Increasing evidence indicates that major neurodevelopmental disorders have potential links to abnormal cerebellar development. However, the developmental trajectories of cerebellar subregions from childhood to adolescence are lacking, and it is not clear how emotional and behavioral problems affect them. We aim to map the developmental trajectories of gray matter volume (GMV), cortical thickness (CT), and surface area (SA) in cerebellar subregions from childhood to adolescence and examine how emotional and behavioral problems change the cerebellar development trajectory in a longitudinal cohort study.This population‐based longitudinal cohort study used data on a representative sample of 695 children. Emotional and behavioral problems were assessed at baseline and at three annual follow‐ups with the Strengths and Difficulties Questionnaire (SDQ).Using an innovative automated image segmentation technique, we quantified the GMV, CT, and SA of the whole cerebellum and 24 subdivisions  with 1319 MRI scans from a large longitudinal sample of 695 subjects aged 6–15 years and mapped their developmental trajectories. We also examined sex differences and found that boys showed more linear growth, while girls showed more nonlinear growth. Boys and girls showed nonlinear growth in the cerebellar subregions; however, girls reached the peak earlier than boys. Further analysis found that emotional and behavioral problems modulated cerebellar development. Specifically, emotional symptoms impede the expansion of the SA of the cerebellar cortex, and no gender differences; conduct problems lead to inadequate cerebellar GMV development only in girls, but not boys; hyperactivity/inattention delays the development of cerebellar GMV and SA, with left cerebellar GMV, right VIIIA GMV and SA in boys and left V GMV and SA in girls; peer problems disrupt CT growth and SA expansion, resulting in delayed GMV development, with bilateral IV, right X CT in boys and right Crus I GMV, left V SA in girls; and prosocial behavior problems impede the expansion of the SA and lead to excessive CT growth, with bilateral IV, V, right VI CT, left cerebellum SA in boys and right Crus I GMV in girls.This study maps the developmental trajectories of GMV, CT, and SA in cerebellar subregions from childhood to adolescence. In addition, we provide the first evidence for how emotional and behavioral problems affect the dynamic development of GMV, CT, and SA in the cerebellum, which provides an important basis and guidance for the prevention and intervention of cognitive and emotional behavioral problems in the future. This study maps the developmental trajectories of gray matter volume (GMV), cortical thickness (CT), and surface area (SA) in cerebellar subregions from childhood to adolescence and provides the first evidence for how emotional and behavioral problems affect the dynamic development of GMV, CT, and SA, which provides an important basis and guidance for the prevention and intervention of cognitive and emotional and behavioral problems in the future. The cerebellum's unique protracted developmental trajectories, gender dimorphism, preferential vulnerability to environmental influences, and frequent impact on childhood developmental disorders such as autism and attention deficit hyperactivity disorder (ADHD) make it a major target for pediatric neuroimaging studies.N = 2515 images), the developmental trajectories among children and adolescents were not depicted.To date, only a few studiesAll of these cerebellar studies on cerebellar development mainly focused on gray matter volume (GMV). However, the brain GMV consists of surface area (SA) and cortical thickness (CT), each exhibiting differentiated developmental patterns during adolescence.The human cerebellum  has a longer developmental timeline than the neocortex ,Thus, our first aim was to delineate the development of cerebellar subregions from childhood to adolescence using the GMV, CT and SA profile to describe the trajectories. Second, we sought to explore how emotional and behavioral problems affect cerebellar development trajectory and eventually lead to cerebellar‐related neurodevelopmental disorders to provide evidence support for understanding the biological mechanisms underlying the behavior. We used a novel automated segmentation pipeline applied to 1319 MRI scans from 695 children and adolescents aged 6–15 years, drew the development trajectory of cerebellar GMV, CT, and SA, and investigated the effects of emotional and behavioral problems on cerebellar developmental trajectory from five aspects: emotional symptoms, conduct problems, hyperactivity/inattention, peer problems, and prosocial behaviors.22.1We used a longitudinal structural MRI dataset comprising 770 normally developing children aged 6–15 years (F/M = 334/436) from the Children School Functions and Brain Development project . Participants underwent 2–4 repeated MRI scans at approximately 1‐year intervals, leading to 1623 structural MRI scans in total. All participants were recruited from primary schools in Beijing with normal cognitive ability, assessed by a well‐validated Chinese standardized cognitive ability test.2.2https://www.sdqinfo.org/py/sdqinfo/b0.py), which includes multiple language versions. The SDQ included 25 items that comprised five subscales .The parent‐reported version of the Strengths and Difficulties Questionnaire (SDQ) was applied to assess emotional and behavioral problems in children and adolescents. The Chinese version was retrieved from the SDQ website  Individual images were carefully visually examined by an experienced radiologist to rule out incidental abnormalities, such as neuroepithelial cysts, arachnoid cysts, and other intracranial space‐occupying lesions. (ii) Five experienced raters each performed careful visual inspections with a scan rating procedure, with a protocol similar to that of the Human Connectome Project.2.5https://www.volbrain.upv.es).We computed the cerebellar GMV, CT, and SA using the automated software pipeline CERES . To investigate the developmental trajectories of the GMV, CT, and SA of the total cerebellum and each of the 12 cerebellar subregions and the effects of sex, we used mixed models with the nlme package.We performed statistical analyses using R 4.1.1  was modeled as a random effect.The p < 0.05, we used the quadratic developmental model and then the linear developmental model. Second, we probed whether the developmental trajectories differed by gender. Hence, a full model including the main effects of age and gender, as well as interaction effects, was compared using the Bayesian Information Criterion (BIC) to a simpler model including only the main effects of age and gender. Whether the full model allowing for differences in growth trajectories between genders explained more variance than the simpler model was tested. Third, if no advantage is found for the full model, the simpler model incorporating the main effects of age and gender was compared with the best‐fit developmental model selected in step one that included only age terms. The best‐fit model was again selected using BIC to obtain the model that accounted for the greatest variance in the number of parameters included in the model and to reduce the threat of data overfitting.The best model fit was selected based on a three‐step process. First, cubic, quadratic, and linear age effects were fitted on the GMV, CT, and SA of each ROI (region of interest). If the cubic age effect was not significant at Finally, we investigated how children's emotional and behavioral problems affect the development of cerebellar subregions. Then we added this continuous emotional and behavioral problems score (centered) to the best fitting mixed model and inspected the significance of its main and age interaction terms. To visualize and better show the role of emotional and behavioral problems, the sample was split into two subgroups based on scores for emotional and behavioral problemshttp://www.quantitativeskills.com/sisa/calculations/bonfer.htm).α/number of tests) and would lead to an overly strict correction because the dependent variables are not obtained in independent subgroups. GMV showed a mean correlation coefficient of r = 0.3641, leading to an equivalent corrected α of 0.0066 (number of tests = 24). CT showed a mean correlation coefficient of r = 0.3499, resulting in an equivalent corrected α of 0.0065. SA showed a mean correlation coefficient of r = 0.3103, leading to an equivalent corrected α of 0.0056.To achieve an optimal balance between Type‐I and Type‐II error, we took into account the correlation between the dependent variables (the 12 cerebellar subregions) by using a Bonferroni procedure adjusted for correlated variables , left VI , bilateral crus I  and II , the CT of lobule left VIIB  and the SA of lobules bilateral IV , right V , left VI , left crus I  and bilateral crus II  showed inverted U‐shaped quadratic trajectories. All the other cerebellar subregions of GMV, CT, and SA showed linear growth  than in the low‐risk group . No gender differences were found in the effects of emotions on cerebellar trajectories.Significant age × emotional symptoms interactions were found in the right cerebellar SA Figure  and the 3.2.2Significant age × conduct problems interactions were found in the bilateral cerebellar gray matter Figure , bilater3.2.3β = 0.0142, p = 2.82 × 10−5) than the low‐risk group .Significant age × hyperactivity/inattention interactions were found in the right IV GMV Figure  and righβhigh‐risk = 0.5436, p = 1.33 × 10−15, βlow‐risk = 0.3731, p = 2.82 × 10−5; right VIIIA GMV in boys: βhigh‐risk = 0.0878, p = 3.71 × 10−4, βlow‐risk = 0.0351, p = 0.011; right VIIIA SA in boys: βhigh‐risk = 0.0163, p = 3.51 × 10−4, βlow‐risk = 0.0041, p = 0.089; left V GMV in girls: βhigh‐risk = 0.0523, p = 6.42 × 10−7, βlow‐risk = 0.0296, p = 3.17 × 10−8; left V SA: βhigh‐risk = 0.0087, p = 1.49 × 10−5, βlow‐risk = 0.0040, p = 1.77 × 10−4).There were significant differences between age and hyperactivity/inattention in the right cerebellar GMV Figure , right V3.2.4βhigh‐risk = 0.0459, p = 0.0216, βlow‐risk = 0.020, p = 3.55 × 10−4) and SA , right