Coronavirus disease 2019 (COVID-19) survivors face societal stigma. The study aims to analyze the association of this stigma with the mental health and quality of life of COVID-19 survivors.In this cross-sectional study, we observed 547 adults who were previously documented as severe acute respiratory syndrome coronavirus (SARS-CoV-2) positive by a polymerase chain reaction (PCR) test, treated in a hospital or an emergency hospital and proven to be SARS-CoV-2 negative by their latest PCR test. We adopted the Berger HIV Stigma Scale to measure stigma; the World Health Organization Quality of Life Brief Form to measure quality of life; and the Mental Health Inventory-38 to measure mental health. The chi-square and binary logistic regression tests were used to find the correlation between the variables.The multivariate analysis revealed that medium stigma was more likely related to quality of life and mental health than low stigma. Females were less likely to experience stigma related to mental health than men, and respondents who worked as laborers and entrepreneurs were less likely to experience stigma related to mental health than those who worked as civil workers/army personnel/teachers/lecturers. COVID-19 survivors experienced medium stigma in society and lower quality of life and mental health status. We found that quality of life and mental health were affected by stigma, sex, and occupation.COVID-19 survivors are a vulnerable group that is most at risk when they return to their communities. Creating a safe environment and providing respectful care, including addressing complex stigma factors, is vital for developing appropriate interventions. The coronavirus disease 2019 (COVID-19) pandemic has affected the worldwide population, thus creating a burden of disease and mortality and an unprecedented impact on social life . InsuffiIndonesia is one of the countries that has suffered remarkably in terms of the number of COVID-19 cases. With approximately 270 million inhabitants of over 300 ethnicities scattered across 34 provinces, morbidity and mortality associated with COVID-19 in Indonesia are among the highest the world wide, and together with India and Sri Lanka, Indonesia continues to report the highest number of new cases and new deaths in Southeast Asia, thus contributing to the global burden of COVID-19 cases . Global Although the Indonesian government has implemented certain interventions, including quarantine, travel restrictions, social distancing, and health education , the virus continues to spread through community transmission . The funAs Indonesia continues its effects to reduce the number of new confirmed cases and new deaths, the incidence of stigma toward COVID-19 survivors in social media has become a serious concern . People The Quality of Life (QoL) scale is an important measurement of the impact of COVID-19 infection on the physical, mental, and social domains of COVID-19 survivors. Assessing QoL helps health care providers identify key factors affecting QoL and recognize the aspects of COVID-19 management that can be improved to enhance the QoL of COVID-19 survivors . A totalWe conducted a cross-sectional study of adult COVID-19 survivors in East Java Province, one of Indonesia’s 34 provinces. According to statistics, East Java has one of the highest numbers of confirmed COVID-19 cases, deaths, and recoveries , 9. AdulRecruitment of participants was conducted through an online platform, the COVID-19 Survivors Community registry. Potential respondents were identified from this registry, and an invitation via a one-to-one private WhatsApp message was sent to each survivor. Their responses were obtained by data collectors (aged > 20 years) trained in research methods, and they were neither students nor trainees. Once the data collectors received feedback from the COVID-19 survivors, they were assessed for eligibility against certain criteria. The respondents who met the criteria were provided with brief information about the study, and those who agreed to participate were enrolled and invited to participate via questionnaires, which were circulated through an online platform. Those who were interested in joining the survey were asked to voluntarily fill out the online form.In this study, we measured three aspects of post-COVID-19 life: stigma, QoL, and mental health. All the questionnaires were translated into Bahasa Indonesia and pilot tested prior to the study. Validation was conducted before using this measurement tool. Stigma among COVID-19 survivors was measured using the Berger HIV Stigma Scale questionnaire, which was adapted for COVID-19 stigma . CronbacThe second questionnaire utilized was the WHO Quality of Life Brief Form (WHOQOL-BREF), which originally contained 26 items. The current study used the WHOQOL-BREF Indonesian version , which hThe Mental Health Inventory-38 was used to assess the mental health state of COVID-19 survivors . The queDistributions of the characteristics of respondents were represented using descriptive statistics. The chi-square test was used to determine the association of stigma with mental health and quality of life faced by COVID-19 survivors. Binary logistic regression tests were performed by adjusting all variables with a p value < 0.05. The associations among variables were measured as odds ratios and 95% confidence intervals (CIs). The regression model met the requirements of both the omnibus  and the Hosmer & Lemeshow tests for goodness of fit . All statistical analyses were performed using SPSS version 20 .In the Indonesian setting, confirmed COVID-19 cases and deaths as well as recovered cases continue to be reported; however, research on issues related to stigma, QoL and mental health status among COVID-19 survivors is currently limited. COVID-19 survivors seem to be vulnerable in the community, putting them at the greatest risk in the general population. We found that a high level of stigma was positively correlated with lower QoL and mental health status among Indonesian COVID-19 survivors. Our findings point toward stigmatization among COVID-19 survivors and reveal the need to develop specific programs for targeted groups.Pandemics may increase stigmatization, as previously observed during the severe acute respiratory syndrome (SARS) epidemic and the bubonic plague , 29. TheIn our study, stigma among COVID-19 survivors remained a salient issue that was significantly associated with QoL. A high level of stigma was positively associated with lower QoL. These findings are consistent with those of studies conducted among health care workers in Italy and Egypt , 38. ConIn this study, we found that females were less likely to experience stigma related to mental health than males; however, females present significantly higher levels of stress and anxiety and poorer mental health statuses . Males aWe also discovered that laborers and entrepreneurs were less likely to experience stigma related to mental health. Testing positive for COVID-19 while working as a laborer may cause insecurity due to lost work productivity. However, a good and conducive work environment can provide material, psychological and social support. Previous research has stated that a good work environment can provide support for COVID-19 survivors to recover quickly and promote enthusiasm during quarantine , 51. SucIn our research, the respondents were asked specific questions about several elements of their QoL and mental health. Standardized surveys measuring QoL and mental health status can reduce information bias , which wA considerable proportion of the COVID-19 survivors in this cross-sectional study experienced COVID-19-related stigmatization at a medium level. Stigma among COVID-19 survivors has a close relationship with their QoL and mental health. These findings highlight the need for specific research and targeted interventions to address these issues for COVID-19 survivors. Given that Indonesia has suffered a high number of confirmed COVID-19 cases and deaths, the stigma experienced by COVID-19 survivors should be more broadly studied. COVID-19 survivors are a vulnerable group, and it is essential to identify new strategies to promote the well-being of this group as soon as possible. Our findings can inform policymakers to ensure the availability of a safe environment supported by respectful care. Urgent action is required to destigmatize COVID-19 at every level, ranging from personal to policy.


Circular RNAs (circRNAs) are non-coding RNAs with covalent closed-loop structures and are widely distributed in eukaryotes, conserved and stable as well as tissue-specific. Malignant solid tumors pose a serious health risk to children and are one of the leading causes of pediatric mortality. Studies have shown that circRNAs play an important regulatory role in the development of childhood malignant solid tumors, hence are potential biomarkers and therapeutic targets for tumors. This paper reviews the biological characteristics and functions of circRNAs as well as the research progress related to childhood malignant solid tumors. Although rare, childhood malignancies are now the second leading cause of pediatric death . FurtherCircular RNAs (circRNAs) are a class of non-coding RNAs (ncRNAs) formed by the 3 and 5′ ends of mRNAs, which are mainly generated from introns or exons by reverse splicing or lassoing introns . PreviouRecently, circRNAs have been found to have important biological roles in childhood malignant solid tumors, hence the potential to become tumor markers and therapeutic targets. This paper reviews the formation and biological functions of circRNAs, as well as the research on circRNAs in childhood malignant solid tumors to provide new perspectives for the clinical diagnosis and treatment of childhood malignant solid tumors.CircRNAs are formed by splicing of precursor mRNAs (pre-mRNAs) and are mostly endogenous. They have no 3′ tail or 5′ cap end and are covalently closed loops  formed bDrosophila , binding RBPs, regulating parental gene expression, and translating proteins or polypeptides.CircRNA contains a miRNA response element (MER), which can act as ceRNA to affect gene expression by competitively binding miRNA sites and inhibiting the regulatory effect of miRNA on downstream genes . For exaCircRNAs can bind directly to RBPs to form RNA-protein complexes to regulate RBPs and further affect the expression and biology of downstream proteins . MusclebSome circRNAs also regulate the transcription of parental genes. Although most circRNAs are located in the cytoplasm, some circRNAs such as ElciRNA are in the nucleus of eukaryotic cells and play an important role at the transcriptional level . ElciRNAMost circRNAs generated by reverse splicing are found mainly in the cytoplasm, and translation of linear mRNAs usually requires the structure of a 5′ end cap and a 3′ end poly(A) tail to remain stable. CircRNAs are not normally considered to have a translational function  but theyM6A methylation modification is the most common modification method in eukaryotic RNA, and it mainly affects mRNA splicing, nucleation and translation. Recent studies show that modification of m6A can affect the nuclear localization of circRNA, and circRNA can also bind to the corresponding regulatory protein of m6A to affect its stability. Internal ribosome entry site (IRES) can promote the initiation of circRNA translation, and circRNA with single or multiple sites of m6 A modification can also initiate translation through IRES, which indicates that the presence of m6 A modification may affect the process of circRNA translation . CircZNFMany circRNAs are aberrantly expressed in pediatric malignant solid tumors and regulate tumor development..The stabHepatoblastoma (HB) is an embryonal malignancy originating in the liver and is the most common malignant solid tumor of the liver in childhood . The incIn vivo experiments showed that circ_0043800 promoted HB tumor growth by upregulating Gli2 and STAT3  is upregulated in HB tissues and cells, and inhibition of circ-STAT3 significantly inhibits HB cell growth, migration, and stemness. Circ-STAT3 can act as ceRNA by binding miR-29a/b/c-3p to upregulate Gli2 and STAT3, while Gli2 can activate the transcription of circ_0043800. nd STAT3 . Circ-HMtabolism . Hsa_cirtabolism .Neuroblastoma (NB) originates from primitive sympathetic ganglion cells  and is ain vivo. Mechanistic studies suggest that hsa_circ_0132817 can promote tumorigenesis