EGFR) testing, patient characteristics, and overall survival (OS) among patients with early‐stage NSCLC in Denmark. Patients with early‐stage NSCLC registered in the Danish Lung Cancer Registry in 2013–2018 were followed through 2019. We described EGFR testing, descriptively summarised patient characteristics, and calculated OS by EGFR testing and mutation status. The association between EGFR mutation (EGFRm) and all‐cause mortality was estimated using Cox proportional‐hazards regression, in subgroups defined by stage at diagnosis, age at diagnosis, comorbidity, and receipt of surgery. In 2013–2018, 21,282 patients with NSCLC were registered in the Danish Lung Cancer Registry, of whom 8758 were diagnosed at an early stage. Of those, 4071 (46%) were tested for EGFRm at diagnosis. Median OS was 5.7 years among patients with EGFRm‐positive status (n = 361) and 4.4 years among patients with EGFRm‐negative status (n = 3710). EGFRm‐positive status was associated with lower all‐cause mortality in all subgroups. This study contributes to population‐based evidence on the epidemiology of early‐stage NSCLC treated in routine clinical practice.Non‐small cell lung cancer (NSCLC) accounts for the majority of all lung cancer diagnoses, and approximately 35% of patients with NSCLC are diagnosed at an early stage (I–IIIA). This study aimed to describe epidermal growth factor receptor  testing and overall survival among patients with early‐stage (I‐IIIA) non‐small cell lung cancer (NSCLC) in Denmark. Patients identified in the Danish Lung Cancer Registry in 2013‐2018 were followed through 2019. Of 8758 patients diagnosed with early‐stage disease, 4071 (46%) were tested for EGFR mutation at diagnosis; EGFR mutation‐positive status was associated with longer overall survival. This study contributes to population‐based evidence on epidemiology of early‐stage NSCLC treated in routine clinical practice. With its population of 5.8 million and tax‐supported universal health care, Denmark has multiple registries of routinely collected data, including information on inpatient and outpatient hospital diagnoses, migrations, and deaths.Patients with early‐stage (stage I–IIIA) NSCLC registered in the Danish Lung Cancer Registry (DLCR) between 2013 and 2018 were eligible for inclusion in this study. The DLCR is a population‐based nationwide clinical quality database that tracks all patients with incident primary lung cancer diagnosis in Denmark since 2003. The DLCR contains data on age, sex, mutation testing status and results, tumour stage at diagnosis, lung function, performance status, and initial treatment. It links data on comorbidity from the Danish National Patient Registry; on histology from the Danish National Pathology Registry; and on death from the Danish Civil Registration System.EGFR testing status, mutation status among the tested, forced ejection volume (FEV1), Eastern Cooperative Oncology Group (ECOG) performance status, Charlson Comorbidity Index (CCI) score, smoking history, tumour histology, and initial treatment. OS was defined as time from the date of lung cancer diagnosis to date of death from any cause, with follow‐up censored at emigration, or end of observation, on 10 September 2019. We reported median OS by EGFR testing status and, among patients who were tested, by mutation status. Among the patients who were tested, we plotted OS according to EGFR mutation status and estimated the association of EGFR mutation status with all‐cause mortality in subgroups defined by stage at diagnosis, age at diagnosis, sex, CCI score category, and receipt of surgery. In each subgroup, we estimated hazard ratios (HRs) adjusted for sex, age, calendar year of diagnosis, FEV1, ECOG performance status, and CCI score category. OS was estimated using the Kaplan–Meier method, and adjusted HRs and 95% confidence intervals (CIs) were estimated using Cox proportional‐hazards regression.Descriptive statistics of patient characteristics at the time of lung cancer diagnosis were: calendar year of diagnosis, median follow‐up time to death or censoring, stage at diagnosis, sex, age, To avoid disclosure of personal data and according to Danish data protection regulation, implicit or explicit nonzero cell counts <5 were masked. Implicit cell counts, i.e., those that allowed recomputation of <5 cells from surrounding data, were marked as non‐reportable (NR) regardless of the observed cell count.3EGFR mutation at diagnosis. Table EGFR mutation (86.1%) had the most common histological NSCLC subtype of adenocarcinoma,EGFR mutation. Patients tested for EGFR had a higher prevalence of females (57.5% vs. 45.0%), a lower prevalence of FEV1 below 40% predicted (29.1% vs. 37.0%), and a higher prevalence of ECOG performance status of 0 (55.8% vs. 49.9%) than untested patients  patients. There were 8758 (44.0%) patients with early‐stage (I–IIIA) NSCLC at diagnosis, and 4071 (46.5%) of these had been tested for EGFR mutation, 361 (8.9%) were EGFRm‐positive, and 3710 (91.1%) were EGFRm‐negative. Of the patients with EGFRm‐positive status, a higher proportion were female (70.4% vs. 56.3%), a higher proportion had an ECOG performance status of 0 (67.8% vs. 54.6%), a higher proportion had low comorbidity burden (51.2% vs. 41.4%), and a higher proportion received surgery (70.1% vs. 59.2%) compared with patients with EGFRm‐negative status. Of note, fewer than half  of the 5188 patients who received surgery were tested for EGFR mutation. Furthermore, a slightly higher proportion of patients with EGFRm‐positive status than patients with EGFRm‐negative status received adjuvant therapy (20.8% vs. 17.5%). Age distributions were similar across mutation status groups . Among the 361 patients with EGFRm‐positive status, median follow‐up until death or censoring was 2.8 years (IQR: 1.6–4.1 years), and median OS was 5.7 years. Among the 3710 patients with EGFRm‐negative status, median follow‐up until death or censoring was 2.1 years (IQR: 1.1–3.5 years), and median OS was 4.4 years. Figure EGFR mutation, overall and by stage at diagnosis.Among the 4071 patients tested for EGFRm‐positive versus negative status and according to the subgroups. EGFRm‐positive status was associated with a lower all‐cause mortality in all strata of stage at diagnosis, age, sex, comorbidity, and regardless of receiving surgery: adjusted subgroup‐specific hazard ratios for all‐cause mortality associated with EGFRm varied from 0.48 to 0.83  non‐small cell lung cancer in Denmark, nearly half of the patients underwent EGFRm in our study population (8.9%) was slightly lower than that reported in systematic reviews for European populations,EGFRm‐negative status (the largest group) was comparable to sex‐ and stage‐specific estimates reported recently for a population of patients with NSCLC from Australia, Canada, Denmark, Ireland, New Zealand, Norway, and the UK by Araghi et al (2021).The prevalence of EGFRm‐positive early‐stage (I–IIIA) NSCLC showed an improved 2‐year disease‐free survival (DFS) compared with historic genotype‐matched controls.EGFR‐mutated NSCLC .EGFR‐expressing NSCLC, nor in the subgroup with EGFRm‐positive status.EGFRm–positive NSCLC, adjuvant treatment with osimertinib reduced the risk of disease recurrence or death compared with placebo by 83% in patients with stage II and IIIA disease , and by 80% in the overall trial population of patients with stage IB–IIIA disease ; 88% of patients with stage IB disease, were alive and disease‐free at 24 months . Adjuvant osimertinib also reduced the risk of central nervous system‐related disease recurrence or death by 82% compared to placebo in patients with stage IB–IIIA disease (HR: 0.18. 95% CI: 0.10–0.33).EGFR testing in patients with stages I–IIIA NSCLC to identify these patients as early as possible for appropriate adjuvant treatment selection.The advent of precision medicine has led to the availability of treatments for genomic alterations in NSCLC. The SELECT study of adjuvant erlotinib in patients with EGFR mutation status, and the DLCR captures the entire eligible population. Limitations of this study include lack of data on cause of death in the analysis dataset; potential unmeasured confounding, and the small number of observations in some subgroups, resulting in low precision of the corresponding estimates.Data used in this study were collected for quality assurance, independent of a research question, with data quality likely independent of EGFR mutation status in early‐stage disease is recommended given that EGFR‐TKI is an approved adjuvant treatment for patients with EGFR‐mutated NSCLC. Characterising patients with early‐stage disease, and understanding the impact of EGFR mutation in this population, will help guide future treatment decisions.This study contributes to population‐based evidence regarding selected demographic and clinical characteristics and survival of patients with early‐stage NSCLC treated in routine clinical practice in Denmark. Testing for Vera Ehrenstein, Aliki Taylor and Erik Jakobsen provided substantial contribution to the conception and design of the work, drafting of the work and revising it critically for intellectual content. Katrine Eriksen conducted the data analysis and provided substantial contribution to the conception and design of the work, drafting of the work and revising it critically for intellectual content. Leslie Servidio provided substantial contribution to the conception and design of the work, drafting of the work and revising it critically for intellectual content. All authors approved the final version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.This study received institutional research funding from AstraZeneca to and administered by Aarhus University.Vera Ehrenstein and Katrine Eriksen are salaried employees of Aarhus University or Aarhus University Hospital. Aliki Taylor and Leslie Servidio are or were at the time of the study conduct salaried employees of AstraZeneca. Erik Jakobsen is a salaried employee of Odense University Hospital.This study underwent mandatory registration with the Danish Data Protection Agency at Aarhus University  and at the Southern Denmark Region . An ethical approval or an approval from an Institutional Review Board is not required for studies based on routinely collected data according to Danish law.