VIIIA surface , right IV CT  and right X CT .Significant age × peer problems interactions were found in the right VIIB GMV Figure  and SA , right Vβhigh‐risk = 0.0156, p = 0.001, βlow‐risk = 0.0044, p = 0.097; right IV CT in boys: βhigh‐risk = 0.0176, p = 0.003, βlow‐risk = 0.0043, p = 0.089; right X CT in boys: βhigh‐risk = 0.0945, p = 2.21 × 10−4, βlow‐risk = 0.0307, p = 0.003; left V SA: βhigh‐risk = 0.0079, p = 0.101, βlow‐risk = 0.0046, p = 1.81 × 10−8), the right Crus I GMV in girls followed quadratic trajectories both in the high‐ and low‐risk peer problems groups, and the high‐risk group  peaked later than the low‐risk group .There were significant differences between age and peer problems in the bilateral IV CT Figure , right X3.2.5βhigh‐risk = 0.0161, p = 6.68 × 10−5, βlow‐risk = 0.0030, p = 0.339; right: βhigh‐risk = 0.0183, p = 3.01 × 10−6, βlow‐risk = 8.16 × 10−4, p = 0.777), bilateral V CT  and right VI CT  in boys. The left cerebellar SA in boys and the right Crus I SA in girls followed quadratic trajectories both in the high‐ and low‐risk prosocial behavior groups, and the high‐risk group  peaked later than the low‐risk group .Significant age × prosocial behavior problem interactions were found in the left cerebellum SA Figure , bilater4In this study, we explored the developmental trajectories and behavioral correlates of the cerebellum in a 4‐year follow‐up cohort study. This is the first study to map cerebellar development in 24 subregions of children and adolescents using a longitudinal cohort from three dimensions of GMV, CT, and SA. In addition, emotional symptoms, conduct problems, hyperactivity/inattention, peer problems, and prosocial behaviors change cerebellar developmental trajectories from childhood to adolescence.4.1The GMV consists of CT and SA, and studies based on the cerebral cortex have found that CT and SA have independent genetic originsWhy was the developmental trajectory of gray matter determined primarily by the changes in SA? The human and animal anatomy shows that although the cerebellum accounts for only 10% of the total brain volume, the mature cerebellum contains more than half of all neurons; therefore, the cerebellum folds into this compact space like an accordion (a narrow band 10 cm wide and nearly a meter long).4.2This study identified three types of differences in cerebellar development trends between boys and girls. First, both girls and boys showed inverted U‐shaped quadratic trajectories in the SA of the left whole cerebellum and left lobule VI, and girls peaked earlier than boys. This is very similar to previous findings. Tiemeier et al.Why do boys and girls show these three different patterns of cerebellar development? This may be caused by differences in age distribution. In Tiemeier et al.'s study, the age distribution was 5–24 years, and they found that girls peaked at 11.8 years and boys peaked at 15.5 years. In our study, the age distribution was 6 to 15 years, an age range when cerebellar development in boys is still rising in the first half of the inverted U‐shaped curve, so we can only observe a linear rise. Girls are more likely to be identified as inverted U‐shaped because they peak earlier. For example, similar results were found in Tiemeier et al.'s study; corps medullare was found to be the area that reached the peak at the latest. Therefore, only girls were identified to reach the peak at the age of 17.2, while boys were always in a linear rise.There were few research studies showing gender difference in brain development particularly in cortical and subcortical structures, and the difference might be due to effect of puberty.4.3This is the first comprehensive study to examine the effects of emotional and behavioral problems on the developmental trajectories of cerebellar GMV, CT, and SA subregions from childhood to adolescence. We found that emotional and behavioral problems changed the developmental trajectory of the cerebellum in different ways.First, emotional symptoms impede the expansion of the SA of the cerebellar cortex. We found that the high‐risk group for emotional problems peaked on the right cerebellar SA approximately 1.7 years later than the low‐risk group and showed a stronger linear trend in the right VIIIB SA. This indicates that emotional problems lead to delayed development of the cerebellar cortex, which is mainly reflected in the expansion of the cortical SA. Previous studies of mood disorders such as bipolar disorder and depression have found that cerebellar GMV decreases,Second, conduct problems lead to inadequate cerebellar GMV development in girls. We found that conduct problems regulated the development of GMV of the bilateral cerebellum, bilateral VI, and left Crus I that they follow cubic developmental trajectories in girls, and the high‐risk group of conduct problems entered the gradual decline later but also ended earlier, indicating that the GMV in these areas may not be sufficiently developed in the high‐risk group for conduct problems. Previous studies have found that a delay in cortical maturation in several areas related to decision‐making, morality, and empathy is a significant cause of conduct problemsWhy do conduct problems cause cerebellar GMV underdevelopment in girls but not boys? According to previous research, in general, girls tended to have fewer behavioral problems, and high‐risk girls may have more behavioral problems than high‐risk boys when assessed in multiple domains.Third, hyperactivity/inattention delayed the development of cerebellar GMV and SA. ADHD is characterized by a delay in cortical maturationIn terms of gender differences, boys at high risk of hyperactivity/inattention showed developmental delays in the left cerebellar GMV, right VIIIA GMV and SA in boys, while high risk girls only in the left V GMV and SA. Both V and VIII have been shown to be associated with motor and somatosensory representations in a meta‐analysis of neuroimaging studies.Fourth, peer behavior problems disrupt CT growth and SA expansion, resulting in delayed GMV development. At present, no studies have explored the relationship between peer problems and cerebellar development. Therefore, we can only refer to the results of ASD research with social impairment at its core. A study based on a mouse model found that a decrease in Purkinje cell functioning leads to abnormal social behaviors.In addition, differences were found in the IV and X lobules in high‐risk boys and Crus I and V in high‐risk girls. The study found that girls were more likely to seek non‐parental relationships and were more likely to be influenced by their peers,Fifth, prosocial behavior problems impede the expansion of the SA and lead to excessive CT growth in the cerebellum. The neuronal mechanisms underlying prosocial behavior are still elusive. Recently, Wang et al.This study found that prosocial behavior problems mainly affected the bilateral IV, V, right VI lobules, and the left cerebellum in boys, while only influencing Crus I lobule in girls. This may be related to the different characteristics of prosocial behavior problems in boys and girls. Previous studies showed that prosocial behavior in girls was more associated with emotional symptoms (such as depression),In this study, we found environment and biology are important influencing factors. First, gender, as the biological factor mainly considered in this study, regulates the developmental trajectories of different subregions of the cerebellum. Overall, the cerebellum of girls matured earlier, but unfortunately, due to the lack of puberty data, it could not be determined whether it was related to puberty. Second, children's emotional and behavioral problems, as important influencing factors, also affect the developmental trajectory of children's cerebellum to varying degrees, resulting in a variety of developmental trajectory abnormalities including developmental delay, inadequate and disorder. Third, the influence of children's emotional and behavioral problems on the developmental trajectories of cerebellar subregions was also regulated by gender. Due to the different characteristics and mechanisms of emotional and behavioral problems in boys and girls, the influence of emotional behavior problems on the developmental trajectories of cerebellar subregions was also different, respectively. In summary, the developmental trajectories of cerebellar subregions were the result of a combination of biological and environmental influences.5The study has some shortcomings. First, the sample involved only children/adolescents aged 6–15 years. The human cerebellum has a protracted developmental timeline compared with the neocortex; therefore, it may take a longer age span to capture the peak of cerebellar development.6In this study, we first mapped the developmental trajectories of gray matter, CT, and SA in cerebellar subregions from childhood to adolescence based on a longitudinal cohort study, and then we investigated in detail how emotional and behavioral problems can cause involvement damage by changing the cerebellar development trajectory. The results showed that emotional symptoms impede the expansion of the SA of the cerebellar cortex; conduct problems lead to inadequate cerebellar GMV development in girls; hyperactivity/inattention delays the development of cerebellar GMV and SA; peer behavior problems disrupt CT growth and SA expansion, resulting in delayed GMV development; and prosocial behavior problems impede the expansion of the SA and lead to excessive CT growth in the cerebellum. This study provides the first evidence for how emotional and behavioral problems affect the dynamic development of GMV, CT and SA, which provides an important basis and guidance for the prevention and intervention of cognitive and emotional behavioral problems in the future.This study has been supported by the STI 2030—Major Projects 2021ZD0200500.Dr. Yong is an Editorial Board member of CNS Neuroscience and Therapeutics and a co‐author of this article. To minimize bias, they were excluded from all editorial decision‐making related to the acceptance of this article for publication.Tables S1–S9Click here for additional data file.