in NB cells by upregulating NOL4L and acting as a sponge for miR-432-5p  is the most common primary malignancy of the kidney in children, accounting for 90% of all renal malignancies . The etiThe expression of circ-CDYL is significantly downregulated in WT tissues compared to adjacent non-tumor tissues and upregulation of circ-CDYL inhibited cell proliferation, migration, and invasion. Circ-CDYL acts as a miRNA sponge reducing the expression of miR-145-5p and further upregulating TJP1 expression . Cao et Rhabdomyosarcoma (RMS) is a malignant tumor arising from embryonic mesenchymal tissue, accounting for 15% of solid tumors and 50% of soft tissue sarcomas in children . The divp-Akt protein levels and altered pRb/Rb ratios. In contrast, the knockdown of circ-ZNF609 had no significant effect on ARMS-derived cells but the exact reason for this is unclear  and alveolar RMS (ARMS). Knockdown of circ-ZNF609 in ERMS cell lines inhibited the cell cycle and led to a strong reduction in  unclear . Circ-VA unclear .Lymphoma is a highly heterogeneous disease  with incin vitro and tumor growth in vivo. Mechanistic studies revealed that circ-APC acts as a sponge for miR-888 in the cytoplasm to upregulate APC, whereas, in the nucleus, circ-APC binds to the APC promoter and recruits the DNA demethylase TET1, which transcriptionally upregulates APC, thereby inhibiting the typical Wnt/β-catenin signaling pathway by reducing the accumulation of β-catenin in the nucleus and the catenin signaling pathway by reducing the accumulation of β-catenin in the nucleus, retarding the growth of DLBCL  and found that circ-LAMP1 was significantly increased in T-LBL. Mechanistic studies revealed that circ-LAMP1 promotes cell proliferation and inhibits apoptosis through the miR-615-5p/DDR2 signaling axis, which in turn leads to malignant progression of T-LBL . Circ-NSof DLBCL . In DLBCof DLBCL . Circ-OTof DLBCL . Circ-NSof DLBCL .Medulloblastoma (MB) is a highly malignant neuroepithelial tumor of the central nervous system and is a common solid tumor in children , particuin vivo. Mechanistic analysis suggests that circ-SKA3 directly targets miR-326 to increase ID3 expression  is a malignant endocrine tumor  that typMany circRNAs that are highly expressed in blood have relatively low expression of their corresponding linear RNAs . The uniZheng et al. analyzed the association between circ-HMGCS1 expression levels and clinical features of HB finding a significant correlation between circ-HMGCS1 and AFP. The subject operating characteristic (ROC) curve was applied to investigate the diagnostic value of circ-HMGCS1 in distinguishing HB tissue from normative tissue showing that circ-HMGCS1 had diagnostic value (Area Under Curve (AUC) = 0.8283), with high expression of circ-HMGCS1 predicting poor prognosis in HB patients by Kaplan-Meier survival curve analysis . The expAt present, there are few circRNAs studies in pediatric malignant solid tumors with variable results. However, all studies have shown that the expression of multiple circRNAs is dysregulated in pediatric malignant solid tumors, and some of the circRNAs target binding miRNAs that are involved in tumorigenesis and progression. The current exploration of tissue-specific circRNAs and studies of circRNAs-miRNAs-mRNAs networks have revealed the association of circRNAs with childhood malignant solid tumors. In addition, cellular and animal experiments have confirmed that for certain circRNAs that promote tumor growth, reducing their levels by targeted knockdown or RNA interference can inhibit further tumor growth and metastasis, suggesting that circRNAs are novel therapeutic approaches and drug targets for malignant tumors.However, the main mechanism currently revolves around circRNA as ceRNA to regulate the function of tumor cells. Whether circRNA may affect the occurrence and progression of childhood solid tumors through other mechanisms The regulation is still unclear, and this needs to be further explored. In addition, most of the discoveries of circRNAs were not screened by next-generation sequencing technology, but were studied by referring to other articles, which lacked novelty. More importantly, only through high-throughput sequencing technology, the establishment of circRNAs expression profiles in childhood solid tumors is conducive to in-depth exploration of possible mechanisms.Currently, the detection of circRNAs in tumors is mainly focused on tissue samples, which are more invasive and not suitable for early clinical tumor diagnosis  comparedMoreover, the application of circRNAs as biomarkers for the diagnosis of malignant solid tumors in children is challenging. Although circRNAs are a promising biomarker due to their high organ-tissue specificity, current studies have not demonstrated that the sensitivity of candidate circRNAs is superior to that of known classical serum biomarkers. Compared to tissue biopsies, liquid biopsies have the advantages of non-invasiveness and reproducibility, however, circRNAs are low in body fluids, clinical data from peripheral blood studies are limited, and most circRNAs have not been shown to have excellent prognostic or diagnostic significance in large samples. Further studies should be conducted to evaluate the expression of circRNAs in serum and disease-related body fluids and there should be a consensus regarding sample handling, detection methods, and threshold values to enable the development of circRNAs as clinical diagnostic biomarkers. In addition, co-detection of tumors may lead to higher sensitivity and accuracy of diagnostic results.In summary, although much is now known about the formation of circRNAs and their biological role and clinical application in childhood malignant solid tumors, the use of circRNAs for the treatment of pediatric malignant solid tumors requires further investigation. In addition, since many reviews on circRNAs in osteosarcoma have been published, osteosarcoma-related circRNAs were not included in the present review.


MYH14p = 1.4 × 10−7), rs11243705 in SETX (p = 8.2 × 10−6), and rs16885 in ATXN1 (p = 1.3 × 10−5). One of the identified variants, rs33985936 in SCN11A, was successfully replicated in an independent study, and three of the variants were found to be associated with blood-related quantitative traits according to the UK Biobank data . Moreover, we show that a risk score based on these variants can predict the severity and outcome of hospitalization in our cohort of patients. Given these findings, we believe that our work may serve as proof-of-concept study demonstrating the utility of quantitative traits and extensive phenotyping for identification of genetic risk factors of severe COVID-19.The COVID-19 pandemic has drawn the attention of many researchers to the interaction between pathogen and host genomes. Over the last two years, numerous studies have been conducted to identify the genetic risk factors that predict COVID-19 severity and outcome. However, such an analysis might be complicated in cohorts of limited size and/or in case of limited breadth of genome coverage. In this work, we tried to circumvent these challenges by searching for candidate genes and genetic variants associated with a variety of quantitative and binary traits in a cohort of 840 COVID-19 patients from Russia. While we found no gene- or pathway-level associations with the disease severity and outcome, we discovered eleven independent candidate loci associated with quantitative traits in COVID-19 patients. Out of these, the most significant associations correspond to rs1651553 in  It was caused by a new strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and was later named COVID-19 [For two years, researchers have been trying to understand the association between the various symptoms of the disease and host genetics. Identifying specific single nucleotide polymorphisms (SNPs) and other genome variations associated with COVID-19 severity and other symptoms is very important as it can help scientists and clinicians better understand disease pathology. Prediction of genetic susceptibility to COVID-19 might aid clinicians to choose the right treatments for patients .SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1 for 3p21.31 and ABO—for 9q34.2.Recent studies reported several dozen associations between genetic variants and morbidity, severity, mortality of COVID-19 among different ethnicities . For exDespite the large number of reported associations, variants that were discovered in one study can be insignificant in another . To idenACE2 gene [While genome-wide studies have been generally successful in identifying genetic risk factors of COVID-19, the search for new associations in poorly studied populations might be complicated due to limited cohort size and/or in case of limited breadth of genome coverage . Recently, we used targeted sequencing to analyze variants associated with COVID-19 severity in Russia within the CE2 gene . In thisThe study design is an observational clinical trial. We utilized 840 medical records from COVID-19 patients who received treatment in the St. Petersburg State Budgetary Institution of Healthcare City Hospital 40  from 18 April 2020 to 21 November 2020. The size of the sample is expected to provide sufficient power for the identification of quantiative trait loci (QTL) according to recent studies .2 < 95%. Clinical and radiological criteria for severe course were respiratory rate more than 30/min, SpO2 ≤ 93%, PaO2/FiO2 ≤ 300 mmHg, progression of changes in the lungs typical for COVID-19 pneumonia according to CT data, including an increase in the prevalence of revealed changes by more than 25%, as well as the appearance of signs of other pathological conditions, changes in the level of consciousness, unstable hemodynamics , and qSOFA > 2 points. The criteria for an extremely severe course were signs of ARF with the need for respiratory support (invasive ventilation), septic shock, and multiple organ failure.In accordance with the International and Russian Recommendations for the Prevention, Diagnosis and Treatment of New Coronavirus Infection (COVID-19), all patients were divided in three groups of comparable age , exposure period, date of disease onset, date of first admission, and date of confirmation. We analyzed the presence of the following risk factors: obesity, arterial hypertension under treatment and risk factors of Charlson Comorbidity Index and their impact on the severity of COVID-19 .2, and respiratory distress); calculation of the National Early Warning Score (NEWS), a recommended scoring system for use in COVID-19 patients [Medical examination of all patients included physical examination and assessment of hemodynamics and respiratory system  inhibitors , IL-1 inhibitors , JAK inhibitors , Bcr-Abl tyrosine kinase inhibitor (radotinib), and glucocorticoids for some cases. Respiratory therapy, modified antibacterial therapy, extracorporeal membrane oxygenation, sepsis, and septic shock treatment  were performed in a stepwise fashion according to indications [Exome sequencing was performed using either Illumina or MGI sequencing platform. For Illumina, we started gDNA libraries preparation with 500 ng of gDNA sheared using Covaris S2 Focused-ultrasonicator. The fragmented DNA was then converted into DNA-libraries using KAPA HyperPrep Kit . The exome-enrichment of DNA-libraries was done using HyperCap Target Enrichment kit and SeqCap EZ Share Choice XL Probes IDP2_REZ clinical exome probe set , following the manufacturer’s protocol.For MGISEQ, gDNA libraries preparation started with 500 ng of gDNA sheared using Covaris S2 Focused-ultrasonicator. The fragmented DNA was then converted into DNA-libraries using KAPA HyperPrep Kit  in combination with MGIEasy DNA Adapters-96 . The exome-enrichment of DNA-libraries was done using HyperCap Target Enrichment kit and IDP2 clinical exome probe set , according to the manufacturer’s protocol with the following modifications: 1 Paired-end reads no shorter than Each exome sample was aligned onto a GRCh38.p13 reference genome assembly provided in the Genome Analysis ToolKit (GATK)  bundle uhttps://github.com/edm1/rank-based-INT, accessed on 15 June 2021), as normalization of phenotypic data using the inverse IRNT may increase the power of genome-wide association analyses [Prior to association analysis, the phenotypic information was preprocessed in the following way: first, outliers—values that deviate more than 3 