AND/OR ‘SARS-CoV-2′, ‘COVID-19’, and ‘COVID-19 vaccine’. The bibliographies of the retrieved articles were searched thoroughly to find relevant articles.The manuscripts in the English language were selected for further analysis only. The inclusion criteria were published cases, case series, or original articles of patients with the diagnosis of either AMN, PAMM, or both associated with COVID-19 infection or vaccination. We included studies with either definitive diagnosis of COVID-19 or recipients of COVID vaccination (any approved type of vaccine), including booster doses. Cases without a definitive diagnosis of COVID-19 infection were excluded from this analysis . If a caFor the purpose of the study, we collected available demographic data from the published cases, including data such as gender, age, ethnicity, and geographic location (if available). The data concerning previous or current COVID-19 infection and vaccination  were noted. The medical history of the patients available in the published cases, such as history of drug intake, pre-existing medical conditions, previous surgeries, or ocular findings were noted. The details of the ophthalmic examination and imaging findings were noted for each case. The course of the AMN and PAMM lesions were obtained, and treatments prescribed were also noted. Any additional clinical data such as additional diagnoses, complications, or findings were noted.®  version 6.0. The demographic data were expressed in mean and standard deviation. The parameters between two groups, namely, AMN versus PAMM, and AMN or PAMM after vaccination or after COVID-19 infection were compared using Mann–Whitney U test. Binary data between the groups were analyzed using Chi Square test. The clinical findings, course of the disease, complications and other data were described using descriptive statistics. The total follow-up was also described using mean and standard deviation. The statistical significance was denoted by p < 0.05 and 95% confidence interval.The data were collected in a pre-designed data collection sheet by two ophthalmologists. Data analysis was performed using GraphPad Prismn = 40; 70.2%) was higher than PAMM . PAMM was more commonly seen in patients with COVID-19 infections (n = 10) than in patients following vaccinations (n = 4). PAMM was associated with central retinal artery occlusion (CRAO) in two patients with COVID-19 infection and in one patient with cilioretinal artery occlusion [A total of 57 cases of AMN and PAMM were obtained after analysis of the published literature. The mean age of all the patients included in the analysis was 34.9 ± 14.4 years. Among them, 29 developed either AMN or PAMM following COVID-19 infections (50.9%) , and thecclusion ,9,10. Tucclusion . The labcclusion . In anotcclusion ,12,13.p = 0.13). Female predilection was more frequently observed in the cohort with COVID vaccination   (p = 0.29). Among the female patients, 71% were either on or were using oral contraceptive pills or hormonal devices for birth control.In 14 patients (24.6%), the diagnosis or confirmation of COVID-19 infection was preceded by the ocular symptoms and/or signs of AMN/PAMM ,14,15. Bn = 12) was the most common vaccine received by the patients in this group, followed by BNT162b2  (referred to as Pfizer-BioNTech vaccine) and Sinopharm BIBP COVID-19 vaccine (referred to as Sinopharm BIBP vaccine) (n = 5 each), mRNA-1273  (referred to as Moderna vaccine) (n = 4). One patient developed AMN in both eyes after Ad26.COV2.S  (referred to as Janssen Vaccine). Fourteen patients exhibited bilateral involvement in the vaccinated group, and among them, 12 had AMN and in one patient, both AMN and PAMM were described after COVID-19 vaccination. The details are provided in A total of 21 patients developed AMN and PAMM after the first dose of vaccination, Oxford-AstraZeneca (ChAdOx1-S (recombinant) vaccine) (p = 0.001). One patient developed ocular symptoms within 20 min of vaccination, along with systemic symptoms such as malignant hypertension [The cohort of patients with COVID-19 vaccination exhibited significantly early onset of AMN and PAMM when compared with the patients with SARS-CoV-2 infection , which is co-sponsored by the Centers of Disease Control (CDC) and the United States Food and Drug Administration (US-FDA). The VAERS is a self-reporting system available to the general population, as well as physicians, who can enter and report adverse events related to their vaccination. Since this is a freely available resource and voluntary in nature, it does not capture all the details regarding the adverse event, and there could be events that are unreported in VAERS database. In our study, we have not performed a VAERS database search. This is because data from VAERS are not peer-reviewed, and therefore, it is inappropriate to combine it with the data obtained from published research for this manuscript. The disease