This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice.n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process.34 studies were identified (The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design.HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.The online version contains supplementary material available at 10.1007/s11897-023-00615-z. Heart failure (HF) is a heterogeneous, chronic syndrome with high morbidity and high mortality, with 10–20% of patients rehospitalised for HF within 1 year and less than 50% of patients surviving 5 years after diagnosis . Left veAt both ends of the EF spectrum there are limitations in the treatment of patients, which indicates there could be potential for personalisation of care. Treatment of HF follows a “one-size-fits-all” approach, with four main treatments that should be considered for patients with HFrEF. However, with this multitude of evidence-based therapies, an aging population and multimorbidity the management of these patients is complicated . CurrentOnly sodium-glucose co-transporter 2 inhibitor (SGLT2i) have demonstrated benefit in patients with HFpEF , 8. OverGiven the high variation in pathophysiology, symptoms, and comorbidities among HF patients, there is significant potential for personalized care. To address the above-mentioned issues, there has been a surge of studies that aimed to describe the heterogeneity of HF patients in a more multidimensional manner, using clustering to characterize phenotypical subgroups.Unsupervised clustering analysis is a machine learning algorithm that can classify patients according to patient characteristics. Cluster analysis is especially suitable for subgroup discovery when dealing with unknown and complex relationships between variables, as these relationships do not have to be pre-specified to be modelled correctly. A series of clustering studies has been instigated since Shah et al. in 2015 used clustering, which they termed “phenomapping”, to identify clusters of patients with HFpEF . The hypThere is a wide variety of clustering studies in HF, using different clustering methods, identifying variables and HF populations, which makes it difficult to compare these studies. To date, several reviews have discussed clustering, in particular in HFpEF, yet results have not yet been synthesised in a systematic review –13. ThisThe review protocol was previously specified and registered in PROSPERO (CRD42022362925). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used to ensure transparent reporting of review methods.Randomised clinical trials and observational studies  reporting on unsupervised clustering analysis in HF were considered for inclusion. Patients had to be diagnosed with HF, HFrEF, HFmrEF or HFpEF, subpopulations of HF were excluded (e.g. patients with HF and diabetes or patients with HF and destination therapy left ventricular assist devices). Studies were also excluded if the aim of the article was not to define and describe phenotypes within patients with HF or if the analysis did not include unsupervised clustering methods. Clustering studies based on symptoms were excluded. Studies were excluded if they were review articles or case reports. Only studies conducted after 1 January 2010 were considered for synthesis to include contemporary studies on HF and machine learning techniques. The language was restricted to English or Dutch.We included relevant search terms for HF, including HFrEF, HFmrEF, HFpEF. In addition, we searched for clustering methods, general terms such as “machine learning” and “clustering analysis” were combined with specific clustering methods such as “latent class analysis”, “hierarchical clustering” and “phenomapping”. Last, we included the outcome of clustering methods such as “clusters”, “phenogroups” and “subgroups”. MeSH terms that were relevant were included. All searches were combined using the Boolean Operators “AND” and “OR”. The search was conducted in two databases: PubMed and EMBASE. The search strategy was conducted on 13 October 2022. A detailed search strategy can be found in Supplementary Table Final consensus on eligibility, based on title/abstract and full text screening, was reached by two independent reviewers (CM and AU) using the Rayyan web tool.Data was extracted from the included articles according to the following characteristics: 1) general information , 2) study characteristics , 3) characteristics of clustering , 4) data on outcome . A proposed qualitative cluster framework was created summarizing similarities and differences between the cluster models. This framework was developed based on phenotype patterns that we could identify across the different clustering studies. Within the framework characteristic frequency was quantified. Additionally, the characteristics and proportions and prognoses of most common clusters are discussed. Data was extracted by one reviewer (CM) and checked by a second reviewer (AU).. [To assess quality of the clustering studies, the methodology of all studies was compared. We consulted the scoping review of Hond et al. •, and tw. •, 15, 16n = 625) were screened on title/abstract and 52 were selected for full-text review. Of these, 18 were excluded based on wrong methods (i.e. supervised clustering or prediction modelling), wrong study population  or a missing description of phenotypes (i.e. missing outcome). In total, 34 studies were included in the systematic review  [n = 10) [n = 5) [n = 5) [n = 3) [n = 6) [n = 7) [n = 9) [n = 12) [n = 31), echocardiographic variables (n = 7) [n = 4) [n = 1) [n = 1) [We found 34 eligible clustering studies that were performed between 2012 and 2022, and used varying datatypes, clustering methods, and sample sizes (Table n = 14) , 47, 49,[n = 10) •, 46, 48 [n = 5) , 34•, 38 [n = 5) , 42, 44, [n = 3) , 20, 34• [n = 6) •, 46, co [n = 7) , 38, 41,[n = 12) •, 40, 45 (n = 7) , 40, 49, [n = 4) , 38, 41, [n = 1) , and dem [n = 1) . The numBelow, we will discuss a few of the trends that could be observed within the three phases of the quality assessment Table .Table 2Mn = 24). However, only rarely sample size requirements are discussed (n = 2) [n = 29). Description of missingness ranged from not mentioning missing values at all (n = 11) [n = 12) [n = 11) [n = 9) [n = 13) [In over half of the studies the generalizability and representativeness of the participants is evaluated( (n = 2) , 41. Sti(n = 11) •, 44, 48[n = 12) , 46, 47,[n = 11) , 45, 49. [n = 9) •, 46 or [n = 13) , 48, 49.n = 27), only a small part of the studies provided a description of the advantages and pitfalls of their chosen clustering technique (n = 11) [n = 9) [n = 18), some studies select features a priori  (n = 9) [n = 7) [Although most studies often described how they selected the number of clusters and helped the reader to interpret the clustering model with either visual aid or with an explanation ((n = 11) , 45, 47. [n = 9) •, 46, 48 (n = 9) , 39•, 47 [n = 7) , 42, 44.In total, eight studies validated their results in an external validation dataset Table  17, 18,, 37, 38.n = 32); 2) metabolic phenotype (n = 29); 3) cardio-renal phenotype (n = 19); 4) AF phenotype (n = 17); 5) elderly female AF phenotype (n = 16); 6) hypertensive-comorbidity phenotype (n = 14); 7) ischaemic-male phenotype (n = 16); 8) valvular disease phenotype (n = 2); and 9) a devices phenotype (n = 4). The prevalence of the phenotype characteristics of these nine phenotypes are quantified in Table Of the 165 described phenotypes, 149 could be assigned to a proposed qualitative framework of nine phenotypes that transcended studies and EF subtypes Table : 1) younn = 10), lower NT-proBNP levels (n = 7) and milder HF symptoms (n = 7). To some extend lower NYHA (n = 4) and smoking (n = 4) is reported for this phenotype. Sex is not reported consistently; six studies mention more males while five studies mention more females.The young low comorbidity burden phenotype could be assigned in 17/19 studies in HFpEF, 6/7 studies in HFrEF and 7/8 studies in all HF patients. This cluster is characterised by a lower comorbidity burden and younger age, with in addition obesity (n = 10); CKD (n = 8) and an imbalance of lipids (n = 11) are often reported. Several studies observed some form of ischaemia . Sex is not reported consistently; five studies mention more males while four studies mention more females.The metabolic phenotype could be assigned in 17/19 studies in HFpEF, 3/7 studies in HFrEF and 5/8 studies in all HF patients. Patients in this phenotype more often have obesity or are overweight, and have diabetes and