standard deviations from the population average—were eliminated, and the data was normalized using the rank-based inverse normal transformation (IRNT) . Before the analysis, variant- and sample-level quality control was conducted: first, we filtered variants with a call rate  of less than 0.9, the minimal allele frequency of less than 0.05, and the Hardy-Weinberg equilibrium (HWE) p-value of less than 0.001. We also filtered out samples with a call rate of less than 0.95 and a heterozygous-to-homozygous call ratio of more than 3. Finally, we performed principal components analysis (PCA) on the resulting set of genetic variants. No outliers were found during PCA.To find associations between genetic variants and disease-related traits we conducted common variant association analysis using the Hail framework for Python v. 0.2.63 .Variants with p < 10 −4 were selected as candidate associations for traits that showed the presence of association signal on the Q-Q plots. Following the selection of significant associations, variants were grouped into independent loci by using the clumping method in PLINK v. 1.9 [After quality control, a set of 13,983 high-quality SNPs and 757 samples remained for the association analysis. Association test was conducted using a linear regression method, including several covariates: age, sex, sequencing platform and run, PCA scores for two first principal components (estimated using genetic data), and the Charlson comorbidity index. Association results were evaluated using Manhattan and quantile-quantile (Q-Q) plots, as well as the genomic inflation factor  were selected during filtering instead of the common ones. Only high-impact variants were selected according to the VEP annotations, including splice acceptor and splice donor site variants, nonsense variants frameshift variants, as well as variants leading to loss of start and stop codons. After quality control and selection of variants, variant counts were aggregated by calculation of the total number of individuals carrying selected variants in each gene. We used Fisher’s exact test to assess the significance of the differences between individuals with different values of binary traits, such as the outcome of hospitalization (death/recovery), severity (severe/not severe), and the presence/absence of a cytokine storm. Results were evaluated using Manhattan and quantile-quantile plots as described above.p-value less than 0.05 to be successfully replicated.To prove the biological relevance and significance of the identified variants, we sought for additional evidence of the role of the variants in COVID-19 or other relevant complex traits. For replication of the identified associations, we used the results of the COVID-19 HG project  release p < 10−5 were considered as significant PheWAS hits .In order to identify additional (non-COVID-19 related) associations of the identified variants, we searched for information about their association with other complex traits or gene expression levels using Global Biobank Engine  or the Gj, n is the number of lead SNPs, i in patient j, and i.For the validation of the relevance of the identified variants for predicting disease severity and outcome, we calculated a risk score that summarized risk effects from all lead SNPs in each patient. The risk score was calculated as follows:The score was computed for each patient, and then the cohort was divided into two groups corresponding to the top decile of patients with the highest risk  and the remaining patients in the dataset. After that, Wilcoxon-Mann–Whitney U-test and the chi-squared tests were applied to test for differences in the continuous and categorical features , respectively.To analyze the genetic susceptibility factors to severe COVID-19, we used clinical exome sequencing data of 840 individuals from a cohort of patients of the City Hospital No. 40 with confirmed COVID-19 diagnosis.Prior to all further analyses, the sequencing data were uniformly processed (see Methods for more details on the bioinformatic pipeline used) and jointly genotyped. A total of 727,656 genetic variants were discovered in our sample. 98,382 of these variants were non-synonymous variants (including missense and putative loss-of-function (pLoF) variants). After filtering out variants with low quality and/or call rate, 13,983 of the remaining ones were common . Out of the remaining rare (AF < 5%) variants, 1884 variants were annotated as pLoF variants in the canonical transcripts of 1121 protein-coding genes. All individuals were assessed for the presence of monogenic immune system disorders, with no pathogenic variants identified according to the ClinVar database .Identification of significant genome- or exome-wide associations can be difficult in cohorts with limited size; hence, we decided to undertake a more systematic approach and analyze the genetic factors of COVID-19 using an extensive collection of phenotypic data available for our cohort of patients. A broad set of more than 100 quantitative and binary traits were collected for each patient. The set of traits included the major parameters that serve as predictive risk factors of severe COVID-19 according to a recent publications : serum lNext, we used the obtained set of phenotypes to search for genes and genetic variants associated with COVID-19 severity. To do so, we applied a multi-perspective analysis strategy by using both common and rare variant association tests b. For biWe first conducted common- and rare-variant association analysis with binary traits (death and severity). Common variant association analysis identified no significant associations and no evidence for the exome-wide association signal as shown by the quantile-quantile plots .We next tested the involvement of rare variants in clinically significant genes by conducting a series of rare variant association tests using both gene- and pathway-level aggregation of variant counts . To enhWe next turned to the analysis of single-variant associations using a broad panel of quantitative trait data. In this analysis, we performed exome-wide association analysis using a set of 13,983 common (MAF > 0.05) variants discovered in our genotype dataset and a set of 53 pre-processed quantitative traits with low missingness rate  or expression levels of the corresponding gene.Of the 11 independent variants identified in our analysis, 9 corresponded to significant cis-eQTLs according to the Genotype Tissues Expression (GTEx) data. Four of these variants corresponded to cis-eQTLs affecting the expression of multiple genes across multiple tissues. Of these, three variants had the most significant effect on neighboring genes: the rs2276638 intron variant in the While we identified 11 independent genetic variants that are associated with quantitative traits that are directly connected to the disease severity and outcome, it is important to note that the significance level of these associations is not sufficient for making a confident conclusion about the effects on the patient phenotype. This predicates the need for additional replication of the observed associations and validation of their true role in the pathogenesis of COVID-19.To obtain such a validation, we first questioned whether the identified variants can be used to directly predict the severity of the disease and/or outcome in our cohort. We began by constructing a simple risk score by computing the weighted sum of risk alleles in the genotype of each patient (see Methods for details). The score had a nearly normal distribution a. To tesHaving demonstrated the general relevance of the identified variants for predicting the severity of the disease and its outcome, we then analyzed the enrichment of gene sets for the identified loci to test for common function among genes harboring top associations. We performed such an enrichment analysis using several tools designed for GWAS data, including LSEA  and FUMASCN11A) which showed modest significance in the analysis of COVID-19 patients vs. population (C2 comparison in COVID-19 HG). We also attempted replicating our findings in the results of the Severe COVID-19 GWAS Group . No genetic variants were successfully replicated in this study  complex traits associated with the identified variants, To this end, we performed phenome-wide association analysis (PheWAS) using the Global Biobank Engine . All assTo sum up, we identified a set of 11 genetic variants showing modest association with quantitative and nominal traits linked to COVID-19 severity. For three of these variants, we were able to find supporting evidence substantiating their role in the COVID-19 pathogenesis.The COVID-19 pandemic has drawn significant attention to the interactions between the host genome and pathogens in infectious disease pathogenesis. Over the last two years, a series of publications addressed the issue of hereditary predisposition to SARS-CoV-2 infection and severe disease . AnalysiDNAJB2, rs34600315 in PIEZO1). Few of the identified variants affect gene expression in the lungs. This observation may be explained by the specifics of the analysis strategy which is mostly focused on blood-borne traits in COVID-19 patients.Only one of the identified variants successfully passed replication in external cohorts, with two additional variants demonstrating nominal significance in independent studies. These results are similar to the ones obtained by Li et al. . As arguMYH14 gene encoding for nonmuscle myosin II C (NMIIC) predominantly expressed in the inner ear, including the organ of Corti [MYH14 increase risk of neurological progression of type 2 diabetes and peripheral neuropathy [MYH14 function may thereby increasing the severity of respiratory failure in COVID-19 infection. However, further investigations are needed to establish the exact role of MYH14 in disease progression.Several genes belonging to the top associated loci in our study deserve a detailed discussion. First, the most significant (and the only significant on the exome-wide level) variant corresponded to the of Corti . Non-synuropathy ,30. Suchuropathy . Hence, DNAJB2 gene encodes an important protein of the Hsp40 chaperone group. Such proteins are known to contribute to the substrate-specificity of other chaperones and mediate the stress response [DNAJB2 gene variation in the levels of lung damage upon SARS-CoV-2 infection is interesting and may point to the role played by the stress response pathways and protein quality control in disease severity. it can be hypothesized that the heat stress response is important for alleviation of negative effects of inflammation on the structure of the tissue; thus, deviation in the regulation of response to heat dresses may trigger the destruction of the lung tissue and cause lung fibrosis and respiratory problems in COVID-19 patients.Second, the response ,32. The LZTR1 gene is also notable as this gene encodes an important leucine-zipper transcriptional regulator that is linked to cell proliferation in cancer [THAP7 gene and its antisense transcript, THAP7-AS1. THAP7 encodes a transcriptional repressor that acts via histone deacetylation [THAP7 overexpression induces permissiveness of human hepatoma Huh7 cells to hepatitis C viral invasion [LZTR1 per se, as target genes affected by rs112544, may be involved in immune system function and anti-viral response. PIEZO1 codes for mechanically-gated ion channels with multiple roles in human organisms, mutations in this gene are comorbid with pathological conditions, namely, hereditary anemia [PIEZO1 may affect the immune response to SARS-CoV-2.The association of the locus spanning the n cancer . The leatylation ). Notablinvasion . These dy anemia , congeniy anemia , lymphaty anemia , and othPKHD1 gene) worsen the ramifications of the autosomal recessive polycystic kidney disease (ARPKD) in children and adults [ATXN1, GABBR2, SETX, CDH23) are also implicated in nervous system pathology but are not clearly linked to immunity and/or infectious disease response [Variants in fibrocystin  variants, suggesting that the actual causal variants might be located further from covered exome regions. However, our results demonstrate the utility of deep laboratory phenotyping of COVID-19 patients for the identification of novel genetic variants affecting the severity and/or outcome of the disease. Hence, we believe that our work may serve as an example of successful indirect identification of genetic risk factors of severe COVID-19.