manifestations of AMN and PAMM may also be very subtle in nature. Considering the self-limiting course of these two conditions and possible minimal clinical signs and symptoms, many such cases of AMN/PAMM may have been unnoticed and, hence, were not reported, leading to a reporting bias.A significant risk of systemic thrombotic complications leading to complement-mediated thrombotic microangiopathy in SARS-CoV-2 infection is now well proven. Our review showed a higher number of PAMM cases in patients with COVID-19 infection. Patients with COVID-19 infection are, thus, at higher risk of developing the acute phase of ischemia of retinal capillary plexuses that may lead to PAMM. To add to it further, PAMM was reported in patients with pulmonary embolism  in assocOur study has various limitations including reporting bias, as stated earlier. Other limitations include lack of uniform diagnostic testing and follow-up and lack of treatment guidelines or uniformity. There may be cases where the cause-and-effect association may be weak. It is possible that there could be multiple confounding factors that could exist, leading to the development of AMN and PAMM. Due to the significant interest in the ocular and systemic effects of COVID-19 in the scientific community, it is possible that there could be a publication bias leading to a higher number of reports on AMN and PAMM following either COVID-19 infection or vaccination. The pathophysiological mechanisms of the development of deep retinal ischemia such as AMN or PAMM after vaccination (including vaccines with different mechanisms of action) and COVID-19 infection are still speculative and under investigation. Based on the available reports, it is difficult to conclude which vaccine confers a higher risk of either AMN or PAMM in the recipients.In summary, both COVID-19 infection and vaccination against COVID-19 have been implicated with the development of AMN and PAMM detected using clinical examination and multimodal imaging using OCT and OCTA. The exact strength of this association, or the level of contribution of COVID-19 vaccine/infection in comparison with other risk factors such as OCP, is yet to be ascertained. Patients developing AMN or PAMM after COVID-19 vaccination tend to be younger and develop the disease within a shorter duration of time compared to those developing the disease after COVID-19 infection.In the future, it is necessary to focus research on the true risk of ischemic events arising from both COVID-19 infections and vaccinations and if there are certain exogenous or endogenous risk factors that contribute to a higher risk of thromboembolic events compared to the general population. With a hope that the future burden of COVID-19 disease will reduce, it will become imperative to understand the real safety and efficacy of the vaccination itself and if there could be improvements in the molecular composition that may reduce adverse events attributed to the vaccine.


Coronavirus disease 2019 (COVID-19) is a viral respiratory infection caused by the severe acute respiratory syndrome virus (SARS-CoV-2). Vaccines that protect against SARS-CoV-2 infection have been widely employed to reduce the incidence of symptomatic and severe disease. However, adenovirus-based SARS-CoV-2 vaccines can cause a rare, thrombotic disorder termed vaccine-induced immune thrombotic thrombocytopenia (VITT). VITT often develops in the first 5 to 30 days following vaccination and is characterized by thrombocytopenia and thrombosis in unusual locations . The diagnosis is confirmed by testing for anti-PF4 antibodies, as these antibodies are capable of platelet activation without any cofactor. It can be clinically challenging to differentiate VITT from a similar disorder called heparin-induced thrombocytopenia (HIT), since heparin is commonly used in hospitalized patients. VITT and HIT have similar pathobiology and clinical manifestations but important differences in testing including the need for PF4-enhanced functional assays and the poor reliability of rapid immunoassays for the detection of anti-platelet factor 4 (PF4) antibodies. In this review we summarize the epidemiology of VITT; highlight similarities and differences between HIT and VITT; and provide an update on the clinical diagnosis of VITT. Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a recently-described hematological disorder that emerged during the mass coronavirus disease 2019 (COVID-19) vaccination programs , 4. VITTIn this review we provide a pragmatic overview of VITT. We review the differences in clinical presentation between HIT and VITT; VITT pathophysiology; and unique aspects of laboratory investigations.VITT is characterized by new-onset thrombocytopenia and thrombosis following vaccination with an adenoviral COVID-19 vaccine . ExampleA complete blood count, D-dimer, coagulation tests , and liver enzymes should be ordered in suspected VITT patients. The median platelet count in VITT ranges from 25,000 to 47,000 based on several reports , 7, 8. DA recent report described eleven VITT patients who presented to hospital with no thrombosis on initial assessment. All eleven patients had thrombocytopenia and severe headache following adenoviral vaccination . They alA review of VITT pathobiology is essential to understand the clinical application of specialized screening and diagnostic tests. VITT is a prothrombotic, autoimmune disorder that is a result of pathologic anti-PF4 antibodies –13. Thesin vitro since they both bind to the same region  and rapid assay . These iEIAs quantify the optical density emitted from anti-PF4/heparin antibodies in patient serum when mixed with a secondary antibody. They consist of microtiter plates containing antigen complexes (PF4/heparin) bound to the well surface. Patient sera is added to the well and anti-PF4/heparin antibodies bind to the antigen. A secondary antibody (anti-human IgG) that can emit light is added and the degree of transmission is quantified. Commercially available EIAs for HIT detect either all immunoglobulin classes  or IgG-only antibodies. When used for VITT testing, commercial EIA has been reported to have a sensitivity of 100% and the specificity of commercial, in-house, and anti-P4F/heparin ranging between 95.6 and 97.4% , 31. ThiPlatton et al.  tested 2Therefore, while rapid assays are often used to rule-out the presence of anti-PF4/heparin antibodies in HIT, they perform poorly in VITT. EIAs are the preferred screening assays as they can accurately identify anti-PF4 VITT antibodies. The Lifecodes Immucor PF4-EIA has demonstrated the best performance in this regard. Reference laboratories should be able to perform more than one type of EIA, given that no single assay detects all VITT sera. Future work should look to evaluate the diagnostic capabilities of EIAs to further substantiate their usefulness as a diagnostic test for VITT due to the limited sample size of these studies. However, functional platelet activating assays remain the gold-standard test for laboratory confirmation of VITT.via turbidity change , while the HIMEA measures aggregation by electrical impedance; the PEA measures P-selectin expression on the platelet surface; and the SRA measures radioactive serotonin release from platelet granules  of platelet activation in VITT can be explained by the recent finding that the majority of anti-PF4 VITT antibodies bind to the heparin-binding site on PF4  . The senFunctional assays can also potentially identify disease resolution in the context of persistent anti-PF4 antibodies following VITT. In a longitudinal study of 35 VITT patients, 32 (91%) continued to test positive on anti-PF4/heparin IgG EIA, albeit with significantly reduced optical density results . In thosVITT functional assays are thus a useful adjunct and diagnostic tool for patient cases that are atypical or require longitudinal monitoring. When testing well clinically defined VITT patient samples, VITT functional assays have reliable and accurate detection. Neither the PF4 serotonin release assay (PF4- SRA), the PF4-induced platelet activation assay (PIPA), or flow-cytometry based assay (PIFPA) missed any of the clinically diagnosed VITT samples, whereas the HEMA missed three samples in two different studies. Of these three samples, one was inconclusive while the other two were patients undergoing IVIG treatment  1, 8, 44–46. GivVITT is a prothrombotic disorder that has been characterized through application of existing knowledge related to HIT. Although these disorders share many similarities, clinicians should be aware of several important differences: First, HIT antibodies and VITT antibodies have different binding sites on PF4. Second, anti-PF4/heparin EIAs have high sensitivity and high specificity for the VITT diagnosis, whereas the specificity is low for HIT. Third, rapid diagnostic assays developed for HIT often yield false-negative results for VITT and should not be used as a diagnostic test for VITT.SJ, DA, DM, NI, A-LB, and IN were all involved in the conceptualization, writing, and editing of the manuscript. All authors contributed to the article and approved the submitted version.Funding was received from the Canadian Institutes of Health Research (CIHR 452655) awarded to IN.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