hypertension. In addition, younger age (n = 7), hypertension (n = 5) and diabetes (n = 5). Several CVDs are observed in this phenotype, including myocardial infarction, valvular disease and coronary artery disease. Sex is not reported consistently; two studies mention more males while four studies mention more females.The cardio-renal phenotype could be assigned to 10/19 studies in HFpEF, 3/7 studies in HFrEF and 6/8 studies in all HF patients. Patients clustered in this phenotype had CKD or worse renal function, were older and more often had AF. Also more often reported were anemia (n = 8) as well as hypertension (n = 4). There are inconsistencies between clusters assigned to this phenotype, some studies report younger patients (n = 2) whereas others report older patients (n = 2).The AF phenotype could be assigned in 8/19 studies in HFpEF, 4/7 studies in HFrEF and 2/8 studies in all HF patients. This phenotype mainly includes patients with AF. Male sex is more reported (n = 4), higher BNP/NT-proBNP (n = 4) and HFpEF (n = 2) are reported.The older female phenotype could be assigned in 8/19 studies in HFpEF, 1/7 studies in HFrEF and 5/8 studies in all HF patients. Patients in this phenotype are elderly, have AF and are more often female. In addition, hypertension (n = 5); IHD (n = 4) and COPD (n = 3) are often reported. Several studies reported anemia, hyperlipidaemia or diabetes . Sex is not reported consistently; two studies mention more males while five studies mention more females.The hypertensive-comorbidity phenotype could be assigned in 7/19 studies in HFpEF, 3/7 studies in HFrEF and 4/8 studies in all HF patients. Patients clustered to this phenotype have hypertension as main comorbidity. In addition, older age (n = 4); revascularisation (n = 3) are more often reported. Several studies also reported higher NYHA (n = 2). Nine studies reported more males in this phenotype.The ischaemic-male phenotype could be assigned in 2/19 studies in HFpEF, 4/7 studies in HFrEF and 5/8 studies in all HF patients. Patients assigned to this phenotype more often have ischaemic heart disease, CAD or previous myocardial infarction. In addition, angina  and ischaemic cardiomyopathy (n = 2). Age is not reported consistently; 3 studies mention younger patients while 2 studies mention older patients.The devices phenotype could be assigned in 1/19 studies in HFpEF, 2/7 studies in HFrEF and no studies in all HF patients. Patients assigned to this phenotype more often have implantable devices such as ICD or CRT. In addition, they have milder HF  and one based on a clinical trial (tertiary care or quaternary care) , 45. ValThere was significant overlap in the clustering outcomes between the various studies •, 44–49.We hypothesize that the differences in phenotype descriptions could be due to differences between the data sources, as phenotypes characteristics are relative to their patient population. This limits the reproducibility and generalisability of the cluster models to other patient populations and use in routine clinical care. A potential solution could be readjusting or fine-tuning the current models using site specific information to increase the generalisability.In addition, it is important to underline that we grouped the clusters based on reported characteristics to one of the nine phenotypes. It could be that there are unreported characteristics that would categorize a cluster to a different phenotype if they were known.Ideally, this systematic review could identify one or multiple clustering studies of sufficient quality for implementation in clinical trials or in clinical practice. Due to the high heterogeneity and absence of a gold standard, this is not possible. Nonetheless, the findings of this review suggest that clustering is a suitable and fruitful approach for capturing the underlying heterogeneity of patients with HF.Future studies should be aware of the methodological caveats of clustering research and take this into account when performing these studies. Efforts should be directed towards improving the development and validation of clustering as machine learning model . ValidatMost importantly, we found that clusters transcend across the EF spectrum, indicating that clusters might not be limited to heterogeneity in HFpEF, but could also play a role in HFrEF. There were significant differences based on prognosis that are worth to be explored further. In addition, it has not yet been investigated whether patients could change between clusters over time. Longitudinal data is necessary to uncover any transitions over time.Current studies should therefore be considered as hypothesis generating. In the future it would be potentially be possible to investigate differences in prognosis and treatment benefit in clinical trials. Differences in prognosis could be used to guide future trial inclusion to optimise and enrich clinical trials. Moreover, patients in trials could be stratified based on clustering models to see whether there are different treatment effects. Currently, there is limited data on treatment heterogeneity across clusters and should be studied further. Investigating this would mean a step forward towards finding beneficial treatment options or strategies on subgroup patient level and in the future on individual patient level.One of the strengths of this systematic review is that both results and methodology of the clustering studies were compared. This gives context to the results and can provide nuance in the discussion on the reliability and validity of the clustering studies. In addition, the large amount of clustering studies and the heterogeneity of their study designs increase the meaningfulness of their similarities regarding their phenotype models. This enables us to quantify the degree of certainty to some extent regarding phenotype characteristics. Moreover, this systematic review showed a general overview of the requirements of unsupervised clustering. In cardiology, clustering is an increasingly common technique for subgroup discovery, and a basic understanding of the strengths, limitations, and pitfalls of clustering can help facilitate critical evaluation of these studies. Furthermore, at this moment we are the first systematic review that has performed rigorous review methodology and that gives an elaborate overview of both the discovered phenotypes and methodology.. [In this systematic review, we developed a methodological quality assessment, as current tools suitable for clustering meta-analysis are non-existent and validated guidelines on reviewing clustering studies are lacking. Therefore, a more descriptive approach has been used. To compare methodologies, the quality assessment was based on a scoping review of Hond et al. •, and tw. •, 15, 16The issue of generalizability across different ethnic or social economic backgrounds in patients with HF has been debated. Some have argued that these differences may lead to variations in the presentation and treatment of HF that require separate subgroup analyses . CurrentLastly, we grouped the clusters according to the reported characteristics for each study. It could be that there are other underlying characteristics that would change the phenotype assignment.There were many differences between the clustering studies regarding the sizes and types of the datasets, variable selection, and algorithms, but they yielded comparable phenotypes which implies that clustering is a fruitful approach for phenotype discovery. Specifically, of the 165 phenotypes that were described, 149 could be assigned to one of nine most common phenotypes: young-low comorbidity burden; metabolic; cardio-renal; AF; elderly female AF; hypertensive-comorbidity; ischaemic-male; valvular disease; and a devices phenotype. These phenotypes are not limited to a particular EF, but rather transcended across the EF spectrum. Comparing methodologies of the studies showed that there was still room for improvement on topics concerning validity and reliability, especially regarding external validation. These methodological aspects limit the current implementation into clinical practice and effort should be directed towards improving the clinical utility of cluster analysis. Altogether, this systematic review is hypothesis generating and lays the groundwork for future research into a more precise and reliable phenotype model that can serve as a stratification and decision tool in clinical trial design and personalised medicine for patients with HF.Supplementary file1 (DOCX 95 kb)Below is the link to the electronic supplementary material.


