The randomized Early Treatment of Atrial Fibrillation for Stroke Prevention Trial found that early rhythm control reduces cardiovascular events in patients with recently diagnosed atrial fibrillation (AF) compared with usual care. How genetic predisposition to AF and stroke interacts with early rhythm-control therapy is not known.Array genotyping and imputation for common genetic variants were performed. Polygenic risk scores (PRS) were calculated for AF (PRS-AF) and ischaemic stroke risk (PRS-stroke). The effects of PRS-AF and PRS-stroke on the primary outcome , its components, and recurrent AF were determined.P < 0.001]. The randomized intervention, early rhythm control, did not interact with PRS-AF (interaction P = 0.806) or PRS-stroke (interaction P = 0.765). PRS-AF was associated with recurrent AF . PRS-stroke showed an association with the primary outcome , driven by more heart failure events  without differences in stroke  in this well-anticoagulated cohort. In a replication analysis, PRS-stroke was associated with incident AF  and with incident heart failure in the UK Biobank . The association with heart failure was weakened when excluding AF patients .A total of 1567 of the 2789 trial patients were analysed . Baseline characteristics were similar between randomized groups. Early rhythm control reduced the primary outcome compared with usual care [HR 0.67, 95% CI: , Early rhythm control is effective across the spectrum of genetic AF and stroke risk. The association between genetic stroke risk and heart failure calls for research to understand the interactions between polygenic risk and treatment.www.easttrial.orgISRCTN04708680, NCT01288352, EudraCT2010-021258-20,   Bourier and H. Schunkert, 1.1 Several sub-analyses demonstrate that early rhythm control is effective independent of atrial fibrillation (AF) symptoms,2 HF,3 and AF pattern.4 The treatment effect appears to be mediated by sinus rhythm,5 suggesting that factors that render rhythm control more difficult may interact with the treatment effect of early rhythm control.The Early Treatment of Atrial Fibrillation for Stroke Prevention Trial (EAST-AFNET4) showed that early rhythm control (ERC) reduces a composite of cardiovascular death, stroke, and hospitalization for worsening heart failure (HF) or acute coronary syndrome compared with usual care (UC) when added to oral anticoagulation and therapy of concomitant cardiovascular conditions.7 Polygenic risk scores (PRS) for AF using data from large genome-wide association studies (GWAS) can quantify the genetic risk for incident AF with a three- to six-fold risk difference.8 Additionally, studies using PRS for stroke can identify AF patients with a four-fold increase in stroke risk when otherwise classified as low risk by CHA2DS2-VASc.9 Similarly, integration of the genetic risk for stroke with clinical risk factors increased the risk prediction for stroke.10 Several observational data sets suggest that AF risk variants are associated with recurrent AF on different rhythm-control therapies.11–13 In coronary artery disease, high genetic risk by PRS has been shown to predict benefit from lipid-lowering therapy.14 A recent scientific statement document from the American Heart Association called for more research into PRS and rhythm-control therapy in patients with AF.15 Whether genetic risk is associated with adverse events or response to treatment in AF is not known. Here, we analysed the interaction between genetic AF and stroke risk and cardiovascular events in the EAST-AFNET4 biosample study.The heritability of AF has been described as high as ∼62% in twins and ∼22% in the general population.2.2.12 of body surface area)], and left ventricular hypertrophy . In the main trial, 2789 patients across 135 sites were 1:1 randomized to either ERC (n = 1395) or UC (n = 1394).1 Patients randomized to ERC received anti-arrhythmic drugs, catheter ablation, or cardioversion directly after randomization.The EAST-AFNET4 trial was a multi-centre investigator-initiated, parallel-group, open, blinded-outcome-assessment trial. Patients with recently diagnosed AF (<1 year) and cardiovascular risk factors were randomized to early rhythm control or usual care. Inclusion criteria were either patients aged >75 years or prior stroke or two of the following criteria: age >65 years, female sex, HF, hypertension, diabetes mellitus, severe coronary artery disease, chronic kidney disease , female sex , and mean CHA2DS2-VASc score (Table 1). Similar to the entire trial cohort,22 more than 90% of patients in both randomized groups received anticoagulation and had well-controlled concomitant cardiovascular conditions .After genotyping, imputation, and stringent quality control, 1567 patients were available for the PRS analyses  years, intermediate risk: 71.0  years, high risk: 70.0  years) and sex  were similarly distributed (see Table S1). Patients with a high genetic risk for AF were younger, had less previous cardioversion and more HF (see Table S1).The distribution of genetic risk followed a normal distribution for each PRS per treatment group see . In the categorized groups, median age , and sex .The same was found in the PRS-stroke3.21 ERC reduced the primary outcome compared to usual care in the biosample sub-population . ERC was associated with reduced hospitalizations for acute coronary syndrome , reduced cardiovascular deaths , and reduced strokes . ERC also reduced recurrent AF  compared to UC. The genetic risk for AF did not interact with the treatment effect on the primary composite outcome (interaction P = 0.806) nor its components or recurrent AF (see Table S3). Similarly, the PRS-stroke score and treatment group did not interact with the primary composite outcome (interaction P = 0.765) .Consistent with the main trial,3.3P = 0.867) (Table S2). The PRS-AF score was associated with recurrent AF with a moderate effect size . When looking at genetic risk as categorical values, a trend toward lower risk for recurrent AF in patients with low PRS-AFcategorized risk was observed compared to patients with an intermediate risk .The continuous genetic risk for AF was not associated with the primary composite outcome   but was not associated with stroke  in this well-anticoagulated population . The genetic stroke risk was associated with worsening of HF  but not with recurrent AF . In the categorized analysis, high PRS-strokecategorized risk was associated with an increased risk of hospitalization for worsening HF in comparison to intermediate risk .The continuous genetic risk for stroke showed an association with the primary composite outcome . This association was weaker when excluding participants in the UKB who first developed AF . The PRS-stroke showed a robust association with AF risk  in the UKB, in addition to the expected association with ischaemic stroke .To further investigate this finding, we applied the PRS-stroke to up to 407 311 UKB participants, where we found an association of the risk score with incident HF (3.4Tables  and 4b).Safety events were rare and not different across the PRS-AF and PRS-stroke risk categories, including events related to anti-arrhythmic drug therapy like non-fatal cardiac arrest and drug-induced bradycardia  reflecting the effectiveness of early rhythm-control therapy across the spectrum of genetic AF risk. Unexpectedly, PRS-stroke was associated with HF hospitalizations but not with stroke in this well-anticoagulated cohort. The association of the PRS-stroke with HF hospitalization was validated in the UK Biobank. This study shows that early rhythm control is effective cross the spectrum of genetic AF and stroke risk and that comprehensive AF therapy including anticoagulation on >90% of the patients and therapy of concomitant cardiovascular conditions reduces the otherwise observed association of PRS-stroke with stroke.23 Pathophysiological consideration and prior observations suggest that a higher genetic AF risk would make rhythm-control therapy more difficult, leading potentially to a dampened treatment effect or an increase in adverse events of early rhythm control in patients with a high genetic AF risk.25 While limited power to detect differences in events is a caveat, this analysis found that early rhythm control is effective for patients across the spectrum of AF PRS risk scores. This is most likely due to the good effectiveness and safety of modern rhythm-control therapy as applied in the EAST-AFNET 4 trial, including in patients with multiple comorbidities,26 that has been replicated in other, recent rhythm control trials.28 While we did not find an interaction of genetic risk with adverse events of rhythm-control therapy, larger genetic studies are needed to detect interactions between genetic risk and rare adverse events in AF treatment.One of the goals of genomics in medicine is to tailor therapies to an individual patient to maximize efficacy while maintaining safety.15 A recent large analysis demonstrated that the genetic risk scores that were developed in large, homogeneous, but not very deeply phenotyped populations predict incident AF in patients with cardiovascular diseases.29Using this data set from a randomized trial with capture of adjudicated events over a mean follow-up of over 5 years provides insight into the role PRS could have in AF care. While polygenic risk is thought to provide an additional layer of risk information that can be independent of clinical risk factors, the use in patients who have disease to inform treatment strategies is less well understood.30 In contrast with this, a meta-analysis by Shoemaker et al. tested whether a PRS for AF is associated with recurrent AF after catheter ablation. They found high genetic risk for AF to be associated with younger age and fewer risk factors but not with AF recurrence.31 There are several factors that explain the divergence with our findings. For one, the Shoemaker study was a meta-analysis of 10 centres that had heterogenous patient cohorts and catheter ablation