Rice is a major staple food crop for more than half the world’s population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.The online version contains supplementary material available at 10.1186/s13007-022-00839-5. Oryza sativa) is a staple food crop for nearly half the world population [Triticum aestivum) (765 million tonnes) and maize (Zea mays) (1.1 billion tonnes). As the global population is expected to reach 9.7 billion by 2050 [Rice  a reduction in carbon capture, photosynthetic efficiency, or the duration of the grain-filling period inhibits the plant’s ability to provide a sufficient amount of assimilates to the seed, (2) reduced activity of starch metabolism enzymes, which are used to convert sugars to starch, and (3) hormonal imbalance between ABA and ethylene as a high ABA-to-ethylene ratio is vital during grain-filling . PhysiolTraditional grain phenotyping has been performed by manual inspection . As suchIn particular, several automated approaches for rice grain chalkiness classification, segmentation and/or quantification have been developed. For example, the K-means clustering approach performs instance segmentation  by grouping pixels based on their values . One advIn addition to the K-means clustering approach, threshold based approaches have been used for chalkiness identification and quantification. For example, a multi-threshold approach based on maximum entropy was used for chalky area calculation  and anotIn recent years, the use of deep learning approaches for image classification and segmentation crop science tasks have led to state-of-the-art high-throughput tools that outperform the results from traditional machine learning and image analysis techniques , 41, enaTo address the limitations of the Mask R-CNN approach, we framed the problem of detecting chalkiness as a binary classification problem  and used CNNs combined with class activation mapping, specifically Grad-CAM , to idenWe proposed to use a weakly supervised approach, Grad-CAM, to classify rice grains as chalky or non-chalky and subsequently detect the chalkiness area in chalky grains.We experimented with the Grad-CAM approach (with a variety of CNN networks as backbone) on polished rice seeds and evaluated the performance using both instance classification and segmentation metrics as well as time and memory requirements.We compared the weakly supervised Grad-CAM approach with the Mask R-CNN segmentation approach on polished seeds and studied its transferability to unpolished rice seeds .We tested the applicability of the tool in determining the level of chalkiness in rice plants exposed to high night temperature (HNT) and quantified the differential level of chalkiness among tillers within a plant exposed to HNT stress.The Grad-CAM based approach to rice chalkiness detection has the potential to help rice phenomics catch up with the developments in rice genomics  as well We address the rice chalkiness segmentation problem using a weakly supervised Grad-CAM approach, which requires binary  image-level labels as opposed to more expensive pixel-level labels.The Grad-CAM approach includes two main components: (i) a deep CNN network  that is trained to classify seed images into two classes, chalky or non-chalky; and (ii) a class activation mapping component, which generates a rice chalkiness heatmap as a weighted average of the feature maps corresponding to a specific layer in the CNN network. The chalkiness heatmap can be further used to calculate a chalkiness score, which quantifies the degree of chalkiness in each individual grain, and to estimate the chalkiness area for each grain. An overview of the approach is shown in Fig. Models based on CNNs have been successfully used for many image classification and segmentation tasks –58. A CNThe ImageNet competition (where a dataset with 1.2 million images in 1000 categories was provided to participants) has led to many popular architectures, including highly competitive architectures in terms of performance as well as cost-effective architectures designed to be run efficiently on low-cost platforms generally present in embedded systems . We antiK feature maps. Let c . Grad-CAM averages the gradients of N pixels c, according to the importance weights F is a non-linear activation function. Traditionally, ReLU has been used as the activation function to cancel the effect of the negative values while emphasizing areas that positively contribute to the category c. The heatmap, c, and implicitly to localize the category in the input image. More specifically, the localization is obtained by binarizing the input image using a threshold T on the values of the pixels in the heatmap as first proposed by Zhou et al. [T is dependent on the data and task at hand, but can be found using a trial-and-error process as shown in related prior works [The Grad-CAM approach was originally proposed by Selvaraju et al.  in the cu et al. . The valor works . Regardior works , under tGrad-CAM is a strong candidate for being used as an explainability/localization approach in the weakly supervised framework, as evidenced by many recent studies that have employed Grad-CAM , 73–76. Z represents the total number of pixels in the GrainArea in the original image. The resulting chalkiness score has a numerical value between 0 and 1, where 0 means that the grain shows no chalkiness and 1 means that the grain has severe chalkiness all over its surface. Finally, the heatmap is used to create a binary mask for the chalkiness area using a threshold on the intensity of the pixels . The masked area can be used to estimate the area of the chalkiness as a percentage of the total grain area. The numeric scores, including the chalkiness score and the chalkiness area, obtained from large mapping populations can be used in determining the genetic control of chalkiness in rice. We used the Grad-CAM approach to generate chalkiness heatmaps for rice grain images. The heatmaps show the concept of chalkiness using soft boundaries through a color gradient. This representation is very appropriate for localizing the concept of chalkiness, which exhibits different levels of intensity and, thus, has inherently soft boundaries that separate the chalky area from the non-chalky area. The heatmap, Mask R-CNN is an object instance segmentation approach, i.e., an approach that identifies instances of given objects in an image  and labels their pixels accordingly. Mask R-CNN extends an object detection approach, specifically Faster R-CNN , to perfIn this section, we describe plant materials and the biological experiment that generated the data  used in this study.Six genotypes  with contrasting chlorophyll index responses to a 14-day drought stress initiated at the agronomic panicle-initiation stage were used in this study . The exp2O5, and 12%K2O) was applied (5 g per pot) before sowing along with 0.5 g of Scotts Micromax micronutrient  at the three-leaf stage. The plants were well-watered throughout the experiment and a 1–cm water layer was maintained in the trays holding the pots. Seventy-two plants were grown with at least 12 plants per genotype wherein 6 plants were used for control and the remainder for HNT. Plants were grown in controlled environment chambers maintained at control temperatures of 30/21 °C  and relative humidity (RH) of 70% until treatment imposition. Both control and HNT chambers were maintained at a photoperiod of 11/13 h  with a light intensity of  850 Seeds obtained from the Germplasm Resources Information Network (GRIN) database were sown at a depth of 2 cm in pots  filled with farm soil. Seedlings were thinned to one per pot at the three-leaf stage. Controlled-release Osmocote  fertilizer , two primary panicles (tillers that followed the main panicle), and other remaining panicles for each plant from each treatment and hand threshed separately. Subsequently, the grains were de-husked using the Kett, Automatic Rice Husker TR-250.In addition to the unpolished grains, polished grains were also used in the initial model development and testing, as polished grains are easier to analyze and label with respect to chalkiness and could potentially be beneficial in terms of knowledge transfer to unpolished rice. The polished grains were obtained from Rice Research and Extension Center in Stuttgart Arkansas, University of Arkansas for preliminary testing and to establish the model. The polished rice grains composed of both medium and long grain rice. For each grain size, there are three degrees of grain chalkiness (roughly estimated by a domain expert): low, medium, and high chalkiness. Thus, based on grain size and degree of chalkiness, the grains were grouped into six categories: (1) long grain, low chalkiness; (2) long grain, medium chalkiness; (3) long grain, high chalkiness; (4) medium grain, low chalkiness; (5) medium grain, medium chalkiness; and (6) medium grain, high chalkiness.Both polished and unpolished grain samples were arranged in transparent 90 mm Petri-plates with three Petri-plates for each sample. A sample corresponds to a size/chalkiness combination in the case of polished rice and a genotype/tiller/condition combination in the case of unpolished rice. Three replicates  were randomly selected (without replacement) for each sample. The grains were scanned using an Epson Perfection V800 photo scanner attached to a computer  was approximately leration . Furtherentclass1pt{minimaThe total number of individual seeds extracted from the images containing Petri-plates with polished rice grains was 1645 out of the total of 1654 grains in the original set of 18 images. Nine seeds got truncated and were removed from the dataset. The exact number of polished seeds in each image and the corresponding number of extracted seed images are shown in Additional file Two types of manual annotations were performed and used as ground truth in our study, as shown in Fig. Out of 1645 polished grains used in our study, 660 grains were labeled as chalky and 985 grains were labeled as non-chalky. The exact numbers of chalky and non-chalky grains in each of the eighteen high-resolution images with polished rice are shown in Additional file Similarly, out of 13,101 unpolished grains, 4085 grains were labeled as chalky and 9,016 grains were labeled as non-chalky. The exact numbers of chalky and non-chalky grains in each of the 108 high-resolution images of unpolished rice are shown in Additional file To train, fine-tune and evaluate our models, we created training, development and test datasets for both polished and unpolished 12) grain images. In the case of polished grain images, for each grain size and chalkiness degree combination, we aimed to use one of the three replicates for training, another one for development and the last one for testing. However, to ensure a larger number of images in the training set (which is common practice in machine learning), we moved some of the instances from the development and test replicates/subsets to the training subset, so that the final distribution of the data split was approximately 2:1:1. In the case of unpolished seed images, for each genotype, tiller and condition combinations, we used a similar procedure to split the three replicates into training training, development and test subsets. The specific distribution of chalky/non-chalky grain images in the training/development/test subsets is shown in Table  grain imIn this subsection, we state several research questions that we aim to address and describe the experiments