Triticum aestivum L.) is a major food crop. Speed breeding (SB) and speed vernalization (SV) techniques for spring and winter wheat have recently been established. However, there are few practical examples of these strategies being used economically and efficiently in breeding programs. We aimed to establish and evaluate the performance of a breeder-friendly and energy-saving generation acceleration system by modifying the SV + SB system.Crop breeding should be accelerated to address global warming and climate change. Wheat  was established and evaluated using an energy-efficient extended photoperiod treatment. A glasshouse with a 22-hour photoperiod that used 10 h of natural sunlight and 12 h of LED lights, and minimized temperature control during the winter season, was successful in accelerating generation. Even with one or two field tests, modified speed breeding (mSB) combined with a speed vernalization system (SV + mSB) reduced breeding time by more than half compared to traditional field-based methods. When compared to the existing SV + SB system, the SV + mSB system reduced energy use by 80% to maintain a 22-hour photoperiod. Significant correlations were found between the SV + mSB and field conditions in the number of days to heading (DTH) and culm length (CL). Genetic resources, recombinant inbred lines, and breeding materials that exhibited shorter DTH and CL values under SV + mSB conditions showed the same pattern in the field.The results of our SV + mSB model, as well as its practical application in wheat breeding programs, are expected to help breeders worldwide incorporate generation acceleration systems into their conventional breeding programs.The online version contains supplementary material available at 10.1186/s13007-023-01083-1. Triticum aestivum L.) is a major global food crop that accounts for 20% of the calories and proteins in the human diet [Wheat  system with an extended photoperiod (22 h) was developed, allowing for up to six generations of spring wheat and spring barley per year . An advaThe SV + SB system contributed to a reduction in breeding time. However, to optimize breeding efficiency, adequate phenotypic and genotypic selection must be carried out during breeding cycles. Under SB conditions, resistance to multiple diseases, including tan spots, stripe rust, leaf rust, and crown rot, can be evaluated 4–6 times using a large numbers of plant materials, whereas in field conditions, it can only be conducted once a year –13. The Wanga et al.  emphasizBy adapting the SV + SB system, we established and tested the efficacy of a breeder-friendly and energy-saving generation acceleration system that enables four generations of spring and winter wheat per year. A large amount of breeding material was used to test the modified SB (mSB) system combined with SV (SV + mSB), which uses a glasshouse and natural sunlight, to ensure that this system can be applied to breeding routines. To ascertain whether intuitive phenotypic selection could be carried out under the SV + mSB settings, phenotypic correlations between the field and SV + mSB conditions were also assessed.2 concentration [The photoperiod for mSB conditions was 22 h per day, consisting of 10 h of natural sunlight and 12 h of LED light Fig. . As a rentration , 21–23. ntration  reportedFigure 6 generation typically takes 68–71 months to develop. When large breeding materials from various cross combinations were developed using the SV + mSB system, four generations could be stably advanced in one year, with additional time for sufficient seed maturity before evaluating them in the field , the SV + mSB reduced the time from artificial crossing to sowing wheat grains in the field by more than half when compared to conventional field breeding systems Fig. b. Under nt lines , 25, ther = 0.691***) and culm length (CL) (r = 0.854***) were found  are highly heritable under a variety of field conditions –28. Spriund Fig. a. RILs h1F4 lines under the SV + mSB conditions , and shorter CL than Joongmo2008 (early heading and long CL)  BCCL) Fig. c and d. However, generation acceleration and phenotypic selection in the SV + mSB conditions remain limited owing to indoor testing across multiple generations. The annual evaluation of breeding lines in local fields allows better-adapted cultivars to be selected –32. BorlWe modified SB conditions in this study to create a breeder-friendly and energy-saving generation acceleration system. The SV + mSB system uses 10 h of natural sunlight and 12 h of LED light, resulting in an 80% reduction in energy consumption compared to the conventional SV + SB system. Four generations of wheat can be developed using the SV + mSB system as standard procedure in breeding programs, enabling the production of a sizable amount of breeding material. When the SV + mSB system was combined with field tests, the breeding time was reduced by more than half compared to the traditional field-based breeding method. Visual phenotypic selection was effective in the SV + mSB condition. Genetic resources, RILs, and breeding lines that had shorter DTH and CL under SV + mSB conditions also had shorter DTH and CL under field conditions. The findings of this study, which involved the successful use of phenotypic selection while accelerating the development of four generations of bulk breeding materials, are expected to serve as a model for breeders worldwide. However, it is still difficult to evaluate major yield-related traits under SV + mSB conditions. Therefore, developing and utilizing gene-specific markers that match the genotype and phenotype in each field condition would enhance both speed and efficiency for wheat breeding.2 in the modified speed breeding (mSB) glasshouse  were mounted at a height of one meter from the shelves where the plants were placed, with five LED lights per 1.5 m 2 Table . The glahttps://data.kma.go.kr/) was used to collect outdoor temperature data.The temperature inside the glasshouse was adjusted by opening and closing the windows, without a cooling system, and a heating system was used for 192 days per year, when the mean minimum temperature was less than 10 °C (late October to March). A Testo 174 H logger  was used to record the temperature inside the glasshouse. The weather data supplied by the Korea Meteorological Administration Open MET Data Portal  and horticultural soil  in a 2:1 ratio. All plants were moved to the SB or mSB conditions after four weeks of vernalization. The SB condition was the same as that used by Cha et al. [Using the SV method developed by Cha et al. , the vera et al. , which mTo compare the growth characteristics under SV + SB and SV + mSB conditions with different seeding dates, ten wheat cultivars, including both spring- and winter-type, were evaluated using five plants per cultivar (Table S4).To evaluate the correlation between agronomic traits in SV + mSB and field conditions, 609 worldwide wheat genetic resources reported by Min et al.  and 184 1F5 lines were developed, as shown in Fig. S3. Visual selection under SV + mSB conditions was conducted in the BC1F5 generation. Phenotypic evaluation in the field was carried out in 2022.To assess the impact of phenotypic selection under SV + mSB conditions, 37 Joongmo2008*2/Tapdong BCRStudio  was used for all statistical analyses. Correlation analysis, analysis of variance, and Duncan’s multiple tests were conducted using ggplot2, agricolae, and GGally packages.Below is the link to the electronic supplementary material.Supplementary Material 1