Currently, we cannot predict whether a pre‐school child with asthma‐like symptoms will have asthma at school age. Whether genetic information can help in this prediction depends on the role of genetic factors in persistence of pre‐school to school‐age asthma. We examined to what extent genetic and environmental factors contribute to persistence of asthma‐like symptoms at ages 3 to asthma at age 7 using a bivariate genetic model for longitudinal twin data.n = 21,541 twin pairs). Bivariate genetic models were fitted to longitudinal data on asthma‐like symptoms reported by parents at age 3 and 7 years to estimate the contribution of genetic and environmental factors.We performed a cohort study in monozygotic and dizygotic twins from the Netherlands Twin Register ; hence, contribution of environmental factors was low. Persistence was explained by a high (rg = 0.807) genetic correlation.Parental‐reported asthma‐like symptoms at age 3 and asthma at age 7 are highly heritably. The phenotype of asthma‐like symptoms at age 3 and 7 was highly correlated and mainly due to heritable factors, indicating high persistence of asthma development over ages 3 and 7. Aadditive genetic effectsALSPACAvon longitudinal study of parents and childrenAPRSasthma polygenic risk scoreCshared or common environment effectsDZdizygoticEnon‐shared environmental effectsGWASgenome‐wide association studiesNTRNetherlands twin registerMZmonozygoticSNPssingle nucleotide polymorphismsPIAMAprevention and incidence of asthma and mite allergyA large part of the variance in liability to parental‐reported asthma‐like symptoms at age 3, and asthma at age 7 was explained by genetic factors.The high phenotypic correlation between asthma‐like symptoms at age 3 and asthma at age 7, was almost entirely explained by persistence of genetic effects.1Asthma often originates early in life,The classical twin study is a common tool to estimate genetic and environmental contributions to complex human traits, including disease, based on the fact that monozygotic (MZ) twins are genetically (nearly) identical and dizygotic (DZ) twins share on average 50% of their segregating genes. Both types of twins share their prenatal environment and grow up in the same family environment. A larger resemblance in MZ compared to DZ twins for a trait or disease is consistent with a genetic contribution to that trait.n = 7,045 children), 244 independent single nucleotide polymorphisms (SNPs) and 13 SNPs from 17q21 were associated with asthma and investigated whether these were associated with six wheezing phenotypes.ORMDL3, IKZF3, and GSDML were associated with intermediate‐onset and persistent, but not early‐onset wheezing.IL33 and IL1RL1 were associated with asthma, intermediate and late‐onset wheeze phenotypes.Only a few genetic studies analyzed asthma‐like symptoms in children.In this large, longitudinal twin study, we aimed to estimate the extent to which genes and environment contribute to asthma‐like symptoms at 3 and 7 years and in persistence of the asthma phenotype from age 3 to 7. Our main interest lies in genetic persistence as expressed in the genetic correlation across these ages and contribution of genetic effects to these phenotypic correlations.22.1We conducted a cohort study of twins aged 3 and 7 years included in the Netherlands Twin Register (NTR), a birth cohort initiated in 1987.2.2A child was considered to have asthma‐like symptoms at age 3 and asthma at age 7 based on the survey question: “Can you indicate for each of the following conditions whether it applies?” followed by a checked box for asthma, chronic bronchitis, or chronic non‐specific respiratory disorders. Since pre‐schoolers cannot reliably perform lung function testing and, thus, cannot have an asthma diagnosis confirmed, we refer asthma‐like symptoms instead of asthma at age 3. Clinicians refer to asthma persistence, whereas in genetic models, the statistical term is stability. Here, we use persistence of asthma‐like symptoms at ages 3 and 7.2.3In addressing genetic continuity, we included an asthma polygenic risk score (APRS), defined based on SNPs identified in previous genetic association studies. To calculate the APRS, we included 26 top hits associated with asthma‐like symptoms or childhood asthma phenotypes from six genome‐wide association studies (GWAS) investigating SNPs associated with childhood‐onset asthma and two candidate‐gene replication studies investigating whether previously reported SNPs were associated with childhood asthma phenotypes Table . The APR2.4We analyzed longitudinal twin data at ages 3 and 7 by genetic covariance structure analysis11, a21, and a22 and likewise for the A and E part. In terms of these parameters, estimates of the total A variance at ages 3 and 7 are a112 and a212+a222, respectively, with similar application to the C and E part, The total liability variance is the sum of these components, that is, a112 + c112 + e112 at age 3, and a212+a222 + c212+c222 + e212+e222, at age 7. Note that the liability is standardized to that the components add up to 1 at age 3 and age 7. This implies that a112, a212+a222 etc. can be interpreted as proportions of liability variance. The contribution of A, C, and E to the phenotypic stability is determined by the products a21*a22, c21*c11, and e21*e11. In Figure 21*a11)/ (a112* a212+a222)½. Similarly for E correlation, we have rE = (e21*e11)/ (e112* e212+e222)½.The contribution of additive genetic effects (A), shared or common environment (C), and non‐shared environment (E) to asthma liability variance at age 3 and 7 was estimated by fitting a genetic covariance structure model to the twin data. We obtained estimates of the contributions of A, C, and E to the variance and the covariance of the liabilities at ages 3 and 7. A path diagram is shown in Figure 3 and b7. By regressing the asthma liability on the APRS, we estimated contributions of APRS to liability variance at ages 3 and 7, and liability covariance between ages 3 and 7. Parameters b3 and b7 represent age‐dependent influences of the APRS at ages 3 and 7, respectively. To obtain parameter estimates, we fitted this model using OpenMx in R version 3.5.3  by raw‐data maximum likelihood, including all available data. We included gender as a covariate . The last test also includes a test for qualitative sex differences.4Heritability accounted for a large part of the variance in liability to parental‐reported asthma‐like symptoms at ages 3 and 7. The high phenotypic correlation between asthma‐like symptoms at ages 3 and 7 was almost entirely explained by persistence of genetic effects.The heritability of asthma at 3 and 7 years corresponds with previous twin studies.To our knowledge, none of the previous twin studies have incorporated an APRS to their model to estimate contribution of currently known genetic loci to asthma phenotypes and our APRS has not been incorporated in a non‐twin study. We did not find an effect of the APRS which was defined by a count of asthma risk alleles (0/1/2) per earlier identified SNP. This may indicate that childhood asthma is highly polygenic and heterogeneous.n = 756,363) and singletons  indicated that the observed higher risk of childhood asthma in twins compared to singletons appears to be mediated by differences in gestational age and birthweight between these populations.A strength of our study is the large cohort including over 20,000 Dutch twin pairs of which over 7,500 twin pairs had longitudinal data describing whether they had asthma‐like symptoms at ages 3 and 7. Our design can estimate the contribution of all genetic and environmental factors (shared and non‐shared). There is uncertainty whether asthma‐like symptoms at young age help predicting childhood asthma onset at school age. Therefore, our main focus was on contribution of genes and environment to asthma persistence over age 3–7 which has yet to be investigated in a bivariate twin model. It was thought that twins are not representative to estimate risk of asthma, but this concern has been addressed previously.This study also had some limitations. Firstly, outcome misclassification may have occurred since clinical data of asthma‐like symptoms and asthma were not available. The asthma‐like symptom phenotype at age 3 is less precise due to difficulty in the diagnosis. Nevertheless, data were retrieved from surveys sent to parents at age 3 and 7, minimizing recall bias. Second, gene‐environment interactions were not investigated in our model. However, in 5‐year‐old Dutch twins overlapping to a large extent with our study population, a dedicated set of analyses tested for interactions with gestational age, time in incubator, breastfeeding, maternal educational attainment, maternal smoking during pregnancy, current parental smoking, presence of older siblings, and of childcare outside home, gene‐environment interactions were barely identified. Several investigated environmental factors did increase risk of asthma, but did not modify genetic influences on asthma, with only one exception, namely prematurity.Genes play a substantial role to persistence of asthma over ages 3 and 7, and we could use this knowledge to define high versus low‐risk groups. Prediction of asthma risk and persistence is likely improving with larger GWAS and potentially when results such as (epi)genetic and gene‐gene interaction scores are added to prediction models.To conclude, our results show that parental‐reported asthma‐like symptoms at age 3 and asthma at age 7 are highly correlated and heritable, indicating high persistence of asthma development over ages 3 and 7. Future studies should elucidate genetics of well‐diagnosed, persistent childhood asthma further and identify mechanisms that could be targets of future interventions.AHM has received research grants outside the submitted work from Lung Foundation of the Netherlands, GSK, Boehringer Ingelheim and VERTEX, she is the PI of a P4O2 (Precision Medicine for more Oxygen) public private partnership sponsored by Health Holland involving many private partners that contribute in cash and/or in kind , and she has served in advisory boards for AstraZeneca, GSK and Boehringer Ingelheim with money paid to her institution. G.H. Koppelman reports grants from Lung Foundation of the Netherlands, TEVA the Netherlands, UBBO EMMIUS Foundation, TETRI Foundation, GSK, and VERTEX, outside the submitted work; and G.H. Koppelman participated in a global advisory board on pediatric asthma for GSK and an advisory board meeting to PURE‐IMS. C. Longo, C.E.M. van Beijsterveldt, S.J.H. Vijverberg, D.I. Boomsma, C. Dolan, M. Bartels, J.J. Hottenga, M.W. Pijnenburg and E.M.A. Slob report no conflict of interest for this study.Elise Margaretha Adriana Slob: Conceptualization ; Formal analysis (supporting); Investigation (lead); Methodology (supporting); Visualization ; Writing – original draft (lead); Writing – review & editing . Cristina Longo: Conceptualization (lead); Formal analysis (supporting); Investigation (supporting); Methodology ; Supervision (lead); Writing – review & editing . Susanne Vijverberg: Conceptualization (supporting); Funding acquisition (supporting); Supervision (supporting); Writing – review & editing (supporting). Toos C.E.M. van Beijsterveldt: Data curation ; Investigation (supporting); Project administration ; Supervision (supporting); Writing – review & editing . Meike Bartels: Data curation ; Investigation (supporting); Project administration ; Resources ; Writing – review & editing . Jouke Jan Hottenga: Conceptualization (supporting); Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Resources (supporting); Software (supporting); Visualization (supporting); Writing – review & editing . Mariëlle W Pijnenburg: Funding acquisition (supporting); Supervision (supporting); Writing – review & editing . Gerard H Koppelman: Funding acquisition (supporting); Supervision (supporting); Writing – review & editing . Anke‐Hilse Maitland ‐ van der Zee: Conceptualization ; Funding acquisition ; Methodology ; Supervision (supporting); Writing – review & editing . Conor Dolan: Formal analysis (lead); Investigation ; Methodology ; Software (lead); Writing – original draft (supporting); Writing – review & editing (lead). Dorret Boomsma: Conceptualization ; Funding acquisition ; Investigation ; Methodology (lead); Supervision (lead); Writing – original draft (supporting); Writing – review & editing (lead).https://publons.com/publon/10.1111/pai.13762.The peer review history for this article is available at Supplementary MaterialClick here for additional data file.