protocols. Secondly, EAST-AFNET4 enrolled patients with a mean time of AF diagnosis of around 81 days where AF-related atrial remodelling might be less important for AF recurrence than in patients with longer AF durations. Finally, this study used a different method to estimate the genetic AF risk (LDPred in the Khera score and Pruning + Thresholding in the Shoemaker study).31Our findings are in accordance with earlier reports showing an association between the first genetic risk variant on chromosome 4q25, the locus that remains prominent in current GWAS for incident AF risk, with recurrent AF on different rhythm control interventions.32 Additionally, PRS have been used to identify subgroups in clinical trials that benefit from treatments more than others. For instance, a sub-study of the FOURIER trial showed that patients without clinical risk factors or high genetic risk (highest quintile of PRS) did not benefit from evolocumab in the reduction of vascular or coronary events.14 In addition, a meta-analysis of three trials for primary prevention of coronary heart disease, those with a high genetic risk had the greatest relative risk reduction (RRR 44% vs. 24%).33 However, evidence of a role of PRS in treatment guidance in other cardiovascular diseases than coronary artery disease is sparse which is acknowledged in the recent AHA scientific statement on PRS.15 This study is providing important evidence to fill that gap for AF, illustrating that early rhythm-control therapy is effective and safe across the spectrum of genetic AF and stroke risk and highlighting that treatment factors, possibly including anticoagulation, modify the genetic risk for stroke.Clinical risk factors for AF such as obesity, alcohol, or hypertension not only enable risk assessment but also provide an actionable target to modify the risk of events such as stroke whereas PRS are not modifiable.34In the present data set containing well-anticoagulated patients with AF, this PRS-stroke was not predictive of stroke but the genetic risk was associated with an increased risk of HF hospitalization. In the MEGASTROKE GWAS on which the PRS-stroke is based, the association of cardioembolic stroke with known loci for incident AF risk may provide an explanation for this observation and hint to not-anticoagulated AF as a confounding factor. The lack of association of the PRS-stroke with stroke in the EAST-AFNET 4 data set could be due to low power (51 stroke events in this sub-sample of EAST-AFNET 4). Alternatively, it could also be a consequence of the high anticoagulation rate in the trial (>90%) which is much higher than in the observed in the UK Biobank (∼30%).PITX2 locus were also reported in GWAS meta-analysis of HF highlighting the link of AF and HF.35 If AF is the mediator of the observed increased risk for HF with genetic stroke risk, as suggested by partially shared genetic architecture (PITX2) across all three conditions, the observed HF events might be tachymyopathy-driven events. The association was weakened when patients with incident AF were excluded, suggesting that the association is at least partially mediated by patients with AF and HF. However, limiting this hypothesis is the fact that we did not observe an increased risk of HF with genetic AF risk. Follow-up studies to elucidate our understanding of our observation may include functional genomics to study how stroke genes could lead to HF and the pathways associated with it. Furthermore, other genetic instruments such as rare-variant-burden testing in patients who have strokes and HF might help prioritize genes of interest. Whole-exome data on a large scale are available via the UKB and the All-of-Us programme.36Our analysis in the UKB confirms the finding in the EAST-AFNET4 data set that the PRS-stroke score is associated with HF events. Variants on the 37 without a deeper understanding of the interaction between PRS, cardiovascular conditions, and their treatment.These hypothesis-generating findings call for further research characterizing the interactions between genetic risk for stroke and AF and cardiovascular therapies. Our data highlight that it may be premature to use PRS as selection or inclusion criteria in prospective trials as recently suggested4.2P-values are given, so descriptive and confidence intervals cannot be used to infer treatment effects. Independent validation is clearly desirable.Collection of analysable biosamples in a large sub-population (69%) of eligible patients randomized in the EAST-AFNET 4 trial and systematic collection of adjudicated outcomes over a 5.1-year follow-up time are strengths of this analysis. Another strength is the continuous delivery of therapy of AF including anticoagulation and treatment of cardiovascular conditions. Both genotyping and imputation for all participants were carried out in the same sequencing centre to avoid systematic bias. Although the sample size contains PRS and outcomes over a mean follow-up duration of 5 years in 1567 patients, the power is too small to detect or rule out weak interactions between PRS and treatment. Information on recurrent AF was not continuously collected in both groups and was included as a time-to-event variable in this analysis. A strength is the use of PRS as continuous risk in addition to quintile groups. The grouping of genetic risk by quintiles is largely arbitrary although common practice in genetic studies. No gold standard PRS for AF or stroke is established and different methods for construction were not compared in this study. Our study focuses on a European sample and did not consider ethnicity in the enrolment of the trial. External validity in cohorts of diverse ethnicities is desirable. The choice of anti-arrhythmic drug therapy can be different depending on the country and catheter ablation was only partly used in this study. Therefore, these findings might not generalize to other cohorts of rhythm control. Due to the explorative design of the study, unadjusted Cardiovascular Research online.cvad027_Supplementary_DataClick here for additional data file.


Background: In healthcare facilities, hand hygiene is important for infection control. The WHO recommends monitoring the consumption of alcohol-based handrub (ABHR) and direct observation of hand hygiene practices to ensure compliance with hand hygiene practices. Monitoring of ABHR is widely used, but direct observation is not widely performed, particularly in small facilities and non–acute-care facilities. We evaluated the effects of direct observation of hand hygiene practices and monitoring of ABHR consumption, with feedback to staff, on ABHR consumption and hand hygiene compliance. Methods: We conducted a prospective intervention study over a 4-year period. Monitoring of ABHR consumption and direct observation of hand hygiene practices, with periodic feedback to staff, was implemented in 17 facilities of varying types: 5 large-scale acute-care facilities, 6 middle-to-small-scale acute-care facilities, and 6 non–acute-care facilities. Statistics for ABHR consumption were calculated before and after the implementation of direct observation of hand hygiene practices, and the change in ABHR consumption was calculated. The paired t test was used to assess the statistical significance of changes. A generalized linear mixed model analysis was performed to assess factors associated with ABHR consumption. Results: The total observation time was 1,225 months , and the average observation time per facility was 36.0 months (± 27.5). All facilities implemented ABHR consumption monitoring within 1 month of starting the study. However, the mean time required to implement direct observation of hand hygiene practices was 24.7 (±19.1) months. There was a significant increase in ABHR consumption in large and middle-to-small-scale acute-care facilities (P < .0001) after implementing the direct observation. However, there was not a significant increase for ABHR consumption in non–acute-care facilities (P = .14). Multivariable regression analysis showed that the hospital ward type, duration of ABHR consumption monitoring, and duration of direct observation of hand hygiene practices were independently associated with ABHR consumption. Conclusions: ABHR consumption increased in all facilities that implemented direct observation, but the change was not statistically significant in non–acute-care facilities. The generalized linear mixed model analysis showed significant associations between ABHR consumption and hospital ward type and time to monitoring of ABHR consumption and direct observation of hand hygiene practices. Direct observation of hand hygiene practices should be implemented more widely. The effect of intervention intensity should be evaluated in future studies.Disclosures: None