It is difficult for radiologists to differentiate adrenal lipid-poor adenomas from non-adenomas; nevertheless, this differentiation is important as the clinical interventions required are different for adrenal lipid-poor adenomas and non-adenomas.To develop an unenhanced computed tomography (CT)-based radiomics model for identifying adrenal lipid-poor adenomas to assist in clinical decision-making.Patients with adrenal lesions who underwent CT between January 2015 and August 2021 were retrospectively recruited from two independent institutions. Patients from institution 1 were randomly divided into training and test sets, while those from institution 2 were used as the external validation set. The unenhanced attenuation and tumor diameter were measured to build a conventional model. Radiomics features were extracted from unenhanced CT images, and selected features were used to build a radiomics model. A nomogram model combining the conventional and radiomic features was also constructed. All the models were developed in the training set and validated in the test and external validation sets. The diagnostic performance of the models for identifying adrenal lipid-poor adenomas was compared.A total of 292 patients with 141 adrenal lipid-poor adenomas and 151 non-adenomas were analyzed. Patients with adrenal lipid-poor adenomas tend to have lower unenhanced attenuation and smoother image textures. In the training set, the areas under the curve of the conventional, radiomic, and nomogram models were 0.94, 0.93, and 0.96, respectively. There was no difference in diagnostic performance between the conventional and nomogram models in all datasets .Our unenhanced CT-based nomogram model could effectively distinguish adrenal lipid-poor adenomas. The diagnostic power of conventional unenhanced CT imaging features may be underestimated, and further exploration is worthy. With the marked escalation in the use of diagnostic imaging, incidental detection of adrenal nodules is increasing , 2. BeniRadiomics, with features extracted from medical images  can produce accurate and robust evidence to assist in clinical decision-making. Radiomics can use high-throughput methods to extract and analyze quantitative information that cannot be assessed by visual inspection of CT, as well as other clinical images, based on intensity, shape, size, and texture . PreviouThe purpose of our study was to assess the performance of radiomics in the identification of adrenal lipid-poor adenomas and to develop a diagnostic model to assist in clinical decision-making.This retrospective study analyzed consecutive patients admitted to two independent institutions between January 2015 and August 2021, who met the following inclusion criteria: adult patients (aged ≥18 years) with adrenal lesions, who underwent adrenal or abdominal unenhanced and contrast-enhanced CT.We excluded lesions showing unenhanced attenuation of ≤ 10 HU or macroscopic fat ; lesions without a solid component, defined as a change in pre- and post-contrast imaging of > 10 HU ; cases without an adequate reference standard, namely adrenal lesions exhibiting an increase of 10%–30% in maximum diameter; lesions with prior systemic or focal therapy; lesions < 10 mm in maximum diameter, which was our cut-off value to avoid partial volume effects; and cases with poor image quality, such as severe motion artifacts.Institution-based sampling was applied. Cases from institution 1, which had a large number of patients, was randomly divided into a training set and a test set at a ratio of 7:3, while cases from institution 2 were used as the external validation set.This retrospective study was approved by the Institutional Review Board of institutions 1 and 2, and the requirement for obtaining written informed consent was waived.18F-fluorodeoxyglucose (FDG) uptake, but which satisfied the adenoma criteria, were diagnosed as adenomas. Adrenal lesions in patients with extra-adrenal malignancies were classified as metastases based on at least one of the following criteria: pathologic diagnosis; newly developed or increase in size (at least 30% increase in maximum diameter) within 12 months during the imaging follow-up, and/or interval regression in size following systemic chemotherapy; or abnormal 18F-FDG uptake (defined as avid uptake relative to liver parenchyma). The diagnoses of pheochromocytomas and adrenocortical carcinomas were histopathologically confirmed, whereas hematomas and cysts were diagnosed based on typical CT findings and interval follow-up.The final diagnosis of all analyzed lesions was established based on histopathological findings or widely accepted imaging criteria. Lipid-poor adenomas were diagnosed based on histopathology or size stability (< 10% transverse diameter) for at least 12 months during the imaging follow-up. Lesions that exhibited abnormal For histopathologically confirmed lesions, preoperative unenhanced and contrast-enhanced CT images were used for analysis, whereas for those confirmed by imaging follow-up, the earliest CT images were analyzed.All unenhanced and contrast-enhanced CT images from the two institutions were obtained using multislice spiral CT scanners . The imaging protocols are described in the Appendix. The image was reconstructed to 2.5-mm, or 5-mm thickness using a standard algorithm and was then uploaded to the image archiving and communication system (PACS).When a patient had multiple