performed to answer these questions. We also discuss the metrics used to evaluate the models trained in our experiments and the hyper-parameters that were fine-tuned to obtain the most accurate models.RQ1Among different CNN networks used as the backbone in the Grad-CAM models for polished rice, what network is the best overall in terms of chalky/non-chalky classification performance versus time and memory requirements? Also, what network is the best overall in terms of chalkiness segmentation?RQ2How does the Grad-CAM weakly supervised approach to chalkiness segmentation compare with the Mask R-CNN segmentation approach to chalkiness detection in polished rice?RQ3What is the performance of the Grad-CAM models for unpolished rice? What is the performance of the polished rice models when used to make predictions on unpolished rice? Does the performance improve if we fine-tune the polished rice models with unpolished rice?We aim to answer the following research questions (RQ): To answer RQ1, we trained Grad-CAM models with several CNN networks as backbone, including variants of AlexNet, DenseNet, ResNet, SqueezeNet, VGG and EfficientNet pre-trained on ImageNet. We compared the models in terms of classification performance, memory and time requirements. We also identified the best model/network for each type of architecture. Subsequently, we study the variation of those best models with respect to the layer used to generate the heatmaps and the threshold used to binarize the heatmaps when calculating the average Intersection-over-Union (IoU). The goal is to identify the best overall layer and threshold for each type of network. The best models (with the best layer and threshold) are used to evaluate the localization accuracy, both quantitatively and qualitatively, for chalkiness detection in polished rice. To answer RQ2, we also trained Mask R-CNN models (with the default ResNet-101 as backbone) and compared them with the best weakly supervised Grad-CAM approach. Finally, to answer RQ3, we first trained and evaluated a Grad-CAM model (with ResNet-101 as backbone) on unpolished rice. We compared the performance of the resulting model with the performance of a model trained on polished rice and also with the performance of the polished rice model fine-tuned on unpolished rice.We evaluated the performance of the Grad-CAM approach along two main dimensions. First, we evaluated the ability of the approach to correctly classify seeds as chalky and non-chalky using standard classification metrics such as accuracy, precision, recall and F1 measure. Second, we evaluated the ability of the approach to perform chalkiness segmentation  using standard segmentation metrics. Specifically, we calculated average IoU , as wellDeep learning models, in general, and the ResNet, VGG, SqueezeNet, DenseNet EfficientNet networks, in particular, have many configurable hyper-parameters. We tuned several hyper-parameters shown to affect the performance of all models. More specifically, we tuned the batch size used in gradient descent to control the number of rice seeds processed before updating the internal model weights. Furthermore, we tuned the learning rate which controls how much we are adjusting the network weights with respect to the gradient of the loss function. The specific values that we used to tune the batch size were 16, 32 and 64. The values used to tune the learning rate were 0.1, 0.01, 0.001, 0.0001 and 0.00001. For each network, the best combination of parameters was selected based on the F1 score observed on the validation subset. Each model was run for 200 epochs and the best number of epochs for a model was also selected based on the validation subset. Overall, our hyper-parameter tuning process revealed that the performance did not change significantly with the parameters considered. All the models were trained on Amazon Web Services (AWS) p3.2xlarge instances. According to AWSAs opposed to the models used as backbone for the Grad-CAM approach, the Mask R-CNN network with ResNet-101 as backbone could only be trained with a batch size of 8 images on AWS p3.2xlarge instances. The same learning rate values as for the CNN networks were used for tuning. However, this network was trained for a total of 600 epochs, as opposed to just 200 epochs for the other models. No other hyper-parameters specific to Mask R-CNN network were fine-tuned.Table As can be seen from Table T, used to binarize the heatmaps when calculating the IoU. The best layer/threshold combination was selected independently for each type of network using both qualitative and quantitative evaluations. Based on preliminary visual inspection of the heatmaps, we observed that heatmaps corresponding to lower level layers in a network result in better approximations of the chalkiness area, possibly because the progressive down-sampling along the convolutional layers of the backbone CNN makes it hard to precisely recover the chalkiness information from the higher level feature maps [T, varied from To produce accurate detection of chalkiness area, we first studied the variation of the average IoU with respect to the layer used to generate the heatmaps and the threshold, ure maps . TherefoAs shown in Table nt}T=15%  or \docunt}T=20%  thresholTo gain more insights into the heatmap layer and threshold, Fig. The same conclusions regarding the superiority of ResNet-101 for chalkiness segmentation are supported by a quantitative evaluation of the networks in terms of localization metrics computed over the whole test set. The results of this evaluation are shown in Table To understand if the results obtained with Grad-CAM and ResNet-101 can be further improved with an alternative localization approach, we compared Grad-CAM with Grad-CAM++ and Score-CAM (using ResNet-101 as the backbone CNN). The results of the comparison are shown in Table polished model; (2) a model trained on unpolished seed images, called unpolished model; and (3) a model originally trained on polished seed images and subsequently fine-tuned on unpolished seed images, called mixed model. All models were evaluated on the 240 seed images in the unpolished test set, which were manually annotated in terms of chalkiness area. These images belong to one of the 12 combinations corresponding to the Kati and CO-39 genotypes, i.e., unpolished (12) set. The training and developments sets used to train the unpolished and mixed models belong to the unpolished(12) set as well . This is important as researchers working on large breeding populations involving hundreds of lines do not obtain large sample sizes and would not have access to polish a small amount of seeds, which requires models that can effectively operate on unpolished seeds. To address this objective, we performed experiments with three models that use ResNet-101 as their backbone: (1) a model trained on polished seed images, called d model;  a model To visually illustrate the output of each model, Fig. As mentioned in Section "To answer RQ1, we evaluated several CNN architectures in terms of classification accuracy, memory and time requirements, and also chalkiness detection performance in polished rice. While the architectures studied have comparable classification performance, the ResNet-101 network was found to be superior with respect to chalkiness detection in polished rice and has relatively small memory and time requirements. Furthermore, we compared the best weakly supervised Grad-CAM models with the Mask R-CNN segmentation model to answer RQ2 and found that the Grad-CAM models performed better than Mask R-CNN, which needs more expensive pixel level annotation. Overall, the chalkiness detection results obtained for polished rice are remarkably good, with an average IoU of 68.11%, GT-known accuracy of 83.34% and localization accuracy of 83.13%. Finally, to answer RQ3, we used Grad-CAM models trained on polished rice, unpolished rice, and a mix of polished and unpolished rice and evaluated them on unpolished rice. When studying the transferability of the models trained on polished rice to unpolished rice, we found that fine-tuning on unpolished rice is necessary. In fact, models trained directly on the unpolished rice performed the best in our study. More specifically, our evaluation on unpolished rice grain images showed that the best model trained directly with unpolished rice had an average IoU of 51.76%, while both the GT-known accuracy and the localization accuracy were 63.75%. It is not surprising that the models perform better on polished rice as chalkiness is easier to detect after the interfering aluerone layer is removed through milling.While the use of the Grad-CAM approach for rice chalkiness segmentation was extremely successful, one challenge that we encountered was the tuning of the layer to be used for generating the heatmaps as well as the threshold for producing the binary masks for chalkiness area. Our goal was to find a good overall layer and threshold for a model to avoid the pitfall of tuning the threshold for each type of rice seed. Our analysis showed that a lower layer generally results in better chalkiness detection. One explanation for this is that higher levels undergo more extensive down-sampling (through successive applications of pooling layers) and this causes loss of information that cannot be recovered in the chalkiness heatmaps. Regarding the threshold for binarization, our results showed that a higher threshold . However, the approach may not work as well for coloured rice such as brown or black rice, given the more opaque nature of coloured rice grains.T for binarization and convolution layer to be used for the heatmap) for other types of rice, or for images taken under different conditions.Our experiments showed that models cannot effectively be transferred from polished rice to unpolished rice. Instead, models trained specifically on unpolished rice have better accuracy. This result suggests that new models may need to be trained and fine-tuned  perform high-throughput rice seed image analysis to identify chalky seeds and generate chalkiness maps, (ii) replace the expensive error-prone human annotations with rapid and continuous annotations without compromising the accuracy, and (iii) provide quantitative measures for chalkiness area. We successfully demonstrated the application of this tool in accurately capturing the HNT induced differential level of chalkiness in different tillers in rice. The models trained in this study are made publicly available. Being already trained, they will be easy-to-use, scalable and can be readily utilized in ongoing rice breeding programs, without requiring researchers to have computer science or machine learning expertise.Additional file 1: Fig. S1. Steps for rice chalk seed image scanning.Additional file 2: Fig. S2. Image scan of rice seeds.Additional file 3: Table S1. Polished rice seeds statistics.Additional file 4: Table S2. Unpolished rice seeds statistics.Additional file 5: Table S3. Variation of the average IoU with the layer and threshold.Additional file 6: Fig. S3. Examples of Grad-CAM/SqueezeNet-1.0 heatmaps.Additional file 7: Fig. S4. Examples of Grad-CAM/DenseNet-121 heatmaps.Additional file 8: Fig. S5. Examples of Grad-CAM/VGG-19 heatmaps.Additional file 9: Fig. S6.Grad-CAM/Grad-CAM++/Score-CAM heatmaps.Additional file 10: Fig. S7. Examples of predictions on unpolished rice.Additional file 11: Fig. S8. Example of false positive images.Additional file 12: Table S4. Number of grains with and without chalk.