The impact of COVID-19 on children and adolescents’ mental healthHafiz & Aljadani address the mental and social health needs of children and adolescents after the pandemic. Children and adolescents are more susceptible to the formation of mental health issues, since their brains are still developing. There are still issues to be resolved to give children and adolescents in many regions of the world with quality, rights-based, and culturally relevant mental health care. It is essential to understand the extent of the issue to devise effective strategies and interventions with which to tackle it. Such measures can deal with fears, helping to develop positive psychological behaviors which can lead children and adolescents to overcome such an unprecedented situation and its associated stressors.see page 1183 The role of intra-articular injection of autologous platelet-rich plasma versus corticosteroids in the treatment of synovitis in lumbar facet joint diseaseKotb et al compare the efficacy between platelet-rich plasma (PRP) and corticosteroids (CS) in improving magnetic resonance imaging (MRI)-detected synovitis in correlation with clinical complaints among patients with lumbar facet joint (FJ) disease. A prospective, randomized, comparative, single blinded study included 30 patients with lumbar FJ disease, divided into 2 equal groups, received PRP and CS injections. Both groups showed a significant improvement in all mentioned parameters at follow-up after 3 months. Both PRP and CS injections were effective in improving MRI-detected FJ synovitis while concurrently improving all examined parameters at follow-up after 3 months.see page 1200A prospective Delphi-studyNational research guideline for prehospital emergency medical care. Alghamdi et al identify 100 prehospital experts in Saudi Arabia were invited to participate in all 3 rounds. Of these, 47 responded in round I, 34 in round II, and 39 in round III. In round I, participants submitted 278 research topics. After deduplication and sorting, 78 topics were assessed in the other 2 rounds. The study was carried out between November 2021 and February 2022. A 3-round modified Delphi consensus methods were used to determine high-priority research topics. Round I included an open-ended question to list all high-priority research topics in a prehospital setting in Riyadh, Saudi Arabia. Rounds II and III included ranking evaluation and consensus agreement. e included topics were listed based on the agreement of ≥70% of the experts participating in the study. e study’s implication is to determine research priorities which will be provided to the Saudi Research, Development and Innovation Authority, Ministry of Health, Saudi Red Crescent Authority, and research centers to disseminate our results, apprise future prehospital research plans, and prioritize funding. Overall, the results of this study can significantly improve the care and quality of emergency medical service.see page 1265 Acute myocarditis after COVID-19 vaccinationSaadi et al evaluate the complications that faced an 18-year-old male who received a COVID-19 vaccine and diagnosed with myocarditis/ pericarditis. He was complaining of a severe diffuse chest pain for 5 hours that prevented him from sleeping and there is change in breath and standing, without dyspnea or palpitation. He denied any recent viral illness. He never had history of any drug intake or previous surgeries. He felt these drawbacks only after receiving COVID-19 vaccination on the 5th of February 2022.see page 1270


Although genetics is known to have a role in sickness absences (SA), disability pensions (DP) and in their mutual associations, the empirical knowledge is scarce on not having these interruptions, i.e., sustainable working life. Hence, we aimed to investigate how genetic and environmental factors affect individual variation in sustainable working life in short-term (two consecutive years) and in long-term (22 years of follow-up) using the classical twin modeling based on different genetic relatedness of mono- and dizygotic twins. The final sample (n = 51 071) included Swedish same-sex twins with known zygosity born between 1930 and 1990 (53% women) with complete national register data of employment, SA, DP, unemployment, old-age pension, emigration, and death. For the short-term sustainable working life, genetic factors explained 36%  31–41%), environmental factors shared by co-twins such as family background 8% (95% CI 5–14%) and environmental factors unique to each twin individual 56% (95% CI 56–56%) on the individual differences. For the long-term sustainable working life, the largest proportions on individual differences were explained by environmental factors shared by co-twins  and unique to each twin individual (37% 95% CI 36–38%) whereas a small proportion was explained by genetic factors . To conclude, short-term sustainable working life was explained to a large extent by unique environment and to lesser extent by genetic factors whereas long-term (22 years) sustainable working life had both moderate unique and common environmental effect, and to lower extent genetic effects contributing to individual differences. These findings suggest that sustainable working life have different short- and long-term predictors. The European Union has adopted a Sustainable Development Strategy  that incThe role of genetics for SA , 6, DP –9 and thIn this Swedish twin cohort study, we aim to investigate the causes of individual variation in sustainable working life using genetic twin design, which allows simultaneously estimating the role of genetic factors and childhood environment shared by co-twins. We will investigate the relative importance of genetic factors as well as environmental factors shared by co-twins and unique for each individual for short term and long-term sustainable working life.The Swedish Twin project Of Disability pension and Sickness absence (STODS) includes the twins identified in the Swedish Twin Registry (STR) who were born between 1925 and 1990, the full sample being 119 907 twin individuals. Zygosity  was determined at the time of STR registry compilation based on survey questions about childhood resemblance and then updated in later surveys by STR for those twins not previously diagnosed or with uncertain diagnosis. This survey method has 98% accuracy when validated against serological and micro-satellite markers , 27. In We utilized two measures of sustainable working life. The first measure of short-term sustainable working life was defined in the bases that an individual had been employed at least two consecutive years , whereas being on SA (>14 days), DP, or unemployed were assumed as interrupted working life. Emigration, old-age pension, or death were censored. The second measure, long-term sustainable working life, was defined as having 22 years of sustainable working life, i.e., we limited the analysis to those who were employed all years from 1994 to 2016 without any interruptions  leading to 12931 individuals while censoring as above.First, the within-pair similarity  in sustainable working life was assessed by calculating tetrachoric correlations, casewise and pairwise concordance rates and prevalence of sustainable working life to measure concordance within twin pairs , 30. TheThe study protocol was designed and performed according to the principles of the Helsinki Declaration. The ethical vetting was performed and approved by the Regional Ethical Review Board of Stockholm, Sweden . For this project the Regional Ethical Review Board of Stockholm stated that the consent to participate was not applicable in these type of large register studies. Authors only had access to pseudonymized data.Descriptive statistics for the short-term (two consecutive years) of sustainable working life are presented in First, we fitted a full model with A, C and E variance components for the two consecutive years of sustainable working life . The ACEThe prevalence of long-term (22 years) sustainable working life was 22–26% . TetrachFor the long-term sustainable working life, we first fitted a full univariate model with A, C and E variance components . The ACEThis Swedish twin cohort study aimed to investigate prospectively the determinants of individual variation in sustainable working life defined as the absence of interruptions of working careers due to SA, DP, or unemployment. We measured short-term and long-term sustainable working life: a) two consecutive years of sustainable working life, and b) 22 years of sustainable working life. The relative importance of genetics was 36%, common environmental factors associated with the family (shared by family members) 8% and individual (nonshared) environmental factors 56% for two consecutive years of sustainable working life. Instead, with 22 years of sustainable working life, common environment was most important (46%) and genetic factors explained 18%, while individual environment accounted for 37% of the variance. These results complement findings of the earlier twin studies that have focused on the “unsustainable” part of the working life i.e., SA/DP , 31. AssDifferent results were shown for short-term and long-term sustainable working life, which might be due to mechanisms related to the role of genetics in the components of sustainable working life  but aThis study included several strengths since we had access to a large Swedish twin cohort and utilized comprehensive and high-quality national register data without loss to follow-up or recall biases for sustainable working life. The register data enabled both short- and long-term assessment of sustainable working life while assessing and censoring due to SA, DP, or unemployment but also due to old-age pension, emigration, or death was possible. Third, the age span at baseline of the final sample was 18–65 years providing wide coverage of individuals for assessment of sustainable working life. We need to note that due to this age range, i.e., some individuals were at their beginning of working life at baseline when follow-up started, whereas others at the middle  whereas we also had individuals at their end of working life (around retirement age at their 60’s) the working life prospects have varied considerably across age groups. Therefore, further studies could consider assessing age specific genetic effects on sustainable working life. However, we accounted for age effects in the models and ran sex-specific assessments which together add generalizability, especially to both sexes. A potential limitation might be our measures of sustainable working life. However, since sustainable working life is a rather new concept , utilizaTo conclude, short-term (two consecutive years) sustainable working life was explained to a large extent by unique environment and to lesser extent by additive genetics whereas long-term (22 years) sustainable working life had both moderate unique and common environmental effect, and to lower extent genetic effects contributing to individual differences. These findings suggest that sustainable working life have different short- and long-term predictors whereas both are liable for effects from societal level, welfare, and other events during adulthood.


Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains.Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models.While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely.This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material. Hence, accelerating breeding for improved reforestation material is becoming essential for traits related to wood quality , biotic To fully harness the power of GS, models need to fit and rely on both additive and non-additive genetic variances for delivering the most optimal selections for the propagation of elite material. Non-additive genetic variance can be partitioned into dominance and epistasis components; that is the interaction of alleles within genetic loci, and the interaction of alleles among different loci, respectively . SimulatIn forest genetics, several studies have attempted to disentangle additive and non-additive effects. Dominance may be separated from additive effects with structured schemes of full-sib crosses, such as in diallel experiments, where each parent is mated with several others. In addition, the partition of additive, dominance, and epistasis components requires clonal repetition of individual genotypes from full-sibs . In treeG) used in GBLUP can estimate the realized relationships based on the fraction of the genome shared between individuals. Furthermore, the G-matrix allows the detection of inbreeding, hidden co-ancestry, and unknown parentage in breeding populations with shallow pedigree, such as for forest trees , a widespread Canadian conifer being the subject of major reforestation and breeding efforts  evaluate the ability of GS-based GBLUP and conventional pedigree-based ABLUP models to partition additive and dominance variances for growth and wood traits; (2) compare the predictive ability of ABLUP and GBLUP when predicting for the same families as those included in the training population, or for new unphenotyped full-sib families that were not part of the training population; and (3) investigate the effect of sample size on the precision of genetic parameter estimates and the accuracy of genetic values.22.1The material was sampled from a test series designed to assess the genetic merit of first-generation selections of the white spruce breeding program in Québec, Canada, which had been subdivided into six breeding groups (BGs) delineated by their geographic region Figure 13), excluding the bark, was calculated following For the present study, 4,245 trees were sampled in the six BGs from 151 full-sib families involving 101 parents. The sampled trees were roughly equally distributed among the two test sites . The phenotypic traits tree height (HT), diameter at breast height (DBH), average wood density (WD), and acoustic velocity (AV) were assessed at age 16 since plantation for BGs 1, 2, 5, and 6, and at age 13 since plantation for BGs 3 and 4. The total volume . Trees were genotyped using an Infinium iSelect SNP array  as described in Ns):Genetic diversity within each BG was estimated with the status effective number , and 2) the phenotypes measured at age 13 in BGs 3 and 4, hereafter referred to as “dataset 2” . These two datasets were analyzed separately because the measurements were taken at different ages, thus having different means and variances, and also because there was no genetic relatedness between these two datasets (no parents in common). All analyses were conducted in the R v.4.0.2 environment  the phenotypes measured at age 16 in BGs 1, 2, 5, and 6, hereafter referred to as “dataset 1” , referred to as “ABLUP” models, or using realized genomic relationship matrices , referred to as “GBLUP” models. We ran two additive models (ABLUP-A and GBLUP-A) and two additive-dominance models (ABLUP-AD and GBLUP-AD). The full additive-dominance models were fitted using ASReml-R v.4.1  is the random block within site effect; p(s) is the random plot within site effect; and e is the residual term. The terms b(s), p(s), and e were fitted with heterogeneous  variance among sites, as bg is the random breeding group (BG) effect, with s:bg is the random effect of site-by-BG interaction, with a(s) is the random additive genetic effect nested within site, using the pedigree-based relationship matrix A for ABLUP, with a(s)~NaV, and using the additive genomic relationship matrix aG for GBLUP, with a(s)~N ; d(s) is the random dominance genetic effect nested within site, using the pedigree-based dominant relationship matrix D for ABLUP, with d(s)~NV, and using the dominant genomic relationship matrix dG for GBLUP, with d(s)~NV. The term d(s) was not included for the additive ABLUP-A and GBLUP-A models. The matrices X and Zx  are incidence matrices of their corresponding effects. The matrices I are identity matrices of their appropriate size. The symbols ⊕ and ⊗ refer to the direct sum and Kronecker product of matrices, respectively.where Va is a 2 x 2 variance-covariance matrix defined by the correlation of additive effects between sites (rBa) and unique additive variances for site ASS (The matrix Vd was defined by the correlation of dominance effects between sites (rBd) and unique dominance variances for site ASS  and its inverse were computed from the corrected pedigree using the “Amatrix” and the “ainverse” functions of the R packages AGHmatrix  following D matrix using the same proportions as in Eq. [6] before computing its inverse (“solve” function). Blending aG with A, or dG with D, using different proportions of xG  did not change the genetic parameter estimates (results not shown).The dominant relationship matrix nd nadiv , respectbg and s:bg were small with large standard errors, we removed these terms from the final models. The final additive-dominance models were then expressed as:Variance components estimated using the full GBLUP-AD models (Eq. [3]) are presented in where the terms are defined in Eq. [3]. The final models (Eq. [7]) had a similar AIC compared to the full models , but the BIC was always smaller for the final models (ΔBIC: -7 to -15), indicating that the final models were the most parsimonious for all traits S2. SimiThe equations used to obtain genetic parameter estimates within each site from the final additive and additive-dominance models are presented in where For the additive-dominance models, across-site estimates of individual narrow-sense heritability ). Estimated genetic values of individual trees on each site were obtained by adding the dominance deviations (BLUPs of the dominance effect d(s)) to the breeding values.Standard errors of genetic parameter estimates were obtained using the delta method (“vpredict” function from the ASReml-R v.4.1 package). Estimated breeding values of individual trees on each site were obtained as the best linear unbiased predictions (BLUPs) of the random additive effect (2.4PA) and prediction accuracy (PACC) of ABLUP and GBLUP models were estimated using two cross-validation (CV) procedures. The CV1 scenario evaluated the potential for predicting additional unphenotyped progeny trees within the same families as those included in the training population, while the CV2 scenario evaluated the prediction of new unphenotyped full-sib families. For CV1, trees were randomly split into 10 folds, making sure that each fold contained ~10% of the trees from each family . For CV2, families were randomly split into 10-folds such that there were only half-sib relationships between the training and validation datasets . For each round of CV, nine folds were used in model training, which was used to predict the breeding and genetic values for the remaining fold . This 10-fold cross-validation was repeated 10 times to obtain the standard deviation of estimates.The predictive ability , within each repetition , which can in turn affect PA and PACC estimates. We reported the mean and standard deviation of PA and PACC estimates across repetitions.All petition . For eacBVPA) or total genetic values (GVPA) and the observed phenotypes, within each site. The prediction accuracy of breeding value estimates (BVPACC) was obtained as GVPACC) was obtained as BVPACC and GVPACC of both ABLUP and GBLUP models, we used the within-site PACC between the corresponding ABLUP and GBLUP models were possible because we used the same heritability estimates for both models.The predictive ability of the models was evaluated as the Pearson’s correlation coefficient between the predicted breeding . This subset of 72 families was used for the two resampling scenarios.To investigate the ability of ABLUP-AD and GBLUP-AD models to estimate additive and dominance effects at different sample sizes, we ran two scenarios of resampling simulations: 1) varying the number of families while keeping the number of trees per family constant, and 2) varying the number of trees per family while keeping constant the number of families. For this analysis, we used dataset 1  and identified 72 families in which at least 26 trees per family were sampled. This was the number of trees per family that allowed us to keep the largest maximum number of trees for these resampling simulations  was conducted to estimate 33.1Genetic parameters were estimated by modeling additive effects in the pedigree-based ABLUP-A and genomic-based GBLUP-A models, or by modeling both additive and dominance genetic effects in ABLUP-AD and GBLUP-AD. In both datasets, the within-site narrow-sense and broad-sense heritabilities differed between sites (16) and average wood density (WD16), were moderately to highly heritable (ABLUP-A: 16), DBH16, and volume (VOL16) exhibited low to moderate