adrenal lesions, only the largest (measured maximum diameter) lesion was analyzed to reduce the clustering effect. Two radiologists , who were blinded to the clinical and histopathological information, independently evaluated the conventional CT image features, including maximum tumor diameter, unenhanced attenuation, and lesion distribution. The maximum tumor diameter and unenhanced attenuation were measured manually on slices with the largest lesion area. Unenhanced attenuation was measured using as large a circular or elliptical region-of-interest (ROI) as possible while avoiding lesion margins, normal adrenal parenchyma, calcification, artifacts, and apparent necrotic or cystic areas. The ROI was determined on the contrast-enhanced image and was then copied onto the unenhanced image. Manual correction was performed, if necessary. All measurements were taken twice in one reading session, and the average values were acquired. The two radiologists also measured 37 samples that were randomly selected 1 month later, to assess the reproducibility of assessing the conventional CT features.The radiomics process included image segmentation, feature extraction, feature selection, and model building.http://www.itksnap.org). The most inferior and superior slices were excluded to minimize the effects of partial volume. The same 37 samples were selected to assess the reproducibility of the radiomics features. To test for interobserver and intraobserver reproducibility, two radiologists  segmented the images again after 1 month. All radiologists were blinded to the patients’ clinical or histopathological information.When a patient had multiple adrenal lesions, only the largest lesion (measured as the maximum diameter) was analyzed. 3D segmentation of the lesions was performed by a radiologist (reader 1), semi-automatically, using the open-source software ITK-SNAP , an open-source Python package. Image standardization was not applied when setting the parameters on the Pyradiomics package because CT values reflect real-world values and should be comparable across different scanners.Radiomic features were extracted and filtered from segmented ROIs using Pyradiomics  algorithm was used for initial feature selection, and 10 features were kept. Then the LASSO algorithm, which was suitable for the regression of high-dimensional data, was applied to select significantly distinguishable feature-based minimum binomial deviance by adjusting the penalty coefficient (λ) to construct the radiomic signature with 10-fold cross-validation . The radAll the models were built on the training set and validated on the test set, and the external validation set was used for further validation. Univariate logistic regression was used to select conventional features that were risk factors for lipid-poor adenomas. Multicollinearity of the variables was tested using the variance inflation factor (VIF). Next, the conventional features with p < 0.05 and VIF ≤ 5 were introduced into a multivariate logistic regression to build a conventional model, with the minimum Akaike’s information criterion as the stopping rule . The radhttps://www.Rproject.org). Data consistent with normal distribution were represented by means and standard deviations. Student’s t-test was used to compare the difference between adrenal lipid-poor adenomas and non-adenomas, as well as the difference between the training and test sets. Data that were not normally distributed are shown as median  values and were compared using the Mann–Whitney U test. Categorical variables were expressed as the number of cases (percentage), and the chi-squared test or Fisher’s exact test was performed. Correlation was evaluated using Spearman’s coefficient. The Hosmer–Lemeshow test was used to evaluate the goodness of fit of the model. The area under the receiver operating characteristic (ROC) curve (AUC) for all datasets was used to evaluate the performance of the model in identifying adrenal lipid-poor adenomas. The cutoff value was set using the maximum Youden index. The differences between ROCs were compared using the DeLong test. The reproducibility of the radiomics features was evaluated using intra-observer correlation coefficients with a two-way random model and absolute type. Statistical significance was set at p < 0.05.All statistical analyses were performed using SPSS  and R software  with adrenal lesions who underwent adrenal or abdominal unenhanced and contrast-enhanced CT were admitted to the two institutions. After excluding 501 patients for various reasons . The 122 non-adenomas included metastatic tumors (n = 60), hemangiomas (n = 2), spindle cell tumors (n = 1), pheochromocytoma (n = 41), lymphoma (n = 5), adrenocortical carcinoma (n = 6), ganglioneuroma (n = 6), and vascular tumors (n = 1). The diagnostic methods of metastases included pathological diagnosis (n = 8), volume increase newly found or within 12 months of