Lung cancer is frequently accompanied by interstitial lung disease (ILD), and the overall survival (OS) of patients with these comorbidities is poor. Thus, we developed a nomogram for the prediction of the OS of patients with advanced non‐small cell lung cancer (NSCLC) and ILD.Patients with wild‐type gene advanced NSCLC with and without ILD who underwent chemotherapy between 2014 and 2019 were enrolled in the present study. The 0.5‐ and 1‐year progression‐free survival (PFS) and overall survival (OS) times of patients with and without ILD were determined using the Kaplan–Meier method. Cox regression was used to assess the prognostic value of clinical factors for patients with ILD. Based on the multivariate regression results, a nomogram for survival prediction was developed. The nomogram was validated using calibration curve.p < 0.001] and 7.0 vs. 15.0 months (p < 0.001), respectively). Multivariate analysis showed that the lymphocyte count , partial pressure of oxygen , and chemotherapy regimen were independently associated with prognosis. The nomogram showed good discriminatory ability [C‐index = 0.69 ]. Calibration curves showed that predicted and actual prognoses were consistent.Data from 155 patients with lung cancer and ILD and 118 matched patients with lung cancer alone who were receiving first‐line chemotherapy were analyzed. The first‐line chemotherapy regimens were paclitaxel + carboplatin, pemetrexed + carboplatin, gemcitabine + carboplatin, and other. The median PFS and OS were significantly shorter in patients with than in those without ILD (3.0 vs. 7.0 months [This nomogram can aid the prediction of the OS of patients with advanced NSCLC and ILD. A nomogram for the prediction of the survival of patients with advanced non‐small cell lung cancer and intersitial lung disease. The cut‐off values used for these factors were based on the literature and the hospital laboratory's parameters.2, and findings of serum protein electrophoresis. The baseline characteristics  of patients in the two groups were not balanced prior to matching . Demographic and clinical variables included in the propensity score model were age, sex, histological type, ECOG status, clinical stage, location, lymphocyte count, chemotherapy regimen , blood gas findings, and pulmonary function. In total, 236 patients matched according to propensity scores were allocated to two groups of 118 patients each. PFS (from first‐line chemotherapy initiation to the confirmation of progression or death) and OS (from diagnosis to death of any cause or last visit) were determined. OS was recorded during follow‐up clinical visits or by telephone. All patients were followed until death or December 2019.We retrospectively analyzed data from 310 patients with advanced NSCLC with and without comorbid ILD who received first‐line chemotherapy at Shanghai Pulmonary Hospital between January 2014 and December 2019. TNM stages were defined according to the 8th edition of the American Joint Committee on Cancer/Union for International Cancer Control guidelines.2.2Diffuse parenchymal lung diseases, often referred to collectively as ILD, form a heterogeneous group of disorders that are classified together because of their similar clinical, radiographic, physiological, and/or pathological manifestations. ILD was classified according to computed tomographic patterns as usual interstitial pneumonia (UIP) and non‐UIP.Chest HRCT (Siemens) was performed with a slice thickness of 1–2 mm. Two thoracic radiologists reviewed the images to diagnose ILD and classify cases as UIP and non‐UIP.2.3AE‐ILD is generally characterized by suddenly progressive and severe respiratory failure, with new lung opacities and diffuse alveolar damage.2.42, percent forced vital capacity, percent forced expiratory volume in 1 s, and albumin level. Kaplan–Meier analysis and the log‐rank test were used to assess the PFS and OS times of patients with and without ILD. Cox proportional‐hazards regression models were used to evaluate age, gender, smoking history, ECOG status, histology, clinical stage, lesion location, lymphocyte count, chemotherapy regimen, ILD pattern, PaO2, and albumin level as predictors of the OS of patients with NSCLC and ILD, with the calculation of hazard ratios (HRs) and 95% confidence intervals (CIs). Multivariate analyses were performed to identify independent prognostic factors. Two‐tailed p values <0.05 were taken to be significant.The data were analyzed using SPSS 22.0 (IBM Corporation) and the R software . Propensity scores were calculated using logistic regression and the following covariates: age, sex, smoking history, ECOG status, histology, clinical stage, lesion location, lymphocyte count, chemotherapy regimen, PaOThe independent risk factors identified were included in a nomogram developed for the prediction of the probability of post‐chemotherapy 0.5‐ and 1‐year OS among patients with advanced NSCLC and ILD. Receiver operating characteristic (ROC) and calibration curves were used to assess the nomogram's performance and discrimination ability, respectively.33.1n = 108]) and internal validation (30% [n = 47]) populations. Their clinicopathological characteristics are summarized in Table n = 296 [95.5%]) patients had ECOG performance status scores of 0 or 1. NSCLC diagnoses were adenocarcinoma (n = 116 [37.4%]), squamous cell carcinoma (n = 94 [30.4%]), and other (n = 100 [32.2%]). Most lesions were peripheral, primarily in the lower pulmonary lobes. The longest follow‐up period was 72 months, and the median follow‐up period was 39 months . At the end of the follow‐up period, 12 patients had been lost and 247 patients had died. Chemotherapy regimens were paclitaxel + carboplatin (n = 43 [13.8%]), pemetrexed + carboplatin (n = 56 [18.1%]), gemcitabine + carboplatin (n = 88 [28.4%]), and other (n = 123 [39.7%]). Other regimens employed single agents and were used for patients of advanced age (≥70 years) who could not tolerate platinum‐based double‐agent chemotherapy. These regimens included docetaxel (n = 33 [10.7%]), vinorelbine (n = 34 [11.0%]), and S‐1 (n = 56 [18.1%]). Bevacizumab was not administered to patients with lung adenocarcinoma due to pre‐existing bleeding, thrombi, and/or non‐coverage by health insurance.Of the 310 patients with advanced NSCLC enrolled in this study , 155 patients also had ILD. All patients with ILD had undergone HRCT examination. The patients were assigned randomly to development , the mean percent forced expiratory volume in 1 s was 81.5% , and the mean diffusing capacity of the lungs for carbon monoxide was 62.9% .The mean PaO3.2p < 0.001] and 7  vs. 15  months [p < 0.001], respectively; Figures The median PFS and OS were significantly shorter in patients with than in those without ILD , chemotherapy regimen , and PaO2  were independent prognostic factors for OS.The univariate analysis revealed significant associations of the lymphocyte count, chemotherapy regimen, and PaO3.49/L), chemotherapy regimen , and PaO2  after primary chemotherapy in patients with breast cancer, which was associated with reduced disease‐free survival.In our study, peripheral blood lymphocyte counts <0.8 × 102 reduction is a frequently reported independent predictor of unfavorable postoperative prognoses, including AE‐ILD, in patients with lung cancer.2 to the fraction of inspired oxygen was associated with all‐cause mortality in patients with AE‐ILD. In our study, PaO2 <80 mm Hg tended to associated with shorter survival times in patients with advanced NSCLC and ILD relative to those without ILD. Mechanisms potentially underlying this association include the development of chemotherapy drug resistance or an aggressive phenotype, which worsen prognoses, due to tumor hypoxia.PaOSeveral limitations of this study should be considered. First, as it was retrospective, selection bias cannot be ruled out fully. Second, all data were collected from a single institution. However, the treatments administered to patients in our sample align with the current National Comprehensive Cancer Network guidelines2, and chemotherapy regimen were identified as independent risk factors for patients with NSCLC and ILD who have undergone first‐line chemotherapy in this study. The nomogram developed and internally validated in this study could be useful for survival prediction for this population in clinical practice. Although further validation of the nomogram is needed, our findings facilitate oncologists' treatment making. Rigorous prospective multicenter clinical trials examining this topic are needed.In conclusion, the lymphocyte count, PaOWeigang Xiu: Investigation ; methodology ; writing – original draft . Jincheng Zheng: Methodology ; software . Yuwen Zhou: Methodology ; software . He Du: Methodology ; validation . Jiayu Li: Methodology ; validation . Wei Li: Methodology ; software . Fei Zhou: Methodology . Caicun Zhou: Conceptualization ; project administration . Fengying Wu: Conceptualization (lead); project administration ; writing – review and editing .This work was supported by Clinical Research Plan of SHDC (No. SHDC2020CR4001), Shanghai Nature Science Foundation (20ZR1447100), and National Nature Science Foundation (81902314).The authors declare that they have no potential conflict of interest related to this research or to authorship or publication of this article.The Medical Research Ethics Committee of Shanghai Pulmonary Hospital Affiliated with Tongji University approved this study. Informed written consent was obtained from all study participants in accordance with the hospital ethics committee's guidelines. The study methodology was based on approved guidelines and relevant regulations.