heritabilities (ABLUP-A: 16 and VOL16 to as much as 0.77 for WD16. The estimates of 16 to 0.41 for WD16. The two wood traits showed very low genotype-by-environment interactions (GxE), as indicated by high narrow-sense .For wood traits and dataset 1, only small dominance effects were detected Figure 2For growth traits and dataset 1, both the AIC and BIC values favored the additive-dominance models over the additive models Table 3.For the second dataset, we found results similar to those of dataset 1, that is moderate to high across-site heritabilities for wood traits, while growth traits presented lower heritabilities (3.2BVPA), that is the correlation between the predicted breeding values and the observed phenotypes, considerably differed between sites for most traits. For simplicity, we present the averaged estimates across sites. For the first cross-validation scenario  in dataset 1 and the additive ABLUP-A and GBLUP-A models, we found BVPA values ranging from 0.48–0.55 for wood traits, and from 0.36–0.39 for growth traits (BVPACC) were similar between wood (BVPACC=0.84–0.90) and growth traits (BVPACC=0.76–0.91).The predictive abilities of breeding value estimates PABV, that isPA). The results were identical in terms of prediction accuracy (PACC) since we used the same heritability estimates for both methods. For CV1, The BVPA were similar between ABLUP-A and GBLUP-A for all traits.To compare ABLUP and GBLUP models, we simply use the predictive ability  than for wood traits, with BVPA values remaining above 0.41. These results translated into much smaller BVPACC for growth (0.44–0.52) than for wood traits (0.71–0.74) under CV2. Again, we obtained similar BVPA values between ABLUP-A and GBLUP-A for all traits.For the second cross-validation scenario (CV2) in dataset 1, in which the predicted unphenotyped full-sib families were not part of the model training , the BVPA and BVPACC estimates for wood than for growth traits.The results for dataset 2 were similar to those of dataset 1 Table S93.3GVPA) for growth traits by 0.02–0.04 compared with that of breeding values (BVPA) from the additive models, but little to no improvement was observed for wood traits  under the additive-dominance models, after standardizing by the broad-sense heritability, compared with that of breeding values (BVPACC) under the additive models, which were standardized by the narrow-sense heritability.For dataset 1 under CV1, and for both ABLUP and GBLUP, the additive-dominance models improved the predictive ability of genetic values , but not for wood traits . We found the opposite trend for ABLUP models under CV2, with a reduction of 0–0.03 from BVPA (ABLUP-A) to GVPA (ABLUP-AD), and a large reduction of 0.11 from BVPACC to GVPACC for the three growth traits.When predicting for new unphenotyped families under CV2, the GVPA) between ABLUP-AD and GBLUP-AD  than for wood traits (increase of up to 0.03 in GVPA). Interestingly, GBLUP-AD also increased the predictive ability of breeding values (BVPA) over ABLUP-AD, again only under CV2, and especially for growth traits (increase of BVPA up to 0.05). Thus, GBLUP-AD was better than ABLUP-AD for predicting both breeding and genetic values for unphenotyped families.Under CV1, we found almost equal predictive ability of genetic values PAGV between GVPA almost doubled using GBLUP-AD versus ABLUP-AD for DBH  and volume .For dataset 2, we obtained very similar results Table S93.416, WD16), the dominance ratio (16), or sometimes both parameters  were generally overestimated using ABLUP-AD, leading to higher broad-sense heritabilities  or accuracy (GVPACC) of genetic values under CV2 steadily increased with increasing the number of families sampled in the training dataset up to the maximum number of families  or 20 (HT16) trees per family for both ABLUP-AD and GBLUP-AD.We found similar results when varying the number of trees per family while keeping constant the number of families. Increasing the number of trees per family decreased standard deviations of 44.1We found that the studied wood traits, acoustic velocity, a proxy for wood stiffness, and average wood density, were under moderate to high genetic control, with almost no GxE interactions, indicating very little rank changes of families between the two contrasting environments tested. These conclusions based on GxE are limited as we only considered two sites. However, these results were confirmed in the two datasets analyzed here, which can be seen as independent replications since they consisted of different breeding populations with no relatedness between them. Our results are also in line with previous studies, which generally found higher across-site heritability and lower GxE estimates for wood quality versus growth traits in white spruce , Norway 13, HT13, and DBH13) were used, although we performed additional SNP and individual quality control filtering following pedigree correction. The reported within-site narrow-sense heritabilities using the ABLUP-A models in Our results for the second dataset can be directly compared to those of Eucalyptus species and their hybrids, the genetic variance of wood density was found to be mostly additive  over the additive-dominance models (GBLUP-AD) for both datasets. The literature is sparse regarding the evaluation of non-additive effects, especially in white spruce , and eveGBLUP-AD . These cGBLUP-AD . In fullGBLUP-AD  and in SGBLUP-AD . In the additive . Thus, oadditive .High narrow-sense heritabilities make wood traits excellent candidates for genetic improvement. The additive genetic variation is of utmost importance to tree breeders because it can be utilized in a simple and efficient random mating design such as open-pollinated seed orchards for the deployment of improved genetic material. Considerable genetic gains can be achieved for wood traits under such conditions . Here, w4.2Improvement for growth has been the main focus in most forest tree improvement programs, yet non-additive effects have not been frequently evaluated or used in tree breeding, including for white spruce . Compare16 in dataset 1; DBH13 in dataset 2), clearly showing the important confounding of genetic variances occurring in the ABLUP-AD models. The reduction of the estimated additive variance when non-additive effects are included in the model has also been reported previously  variances using open-pollinated , 2018 or4.3PA) is obtained as the correlation between the predicted breeding/genetic values and the observed phenotypes, and the prediction accuracy (PACC) is obtained as the correlation between the predicted breeding/genetic values and the true breeding/genetic values. In operational breeding populations, the true breeding/genetic values are unknown and many previous studies used, for this purpose, the BLUPs obtained using the complete dataset  with either ABLUP or GBLUP as a surrogate for the true breeding values  techniques can be used to evaluate model predictive ability and accuracy. The predictive ability . Similar results were found for GBLUP-AD and for dataset 2. Given the differences observed between sites, we suggest that computing PA and PACC within sites as done here is more accurate, especially for traits with higher GxE, and when there is a sufficient number of samples per site.Here, we evaluated n spruce , 2020b. es e.g., , 2020b. en sites . For exa4.4BVPACC) for all traits, datasets, and models .In our first CV scenario (CV1), individuals were divided into 10 folds, with ~10% of individuals from each family in each fold so that all families were well represented in training and validation datasets . Using the CV1 scenario and the additive models, we obtained very high prediction accuracies of breeding values (BVPA) or accuracy (BVPACC as BVPACC is a better estimator to compare across studies because it accounts for the different heritabilities. We found that the accuracies obtained in this study for the GBLUP-A models and dataset 1 were similar to those reported for Norway spruce (Ns). The accuracies obtained for both datasets in this study were larger than those obtained for a white spruce polycross trial with similar Ns, which used a smaller sample size  found either no improvements  or to the expected smaller within- versus among-family genetic variances  and 7K SNPs, but were still able to show an advantage of GBLUP when ranking individuals within full-sib families. Other studies reported significantly positive within-family PA or PACC values, either from full-sib family trials without clonal replication  compared with the predictive ability of breeding values (BVPA) obtained from the additive models. This was observed only for growth traits because wood traits showed little dominance variance. For growth traits, the PA increased from the additive to the additive-dominance models by 6.4% and 8.2% on average for ABLUP and GBLUP, respectively. This increase was expected because the additive-dominance models could predict a larger portion of the phenotypic variation due to the inclusion of significant dominance genetic effects. However, this improvement in PA was rather small considering that the dominance variance accounted for about half of the genetic variance for growth traits and that the broad-sense heritabilities obtained from the additive-dominance models were ~55% higher on average than the narrow-sense heritabilities estimated by the additive models. The standardization of PA as PACC) from the additive to the additive-dominance models for all traits, but more so for growth traits . These results indicated that the