Data on patients with type 1 diabetes mellitus (T1DM) and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infections are sparse. This study aimed to investigate the association between SARS‐CoV‐2 infection and T1DM.Data from the Prospective Diabetes Follow‐up (DPV) Registry were analyzed for diabetes patients tested for SARS‐CoV‐2 by polymerase chain reaction (PCR) in Germany, Austria, Switzerland, and Luxembourg during January 2020–June 2021, using Wilcoxon rank‐sum and chi‐square tests for continuous and dichotomous variables, adjusted for multiple testing.Data analysis of 1855 pediatric T1DM patients revealed no differences between asymptomatic/symptomatic infected and SARS‐CoV‐2 negative/positive patients regarding age, new‐onset diabetes, diabetes duration, and body mass index. Glycated hemoglobin A1c (HbA1c) and diabetic ketoacidosis (DKA) rate were not elevated in SARS‐CoV‐2‐positive vs. ‐negative patients. The COVID‐19 manifestation index was 37.5% in individuals with known T1DM, but 57.1% in individuals with new‐onset diabetes. 68.8% of positively tested patients were managed as outpatients/telemedically. Data analysis of 240 adult T1MD patients revealed no differences between positively and negatively tested patients except lower HbA1c. Of these patients, 83.3% had symptomatic infections; 35.7% of positively tested patients were hospitalized.Our results indicate low morbidity in SARS‐CoV‐2‐infected pediatric T1DM patients. Most patients with known T1DM and SARS‐CoV‐2 infections could be managed as outpatients. However, SARS‐CoV‐2 infection was usually symptomatic if it coincided with new‐onset diabetes. In adult patients, symptomatic SARS‐CoV‐2 infection and hospitalization were associated with age. HighlightsDiabetic ketoacidosis rate and HbA1c were not elevated in pediatric patients with type 1 diabetes mellitus (T1DM) and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection.Pediatric patients with known T1DM did not show an elevated COVID‐19 manifestation index; only pediatric patients with coincident SARS‐CoV‐2 infection and new‐onset diabetes had an elevated rate of symptomatic infection.Adult patients showed an age‐dependent increase in symptomatic SARS‐CoV‐2 infections and hospitalization rate. The DPV registry is maintained by the Institute of Epidemiology and Medical Biometry at the University of Ulm, Ulm, Germany and collects patient data from diabetes centers in Germany, Austria, Switzerland, and Luxembourg, covering >90% of all pediatric diabetes centers in Germany and Austria.Data analysis included all T1DM patients aged >6 months with documented polymerase chain reaction (PCR) tests for SARS‐CoV‐2. All patients tested upon inpatient admission as well as outpatients and externally tested patients were included in the analysis. The data collection period was 01/2020–06/2021, and data were analyzed separately for pediatric (age <18 years) and adult (age ≥ 18 years) patients. Data were aggregated per patient ±10 days around the test. Overall, 162 centers contributed to the data collection, including 150 centers from Germany, 10 from Austria, and one each from Switzerland and Luxembourg. Data from SARS‐CoV‐2‐positive and ‐negative T1DM patients were compared. In a second step, the following subgroups of data were further analyzed: First, data from SARS‐CoV‐2‐positive T1DM patients were analyzed, for whom there was documented information on symptoms (comparison of patients with/without symptoms of coronavirus disease 2019 [COVID‐19]). Second, data from hospitalized SARS‐CoV‐2‐positive and ‐negative T1DM patients were compared. Third, a separate analysis of pediatric patients with new‐onset diabetes or follow‐up examinations was performed with respect to glycated hemoglobin A1c (HbA1c) and the rate of diabetic ketoacidosis (DKA). In addition, we analyzed data from SARS‐CoV‐2‐positive and ‐negative adult T1DM patients .2.22). For the pediatric patients, BMI values were expressed as standard deviation scores (SDS) based on the German KiGGS percentiles .Demographic data included patient age, age at new‐onset diabetes, diabetes duration, sex, and migrant background . Clinical data included HbA1c  and body mass index  on a Windows Server 2019 mainframe to compute the Wilcoxon rank‐sum test for continuous variables and the chi‐square test for dichotomous variables. All analyses were adjusted for multiple testing using the Bonferroni‐Holm method (Bonferroni stepdown). Two‐tailed 33.1p = 0.003). The difference was due to the patients with known and treated T1DM; there was no difference in this respect in the patients with new‐onset diabetes. The rate of DKA was not increased in patients who tested positive for SARS‐CoV‐2 compared to patients who tested negative . There was no statistical difference between negatively and positively tested patients with respect to age, age at new‐onset diabetes, diabetes duration, and BMI‐SDS (see Table p < 0.001 in each case). For SARS‐CoV‐2‐negative patients, the most common reasons for admission were new‐onset diabetes (41.5%) and admission for diabetes education (41.2%). By contrast, 69.4% of patients who tested positive were admitted to hospital due to new‐onset diabetes and only 10.2% were admitted for education, (p < 0.001 in each case).SARS‐CoV‐2‐positive patients received mostly inpatient care during the first 6 months of the pandemic (93.3%) whereas negatively tested patients were only treated in 38% as inpatients. As the pandemic went on, this treatment changed. As for 06/2021, patients who tested negative for SARS‐CoV‐2 received care as inpatients in 75.8% of cases, as outpatients in 24% of cases, and by telemedicine in 0.2% of cases. By contrast, only 31.2% of positively tested patients received care as inpatients, 59.3% as outpatients, and another 9.6% by telemedicine  had COVID‐19 symptoms. In individuals with known T1DM, the COVID‐19 manifestation index was 37.5% (12/32). By contrast, 8/14 (57.1%) children and adolescents with new‐onset diabetes and 5/6 (83.3%) hospitalized adult patients with new‐onset diabetes showed symptoms of COVID‐19 .Our analysis found that SARS‐CoV‐2‐positive patients had significantly lower HbA1c levels than negatively tested patients. Further analysis showed that this difference was due to significantly lower HbA1c levels in patients with known T1DM, while HbA1c levels were the same in positively and negatively SARS‐CoV‐2 tested pediatric patients with new‐onset diabetes. Our database did not contain any information as to why patients were tested for SARS‐CoV‐2, so the reason for the significantly lower HbA1c value in SARS‐CoV‐2 positive patients remains unclear. In the group of SARS‐CoV‐2 negative patients the percentage of patients admitted to the hospital for diabetes education was much higher than in the SARS‐CoV‐2 positive group. The main reason for these admissions was poor diabetes control. This may explain the higher HbA1c level in children and adolescents with negative SARS‐CoV‐2 tests. Population‐wide screening studies among diabetic pediatric patients may provide clarity in this regard. Importantly, no increased HbA1c levels in terms of a risk factor for SARS‐CoV‐2 infection were observed in SARS‐CoV‐2‐positive patients. There were no other significant differences between negatively and positively tested pediatric patients. The results are indicative of the low morbidity in children and adolescents with T1DM and SARS‐CoV‐2 infection, as has also been reported for children from the United States and England.The rate of DKA at new onset of diabetes was significantly higher during the COVID‐19 pandemic than in prior years. Kamrath et al. reported a significantly increased value of 44.7% for 2020.Among the subgroup of patients with new‐onset diabetes and available data on COVID‐19 symptoms, infection was symptomatic in 8/14 cases (57.1%), which was markedly higher than in the overall study population, where the percentage was 43.5%. This finding indicates that new‐onset diabetes may have aggravated COVID‐19 infection, which is consistent with identical observations by Singh et al.4.2In adult patients with T1DM, the proportion of symptomatic infections was almost twice as high as in pediatric patients (83.3% vs. 43.5%). Definitive statements are not yet available regarding the COVID‐19 manifestation index, which depends on multiple co‐variables, including age, preexisting disease, and socioeconomic status of the population.4.3Due to the retrospective design of the study as an analysis of existing database records and the absence of a nondiabetic comparison group, our observations need to be considered tentative and interpreted with caution. Since our analysis compared a relatively small group of patients with SARS‐CoV2‐positive T1DM with a markedly larger group of negatively tested patients, it was repeatedly impossible to measure significant differences between the groups, despite clear differences between the data. Further studies in larger cohorts may be useful to confirm our observations.Our database did not contain any clear information on the reasons why patients were tested for SARS‐CoV‐2. Since SARS‐CoV2 testing was usually performed on hospital admission but not in the case of outpatient appointments, our data originated mainly from inpatients as well as patients with infection symptoms. This limits the statements regarding the above‐mentioned group. Population‐wide screening studies among diabetic pediatric patients may provide clarity in this regard.Our results reflect the COVID‐19 pandemic during the periods when the original and the “delta” variants of SARS‐CoV‐2 were predominant. Infection rates were much higher during the subsequent “omicron” wave, especially in pediatric patients, and some symptoms differed considerably. Hence, results for this later period may differ from the present findings. DPV registry data for this period will become available in the fall of 2022.5Our results are indicative of the low morbidity in children and adolescents with T1DM and SARS‐CoV‐2 infection. Importantly, our study found no increase in DKA rate among patients with positive SARS‐CoV‐2 tests. Patients who tested positive for SARS‐CoV‐2 did not have elevated HbA1c levels compared to those who tested negative. In addition, no association was found between age/diabetes duration/HbA1c/BMI and symptoms of infections. Thus, no evidence emerged that these factors led to increased SARS‐CoV‐2 morbidity in pediatric patients with a previous diagnosis of T1DM in our study population. Most patients with known T1DM and SARS‐CoV‐2 infection could be managed as outpatients. However, when SARS‐CoV‐2 infection coincided with new‐onset diabetes, the infection was usually symptomatic.In adult patients, an association was observed between age and frequency of symptomatic SARS‐CoV‐2 infection as well as hospitalization. Since diabetes sequelae are associated with a more severe course, optimization of patients' metabolic situation is recommended.B.R.B. was responsible for conceptualization, data analysis and interpretation, investigation, and writing of the original draft of the manuscript. S.R.T. was responsible for methodology, data analysis, formal analysis, and review and editing of the manuscript. C.K. was responsible for review and editing of the manuscript. T.R.R. was responsible for conceptualization, investigation, review and editing of the manuscript. R.W.H. was responsible for conceptualization, supervision, funding acquisition, review and editing of the manuscript. B.K., K.K., A.M., E.M.