However, information about the acceptability of COVID-19 vaccines in Zambia is lacking. Therefore, the study assessed the prevalence and factors associated with COVID-19 vaccine acceptance among the general population in Zambia.th April to 21st May 2021. We included adult Zambians who had access to Facebook and WhatsApp. A multivariable logistic regression model was fitted to determine factors influencing vaccine acceptability. Data were analysed using Stata version 16.1.this was an online questionnaire-based cross-sectional study conducted from 13of the 677 participants, only 33.4% (n = 226) would accept the vaccine if made available to them. In multivariable regression analysis, respondents who were older than 41 years compared to the 18 to 23 years age group , those who agreed  or did not know  compared to those who disagreed that the COVID-19 vaccine passed through all the necessary stages to ensure its safety and effectiveness, and those who were aware  compared to those who were not aware that the COVID-19 vaccine reduces virus transmission, were more likely to accept the vaccine. Conversely, entrepreneurs compared to government employees  were less likely to accept vaccination.awareness of the COVID-19 vaccine was high despite low acceptability levels. These findings are significant as they highlight the need to develop strategies for improving vaccine acceptability in Zambia. The coronavirus disease 2019 (COVID-19) has remained a public health threat since its first report in December 2019 in Wuhan Province of China . It has Vaccines have become integral to limiting the interpersonal, community and global spread of COVID-19 , 9. HoweThe drivers of vaccine acceptance include clear and consistent messages from health authorities, creating an atmosphere of trust, effective vaccine campaigns, confidence in regulatory bodies, and a culturally informed health workforce , 18, 19.Despite its immense importance for favourable public health and vaccination program outcomes, vaccine awareness, acceptance and/or hesitancy has not been explored among the general population in Zambia. Therefore, the study assessed the prevalence and factors associated with COVID-19 vaccine acceptability among the general population in Zambia.Study design, setting and population: this was an online cross-sectional study done in Zambia's ten provinces . Data were collected between 13th April and 21st May 2021. The study was conducted among Zambians who gave informed consent and had access to the online questionnaire. A total of 677 individuals took part in the survey. Due to the ongoing pandemic, the research team identified potential participants through Facebook and WhatsApp.Sample size determination: the sample size was determined using Cochrane´s formula [ formula . Accepta formula . A 95% cData collection tool: the questionnaire was administered to participants electronically via Google forms®platform. The data collection tool was adapted from similar African studies and modified to the Zambian setting [ setting , 28. ConThe primary outcome (acceptability) and the secondary outcome (awareness) were assessed on a binary scale (yes/no). These outcomes related to the statements, “If the COVID-19 vaccine was made available, would you accept to be vaccinated?” and “Have you heard of COVID-19 vaccine?”, respectively. The independent variables included age (in years), gender , marital status , education level , employment , source of information about COVID-19 vaccines , religion , areas of residence . In addition, the participants were asked if they knew whether COVID-19 vaccines can reduce SARS-CoV-2 transmission, if the COVID-19 vaccines had passed all the stages of vaccine development to ensure they are safe and effective and if vaccines were only meant for children.Statistical analysis: Stata/IC version 16.1  was used for our statistical analyses. Categorical variables were presented as frequencies and percentages. The bivariate analysis was performed using the Chi-square test. In cases where the assumption of the Chi-square test was not met (expected cell frequency less than 5), Fisher's exact test was used as appropriate. The primary outcome of the study was the acceptance of COVID-19 vaccines. A binary logistic regression was performed to assess the factors that independently affect COVID-19 vaccine acceptance among the Zambian population. Firstly, potential predictors for acceptance of COVID-19 vaccines were screened using univariable analysis, and variables with p-value < 0.2 were considered in the multivariable binary logistic regression. In the multivariable binary logistic regression model, the participants were dichotomised as would accept vaccines .The odds ratio (OR) values and 95% confidence intervals (95% CI) were calculated. To account for clustering in the 10 provinces, we calculated the effect measure estimates using robust standard errors. For the final model, interactions between significant predictors were assessed and none reached any statistical significance. Hosmer Lemeshow test was used to assess model fitness. A non-significant Hosmer Lemeshow test indicated a good model fit. Since almost all the participants were aware of COVID-19 vaccines, we could not fit the logistic regression model for this outcome (perfect prediction). A p-value < 0.05 at 95% confidence level was used to indicate statistical significance.Ethical approval: the approval of the study protocol was done by the University of Zambia Health Sciences Research Ethics Committee (UNZAHSREC) under an approval Ref No: 20190217024. The regulatory approval was done by the Zambia National Health Research Authority (NHRA) under an approval Ref No: NHRA00001/12/04/2021. This study is part of an ongoing study on the awareness and acceptance of COVID-19 vaccines among the Zambian adult population. All participants provided consent to take part in the study.Respondents' socio-demographic characteristics: a total of 677 respondents were included in the analysis. Their socio-demographic characteristics are listed in Awareness and acceptance of COVID-19 vaccines: nearly all the respondents, 99.9% (n=676) were aware of the COVID-19 vaccines. However, less than a third of the respondents, 31.0% (n=210) did not know that the COVID-19 vaccine reduced the transmission of the virus. About 55.8% (n=378) did not think that the COVID-19 vaccine had passed through all stages to ensure its safety. Among the respondents, slightly below one in ten were not aware whether vaccines were only for paediatric use or not. The majority of respondents, 71.8% (324) would refuse vaccination if any COVID-19 vaccine was made available because they were concerned about the safety associated with the vaccine. Approximately 13.3% (38) of respondents were willing to be vaccinated to protect themselves from COVID-19. The summary of the responses to the survey questions on awareness and acceptance of the COVID-19 vaccine are listed in Acceptance of COVID-19 vaccines: overall, 226 of the 677 respondents would accept to receive the COVID-19 vaccine if made available, giving a prevalence of 33.4% (95% CI: 29.8-37.1). Factors influencing COVID-19 vaccine acceptance: the results of univariable and multivariable analysis (binary logistic regression) identified the independent factors that influenced acceptance level, as shown in The study was conducted to assess the prevalence and factors associated with COVID-19 vaccine acceptability among the general population in Zambia. Although 99.9% of the participants were aware of the COVID-19 vaccine, only about 33.4% were willing to accept the vaccine if made available to them. Factors influencing vaccine acceptability included advanced age, being an entrepreneur than a government employee, knowing that the COVID-19 vaccine has passed through all the necessary stages and is safe, effective and reduces virus transmission.The acceptance of the COVID-19 vaccine among the adult Zambian population was lower than that reported in Ethiopia , Jordan Regarding age, the majority of respondents in the current study above the age of 41 years were willing to be vaccinated so that they are protected from COVID-19. A similar study in Libya reported that individuals aged 41-50 years were willing to receive the vaccine . HoweverConsistent with other published studies , 39, 40,Strengths and weaknesses of the study: to the best of our knowledge, this is the first study on COVID-19 vaccine awareness and acceptability among the general population in Zambia. Our findings could help inform the development of strategies to address vaccine hesitancy and increase the uptake of vaccinations among the Zambian population and other sub-Saharan African settings. Moreover, this study highlights the factors that need to be addressed if COVID-19 vaccination programs are to be successful. However, the study had few limitations. Many Zambians who have no access to the social media platforms that were used to collect data did not participate in the study; hence, the generalisation of our findings may not apply to the entire Zambian population, possibly a risk factor resulting in non-response bias. In the analysis, potential confounders were adjusted using a multivariate logistic regression model.Unanswered questions and future research: drivers of vaccine hesitancy and/or acceptance in technologically disadvantaged and rural poor populations in Zambia. The incidence of adverse events among vaccinated individuals and implications for future participation in vaccination programs.The COVID-19 vaccine acceptance was low among the adult Zambian population, while awareness was high. This indicates the unwillingness of the majority of Zambians to be vaccinated against COVID-19. The findings of this study can be used to formulate and implement strategies that can help improve the willingness of individuals to be vaccinated. Also, despite being an online survey, the findings of this study can be used to prepare individuals for vaccinations in future epidemics.COVID-19 is a public health problem that has negatively affected the globe;COVID-19 vaccines have been developed and are being administered to contain the pandemic;Potential adverse effects and concerns about the effectiveness of COVID-19 vaccines are among the factors leading to vaccine hesitancy.This study adds information that can be used to address vaccine hesitancy during pandemics;Increasing COVID-19 vaccine acceptance will lead to increased uptake and achievement of herd immunity;Dissemination of information about the COVID-19 vaccines is more trusted by individuals when it comes from healthcare workers.