additive-dominance models could predict with less accuracy the total genetic values compared with breeding values under CV1.Under CV1 with both datasets 1 and 2, the inclusion of dominance in the additive-dominance models increased the predictive ability of genetic values , which is highly amenable for a large diversity of clonal lines in spruce species (GVPA) for growth traits in this study; 2) the selection intensity could be largely increased by genotyping more individuals; and 3) it dramatically reduces the breeding cycle length because no field testing is required for the SE lines. Thus, we expect that the predictions of total genetic values by GS models, although less accurate than those of breeding values, should still allow increasing genetic gains.The CV1 scenario simulated the prediction of additional genotypes from the same families as those included in the training population, which is within the current generation, and has direct applications in forest tree breeding. For example, GS models could be beneficial for the selection of somatic embryogenesis (SE) lines, for which the pedigree-based methods do not allow within-family ranking. As such, thousands of cryo-conserved SE lines can be genotyped to predict their genetic values. The best individuals, both across and within families, can then be deployed  species . Such cl species . This st4.6BVPA) between the additive ABLUP-A and GBLUP-A models, the results were different for the additive-dominance models. We found that the predictive ability of breeding (BVPA) and of genetic values (GVPA) for growth traits were substantially increased by 22% and 53% on average, respectively, for GBLUP-AD versus ABLUP-AD. For wood traits, there was a slight advantage of GBLUP-AD for wood density (~5–10%), which presented small dominance effects, but no advantage was observed for acoustic velocity.We found different trends under the second cross-validation scenario (CV2), in which the training and validation datasets included different families, thus simulating the prediction of breeding and genetic values for new unphenotyped full-sib families. However, one must keep in mind that those predicted families were half-sib related to a few families in the training dataset. While there were no differences in predictive ability of breeding values  by 5%, but substantially increased the predictive ability of genetic values (GVPA) by 24%, on average, compared with GBLUP-A. This increase in GVPA led to an increase in GVPACC of 10% from GBLUP-A to GBLUP-AD, on average. The trait that showed the largest improvement was DBH in dataset 2 (+54% in GVPA and +27% in GVPACC), which was also the trait that had the highest proportion of genetic variation attributed to dominance under GBLUP-AD /2=1,891 possible ways to combine the 62 parents of the four breeding groups, including crosses between breeding groups, but only 90 crosses were tested in the present study. Although the accuracy was smaller when predicting new families (-25% on average for CV2 versus CV1 in dataset 1), this could be compensated by the large increase in selection intensity from predicting additional parent-pairs and thus, ultimately, could lead to higher genetic gains.Mate allocation using GS models was found to increase the selection response in animal breeding . This onAt the operational level, this strategy would be relatively quick to implement because the parents are already sexually mature and good phenotypic data is available for their tested progeny, also allowing the measurement of new relevant traits, for instance in relation to adaptation to climate . Another4.7GVPA or GVPACC under CV2. Evaluating the minimum number of families needed for accurate predictions of genetic values was more difficult because, as the number of families sampled decreased, some families in the validation datasets became unrelated with the training population in CV2  or trees per family (6–26) sampled in dataset 1. For our breeding population, we found that a large sample size of 60-72 families and the maximum of 26 trees per family was required for accurate estimation of broad-sense heritabilities H^ind2, An important finding of our study is that GBLUP-AD estimated genetic parameters and predicted genetic values with greater accuracy than ABLUP-AD at all sample sizes tested, especially for growth traits. For these traits, smaller sample sizes were needed in GBLUP-AD to obtain similar prediction abilities of genetic values compared to ABLUP-AD under CV2. Similarly, 4.8Using two large full-sib datasets, we evaluated the inclusion of dominance in pedigree-based ABLUP and genomic-based GBLUP models for wood and growth traits. Wood traits were found to be optimal candidates for tree breeding efficiency, as they presented higher narrow-sense heritabilities and lower GxE than growth traits. High accuracies of GS models were even maintained when predicting for new unphenotyped families, which were half-sib related to a few families in the training dataset, a scenario that is closer to the production of the next generation.Predictions for growth traits will highly benefit from application of GS instead of using pedigree-based methods. GBLUP led to more realistic estimates of genetic variances and better partitioning of additive and non-additive variances, thus allowing to better plan the methods of selection for breeding purposes and the propagation of reforestation material. For growth traits, the use of GBLUP-AD led to higher predictive abilities for new families (CV2) than ABLUP-AD, mainly due to the ability of GBLUP-AD to predict dominance. The predictive ability of breeding values was slightly improved under GBLUP-AD for new families, compared with ABLUP-AD. Dominance was generally predicted with smaller accuracy than breeding values in GBLUP-AD (CV1). Still, it was sufficiently accurate to substantially increase predictive abilities and accuracies for unphenotyped families (CV2), and to outperform ABLUP-AD. It can be concluded that the dominance term should always be included into models when significant dominance variance is expected, such as for growth traits in this study.By subsampling various sets of families and trees per family, we found that GBLUP produced better estimates of genetic variances and higher predictive abilities than ABLUP for all subsamples, especially for growth traits. Results also highlight the need to rely on sufficiently large sample sizes to obtain accurate estimates of genetic parameters and predictions of breeding and genetic values.The ability of GBLUP to predict for new unphenotyped families also provided useful insights for next-generation prediction accuracies. Nonetheless, there is a need for next-generation studies in white spruce and other important tree breeding species. Meanwhile, predicting for new crosses may be used to perform mating allocation and maximize the total genetic values for elite family or clonal propagation in the current generation.https://treesource.rncan.gc.ca/en.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: SN, JBe, JBo, and PRNL conceived the study. SN and PRNL wrote the manuscript. JBo, PRNL, SG, JBe, and MP edited the manuscript. SN and SG performed the statistical analyses. JBe, JBo, PRNL, and MP set up and maintained the field trial. JBo, JBe, and PRNL obtained funding to support genotyping and phenotyping. All authors contributed to the article and approved the submitted version.


Medical students have higher risk of psychological disorders due to the relatively stressful environment. Educators are becoming increasingly aware of the impact of stresses on the students general well-being. The objective of the current study was to examine the prevalence of and risk factors for depressive and anxiety symptoms among first-year and fifth-year medical students. Additionally, we aimed to determine whether the COVID-19 pandemic has affected students’ mental well-being.A cross-sectional study was performed at the College of Medicine at King Saud University between September 2020 and January 2021. The target population was first-year and fifth-year medical students. Depressive symptoms were screened using the 9-item Patient Health Questionnaire (PHQ-9), while anxiety symptoms were screened using the 7-item Generalized Anxiety Disorder assessment (GAD-7). Students were also directly asked about the effect of the COVID-19 pandemic on their mental well-being. Outcomes were compared between groups using the chi-squared test and Student’s t test. Multivariate logistic regression analysis was performed to identify factors associated with depressive and anxiety symptoms.p = 0.020) and anxiety symptoms  were higher in the first-year students than in the fifth-year students. Approximately 19.2% of the students were worried about acquiring COVID-19, 49.4% were worried about academic performance, and 30.8% were feeling sad, depressed or anxious during the COVID-19 pandemic. Independent risk factors for depressive symptoms included having concomitant anxiety, being worried about acquiring COVID-19, being worried about academic performance, and feeling sad, depressed or anxious. Independent risk factors for anxiety included having a lower grade point average and having concomitant depressive symptoms.A total of 182 medical students were included. Depressive symptoms (52.9% versus 35.8%, Medical students have an alarmingly high prevalence of depressive and anxiety symptoms, which might have been negatively impacted by the COVID-19 pandemic. There is a need for a special mental health program targeting new and