‐R., F.R., T.R.R. and R.W.H were responsible for data acquisition. S.R.T., C.K., B.K., K.K., E.M.‐R., F.R., T.R.R. and R.W.H. were responsible for scientific discussion of the results and important intellectual content and review and editing of the manuscript. All authors reviewed the draft manuscript for important intellectual content and approved the manuscript prior to submission. S.R.T. and R.W.H. are the guarantors of this work and, as such, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.The DPV is supported through the German Federal Ministry for Education and Research within the German Centre for Diabetes Research (grant 82DZD14A02). The funding organization had no role in design and conduct of the study, collection, management, analysis, and interpretation of the data, preparation, review, or approval of the manuscript, or decision to submit the manuscript for publication.None declared.The ethics committee of the University of Ulm approved the analysis of the anonymized DPV data .Informed consent  to participate in the DPV registry was available from patients and/or their parents.Supporting Information.Click here for additional data file.


We mainly evaluate retinal alterations in Alzheimer's disease (AD) patients, investigate the associations between retinal changes with AD biomarkers, and explore an optimal machine learning (ML) model for AD diagnosis based on retinal thickness.A total of 159 AD patients and 299 healthy controls were enrolled. The retinal parameters of each participant were measured using optical coherence tomography (OCT). Additionally, cognitive impairment severity, brain atrophy, and cerebrospinal fluid (CSF) biomarkers were measured in AD patients.p < 0.05). Moreover, TMV was positively associated with Mini‐Mental State Examination and Montreal Cognitive Assessment scores, IPL thickness was correlated negatively with the medial temporal lobe atrophy score, and the GCL thickness was positively correlated with CSF Aβ42/Aβ40 and negatively associated with p‐tau level. Based on the significantly decreased OCT variables between both groups, the XGBoost algorithm exhibited the best diagnostic performance for AD, whose four references, including accuracy, area under the curve, f1 score, and recall, ranged from 0.69 to 0.74. Moreover, the macular retinal thickness exhibited an absolute superiority for AD diagnosis compared with other enrolled variables in all ML models.AD patients demonstrated a significant decrease in the average, superior, and inferior quadrant peripapillary retinal nerve fiber layer, macular retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL) thicknesses, as well as total macular volume (TMV) (all We identified the retinal alterations in AD patients and found that macular thickness and volume were associated with AD severity and biomarkers. Furthermore, we confirmed that OCT combined with ML could serve as a potential diagnostic tool for AD. APOE genotype. Finally, we found the XGBoost algorithm exhibited the best diagnostic performance for AD.In this study, we found that AD patients demonstrated a significant decrease in peripapillary retinal nerve fiber layer thickness and macular retinal thickness, as well as total macular volume. In addition, these changes were correlated with MRI findings indicative of cerebral atrophy, tau and amyloid‐beta levels in the cerebrospinal fluid, and  It is pathologically characterized by extraneuronal amyloid‐β (Aβ) plaque deposition and intracellular neurofibrillary tangles consisting of hyperphosphorylated microtubule‐associated protein tau or tau.The retina might be an important site of inquiry because it shares the same embryonic precursor as the brain and displays several structural and functional similarities with the organ.High‐definition optical coherence tomography (HD‐OCT) is a cost‐effective and convenient tool that allows for high‐resolution retinal images to be obtained in vivo. With the advent of OCT, the retina has become an appealing subject in many studies on neurodegenerative diseases, including AD.Artificial intelligence (AI), particularly machine learning, has recently emerged as a powerful tool in the field of neuroscience. It refers to the ability of computers to detect complex patterns and construct models by learning from existing data. Machine learning has been developed and applied extensively in the field of medicine, including disease diagnosis and prognosis, disease progression monitoring, and treatment efficacy evaluation.22.1APOE) genotyping. Patients with (1) best‐corrected visual acuity (BCVA) <0.5, (2) refractive spherical equivalent >6.00 D or with astigmatism >3.00 D, and (3) intraocular pressure (IOP) >21 mmHg were excluded. All participants were free of diabetes mellitus and uncontrolled hypertension, as well as other neurologic, psychiatric, and ophthalmological conditions . Figure A total of 159 patients with AD and 299 healthy controls (HCs) were enrolled between December 2017 and November 2019. Patients with AD were recruited from Xiangya Hospital, and HCs were referred from the Health Management Center of the Third Xiangya Hospital. All diagnoses of probable or possible AD were performed by neurologists at Xiangya Hospital, based on the National Institute on Aging‐Alzheimer's Association guidelines.2.2All OCT scans  were performed by the same ophthalmologist, who was blinded to the diagnoses according to standard protocols. Due to time constraints and a high inter‐eye correlation between both eyes, we selected one random eye for the HD‐OCT examination. The thickness of the peripapillary retinal nerve fiber layer (pRNFL) was acquired by taking three consecutive 3.4‐mm circular scans centered on the optic nerve disk Figure . The mac2.3All OCT images were segmented using advanced automated three‐dimensional retinal layer segmentation software (IOWA OCTExplorer v3.8.0).2.4g for 10 min and stored at −80°C, according to a previously established protocol. The levels of Aβ42, phosphorylated tau (p‐tau), and total tau (t‐tau), as well as the Aβ42/Aβ40 ratios in the CSF, were measured using enzyme‐linked immunosorbent assays.All patients underwent 3‐Tesla MRI scans on the same scanner . The severity of brain atrophy was assessed using three rating scales: Medial temporal lobe atrophy (MTA),2.5APOE genotyping in 149 patients with AD. APOE genotyping was performed on DNA extracted from a 10 ml blood sample using previously established protocols.Of the 159 patients with AD, 10 refused to provide their blood samples. Thus, we assessed 2.6t‐tests and Chi‐square tests were used to assess the difference between two variable groups when the sample data were normally distributed; otherwise, the non‐parametric test was used. The covariance analysis was used to compare OCT measurements between AD patients and HCs, with age, sex, IOP, BCVA, and axial length (AL) taken as covariates. Similar analyses were performed for comparisons across AD subgroups and each subgroup versus HCs. We evaluated the correlation of OCT parameters with cognitive function, brain atrophy, CSF biomarkers, and APOE genotypes by using Pearson correlation analysis. Statistical significance was set at p < 0.05. All statistical analyses were performed using SPSS Statistics, version 25.0 (IBM Corp.).Data are expressed as mean ± standard deviation, median or percentages. The Kolmogorov–Smirnov test was used to determine the normality of the data. Student's 2.7p‐value at the above statistical analyses were incorporated into the final diagnosis model. We evaluated the performance of six common machine learning algorithms, including extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (Light GBM), k‐nearest neighbor, Random Forest, Gradient Boost, and Adaptive Boosting (AdaBoost). We randomly selected 70% of the dataset to obtain the models and used the remaining 30% of the dataset to test the performance of the models. Four criteria were used to evaluate the performance of each model, including the accuracy (ACC), the area under the curve (AUC), f1 score, and recall. We used fivefold cross‐validation to validate the stability of these constructed models, and the accumulated ACC, AUC, f1‐score, and recall values were used as the metrics for evaluation. All the construction processes of prediction models were based on Python programming language (version 3.7).OCT variables with a significant 33.1p < 0.001) and lower mini‐mental state examination (MMSE) scores (p < 0.001) than the HCs group . The AD group had a shorter mean duration of education (3.2p < 0.001) and lower TMV .After adjusting for potential confounders, the average, superior, and inferior quadrant pRNFL Figure , mRNFL, n = 51, CDR = 0.5), moderate , and severe  groups. The OCT measurements were compared between each AD subgroup and HCs. The results showed that GCL (p = 0.020) and IPL (p = 0.044) were significantly lower early in the mild AD group , superior (p = 0.015) quadrant pRNFL thickness  thickness  and TMV (p < 0.001) values in the moderate AD group were significantly decreased, and the inferior (p = 0.003), nasal (p = 0.003), and temporal (p = 0.022) quadrant pRNFL thickness  scores: mild , MRT , and TMV  were positively associated with MMSE scores. Moreover, the average , superior , inferior  quadrant pRNFL thickness, MRT , and TMV  were positively associated with MoCA scores  scores in patients with AD. After adjusting for potential confounders, superior quadrant pRNFL thickness , MRT , IPL thickness , and TMV  in AD patients. Moreover, the GCL thickness correlated negatively with the PCA score . No significant correlations were observed between any OCT measure and the GCA score  and negatively associated with CSF p‐tau concentrations  in the AD group. The IPL thickness was positively correlated with CSF Aβ42 concentrations  and negatively associated with CSF t‐tau concentrations  in the AD group. However, no association was found between CSF biomarkers and pRNFL thickness , whereas 83 (55.70%) patients with AD were APOE ε4‐carriers . We divided patients with AD into two groups based on APOE genotypes: the APOE ε4‐noncarrier and APOE ε4‐carrier groups. All retinal parameters were decreased in the APOE ε4‐carrier group compared to the APOE ε4‐noncarrier group, though