Observational studies have suggested an association between coronavirus disease 2019 (COVID‐19) and myasthenia gravis (MG). Here, we aimed to estimate the genetic correlation and causal relationship between COVID‐19 susceptibility, hospitalization, severity, and MG phenotypes using linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) approach.Q test, MR‐PRESSO method, and MR‐Egger intercept test.Summary statistics of COVID‐19 susceptibility, hospitalization, and severity were used as instrumental variables for exposure traits. Large‐scale genome‐wide association study (GWAS) data for MG were used as outcome traits. The inverse variance weighted approach was used for the main MR analysis, complemented by MR‐Egger, weighted median, simple mode, and weighted mode methods. Sensitivity analysis was implemented using Cochran's p > .05). Our MR analysis did not provide evidence supporting a causal effect of COVID‐19 susceptibility, hospitalization, or severity on MG phenotypes (p > .05). Extensive sensitivity analysis strengthened the robustness and consistency of the MR estimates.LDSC analysis did not reveal any genetic correlation among COVID‐19 susceptibility, hospitalization, severity, and MG phenotypes, including MG, early‐onset MG, and late‐onset MG (Our study did not find evidence of a genetic correlation or causal relationship among COVID‐19 susceptibility, hospitalization, severity, and MG. Future studies with more GWAS data are needed to evaluate the association between COVID‐19 phenotypes and MG and its subgroups. No genetic correlation or causal relationship between COVID‐19 susceptibility, hospitalization, severity, and myasthenia gravis.  Specifically, we examined the genetic correlation among COVID‐19 susceptibility, hospitalization, severity, and MG using the regression slope derived from the LDSC analysis.LDSC is a robust and powerful tool used for analyzing genetic correlations between complex diseases and traits. This method involves regression analysis on the chi‐squared statistics derived from GWAS data for one phenotype or the cross‐product of chi‐squared statistics for two phenotypes  and strong IV strength (F statistic > 10)  × EAF, where BETA and EAF represent the genetic estimate strength and the effect allele frequency of the COVID‐19 phenotype IVs  method. The mRnd online platform was used to calculate the statistical power of MR analysis for assessing the impact of COVID‐19 susceptibility, hospitalization, and severity on MG phenotypes  in R software (version 4.2.1).In this study, we used genetic variations associated with exposure as IVs to examine the potential causal relationship between exposure and outcomes using MR approach. The primary MR estimation method used was the multiplicative random effects inverse variance weighted (IVW) method, supplemented by MR‐Egger, weighted median, simple mode, and weighted mode methods. The IVs used in our analysis were genome‐wide statistically significant single nucleotide polymorphisms (SNPs) with a significance threshold of 33.1Rg = .045, p = .607), early‐onset MG , or late‐onset MG . Furthermore, there was no evidence supporting genetic associations between COVID‐19 hospitalization and MG , early‐onset MG , or late‐onset MG . Similarly, we found no genetic associations between COVID‐19 severity and MG , early‐onset MG , or late‐onset MG .As shown in Table 3.2p: .66), hospitalization , and severity  with MG , hospitalization , and severity  with early‐onset MG , hospitalization , and severity  with late‐onset MG (Figure F‐statistics for all genetic variants were greater than 10.Our MR analysis revealed no evidence to support a causal relationship between genetically determined COVID‐19 susceptibility (IVW: OR: 1.09, 95% CI: .74–1.59, G Figure . FurtherG Figure . SimilarG Figure . The F‐sQ test did not detect any horizontal pleiotropy (p > .05, Table p > .05, Table Sensitivity analysis showed that Cochran's 4To our knowledge, this study is the first to evaluate the genetic association and potential causal relationship between COVID‐19 and MG. Using LDSC and MR analysis, no significant genetic or causal association was observed between COVID‐19 and MG phenotypes. The extensive sensitivity analysis performed confirmed the primary findings, thereby enhancing the stability and consistency of the MR analysis.By employing cross‐trait LDSC analysis with two different GWAS datasets at the summary level, this study effectively addresses the issue of sample overlap and allows for the estimation of global correlation (Bulik‐Sullivan et al., In the absence of large‐scale randomized controlled trials, the reanalysis of GWAS data at the summary level using LDSC and MR methods is undoubtedly a valuable avenue of investigation. As preliminary investigations, these approaches aim to shed light on the association and causality between COVID‐19 and MG, thus filling an important gap in the field. Furthermore, by exploring the association between COVID‐19 and MG, it will be possible to identify common pathogenic mechanisms, which in turn may provide new insights into potential molecular mechanisms and therapeutic development (Patrick et al., Previous studies have confirmed that SARS‐CoV‐2 can enter the nervous system and induce a cytokine storm and immune dysregulation (Gu et al., Emerging evidence suggests a possible association between COVID‐19 and MG, but research results remain controversial. Some case reports or series have shown MG following COVID‐19 vaccination or infection (Chavez & Pougnier, Currently, the results from traditional observational studies are inconsistent. Some evidence does not elucidate whether the higher hospitalization and mortality rates are due to multi‐organ failure, COVID‐19, exacerbation of autoimmune dysfunction, or exacerbated MG treatments Galassi, . It cannIn our analysis, the results from LDSC and MR studies did not reach statistical significance. In other words, we did not find a positive association between COVID‐19 and increased risk of MG, which is consistent with some observational studies (Businaro et al., Considering the evidence from these observational studies and our analysis, MG patients show good safety and tolerability to SARS‐CoV‐2 vaccines, and vaccination can be considered to prevent severe and life‐threatening complications.Our study has several strengths. First, we comprehensively evaluated the relationship between COVID‐19 and MG using publicly available GWAS data. Second, our MR analysis minimized the confounding biases and potential for reverse causation. Third, extensive sensitivity analyses strengthened the stability and consistency of our MR estimates. However, there are also limitations to our study. First, our genetic data mainly came from European populations, so our results may not be easily extrapolated to other ethnic groups. Second, LDSC and MR analyses only use summary‐level data from GWAS, which precludes further genetic analyses at the individual level, such as assessing the impact of medications. Third, although we did not find any association between COVID‐19 and MG phenotypes, including all MG, early‐onset MG, and late‐onset MG, it should be noted that MG can also be classified into ocular and generalized MG. In addition, a small percentage of MG patients carry MuSK and/or LRP4 antibodies rather than AchR antibodies. The association between COVID phenotypes and these MG subgroups is still unknown. Validation of the current analysis requires GWAS research focusing on MuSK and/or LRP4 antibodies. In addition, our LDSC and MR studies are preliminary and caution should be used in interpreting the present results. More robust evidence, such as cohort studies, is needed to evaluate the association between COVID and MG.In conclusion, our study found no evidence of genetic association or causal relationship between COVID‐19 susceptibility, hospitalization, and severity and MG. This suggests that COVID‐19 vaccination may be safe in MG patients. Our study provides new evidence to understand the impact of COVID‐19 on neurological immune disorders. Future GWAS data are needed to evaluate the association between COVID‐19 phenotypes and MG and its subgroups.Hongyu Zhou conceived and designed the overall research. Dongren Sun and Liangdan Tu analyzed the data and wrote and revised the manuscript. All authors were involved in the collection and monitoring of the data. All authors reviewed and agreed to the submitted version.The authors declare no conflicts of interest.https://publons.com/publon/10.1002/brb3.3239.The peer review history for this article is available at Table S1 Estimation of MR by MR‐Egger, weighted median, simple mode, and weighted mode.Click here for additional data file.Table S2 MR reanalysis after the removal of confounding factors by PhenoScanner online tool.Click here for additional data file.Table S3. Variance and power of COVID‐19 phenotypes and MG phenotypes.Click here for additional data file.