current medical students.The online version contains supplementary material available at 10.1186/s12909-023-04387-x. Anxiety and depressive disorders are the most common mental disorders worldwide , 2. ApprThe mental health of medical students has received increasing attention in recent years. Several studies from Arabic and Western countries have indicated that medical students suffer from higher levels of anxiety, depression, and psychological distress than their peers in other specialties –7. MedicThe emergence of the COVID-19 pandemic represents a compelling new challenge for the mental health and psychological well-being of the whole world population and medical students in particular –16. EstiThe current study was conducted at the College of Medicine of King Saud University in Riyadh, Saudi Arabia. The college was established more than 40 years ago as the first medical college in the country. The college hosts more than 1400 male and female medical students.A cross-sectional study was carried out between September 2020 and January 2021. The study obtained all required ethical approvals from the institutional review board at the College of Medicine, King Saud University (No. E20-5302). The students were required to provide consent to join the study. The consent emphasized voluntary participation, confidential handling of data, no sensitive data collection, and the anonymous nature of data analysis.The target population was first-year and fifth-year medical students during the 2020–2021 academic year. Students with previously diagnosed psychiatric disorders were also included.It was estimated that 180 students (90 from each year) are required to detect a 20% difference in study outcomes (depressive and anxiety symptoms) between the two years using 80% power and a 95% level of significance. Given the actual number of medical students at King Saud University in the target years, the calculated sample size would be sufficient to detect 40% study outcomes (depression and/or anxiety) with 10% confidence in each group. The sample was collected using convenience sampling. Students were recruited randomly from classrooms, break areas, and building courtyards. We decided to compare first-year and fifth-year students because they represent the beginning and the end of undergraduate education in the College of Medicine. Therefore, their comparison is more likely to show differences in the measured outcomes due to several factors that distinguish between both groups, such as the experience level, level of maturity, ability to handle stress and seek support when needed, etc.A self-administered online questionnaire (using Google Forms) was developed and included sociodemographic characteristics, academic performance, and perceived probable COVID-19 impact on students’ psychology. The questionnaire was reviewed by three experts to assess face and content validity. The experts were clinicians working in the student clinic with experience in assessing the physical and psychological health of medical students. A pilot study was conducted with 10 students to check the applicability and clarity of the questions. The pilot study showed positive feedback to the questions. Additionally, previously validated psychometric tools were used to assess the study outcomes (depressive and anxiety symptoms) , 19. StuDepressive symptoms were screened using the 9-item Patient Health Questionnaire (PHQ-9), while anxiety symptoms were screened using the 7-item Generalized Anxiety Disorder assessment (GAD-7) , 19. Botp < 0.20). Backwards elimination was used to exclude nonsignificant variables from the model. To simplify the analysis of COVID-related questions, responses of  and (most of the time) were analysed as (yes) and  and (sometimes), which were analysed as (no). A P value < 0.05 was considered significant. SPSS  was used for all statistical analyses.Data are presented as frequencies and percentages for categorical data and mean and standard deviation (SD) for continuous data. Sociodemographic characteristics, academic performance, COVID-19-related questions, and study outcomes were compared between the study groups (first year and fifth year). Additionally, the previous factors were examined to detect the univariate risk factors for depressive and anxiety symptoms. Chi-squared or Fisher’s exact tests, as appropriate, were used to examine differences in categorical variables, while Student’s t test was used to examine differences in continuous variables. To identify factors independently associated with depressive and anxiety symptoms, multivariate logistic regression analysis models were run after adjusting for the variables that were potentially associated with awareness in univariate analysis (with p < 0.001), significantly better grade point average (GPA) , significantly more frequent successful academic years , significantly more frequent within-family living status , and significantly more frequent sedentary (not active) lifestyle . There were no significant differences between students in the two academic years in gender, use of psychological therapy, sleeping duration, single social status, and current smoking.A total of 182 medical students were included in the current study. Table p = 0.020). Anxiety symptoms were also higher in the first-year students than in the fifth-year students (35.6% versus 26.3%), but the difference did not reach statistical significance (p = 0.176). Approximately 25.3% of the students had both depressive and anxiety symptoms, and this prevalence was significantly higher in the first-year students than in the fifth-year students . Approximately 49.5% of the students had either depressive or anxiety symptoms, without a significant difference between students in the two academic years .As shown in Fig. p = 0.517). Almost half of the study students were always (15.9%) or most of the time (33.5%) feeling worried or anxious about their academic performance during the COVID-19 pandemic, with significantly higher levels of worry in the first-year students than in the fifth-year students . Approximately 30% of the study students always (8.8%) or most of the time (22.0%) felt sad, depressed or anxious during the COVID-19 pandemic, without significant differences between students in the two academic years (p = 0.648). Approximately 11.2% of the study students were diagnosed with COVID-19 (defined by a positive swab test), with a significantly lower infection rate in the first year than in the fifth year . Half of the study students had at least one of their relatives diagnosed with COVID-19, without significant differences between students in the two academic years (p = 0.764).Table p = 0.021) but not in multivariate analysis. Independent risk factors for depressive symptoms included having concomitant anxiety , being worried about acquiring COVID-19 , being worried about academic performance , and feeling sad, depressed or anxious . Academic year was not a significant predictor of anxiety in either univariate or multivariate analysis. Independent risk factors for anxiety included having lower GPA  and having concomitant depressive symptoms .Table The current study revealed alarmingly high prevalence rates of depressive and anxiety symptoms among medical students during the COVID-19 pandemic. The current prevalence of depressive symptoms 44%) was generally similar to the prevalence reported by previous studies performed in Saudi Arabia and internationally 4% was ge. For exaThe current prevalence of anxiety symptoms (31%) was generally similar to the prevalence reported by previous studies performed in Saudi Arabia –26 and iBoth depressive and anxiety symptoms in the current study were higher in the first-year students than in the fifth-year students; this difference was significant for depressive symptoms. Similarly, some studies have shown that depressive symptoms are higher in new medical students than in clinical students, probably because of a lack of coping mechanisms with respect to the challenging new study environment , 11, 28.The current study showed that worrying about academic performance among medical students (49%) was much higher than worrying about contracting COVID-19 (19%). Additionally, COVID-19-related worries were independent risk factors for depressive and anxiety symptoms. This finding probably indicates the significant impact of the current COVID-19 pandemic on the mental health of medical students. Consistent with this finding, several international studies have reported the significant role of the current COVID-19 pandemic in developing depressive and anxiety symptoms among medical students –14. SimiData on types of worry during the COVID-19 pandemic and their relationship with mental health are very limited , 31. TheOur study is unique in quantifying the burden on the mental health of medical students in different academic years. We used validated psychometric tools to assess depressive and anxiety symptoms , 19. AddIn conclusion, the current study shows an alarmingly high prevalence of depressive and anxiety symptoms among medical students during the COVID-19 pandemic. Both depressive and anxiety symptoms were significantly higher in the first-year students than in the fifth-year students, which reached statistical significance only for depressive symptoms. COVID-19-related worries were independent risk factors for depressive and anxiety symptoms. There is a need for special mental health programs targeting new medical students, especially those with limited psychiatric services during the pandemic.Additional file 1.