follow-up (n = 22), volume reduction in the interval after systemic chemotherapy (n = 18), and abnormally high 18F-FDG uptake (n = 12). All non-adenomas, except for metastases, were confirmed by histopathology.In institution 1, 118 lesions were diagnosed as lipid-poor adrenal adenomas by the following methods: histopathological diagnosis (n = 108), size stability (n = 9), and abnormal 18F-FDG uptake (n = 4). Spindle cell tumors, pheochromocytomas, adrenocortical carcinomas, and ganglioneuromas were confirmed by histopathology.In institution 2, 23 lesions were diagnosed as lipid-poor adrenal adenomas by histopathological diagnosis (n = 19) or size stability (n = 4). The 29 non-adenomas included metastatic tumors (n = 13), spindle cell tumors (n = 1), pheochromocytoma (n = 11), adrenocortical carcinoma (n = 2), and ganglioneuroma (n = 2). The diagnostic methods of metastases included volume increase either newly found or within the 12-months follow-up (n = 7), volume reduction in the interval after systemic chemotherapy (n = 2), and abnormally high 2 vs 23.4 ± 3.5 kg/m2, p < 0.001). There was no significant difference between the training and test sets for all variables . The lipid-poor adenomas had smaller diameters than did the non-adenomas  in all data sets. There was no difference in tumor distribution between lipid-poor adenomas and non-adenomas. There were no significant differences in any of the image characteristics between the training and test sets.Multivariable logistic regression analysis revealed that unenhanced attenuation and tumor diameter were independent predictors of lipid-poor adenomas Table 3.The rad-scores of adrenal lipid-poor adenomas were higher than those of adrenal non-adenomas in all data sets . The rad-scores of adrenal lipid-poor adenomas, pheochromocytomas, and metastases are shown in Detailed information on the performance of the different models is presented in Examples of clinical uses of the nomogram are shown in Adrenal lipid-poor adenomas hamper the distinction of adenomas from non-adenomas; yet, this is an important matter as the differential diagnosis affects clinical decisions. To our knowledge, this is the first study to identify adrenal lipid-poor adenomas and non-adenomas using radiomics with a relatively large sample. This study developed an unenhanced CT-based nomogram model that showed excellent diagnostic performance  in distinguishing lipid-poor adenomas, which means that contrast-enhanced CT can be avoided in patients with adrenal lesions. In the process of feature extraction, customized parameters, including bin-width and voxel array shift according to data characteristics, were applied. Feature extraction is a key step in radiomics studies, and it is necessary to consider customizing the extraction rather than using default parameters in the software package in most situations. The radiomics quality score in this study was 21 . Among tA previous study found that washout CT could effectively identify adrenal adenomas with a sensitivity of 93.9% and specificity of 95.8% ; howeverUnexpectedly, the conventional model combined with unenhanced attenuation and tumor diameter also showed good performance in identifying lipid-poor adenomas, with an AUC of 0.94 in the training set and 0.86 in the external validation set. There was no statistically significant difference between the performance of the conventional model and our nomogram model. Adrenal lipid-poor adenomas had lower unenhanced attenuation, while the two radiomics features of the first order-90 percentile and first order-median were negatively correlated with adrenal lipid-poor adenomas, which was mutually validated. Previous studies might have paid more attention to the unenhanced CT attenuation value of 10 HU as a cutoff point; nevertheless, unenhanced attenuation demonstrates good ability to identify adrenal lipid-poor adenomas, although it was not further explored in some studies , 29. In Our study has some limitations. First, the number of samples of some types of non-adenomas with low incidence  was insufficient, and the ability of the model to differentiate them was uncertain . Second,In this study, we developed a CT-based radiomics nomogram model, which could effectively identify adrenal lipid-poor adenomas and non-adenomas as a novel, non-invasive method for assisting clinical decision-making. In addition, our study implied that the diagnostic power of conventional unenhanced CT imaging features for identifying adrenal adenomas may be underestimated, and further exploration is worthy.The original contributions presented in the study are included in the article/BZ, HZ, WP, and XD: conception and design. HZ, XL, JC, SJ, and WJ: collection and assembly of data. HZ, and BZ: data analysis and interpretation. BZ, XD, and JY: manuscript writing. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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