this difference was only significant in IPL thickness (p = 0.004)  patients with AD were 4.30% pat3.6Three pRNFL  and five macular  variables showed a significant decrease in patients with AD. All participants were randomly split into training (111 AD patients and 209 HCs) and testing (48 AD patients and 90 HCs) sets. The results revealed that all six machine learning algorithms were superior to the traditional Logistic Regression. Moreover, the XGBoost algorithm exhibited the best diagnostic performance in both the training and testing sets. In the testing set, the evaluated results of the XGBoost algorithm were ACC (0.74), AUC 0.69), f1 score (0.70), and recall (0.74) Table . The mos9, f1 sco4APOE genotypes in patients with AD. In addition, we found that the XGBoost algorithm showed the best diagnostic performance for AD by comparing six common machine learning algorithms' performances.This study revealed that OCT measurements were significantly correlated with MMSE and MoCA scores indicative of cognitive function, MTA and PCA scores indicative of cerebral atrophy, tau and Aβ levels in the CSF, and Consistent with previous studies, the present study found pRNFL thinning in the superior and inferior quadrants.We found that retinal measures were positively associated with MMSE and MoCA scores. This suggests that OCT parameters correlate with cognitive function in patients with AD. Oktem et al.An association was also detected between some OCT measures and MTA and PCA scores. This study demonstrated that both axonal and ganglionic cell body losses in the retina were associated with MTA scores and that the GCL thickness was correlated with the PCA score. The hippocampus is known to be involved in the pathogenesis of neurodegenerative diseases, including AD,42/ Aβ40 and negatively correlated with CSF p‐tau levels. Additionally, IPL thickness was positively correlated with CSF Aβ42 levels and negatively correlated with CSF t‐tau levels. There are few reports on the association between retinal thickness and CSF biomarkers. A study found that neither pRNFL nor macular thickness was associated with CSF biomarkers.42 and a higher tau concentration reflect a higher Aβ and tau burden in the retina and brain. Thus, the retinal pathophysiological changes could eventually lead to decreased retinal thickness.Our study showed that GCL thickness was positively correlated with CSF AβAPOE ε4‐carrier group compared to the APOE ε4‐noncarrier group, although this difference was only significant in terms of IPL thickness. In previous studies, patients with AD carrying APOE ε4 have been shown to exhibit increased apoptosis and loss of synaptic integrity.APOE ε4 was also associated with reduced microvascular density and neocortical cerebral blood flow.APOE ε4‐carrier group, possibly due to cholinergic dysfunction in the IPL, as described in previous studies.We found that all retinal parameters were decreased in the In this study, we presented a machine learning model to diagnose AD. The primary objective of our study was to develop a diagnostic prediction model for AD by combining OCT measures and machine learning. Eight quantitative OCT features were screened for inclusion in the final diagnostic models. Obtaining OCT features was inexpensive, noninvasive, and convenient when compared to traditional diagnostic tools . Additionally, our study revealed that the XGBoost algorithm exhibited the best diagnosis performance for AD, including the highest ACC, f1 score, and recall both for the training and testing sets. The superiority of the XGBoost algorithm in diagnostic performance was validated for these evaluation indicators, including ACC, f1 score, and recall by using fivefold cross‐validation. The XGBoost algorithm based on OCT measurements was superior to traditional logistic regression and other five machine learning algorithms and exhibited certain potential in the diagnosis of AD. Additionally, we found that all six machine learning algorithms were superior to the traditional Logistic Regression. This confirmed that machine learning had a wide range of application prospects in clinical practice. Because of its ability to detect complex patterns and construct models, it was available to assist in the clinical diagnosis of AD, and thus, achieve the aim of the early invention and optimizing treatment strategies. Further studies are required to validate these findings.This study had some limitations. Since OCT measurements and neuropsychological assessments require patients' cooperation, patients with more advanced stage AD (CDR = 3) could not be included in our study. Additionally, this was a cross‐sectional study; therefore, we cannot conclude retinal changes over time. However, we compared OCT measurements among patient subgroups according to disease severity and HCs groups, which roughly simulates the process of retinal changes over time. Moreover, MRI and CSF analyses were not available for the HC group.APOE genotypes. In addition, we developed an optimal machine learning algorithm to assist the clinical diagnosis of AD. Overall, this study confirmed that OCT measurements combined with machine learning could be useful for AD diagnosis.In conclusion, this large‐scale study confirmed that retinal structure was significantly altered in patients with AD and that OCT measurements correlated with cognitive function, MRI findings indicative of cerebral atrophy, tau and Aβ levels in the CSF, and XW and BJ designed the experiment and analyzed, interpreted the data, and wrote the manuscript. XW, BJ, HL, YQW, XLH, YZ, SZZ, QX, XXL, YFZ, HJ, JLW, JFG, XXY, and BST collected participants' data and discussed the results. BX and HZX performed all participants' OCT images scan. XLJ and RCZ segmented and calculated the macular single retinal layer thickness of all scans. LS edited the manuscript.The authors have no competing interests to declare.


Symbiodinium sp. Coral animals induce the release of carbon fixation products of symbiotic Symbiodinium sp. through secreting host release factors (HRFs) such as taurine. To study the potential effect of taurine on photosynthesis and release of carbon fixation products of Symbiodinium sp., we compared the growth of Symbiodinium sp. under control and taurine-stimulated conditions. Photosynthesis parameters were detected to monitor the photosynthetic efficiency. Biomass and the contents of total soluble sugar, total insoluble sugar, total protein, total lipids, chlorophyll a were analyzed. Metabolome and transcriptome analyses were performed to analyze the potential effect of taurine on primary metabolism and mRNA transcription. The results revealed that taurine significantly increased the growth, photosynthesis efficiency, total soluble sugar, chlorophyll a, and chlorophyll b and free amino acid content of Symbiodinium sp. while decreased the content of total insoluble sugar. Results of metabolome and transcriptome analyses suggested that taurine might affect metabolic pathways in Symbiodinium sp. by altering the permeability of the algal cell membrane, diverting photosynthetically fixed carbon from storage compounds to translocated compounds, releasing a signal of low concentrations of nitrogen to initiate a series of response mechanisms, and controlling the density of Symbiodinium sp. through the quorum sensing effect. These results help to explore how corals control carbon metabolism in Symbiodinium sp. and to provide theoretical guidance for furthering our understanding of Symbiodinium sp. biology and coral-algal symbiosis.Coral reefs belong to the marine ecosystems and host the richest biodiversity of marine organisms. Coral reefs are formed as a result of the symbiotic relationship between the host coral animal and photosynthetic dinoflagellates, namely  Coral reefs play important roles in the marine ecosystem . Coral rSymbiodinium sp.  such as taurine , trace elements, and vitamins (filter-sterilized).eviously . Brieflyg for 3–5 min, the resulting pellet was dissolved in 50 μl of lysis buffer , and then incubated at 95°C for 5 min. The supernatant obtained after centrifugation was used as a template for the PCR amplification of microalgal 18S rRNA gene fragments. The total reaction volume was 25 μl, which contained 1 μl of template, 1 μl of 18S-F (CCTGGTTGATCCTGCCAG) and 1 μl of 18S-R (TTGATCCTTCTGCAGGTTCA) universal primers, 12.5 μl 2× Rapid Taq Master Mix , and 9.5 μl ddH2O. PCR conditions were set as follows: initial denaturation at 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 1 min, and final extension at 72°C for 10 min. The amplicons were sent to BGI Tech  for sequencing.To identify the isolated microalgae, a total of 1 ml microalgal biomass was harvested by centrifugation at 5,000 × −2 s−1) at ~26°C and a 12:12 light:dark cycle. For treatment with low concentrations of taurine (LT), cells were treated with taurine at a final concentration of 10 μM, and an equal amount of NO3− was added to control cells (LC). For treatment with high concentrations of taurine (HT), cells were treated with taurine at a final concentration of 1 mM, and an equal amount of NO3− was added to control cells (HC). Each treatment was administered in triplicate sets in 1 L flanks. To monitor cell growth, the flank was stirred vigorously to suspend the cells and 5 ml of culture was sampled. A UV/visible spectrophotometer  was used to measure the absorbance at 680 nm (A680 nm). When there were significant differences in growth (LT vs. LC and HT vs. HC) and photosynthesis performance (HT vs. HC) between treatments, algal cells were allowed to settle at the bottom of the flasks, and the supernatant was siphoned out. Cell pellets were collected by centrifugation at 10,000 × g for 10 min. Subsequently, the pellets were frozen in liquid nitrogen and stored at −80°C.Algal cells were cultured in sterilized seawater supplemented with F/2 medium . Culture3− nor taurine was added). Each treatment was administered in triplicate sets in 1 L flanks. The flank was stirred vigorously to suspend the cells and 5 ml of culture was sampled at days 1, 2, 3, 5, 7, and 9 for subsequent PSII activity detection with water-PAM  was measured under shading conditions.For photosynthetic performance measuring, cells were cultured under HC, HT, and NN (neither NOater-PAM . Act-Ligg for 5 min. Supernatants (700 μl) were recovered, lyophilized, and resuspended in 450 μl of distilled water. Then, 50 μl DSS  standard solution  was added, and the solution was mixed by vortexing. The samples were transferred to 5-mm nuclear magnetic resonance (NMR) tubes , and spectra were collected using a Bruker AV III 600 MHz spectrometer. The first increment of the 2D-1H, 1H-NOESY pulse sequence was used for the acquisition of 1H-NMR data and for suppressing the solvent signal. Each run consisted of 100 ms mixing and 990 ms pre-saturation times (~80 Hz gammaB1), and the spectra were