Structural valve deterioration (SVD) of bioprosthetic heart valves (BHVs) has great clinical and economic consequences. Notably, immunity against BHVs plays a major role in SVD, especially when implanted in young and middle-aged patients. However, the complex pathogenesis of SVD remains to be fully characterized, and analyses of commercial BHVs in standardized-preclinical settings are needed for further advancement. Here, we studied the immune response to commercial BHV tissue of bovine, porcine, and equine origin after subcutaneous implantation into adult α1,3-galactosyltransferase-knockout  mice. The levels of serum anti-galactose α1,3-galactose  and -non-Gal IgM and IgG antibodies were determined up to 2 months post-implantation. Based on histological analyses, all BHV tissues studied triggered distinct infiltrating cellular immune responses that related to tissue degeneration. Increased anti-Gal antibody levels were found in serum after ATS 3f and Freedom/Solo implantation but not for Crown or Hancock II grafts. Overall, there were no correlations between cellular-immunity scores and post-implantation antibodies, suggesting these are independent factors differentially affecting the outcome of distinct commercial BHVs. These findings provide further insights into the understanding of SVD immunopathogenesis and highlight the need to evaluate immune responses as a confounding factor. The implantation of bioprosthetic heart valves (BHVs) has increased because of population aging and greater longevity ,2. ReseaThe most common tissues used for BHVs are of animal origin, basically from bovine and porcine pericardium, pig valves, and occasionally from equine pericardium ,15,16. MDespite the advances in the generation of better BHVs, the actual immunogenicity of currently used BHVs and the effect on their durability is still not well established . As a reIn this work, we assessed the feasibility of a simple subcutaneous model using adult α1,3-galactosyltransferase knockout  mice to study the humoral and cellular immune responses toward different commercial BHVs. The age of mice was selected to stand for the age range of patients that pose a greater difficulty in choosing BHV over mechanical heart valves because of SVD .The commercial BHVs used in this work were kindly donated by Medtronic  and Sorin-LivaNova  and kept in their original conditions prior to experimental assessment. The human pericardium was obtained from a patient subjected to cardiac surgery and fixed with 0,6% glutaraldehyde  in phosphate-buffered saline  for one month and subsequently maintained at 0.2% glutaraldehyde. Prior to surgical implantation, small pieces of BHV and human pericardium were generated under sterile conditions, their weight recorded, and extensively washed in tissue-culture-grade PBS. The selected pieces were kept overnight in PBS in sterile 24-well plates. In compliance with current legislation, this project was approved by the ethical clinical committee of Bellvitge University Hospital.n = 5, 12–14 months-old mice) and 4 months . One mouse (M3) was lost for causes unrelated to graft rejection (during blood collection). Next, the experimental setting was established with adult mice (10–14 months old) for a 2-month implantation period. Mice were equally distributed between 5 experimental groups (n = 7–10), including both males and females, as well as mice with different titers of anti-Gal IgM and IgG antibodies pre-determined by ELISA using plates coated with Gal-human serum albumin (HSA) as previously described [The procedures and the care of mice complied with European Commission guidelines and were approved by the local institutional ethical committee and Generalitat de Catalunya. For these studies, we used mice knockout for α1,3-galactosyltransferase  from our colony (B6xCBAx129sv mixed background) , most ofescribed . These 5The grafts were examined macroscopically at the time of collection, fixed in 10% neutral-buffered formalin , processed, and embedded in paraffin for histologic examination. Sections of 3 µm were cut, stained with hematoxylin and eosin (H&E) following standard procedures, and evaluated independently and blindly by two investigators, including an expert pathologist, for signs of immune rejection. A scoring system  of 1–5 wPorcine aortic endothelial cells (PAEC) were obtained from the European Collection of Cell Cultures  and cultured in cell-culture conditions in DMEM/10% FBS supplemented with 100 IU/mL penicillin–100 μg/mL streptomycin and 50 μg/mL endothelial cell growth supplement . Culture flasks  pre-coated with 1% porcine collagen (Merck) in PBS were used.Anti-Gal IgM and IgG antibodies were determined by ELISA, as previously described . Furthert test for single comparisons. The coefficient of determination (R2) was calculated for correlations. Statistical analyses were performed using Graph Pad Prism 6. Differences were considered statistically significant at p ≤ 0.05.Statistical analyses were conducted after confirming data normality with the Shapiro–Wilk normality test. Then, the following tools were used as necessary: the one-way ANOVA and Tukey’s test for multiple comparisons, one-way ANOVA and Dunnett’s tests to compare multiple conditions relative to baseline, and paired Student The Gal KO mice used develop anti-Gal IgM and IgG antibodies spontaneously but with high variability . As a stTo assess the antibody response over time, we first determined serum anti-Gal antibody titers up to 2 months post-implantation (pilot study) by ELISA . Two difGrafts were surgically retrieved at 2 and 4 months post-implantation. Gross examination at 2 months showed that implants were well preserved and surrounded by the vascularized soft tissue of the recipient . HistoloNext, a new set of experiments was performed in adult Gal KO mice by grafting subcutaneously a piece per mouse of different commercial BHVs comprising tissues of equine, bovine and porcine origin, as well as glutaraldehyde-fixed human pericardium for comparison . The expPathologic studies of the grafts at two months post-implantation were also conducted. As observed in the pilot experiment, the initial macroscopic examination showed that the structure of the BHV tissues was basically preserved in all cases . Signs oThe anti-Gal antibody response was also determined in adult Gal KO mice after BHV implantation. Indeed, a very selective antibody response was observed by flow cytometry depending on the implanted tissue. As expected, the Gal-negative human pericardium did not influence the anti-Gal antibody levels . InsteadLooking at a potential link between the antibody response and the outcome of the implanted BHV tissue, we assessed whether the serum anti-Gal antibody reactivity (determined by flow cytometry) correlated with the histology scores on the cellular immune infiltrate . In geneIn the same flow cytometric assays, an anti-non-Gal antibody reactivity was determined for Gal KO mice implanted with BHV tissues using GAS914 pre-treated sera. No significant anti-non-Gal IgM responses were detected for Gal KO mice following this procedure a. Only aIn this work, we found that the cellular and antibody responses induced by the implantation of BHV tissues follow separate mechanisms that may differently affect the integrity of implanted BHVs. This finding provides insights into how the immune response may contribute to SVD and points to the need for multiple strategies of intervention to address SVD. The cell- and antibody-mediated pathways acted as independent factors in the adult Gal KO mouse model. Notably, the anti-Gal and anti-non-Gal antibody responses in BHV-implanted patients have been recently related to BHV calcification and, thus, to SVD ,22,23,24A cellular immune response was observed by histopathological analysis for all BHV tissues at 2 months post-implantation, a time period that was found to be appropriate to assess the cellular immune infiltrate in the pilot experiments. The macroscopic examination did not provide much information, but the histological analysis revealed the presence of macrophages, lymphocytes, and plasma cells surrounding the grafted tissue at this time point. Notably, macrophages and lymphocytes have also been observed in BHV retrieved from patients ,35. The The semi-quantitative differences in the cellular immune infiltrates between the various tissues studied were relevant findings of the present work. The fixed human pericardium, followed by the Hancock II (porcine), triggered the highest cellular immunity. The other tissues assessed, including the ATS 3f, the bovine Freedom/Solo tissue, and the Crown (bovine pericardium with a phospholipid reduction treatment), induced a lower cellular response. Although neither commercial BHVs nor human pericardium has been previously studied in this Gal KO model, other studies evaluated the cellular immune responses to various glutaraldehyde-processed pericardial tissues at 2 and 3 months post-implantation ,38,39. IMajor differences were also found in the antibody responses to the various tissues studied. Evidence for the role of antibodies in SVD has been provided experimentally by demonstrating the contribution of anti-Gal and anti-Neu5Gc antibodies to the calcification process of BHV tissues in various animal models ,40. In oPositive correlations between serum antibody reactivity and histological scores were rarely observed in this study. Exceptionally, a correlation was found between anti-non-Gal IgG reactivity and the histological scores of ATS 3f at two months post-implantation despite these antibodies not reaching a statistically significant elevation at this time point. Nevertheless, the higher amount of cellular immune infiltrate did not generally coincide with increases in specific serum antibodies. In the mouse model used, there were situations that elicited a specific antibody response, others with a predominant cellular immune response, and even a mixed profile was observed. Intriguingly, a statistically significant negative correlation was found between pre-implantation anti-Gal IgG and the histological score for ATS 3f grafts but not for other BHV tissues. Elucidating the mechanism behind this is beyond the scope of this work, but we can hypothesize that it may be caused by the variability of Gal KO mice in Th1/Th2 balances that influence the anti-Gal IgG natural antibody titers and the cellular immune response to the ATS 3f tissue in opposite ways. In summary, our data support the concept of at least two separate immune pathways (antibody- and cell-mediated) participating in BHV-tissue deterioration.Based on our data, it should be considered that some BHV tissues may trigger a response with Th1 predominance (cellular) while others may induce a more Th2-driven  reaction. Furthermore, innate immunity and inflammation, as represented by the well-characterized biomarker C reactive protein, may contribute to these processes and be of interest for inclusion in future studies . Indeed,We found high variability in the induction of cellular and antibody responses by the different commercial BHV tissues implanted subcutaneously in adult Gal KO mice. Penetrating cellular immunity was associated with stronger tissue deterioration. However, no major correlations between the amount of cellular immune infiltrate in the graft and the elicited serum antibody levels were observed. Summarily, it may be concluded that the cellular and antibody responses are independent factors in BHV deterioration in the Gal KO mouse model. This model may provide valuable information for predicting the immunogenicity of commercial and newly developed BHVs.