Spectral-domain optical coherence tomography (SD-OCT) has been used to observe the morphology of the palisades of Vogt (POV) with satisfactory resolutions. In this study, we used SD-OCT to examine the microstructure of the POV in ocular surface disorders with limbal involvement. We detect subclinical limbal pathologies based on five parameters, including (1) decreased epithelial thickness, (2) loss of the sharp stromal tip, (3) loss of the smooth epithelial-stromal interface, (4) dilated stromal vessels, and (5) decreased POV density. Eighteen eyes of 10 patients with advancing wavelike epitheliopathy (AWE) and 15 eyes of 9 patients with phlyctenular keratitis/ocular rosacea were recruited. SD-OCT could detect abnormal changes in the POV in 100% of the lesion sites. In presumed-healthy areas of the diseased eyes diagnosed by slit-lamp biomicroscopy, SD-OCT detected abnormal changes in the POV in 100% of the eyes in both groups. In patients with unilateral disease, abnormal changes in the POV were detected by SD-OCT in 50% and 100% of presumed-healthy eyes diagnosed by slit-lamp biomicroscopy in the AWE group and phlyctenular keratitis/ocular rosacea group, respectively. SD-OCT is powerful in detecting POV changes in ocular surface disorders and can provide useful information that cannot be provided by slit-lamp biomicroscopy. The limbus, a transition zone between the peripheral cornea and conjunctiva, has attracted ophthalmologists’ attention because it provides a niche microenvironment for limbal stem cells4. Damage to stem cells in the POV may sequentially result in persistent epithelial defects, conjunctival ingrowth, corneal opacity, and severe vision loss5. Thus, understanding the detailed microstructure of the POV by using noninvasive modalities is clinically important6.Limbal stem cells are a group of cells located in the basal epithelial layer of the palisades of Vogt (POV) and are involved in maintenance of epithelial homeostasis and corneal clarity8. IVCM can provide detailed information regarding the structure of the POV and limbal stem cells. However, a limitation of IVCM is that it can provide only en face images of a limited area. Compared with IVCM, spectral-domain OCT (SD-OCT) with a corneal anterior module long adaptor lens can provide the cross-sectional images of the POV of both radial and tangential orientations with a satisfactory magnification and resolution. Lin et al. used SD-OCT to examine the role of age and regions in the microstructure of POV in healthy eyes9. In that study, the “atypical” pattern of the radial cross-sectional POV was detected as the abnormal thickness of the limbal epithelium and the abnormal morphology of the subepithelial stroma underneath the epithelium of the POV. Thus, the detailed microstructure of the POV could be easily determined through SD-OCT9. Haagdorens et al. used tangential cross-sectional images obtained by SD-OCT to observe and quantify limbal crypts and the density of POV  and provided useful information for evaluation of the POV10. Furthermore, studies have used SD-OCT to grade the abnormality of the POV and demonstrated the power of SD-OCT to grade disease severity in limbal stem cell deficiency (LSCD)13. All these results have demonstrated the value of SD-OCT for evaluation of the POV in diseased eyes.In addition to the widely used slit-lamp biomicroscopy, various noninvasive imaging modalities are used to determine the structure of the POV, including ultrasound biomicroscopy (UBM), in vivo confocal microscopy (IVCM), and anterior segment optical coherence tomography (AS-OCT). The 50-MHz UBM system can penetrate up to 5–6 mm, thus enabling visualization of the structures of the anterior chamber angle, ciliary body, and posterior chamber. However, UBM is limited by being a close-contact immersion technique. Moreover, the 25-μm axial resolution is inadequate to obtain high-quality images of the POV14, few studies have used this technique to observe changes in the POV of patients with LSCD or potential limbal damage15. To the best of our knowledge, no study has used SD-OCT to observe presumed-healthy limbal areas in the eyes with ocular surface disorders or the presumed-healthy fellow eyes of patients with unilateral ocular surface disorders diagnosed by slit-lamp biomicroscopy. In this study, we enrolled patients with advancing wavelike epitheliopathy (AWE) and phlyctenular keratitis/ocular rosacea with total or partial limbal involvement in one or both eyes. We performed slit-lamp biomicroscopy in all patients to define the normal or abnormal patterns of the POV. In addition, we analyzed the POV by using SD-OCT with both tangential and radial cross-sectional scans in the four quadrants of the limbus in both eyes. We determined the power of SD-OCT to evaluate the microstructure of the POV in these patients and found a higher sensitivity of SD-OCT than conventional slit-lamp biomicroscopy in observing the structure of the POV. SD-OCT can provide useful information for monitoring the POV in these patients. The use of SD-OCT can enable early detection of limbal damage in the cornea with potential limbal involvement, leading to proper intervention and prognosis prediction while treating patients with potential LSCD.Although SD-OCT has been increasingly used to observe the POVSD-OCT images were obtained from 19 patients. Ten patients were diagnosed as having AWE . Nine patients were diagnosed as having phlyctenular keratitis/ocular rosacea . The mean age of patients with AWE and those with phlyctenular keratitis/ocular rosacea was 48.7 ± 14.0 (range: 33–73) years and 30.3 ± 18.2 (range: 9–62) years, respectively. Table Table Slit-lamp biomicroscopy findings revealed that 14 of the 18 eyes of patients with AWE and 14 of the 15 eyes of patients with phlyctenular keratitis/ocular rosacea exhibited a presumed-healthy limbus in at least one quadrant. Table Five patients were found to have unilateral ocular surface disease, which was diagnosed by slit-lamp biomicroscopy . SD-OCT findings revealed abnormal POV in at least one quadrant in the presumed-healthy fellow eyes of four patients. In patients with unilateral AWE, a decreased epithelial thickness of the POV (case 2), loss of the smooth epithelial–stromal interface of the POV (case 2), dilated limbal vessels (case 2), and a decreased density of the POV (case 2) were detected through SD-OCT. In patients with unilateral phlyctenular keratitis/ocular rosacea, a decreased epithelial thickness of the POV , loss of the smooth epithelial–stromal interface of the POV (case 14), dilated limbal vessels (cases 14 and 15), and a decreased density of the POV (case 14) were detected through SD-OCT. These findings were similar to the abnormal pattern of the POV observed in the presumed-healthy sites of the contralateral diseased eyes.Figure Figure 17. In patients in whom the POV could not be detected through slit-lamp biomicroscopy, the limbus was still diagnosed as “healthy” if no perilimbal injection, swelling, infiltration, ischemia, degeneration, malignancy, or neovascularization was found. IVCM is a powerful technique that has been used to visualize cells located in the POV in the past decades20. However, in IVCM, the requirement of direct contact with the eye is a disadvantage for patients with ocular surface disorders. AS-OCT is a rapid, noninvasive, highly repeatable modality that can provide high-resolution images. In the past years, optical coherence tomography (OCT) has become a widely used clinical tool for imaging of the ocular surface, although it was mostly used for the observation of the optic disc and retinal choroidal structure21. Recently, studies have used AS-OCT to visualize the POV in healthy13 and pathological conditions13. Although SD-OCT has not yet been able to identify the structure of individual limbal stem cells, research-grade OCT with a high resolution  could visualize individual pigmented cells along fibrous folds and red blood cells inside the capillaries of the POV22. In addition, Chen et al. reported that full-field OCT could provide both cross-sectional and en face images of the POV at a submicron resolution, and even the corneal nerve could be clearly observed23. Lathrop et al. used SD-OCT to identify the POV and reported an excellent structural correlation with images obtained through IVCM24. Banayan et al. reported the 3D structure of the POV with various scan orientations of SD-OCT , and both the stromal and epithelial components could be identified12. All these evidence demonstrate the potential of SD-OCT in evaluating patients with ocular surface disorders.The POV are a series of radially oriented fibrovascular ridges, similar to fingerprints, located in a 1–2 mm band of the corneoscleral limbus. In this area, the epithelium of the POV projects downward between the subepithelial papillae. Usually, the POV structure could be identified through slit-lamp biomicroscopy in only 20% of the healthy eyes9, the positive rate of typical POV was higher in superior limbus (68–100%) and inferior limbus (25–100%) while relatively lower in nasal limbus (0–69%) and temporal limbus (4–65%). The majority of the study subjects presented with lesions located in the superior or inferior limbus. As the nasal or temporal limbus was evaluated as presumed-healthy sites, the presence of atypical patterns of POV was predicted to be higher. In the current study, the slightly higher percentage of > / = 3 abnormal features might not be considered as a presentation of an earlier phase of the disease.In this study, we proposed five simple but reliable qualitative and quantitative parameters for the radial and cross-sectional images of the POV obtained through SD-OCT: (1) a decreased epithelial thickness, (2) loss of the sharp stromal tip, (3) loss of the smooth interface between the epithelium and stroma, (4) dilated stromal vessels, and (5) a decreased POV density. Using these five parameters, we evaluated the possibility of detecting subclinical changes in the POV through SD-OCT before detection through slit-lamp biomicroscopy. We focused on two types of ocular surface disorders, namely AWE and phlyctenular keratitis/ocular rosacea, which typically involve the limbal areas. We evaluated four quadrants of the limbal area through tangential and radial cross-sectional scanning. According to the result of our previous study22; compared with slit-lamp biomicroscopy, SD-OCT was more sensitive and useful in examining the health of limbal microstructure. To the best of our knowledge, information provided by our study has not been reported thus far. Furthermore, a higher detection rate of abnormal changes in the POV by SD-OCT was demonstrated through recognition of subclinical pathologies in presumed-healthy sites of the diseased eyes or presumed-healthy fellow eyes previously diagnosed by slit-lamp biomicroscopy.In this study, we found a high detection rate of abnormal POV by using SD-OCT on those limbal areas in which slit-lamp biomicroscopy clearly demonstrated pathological changes in both disease groups. SD-OCT provided information regarding epithelial thickness, epithelial–stromal interface, subepithelial stromal morphology, and stromal vessel