collected after obtaining a total of 128 scans in a 15 min period at 25°C. Fourier transformation was performed with the free induction decay (FID) signals in a Chenomx NMR Suite, version 8.3 , and the baseline was corrected. DSS was used as the internal standard, and all spectra were referenced and analyzed against the Chenomx compound library.For metabolome analysis, each group included five biological replications. Metabolome analysis followed the methods described by For transcriptome analysis, each treatment was conducted in three replications. Total RNA was extracted using TRIzol reagent  according to the manufacturer’s instructions. High-quality total RNA  was used to construct cDNA libraries for high-throughput RNA sequencing. One microgram of total RNA was used to construct an RNA-seq transcriptome library using the TruSeqTM RNA sample preparation Kit from Illumina  following the manufacturer’s instructions. Then, cDNA libraries were selected for cDNA target fragments of 200–300 base pairs (bp) in 2% low-range ultra agarose and then amplified using Phusion DNA polymerase (New England Biolabs (Beijing) LTD, Beijing). The amplified cDNA libraries were loaded into a NovaSeq 6000 sequencing system (Illumina).2 (fold-change [|FC|]) > 2 and value of p < 0.05. Functional annotation and enrichment analyses were performed and classified using the GO and KEGG databases. Detail parameters for bioinformatic pipelines are supplied in To generate clean reads, raw sequence reads were trimmed using SeqPrep,3PO4 (containing 1 M KCl) was added and mixed, followed by centrifugation at 5,000 × g for 5 min, then the solvent phase was recovered. This process was repeated three times. The solvent phases were combined, washed three times with distilled water, and then evaporated at ~20°C under a ventilated fume hood.Total lipids were extracted as the methods described by g for 5 min. This process was repeated three times, and the supernatants were merged and evaporated at 85°C to a volume of ~0.3 ml, made up to 1 ml with distilled water, and used for the soluble sugar analysis. To hydrolyze insoluble sugar, 1 ml of 30% perchloric acid was added to the sediment and stirred for 15 min at room temperature (~25°C) and centrifuged at 8,000 × g for 5 min. The supernatant was collected. This extraction was repeated three times and the supernatants were merged and made up to 3 ml with 30% perchloric acid. To determine the content of sugar, 0.2 ml samples were added to 1 ml of anthrone solution [2 g anthrone in 1 L 72% (v/v) H2SO4] and mixed rapidly. The mixtures were incubated in a water bath at 100°C for 8 min, cooled to room temperature, and the absorbance at 625 nm was measured. The soluble sugar content was determined according to a standard curve carried out simultaneously using glucose, while the starch contents were calculated by multiplying the value by 0.9.Total soluble sugar content was determined as the methods described previously  with min2O was added. The mixture was centrifuged at 12,000 g for 15 min and the supernatant was collected. This process was repeated three times, and all the resulted supernatants were merged. Protein concentration was then measured by a bicinchoninic acid protein assay kit using bovine serum albumin as standard.Total protein content was determined as the methods described by g for 10 min, and supernatants were recovered. Chlorophyll a was determined by detecting the absorbance levels at 665 and 652 nm and calculated using the equations described previously  in the growth rate of Symbiodinium sp. between LC and LT groups until day 9, at which time the growth rate of LT was higher  than that of LC (p < 0.05) the growth rate of Symbiodinium sp. since day 3  it (p > 0.05) in Fv/Fm among HT, HC, and NN in early state of cultivation (p < 0.05) than that of HT and HC, indicating that N-limited condition influence the max photosynthetic efficiency in Symbiodinium sp.Since the growth rate was highest in HT, followed by HC, and lowest in NN , the pho0.05) it . There wtivation . While on = 1), amino acids (n = 16), food and drug compounds (n = 2), nucleic acid components (n = 1), organic acids (n = 5), sugar (n = 1), and vitamin/cofactors (n = 1). The regulation patterns of most metabolites were similar in HT compared to the other three groups , indicating that a high concentration (1 mM) of taurine can significantly affect the primary metabolism of Symbiodinium sp. . The contents of organic acids  were not significantly different among the groups (p > 0.05), except for succinate levels, which were downregulated in HC/LC, HT/LC, and LT/LC. Additionally, glucose levels increased significantly under HT/LT, likely because of increased carbohydrate metabolism.The metabolome was compared between algal cells under different culture conditions . A total of 27 differently expressed metabolites were identified , includinium sp. . Of the Symbiodinium sp., we analyzed the transcriptome of Symbiodinium sp. exposed to 1 mM taurine . In total, an average of 58,630,949 raw reads and 58,147,131 clean reads were generated from HT and HC groups; 98.42% of the clean read bases had a value of Q ≥ 20, and 95.31% of the clean read bases had a value of Q ≥ 30  under taurine-stimulated conditions  under taurine-stimulated conditions . There were no significant changes in the content of total protein and total lipids . This indicated that taurine might influence the metabolism of Symbiodinium sp. by decreasing the content of total insoluble (most probably storage compounds) while increasing the synthesis of total soluble sugar (most probably translocated compounds), which might be further secreted into the extracellular for utilization in coral.Compared to HC, HT increased in cellular total soluble sugar but decreased in total insoluble sugar  and taurine-stimulated (HT) conditions. A cell wall-associated hydrolase was upregulated . Besides, some genes involved in hydrolase, including a putative peptidase C1-like protein , an esterase OVCA2 , and a phosphoserine phosphatase , were upregulated. It is reported that HRFs might alter the permeability of cell membrane of symbiotic algae to facilitate the release of photosynthetic products , a putative E3 ubiquitin-protein ligase , an apolipophorin , and an esterase , were upregulated, indicating that taurine might induce the alteration of Symbiodinium sp. metabolism from synthesis of storage compounds to decomposing of them, providing new evidence for previous reports . We speculate that taurine might be a signal of low nitrogen concentration to Symbiodinium sp., which in turn increase in nitrogen assimilation to maintain efficient photosynthetic carbon fixation. In addition, we also found that two of the downregulated genes are related to quorum sensing . We speculated that taurine regulates the Symbiodinium sp. cell density through the quorum sensing effect and maintains a certain number of Symbiodinium sp. in its cells. This helps to keep the corals and Symbiodinium sp. at a state of dynamic balance. However, the results of our study need to be validated in future more extensive studies.Generally, under starvation conditions, corals induce the higher production of photosynthetic products in nium sp. . During nium sp. . In our p < 0.05 as the criterion of biologically significant difference in the transcriptome, only 208 DEGs were identified, including 96 upregulated and 112 downregulated DEGs. This indicates that taurine has little effect on the transcriptome of Symbiodinium sp. after 10 days of treatment. A shorter time point might result in more results. Besides, an exometabolomics analysis would provide further evidences. Therefore, future studies are expected to analyze the role of taurine in coral-algal symbiosis in detail.In this study, we performed transcriptome analysis by taurine-stimulated for 10 days. By setting a 2-fold change in abundance and Symbiodinium sp. while decreased the content of total insoluble sugar. Results of metabolome and transcriptome analyses revealed that taurine might affect metabolic pathways in Symbiodinium sp. by altering the permeability of the algal cell membrane, diverting photosynthetically fixed carbon from storage compounds to translocated compounds, releasing a signal of low nitrogen concentration to initiate a series of responses to external stimuli, and controlling Symbiodinium sp. density via the quorum sensing effect.Taurine significantly increased the growth, biomass, photosynthesis efficiency, total soluble sugar, chlorophyll a, and free amino acid content of 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 in the article/ ZX designed the study. HS and RC performed the experiments. AH, HS, and XC analyzed the data. AH wrote the manuscript. All authors contributed to the article and approved the submitted version.This research was financially supported by the National Natural Science Foundation of China (41876158 and 42006145), Natural Science Foundation of Hainan Province (420QN219), and the Scientific Research Foundation of Hainan University [KYQD(ZR)20060].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Intelligent assistance systems (IAS) are designed to counteract rising cognitive demands caused by increasingly individualized manufacturing processes in assembly. How IAS affect work characteristics which are crucial for promoting work motivation of employees is yet unclear. Based on the cyber-physical systems transformation framework, the model of routine-biased technological change, and a comprehensive model of work design, we expected in- and decreases in motivational work characteristics (MWC) when working with IAS. Furthermore, we posited a buffering effect of the option of voluntary use on decreasing knowledge characteristics. Applying an online case study with experimental vignette methodology (EVM) allowed us to identify effects of the IAS on MWC before it is widely implemented. 203 German and British blue-collar workers evaluated an assembly workplace according to three experimental conditions . We identified enhanced feedback from the job and information processing in work with IAS in contrast to a traditional assembly workplace but found no restrictions (or elevations) in terms of other task  or knowledge characteristics . Thus, our results indicate that the IAS improves some motivational work characteristics of the assembly workplace, although it misses the primary goal of cognitive relief. Our study highlights the need for work design theories that specify the effect of IAS on motivational work characteristics and the potential benefit of IAS in assembly of the future.The online version contains supplementary material available at 10.1007/s10845-023-02086-4. In detail, we then define motivational work characteristics (MWC) and review the existing literature and theoretical models how IAS might affect MWC. Subsequently, we postulate and explain our expectations on the effect of a projection-based, cognitive-assistive IAS on nine different MWC. Then, we present the used methods, in particular outlining the implemented experimental vignette methodology (EVM). We then present the results of our hypothesis testing, and finally discuss the implications of our findings for theory and practice.Technical systems that support people in carrying out activities by taking in and processing information from the environment or by inputting information are defined as intelligent assistance systems (IAS) . For example, researchers have reported contradictory impacts of ICT at work, for example, on motivation  is one of the most influential theories in work design and motivation literature  . Further relevant task characteristics identified are task variety, task significance, task identity, and feedback from the job. Second, knowledge characteristics comprise knowledge, skills, and abilities needed for the execution of a job, and include job complexity, problem solving, information processing, skill variety, and specialization  might help to explain some of the motivational effects on task and knowledge characteristics when introducing IAS at work. First, the l et al.  is basedl et al. . It postl et al. . In detal et al. , the autl et al. . Althougmodel of routine-biased technological change, which postulates a polarization of jobs in the form of an increasing number of low-skilled and high-skilled jobs as the number of middle-skilled jobs decreases due to the introduction of digital technologies. It differentiates between manual and cognitive as well as routine tasks—tasks that “can be performed by machines according to explicitly programmed rules”  Work scheduling autonomy (b) Decision-making autonomy (c) Work methods autonomy is significantly lower in work with IAS than in work without IAS.With regard to feedback from the job, defined as “the degree to which the job provides direct and clear information about the effectiveness of task performance”  lower (b) higher in work with IAS than in work without IAS.Fourth, skill variety reflects the necessary amount of skills that are required for the completion of the work  lower (b) higher in work with IAS than in work without IAS.Fifth, specialization refers to “the extent to which a job involves performing specialized tasks or possessing specialized knowledge and skill” , can be buffered in practice. Using the system voluntarily-allowing employees to use the system or not-could counteract systematic de-skilling of workers based on the model of routine-biased technology change  Job complexity (b) problem solving (c) information processing (d) skill variety (e) specialization is significantly higher in work with voluntary use of IAS than in work with IAS.In our experiment, we investigated the causal impact of a stationary, cognitive-assistive IAS on task and knowledge characteristics and the role of the voluntary use of IAS concerning knowledge characteristics. For this, we manipulated an assembly workstation using EVM in an online experiment realizing three experimental conditions (work without IAS vs. work with IAS vs. work with voluntary use of IAS).https://osf.io/352zd/?view_only=5ff4cf3e692d44edbb0843418bb9bc26). The electronic supplementary material (ESM) including the presented hypothetical assembly workstation, information on the presented IAS, and additional analyses can be found in the OSF.We pre-registered all procedures and hypotheses before data collection  without IAS as well as an approximately 1-min long video of an exemplary assembly process without the support of the IAS.In the second condition, work with IAS, and the third condition, work with voluntary use of IAS, participants were given a page of text description of an assembly workstation with IAS (again including a picture of the workspace) and additional information about the functions of the IAS. Also, the participants watched an approximately 1-min long video of the exemplary assembly process with the support of the IAS. Conditions 2 and 3 differed only in that the participants in Condition 3 received the information that they could turn the IAS off and on at any time on a separate page. The IAS presented in the vignette is a projection-based, cognitive-assistive system that guides workers through each assembly step using short instructional videos projected onto the work surface. If the work steps are performed correctly, the projected video will light up green; if they are performed incorrectly, the video will light up red. The progress of work processes is displayed in the form of a progress bar above the videos with the assembly steps on the work surface. Alternative workflows and assembly sequences can be observed and taught automatically using machine learning. The basis of these functions lies in the recording of the hand and eye movements of the employees with the help of a 3D camera and three eye trackers  about themselves.For recruiting participants, we realized two approaches. First, we invited German blue-collar workers from our personal and professional networks from diverse fields via email and social media to participate in an online experiment. Blue-collar workers perform jobs relatively similar to assembly work, so they are able to adequately assess the work situation presented. The prerequisite for participation was a current job or prior work-related experience in the areas of manufacturing, assembly, or craftsmanship. We offered them a summary of the study results and an opportunity to enter a drawing for vouchers (5 × 20 Euro) for every 50 participants as incentives for participating. Within the collection period from October 2021 to April 2022, 1,041 people clicked on the link to the online experiment. 135 participants completed the survey (response rate of approximately 13%). We excluded six participants who failed the attention check , five participants who had technical problems, and another 23 who classified their job as an office job and had no prior experience working in manufacturing, assembly, or craftsmanship from the analysis. This led to 101 German participants.Second, we collected additional data from British blue-collar workers with an English version of our experiment in April 2022 using Prolific. We paid 2.20 GBP for participating. Of the 147 people who clicked on the link to the study, 127 participants completed the experiment (response rate of approximately 86%). We excluded two participants who failed one of two built-in attention checks. We further excluded 23 participants who reported that they were currently working in an office job or were students and did not have any prior work experiences in manufacturing, assembly, or crafting, resulting in 102 British participants.t(201) = − .826, p = .410, d = − .116, sex, χ2(2) = 0.334, p = .846, φ = .041, or prior experience with working with IAS, χ2(1) = 1.012, p = .314, φ = − .071, between the German and British participants . Furthermore, a chi-square difference test revealed no disproportionate allocation of the German or English subsample to one of the three experimental conditions, χ2(1) = 1.159, p = .560, Cramér’s V = 0.076 .Given the two different data collection efforts, we checked for potential differences in demographic variables. We did not find significant differences in age, N = 203. Ages ranged from 19 to 75 . The majority of the participants were male (70.4%) and had no prior experience with IAS (87%). Among those 13% of participants with prior experience with IAS, virtual reality (n = 10), pick-by-light (n = 6), and augmented reality (n = 3) were the most common. The low number of participants with prior experience with working with IAS in our sample reflects the low use and distribution of IAS in German and British production companies, highlighting the importance of EVM study designs.Therefore, we based our analysis on the combined data, resulting in a sample of equipment use in the hypothetical workplace immediately after reading the text description and watching the short video of an exemplary assembly process. For this, we used three items of a validated German version  to strongly agree (5). We conducted a Helmert contrast to test whether the work without IAS differs significantly from the other two conditions (work with IAS and work with voluntary use of IAS). Equipment use was rated significantly lower in the condition of work without IAS  than in the other two conditions , F = 3.29, p = .039, ηp2 = .032, C = − .314, p = .014, indicating that our experimental manipulation of the assembly workstation was successful.First, to ensure the successful manipulation of the assembly workstation, we asked participants in all three experimental conditions to rate M = 1.92, SD = 0.97) did not differ significantly from the work with IAS condition  in terms of work methods autonomy, t(135) = − 1.468, p = .144, d = − .251, resulting in a failed manipulation of voluntary use. Hence, we combined the work with IAS and work with voluntary use of IAS into the work with IAS condition . This precluded us from testing H8.Second, to ensure the successful manipulation of voluntary use of the IAS, we asked participants to rate work methods autonomy (α = .91) from the WDQ  to strongly agree (5). To cover the hypothetical job situation (and not the current job participants hold), we slightly altered the item wording of some German items (items including “my job” were changed to “the job”). This was not necessary for the English version by Morgeson and Humphrey  = 6.441, p < .001, ηp2 = .231, Wilk’s Λ = .769. Subsequently, we conducted t-tests for every motivational work characteristic separately according to our hypotheses (Table t(201) = 1.909, p = .058, d = .286, decision-making autonomy, t(201) = − 0.149, p = .882, d = − .022, and work methods autonomy, t(201) = 0.330, p = .742, d = .049, thereby rejecting H1a-c. Feedback from job was significantly higher in work with IAS than in work without IAS, t(201) = − 5.701, p < .001, d = − .854, supporting H2. There was no significant difference between work without IAS and work with IAS in job complexity, t(201) =  − 0.790, p = .430, d = − .118, and problem solving, t(201) =  − 0.728, p = .467, d = − .109. Thus, we rejected H3 and H4. Information processing was significantly higher in work with IAS than in work without IAS, t(201) = − 2.205, p = .029, d = − .330, supporting H5b. Finally, the two working conditions did not differ in skill variety, t(201) = 0.369, p = .713, d = .055, and specialization, t(201) = − 1.077, p = .283, d = − .161. Hence, we rejected H6a, H6b, and H7.Table 2As prior research on IAS largely neglected human factors  assessment factors, such as the task completion time, or error count, which have already been investigated in other studies Supplementary file2 (DOCX 908 KB)Below is the link to the electronic supplementary material.