diameters in the POV. SD-OCT could also detect the density of POV with high reliability and repeatability30.These subclinical changes in the POV included changes in the epithelial thickness, an unsmooth interface between the epithelium and stroma, loss of the typical stromal figure, and dilated stromal vessels. Our results suggested that SD-OCT can serve as a useful noninvasive tool for evaluating clinical and subclinical changes in the limbal area in ocular surface disorders and provide higher-quality images compared with those obtained using current technologies. Such high detectability is particularly useful for monitoring partial or subclinical LSCD, eventually aiding in the administration of appropriate treatment. In addition to AWE and phlyctenular keratitis/ocular rosacea, patients with other potential limbal damage can be benefited by SD-OCT, including those wearing contact lenses and those with chemical burns, topical drug toxicity, and Stevens–Johnson syndrome23. Extension of the study method to other ocular surface disorders can be a safe and valuable future clinical application.This study has some limitations. First, SD-OCT image resolution was compromised by the instability of eye fixation toward nonprimary gaze, particularly in patients with severe ocular surface disorders who were sensitive to light. Therefore, careful and adequate application of topical anesthetic eye drops before an examination is required. Second, the site of the radial cross-sectional scans of the POV differed from the interpalisade to palisade area, whereas that of the tangential cross-section scan deviated between the corneal and scleral side. Repeated examination by experienced technicians or doctors is required. Third, the case number is not large enough and not all patterns of ocular surface disorders were included in this study. Fourth, we combined patients with phlyctenular keratitis and ocular rosacea in the same group due to difficulty in the differential diagnoses in some patients, particularly in young adults or children. However, despite the aforementioned limitations, we found some novel results of the structural change of the POV in patients with AWE and phlyctenular keratitis/ocular rosacea by using SD-OCT. With new sub-micrometer axial resolution optical coherence tomography, the high spatial OCT resolution allowed for precise identification of POV by visualization of the capillaries that extend from the inside of the palisade’ ridges to a lateral micro-vascular network located underneath the POVIn conclusion, the POV and limbus structure could be effectively visualized through SD-OCT, and this imaging modality can be used to detect subclinical changes in the POV in the presumed-healthy sites of the eyes in patients with ocular surface disorders. These findings helped us gain novel insights into the management of ocular surface disorders, and can aid in diagnosis, monitoring, treatment-related decision making, and prognosis prediction in clinical practice.In this cross-sectional, observational study, we included 18 eyes of 10 patients with AWE  and 15 eyes of 9 patients with phlyctenular keratitis or ocular rosacea . Slit-lamp biomicroscopy was used for the diagnosis. In AWE, slit-lamp biomicroscopy revealed centripetally advancing waves of the wavy, coarse, irregular epithelium extending from the limbus to the central cornea. In phlyctenular keratitis, corneal phlyctenules presented as round, elevated, gray, or yellow nodules accompanied by a zone of engorged hyperemic vessels emerging from the limbal area. Some degree of ulceration and fluorescein staining was found with the progression of corneal phlyctenules. In ocular rosacea, bilateral telangiectasia and inspissation or obstruction of the meibomian glands associated with typical rosacea facial lesions were observed under the slit-lamp biomicroscope. We enrolled only patients who presented with manifestation of corneal inflammation, including peripheral stromal infiltration, opacification, scarring, or neovascularization, in addition to the typical findings of meibomian gland diseases and facial lesions. We combined patients with phlyctenular keratitis and ocular rosacea in the same group (phlyctenular keratitis/ocular rosacea) due to difficulty in the differential diagnosis in some patients, particularly young adults or children.The study was performed in the Department of Ophthalmology, National Taiwan University Hospital, from Jun, 2018 to Jun, 2019. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of National Taiwan University Hospital . After explaining the study purpose, we obtained written informed consent from all patients. For one participant under the age of 18 years, informed consent had been obtained from the parent and legal guardian. Figure 9 with some modifications as follows. We evaluated all four quadrants of the limbal area through tangential and radial cross-sectional scanning and ensured that the POV area could be clearly seen. Superior, inferior, nasal, and temporal areas were routinely examined. However, oblique orientations were examined in some cases if the most severe part of the limbal pathology was not located in the superior, inferior, nasal, or temporal area. Regardless of the extent of AWE, phlyctenular keratitis, or ocular rosacea, quadrants exhibiting the most severe pathological change were considered the lesion sites. Areas presumed to be healthy on the eyes with lesions or the fellow eyes presumed to be healthy through slit-lamp biomicroscopy were carefully reviewed, and quadrants exhibiting the most severe pathological change, as revealed by SD-OCT findings, were considered the presumed-healthy sites or presumed-healthy eyes, respectively.All patients underwent slit-lamp examination and SD-OCT examination at the outpatient department. The SD-OCT examination was performed as reported in a previous study9. Adaptor lenses  were used in this study and provided the scan with a length of 6-mm- and a depth of 1.96-mm14. Radial and tangential cross-sectional scans were obtained simultaneously. To obtain the accurate pattern and the maximum epithelial thickness of the POV, patients were asked to keep looking at the four directions to maintain the perpendicularity of the OCT beam at the surface of the targeted tissue. To compare the epithelial thickness between the POV and central cornea, we obtained the cross-sectional scans of the central cornea.A SD-OCT system  with a noncontact cornea anterior module was used for the analysis. This system was operated at a wavelength of 840 nm. The scan speed was 26,000 axial scans per second. The width and depth of the scan ranged from 2 to 12 mm and 2 to 3 mm, respectively. The depth of the resolution  was 5 μm in the tissue, whereas the transverse resolution was 15 μm9. In radial cross-sectional scans, the following four parameters were considered the normal pattern of the POV: (1) the maximum epithelial thickness of the POV should be at least 1.5-fold greater than the corneal epithelial thickness, (2) the stroma of the POV should be presented as a sharp tapering tip pointing to the corneal–limbal junction, (3) a smooth and clearly identified interface should be observed between the epithelium and stroma, and (4) the lumen diameter of the limbal vessels should be < 50 μm. We added another parameter; that is, the density of the POV, for the tangential cross-sectional view. The POV was considered to have normal density if we observed dense, distinct shadows of the POV with an average density of > 8.0 shadow/mm22.Regarding the POV structure, we analyzed epithelial thickness, stromal configuration, interface contour between the epithelium and stroma, limbal vessel diameter, and POV density (numbers/mm) in the tangential cross-section scan Fig. . In this9 conducted previously by our team, which revealed the central corneal epithelial thickness was 51.33 μm and 51.67 μm in the group aged 20–39 and 40–59 respectively. The maximum epithelial thickness of POV was 106.9 ± 17.0 μm, 106.2 ± 22.8 μm, 85.6 ± 13.2 μm and 82.8 ± 13.1 μm at superior, inferior, temporal and nasal limbus respectively. Accordingly, the normal pattern of POV has been established with the maximum epithelial thickness of POV at least 1.5 thicker than the central corneal epithelial thickness in the previous study. And the difference of peripheral corneal epithelial thickness and central epithelial thickness is minimal. Thus, we referred to the peripheral corneal epithelial thickness for direct comparison in each OCT scan to evaluate the first and the fourth criteria. At each limbal area, those radial cross-sectional images with high quality which can clearly show the interface of epithelium-stroma were selected for further analysis. For those tangential cross-sectional scans, the best quality scan was obtained by a trained operator based on the visibility and contrast of the line pattern corresponding with the POV. The tangential cross-sectional scans consisted of the best presented shadows of POV  in the scan were selected for further analysis of palisade density. In case without clearly identified shadow of POV, slit-lamp findings were also used to verify the absence of POV. The density of the POV was calculated three times within the central 3-mm width of scanned images in the tangential cross-sectional view where the shadows of the POV were clearly identified14. The vertical shadows underneath the highly reflective, narrow, loop-like structures of POV and projecting through the whole length of cornea were counted.The followings were the five morphological criteria used to determine whether the POV was abnormal or not in this study, including (1) POV epithelial thickness thinner than the corneal epithelial thickness, (2) loss of the sharp stromal tip, (3) loss of the smooth interface between the epithelium and stroma, (4) dilated stromal vessels with diameter > 50 μm, and (5) POV density < 7.0 shadow/mm. The cut off value of the first parameter was based on the studyAll the images were evaluated by two examiners (Y-YC and Y-CS) individually and finally approved by the third examiner (W-LC) to avoid the inter-observer variability. When there was discrepancy between the first two evaluations, the assessment of a third examiner was decisive. All the examiner was unaware of the study subject of the OCT scan under interpretation.In patients with AWE, we performed impression cytology on the lesion site to confirm the presence of conjunctival invasion, which is a typical presentation of LSCD. Samples were collected from four limbal quadrants using cellulose acetate filter papers  in both the eyes. After applying one drop of the topical anesthetic , excess tears were gently removed using cotton swabs. The cellulose acetate filter paper was placed on the surface of the bulbar conjunctiva, limbal area, and peripheral cornea. After applying slight pressure for 30 seconds, the sampling filter paper was removed from the surface and immediately stored in 95% alcohol. The samples were stained with periodic acid-Schiff and hematoxylin stains and photographed at 100 × and 200 × magnifications. All limbal impression cytology specimens were obtained and evaluated by the same technician.