We identified unique mutations in the cfDNA and tumour DNA of each GBM patient, including some of the most frequently mutated genes in GBM according to the COSMIC database . Using our gene–gene fusion database, ChiTaRS 5.0, we identified gene–gene fusions in cfDNA and tumour DNA, such as KDR–PDGFRA and NCDN–PDGFRA, which correspond to previously reported alterations of PDGFRA in GBM . Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors such as imatinib, sunitinib, and sorafenib. Moreover, we identified BCR–ABL1 (in 8% of patients), COL1A1–PDGFB (8%), NIN–PDGFRB (8%), and FGFR1–BCR (4%) in cfDNA of patients, which can be targeted by analogues of imatinib. ROS1 fusions (CEP85L–ROS1 and GOPC–ROS1), identified in 8% of patient cfDNA, might be targeted by crizotinib, entrectinib, or larotrectinib. Thus, our study suggests that integrated analysis of cfDNA plasma concentration, gene mutations, and gene–gene fusions can serve as a diagnostic modality for distinguishing GBM patients who may benefit from targeted therapy. These results open new avenues for precision medicine in GBM, using noninvasive liquid biopsy diagnostics to assess personalized patient profiles. Moreover, repeated detection of druggable targets over the course of the disease may provide real‐time information on the evolving molecular landscape of the tumour.Glioblastoma (GBM) is the most common type of glioma and is uniformly fatal. Currently, tumour heterogeneity and mutation acquisition are major impedances for tailoring personalized therapy. We collected blood and tumour tissue samples from 25 GBM patients and 25 blood samples from healthy controls. Cell‐free DNA (cfDNA) was extracted from the plasma of GBM patients and from healthy controls. Tumour DNA was extracted from fresh tumour samples. Extracted DNA was sequenced using a whole‐genome sequencing procedure. We also collected 180 tumour DNA datasets from GBM patients publicly available at the TCGA/PANCANCER project. These data were analysed for mutations and gene–gene fusions that could be potential druggable targets. We found that plasma cfDNA concentrations in GBM patients were significantly elevated  is challenging owing to tumour heterogeneity. Here, we extracted and sequenced cell‐free DNA (cfDNA) from the plasma of 25 GBM patients and tumour DNA from fresh tumour samples. We found that cfDNA concentrations in the plasma of GBM patients were significantly elevated, as compared to healthy controls. Moreover, we identified unique mutations and gene–gene fusions in the cfDNA and tumour DNA of GBM patients, some of which could be therapeutically targeted by tyrosine kinase inhibitors. SNVs records shared by GB13_Tumor.vcf and GB13_WB.vcf or GB13_cfDNA.vcf and GB13_WB.vcf were not used for downstream analysis.After this filtration, SNVs records that are only private to Step 7: Calculating common SNVs between tumour and cfDNA samples:‘bcftools isec’After removing the germline SNVs from the tumour and cfDNA samples, we compared VCF files of tumour and cfDNA samples to extract common SNV records using bcftools isec GB13_Tumor_Filtered_Germline.vcf GB13_cfDNA_Filtered_Germline.vcf‐p GB13_Tumor_cfDNAe.g., Further, germline variants identified in PBMC DNA were removed from the respective patient tumour and cfDNA variants and were considered as somatic variants.2.10‘VEP’ standalone pipeline:Somatic variants from cfDNA and tDNA were annotated using the standalone Ensembl Variant Effect Predictor (VEP) pipeline (120). Annotation of the final VCF files with common SNVs in tumour and cfDNA samples was performed using the vep ‐i GB13_Tumor_Common.vcf.tsv ‐‐everything ‐‐cache ‐‐force_overwrite ‐‐filter_common ‐‐forke.g., 33.1P < 0.0001, t‐test). We then examined the sizes of cfDNA molecules in all samples. A Bioanalyzer DNA High Sensitivity assay showed that in both GBM and healthy control samples, a cfDNA major peak was detectable at, or close to, 166 bp, which accounted for 85% of the circulating cfDNA. A smaller peak at, or close to, 332 bp accounted for 10% of the cfDNA and another peak at 2000–10 000 bp constituted 5% of cfDNA and likely represent fragments released by necrotic tissue  with 30× coverage (at least 150 million paired end [PE] 100 bp reads per sample). In addition, we sequenced tDNA . We first removed all germline SNPs that appeared in patient gDNA using the variant calling method . Next, we sorted the mutations into “cfDNA only”, “tDNA only”, and “both cfDNA and gDNA” groups , PDGFRA , PIK3CA (intron and upstream gene variants), PIK3R1 (upstream and downstream gene variants), and TP53 . Finally, we found that tumour‐suppressors were mostly absent in GBM due to missense mutations and that oncogenes appeared in the annotated data of mapped cfDNA sequences (data not shown). These results indicate that our liquid biopsy technique captures a broad spectrum of known glioma mutations at similar incidence rates as do standard tumour biopsies.As mentioned above, we compared somatic high‐impact mutations shared by cfDNA and tDNA in our patients with the mutation landscape data obtained from four studies , 45, 46 3.3http://chitars.md.biu.ac.il/). We thus identified unique gene fusions, such as KDR‐PDGFRA (8%), and NCDN‐PDGFRA  that correspond to the previously reported variations in PDGFRA in GBM. Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors, such as imatinib, sunitinib, and sorafenib [We hypothesized that fusion genes contribute to glioma tumour formation, in addition to the point mutations described above, and that specific fusions, as opposed to mutation combinations, may be unique to different gliomas. To test this idea, we analysed cfDNA sequences from 25 control and 25 GBM samples, and from 180 TCGA GBM patients (downloaded from publicly available sources). We searched for fusions using our ChiTaRS 5.0 reference database  datasets to identify hits among the 1207 predicted druggable fusions collected in the ChiTaRS 5.0 database . Predict3.4Since functional mutations and fusions disrupt key metabolic pathways in cancer cells, we considered whether glioma‐specific pathway disruptions could be treated with targeted drug combinations. To test this possibility, we first found that a specific subset of fusions presented above and identified in cfDNA and tDNA encode druggable targets that are likely to respond to the crizotinib analogues entrectinib and larotrectinib and/or imatinib analogues Tables  and 5. W4In this study we showed that plasma cfDNA concentration in GBM patients is higher than in healthy individuals. The direct association of cfDNA concentration and tumour stage was previously reported , 52, 53.In our 25 GBM patient samples, we detected the top 50 GBM mutations in cfDNA and showed that these are also found in tDNA. This suggests that liquid biopsy can provide information on the molecular signatures of GBM. It may serve as a noninvasive longitudinal diagnostic method for detection of molecular evolutions that occur during the disease. We also showed by gene enrichment analysis that these frequently observed fusion genes and the 50 most frequent genes from the glioma mutation landscape share common pathways that are substantial in GBM. These include the ErbB signalling pathway, the VEGF signalling pathway, the choline metabolism pathway, the central carbon metabolism pathway, and the p53 signalling pathway. The ErbB signalling pathway is enriched for both mutations and fusions in GBM. Receptor proteins ErbB1, ErbB2, ErbB3, and ErbB4 belong to the ErbB receptor family of tyrosine kinases. Upon ligand induction, these receptors activate downstream signalling pathways that lead to cell migration, cell proliferation, and antiapoptosis processes . MutatioUsing our fusion gene database , 26, 60,5We showed that liquid biopsy can play an important role in the molecular diagnosis of GBM, and as a potential means for selecting an accurate personalized approach for treatment of this devastating disease. The major advantage of liquid biopsy is its less invasive nature and its ability to provide information on a broad range of mutations and fusions in patients with brain tumours, while avoiding the need to perform invasive procedures to obtain tumour tissue for analysis. As therapeutic druggable fusion gene targets can be identified using liquid biopsy, this easy‐to‐use and noninvasive diagnostic technique will contribute to precise treatment of GBM patients at any stage of the disease.The authors declare no conflict of interest.https://publons.com/publon/10.1002/1878‐0261.13157.The peer review history for this article is available at MFM and TS designed, analysed, and supervised the project. VP and DRS produced all the experiments. MFM, AG, and RD produced bioinformatics analyses. AG provided the result visualization. SP, MK, and MVK ran library preparation. NGS analysed the study., RG, CF, AAK, YL, SHN, and ALB provided the tumour and blood samples of patients. VP, MFM, and TS wrote the article. All authors revised the article.Fig S1. Technical details related to the functional protein domains of BCR‐ABL1, BCR‐FGFR1, MSK‐ALK, TFG‐ALK, NPM1‐ALK, GOLGA5‐RET, AKAP9‐BRAF fusions observed in patients with GBM.Click here for additional data file.Fig S2. Technical details related to the functional protein domains of KIF5B‐RET, SDC4‐ROS1, TPM3‐ROS1, NIN‐PDGFRB, TPM3‐NTRK1, COL1A1‐PDGFB, EWSR1‐ATF1, KMT2A‐FLNA, KMT2A‐MAML2 fusions observed in patients with GBM. All the protein domains preserved in the sequence of the fusions have been mapped specifically to the reference human genome (query sequence) to show their potential druggable features.Click here for additional data file.Table S1. Age and gender details of the healthy controls used in this study.Click here for additional data file.Supplementary Material Detection of gene mutations and gene–gene fusions in circulating cell‐free DNA of glioblastoma patients ‐ an avenue for a clinically relevant diagnostic analysisClick here for additional data file.