Syringin is a natural chemical compound first isolated from the bark of lilac and is known to have neuroprotective effects in middle cerebral artery occlusion (MCAO). Volume regulated anion channel (VRAC) is a cell swelling‐activated anion channel, which is implicated in brain ischemia. However, the mechanism underlying the syringin protecting the neuron from damage in MCAO is still unclear. We hypothesized that syringin has an inhibitory effect on the opening of VRAC channels. To access the effect of syringin on VRAC currents and predict how syringin interacts with VRAC proteins, we performed whole‐cell patch‐clamp experiments using HEK293 cells. Initially, HEK293 cells were perfused with isotonic extracellular solution, followed by hypotonic extracellular solution to stimulate endogenous VRAC currents. Once the VRAC currents reached a steady state, the hypotonic solution containing syringin was perfused to study the effect of syringin on VRAC currents. The potential interaction between syringin and the VRAC protein was investigated using molecular docking as a predictive model. In this study, we found that syringin moderately inhibited VRAC currents in a dose‐dependent manner. The potential binding of syringin to LRRC8 protein was predicted through in silico molecular docking, which suggests an affinity of −6.6 kcal/mol and potential binding sites of arginine 103 and leucine 101. Our results herein characterize syringin as an inhibitor of the VRAC channels, which provides valuable insights for the future development of VRAC channel inhibitors. To record wild‐type volume regulated anion channel (VRAC) currents, HEK293 cells were plated on glass coverslips and treated with a cellular hypotonic solution. The inhibitory effect of syringin on VRAC currents was tested using patch‐clamp techniques. Results showed that syringin inhibited VRAC channel activity. ECextracellular domainILintracellular linker domainLRRleucine‐rich repeat domainMCAOmiddle cerebral artery occlusionROSreactive oxygen speciesRVDregulatory volume decreaseVRACvolume regulated anion channel1Volume regulated anion channel (VRAC) is an anion channel ubiquitously expressed in the membranes of vertebrate cells. Changes in osmotic pressure can cause cells to swell, and the process of regulatory volume decrease (RVD) is responsible for restoring the abnormal cell volume. VRAC channels play a critical role in RVD by facilitating the regulated passive efflux of chloride and small organic compounds, thereby enabling water to exit the cytoplasm.DCPIB is a commonly used specific inhibitor of VRAC channels, with an IC50 of 4.1 μM in CPAE cells.Cell swelling is relevant to ischemia‐induced brain damage. Astrocytes are susceptible to swelling in the central nervous system injury, and this process is mediated by the opening of VRAC channels. The activation of VRAC channels facilitates excitatory glutamate efflux from astrocytes, resulting in neuronal damage. Studies have shown that defects in astrocytic VRAC channels attenuate glutamate‐dependent neuronal excitability and protect mice from brain damage.Syringin (4‐[(1E)‐3‐hydroxyprop‐1‐en‐1‐yl]‐2,6‐dimethoxyphenyl β‐D‐glucopyranoside), also known as eleutheroside B, is a monosaccharide derivative that is abundantly found in Tinospora sinensis (Lour.) Merr., Tinospora crispa, and Eleutherococcus senticosus. Syringin exhibits various pharmacological properties, including anti‐oxidation, anti‐inflammation, and immune inhibition.In the present study, we recorded the endogenous VRAC currents induced by hypotonic extracellular solution in HEK293 cells. We measured the VRAC currents before and after adding syringin at a potential of −80 mV. Additionally, we utilized molecular docking to predict the potential binding sites of the syringin.22.1Syringin  was purchased from Herbpurify, dissolved in DMSO to make a stock solution, and stored at −20°C. Solutions were freshly prepared from stock solutions before each experiment and protected from light. The final concentration of DMSO was less than 0.1%.2.22. When cells were cultured to 70%–80% confluence, HEK293 cells were split by trypsin–EDTA (Gibco) and replated onto HD coverslips coated with 0.1 mg/mL poly‐L‐lysine (Sigma‐Aldrich) for electrophysiological recording.HEK293 cells were cultured in DMEM (Solarbio) supplemented with 10% FBS (Hyclone) in flask T25 and maintained at 37°C with 5% CO2.3Volume regulated anion channel currents were recorded in the standard whole‐cell configuration at 15–25°C using EPC 10 USB patch‐clamp amplifier and PATCHMASTER NEXT software . The sampling rate was 20 kHz and digitally filtered at 2 kHz. Series resistance compensation was set to 60%–80%. The electrodes were pulled by SUTTER P‐1000 puller, and the resistance was maintained at 3–5 MΩ. In the patch‐clamp experiments, bathing solution perfusion was delivered by eight‐channel perfusion valve control system (Warner Instruments).2, 1.5 CaCl2, 10 glucose, and 10 HEPES . To elicit VRAC currents, cells were exposed to a hypotonic solution containing (in mM) 105 NaCl, 6 CsCl, 1 MgCl2, 1.5 CaCl2, 10 glucose, 10 HEPES . Capillary glass electrodes were filled with solution containing 140 mM CsCl, 1 mM MgCl2, 5 mM EGTA, 4 mM Na2ATP, and 10 mM HEPES .For VRAC currents whole‐cell recording, the isotonic extracellular solution contained (in mM) 150 NaCl, 6 KCl, 1 MgClThe standard test protocol consisted of a 600 ms step to −80 mV followed by a 2600 ms ramp from −100 to +100 mV from a holding potential of −30 mV, applied at 1500 ms intervals. The full voltage protocols consisted of a 2000 ms step protocol from −120 to +120 mV in 20 mV increment from a holding potential of −80 mV applied every 5000 ms.2.4https://www.rcsb.org/). The solvent and organic were removed from VRAC protein by PyMOL software. The 2D structure of syringin (PubChem CID: 5316860) was acquired from PubChem (https://pubchem.ncbi.nlm.nih.gov/). The energy of syringin was reduced by the MM2 method in Chem 3D 19.0. The charge was added to syringin by Autodock Vina MGLTools 1.5.6. software.The 3D structure of the VRAC protein (PDB number: 6NZW) was obtained from the RCSB PDB Protein Data Bank .2.6http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY,Key protein target and ligand in this article are hyperlinked to corresponding entries in 33.1Syringin is a monosaccharide derivative, composed of trans‐sinapyl alcohol that is linked to a β‐D‐glucopyranosyl residue at position 1 through a glycosidic linkage . . , 0.79 ± 0.02 (n = 5), and 0.61 ± 0.02 (n = 5), respectively. ,2O2 in H9c2 cells.50 curve for syringin and testing its specificity for VRAC channels.In conclusion, our study investigated the effects of syringin on endogenous VRAC currents induced by hypotonic extracellular solution in HEK293 cells. Syringin showed a dose‐dependent inhibition of VRAC current. Additionally, in silico calculations indicated that syringin may have a high affinity for LRRC8/VRAC protein, suggesting that syringin may have a potential direct binding effect on VRAC channels. Given that VRAC channels mediate the osmotic excitotoxic substances efflux from astrocytes in ischemic injury, we propose that syringin may have the potential to rescue swollen astrocytes in ischemic injury by inhibiting VRAC channels, which could reduce the release of glutamate and potentially decrease neurotoxic damage to neurons. Syringin has been shown to possess anti‐oxidant properties, as evidenced by its ability to inhibit reactive oxygen species (ROS) levels in the brains of APP/PS1 transgenic mice,50: 7.11 ± 0.94 μM). Interestingly, 6u also acts as a dual inhibitor, as it is able to inhibit TREK1 channels.Due to the critical role of VRAC channels in cell apoptosis, insulin secretion, anti‐viral immunity, and particularly in cerebral ischemia, there is a pressing need to develop regulators of VRAC channels. To date, DCPIB is widely recognized as a specific and potent inhibitor of VRAC channels. However, accumulating evidence demonstrates that DCPIB not only inhibits the VRAC channels but also suppresses the other proton pumps or ion channels such as H+/K+ ATPase, connexin hemichannels, K2P channels, including TRESK, TASK1, TASK3. DCPIB even displays the agonist function toward TREK1, TREK2, and TRAAK channels .In the current study, we first characterize syringin as an inhibitor for the VRAC channels. Syringin is the first VRAC regulator with a monosaccharide derivative structure, which provides a perspective for future VRAC channel regulator development. We hope that the inhibitory effect of syringin on VRAC channels will provide a theoretical basis for the inhibitory mechanism of syringin on brain edema.Participated in research design and performance: Ziwei Xu, Wei Yao, Minyan Liang, Song Wang, Longhui Lu, Jingjing Wang, and Na Zhu. Wrote or contributed to the writing of the manuscript: Ziwei Xu and Liping Huang.We gratefully acknowledge the financial support for this study provided in part by the National Natural Science Foundation of China (82060759), the Jiangxi University of Chinese Medicine Graduate Student Innovation Special Fund (at the University Level) (JZYC22B03), and the Jiangxi University of Chinese Medicine Science and Technology Innovation Team Development Program (CXTD22007).All authors declare that they have no conflict of interest.None.


In low‐ and middle‐income countries, protein‐losing enteropathy is a diagnosis of exclusion. SLE should be on the list of differential diagnoses of protein‐losing enteropathy, especially if the patient had a long history of GI symptoms and ascites.Protein‐losing enteropathy can rarely be the initial presentation of systemic lupus erythematosus (SLE). Protein‐losing enteropathy is a diagnosis of exclusion in low‐ and middle‐income countries. Protein‐losing enteropathy in SLE should be in the list of differential diagnosis of unexplained ascites, especially if patient had long history of gastrointestinal symptoms. We present a case of 33 years old male with long standing gastrointestinal symptoms and diarrhea attributed previously to irritable bowel syndrome. Presented with progressive abdominal distension, and diagnosed with ascites. Workup for him showed leucopenia, thrombocytopenia, hypoalbumenemia, elevated inflammatory markers , high cholesterol level (306 mg/dL), normal renal profile and normal urine analysis. Ascitic tab pale yellow with SAAG 0.9 and positive for adenosine deaminase (66 u/L) sugesstive for tuberculous peritonitis although quantitative PCR and geneXpert for MBT was negative. Antituberculous treatment was started and his condition deteriorated, immediately antituberculous was withdrawal. Further tests revealed positive serology for ANA (1:320 speckled pattern) with positive anti‐RNP/Sm, positive anti‐Sm antibodies. Complements level were normal. He started immunosuppressive therapy . In addition, his condition is improved Diagnosis was made as SLE with Protein‐losing enteropathy based on hypoalbumenemia , ascites, hypercholesrtolemia and exclusions of other mimics as explained later. As well as positive response to immunosuppressive medications. Our patient diagnosed clinically as SLE with protein‐losing enteropathy. Protein‐losing enteropathy in SLE is challenging in diagnosis because of its rarity as well as limitations in its diagnostic tests. It has a strong genetic predisposition along with environmental factors, which act as triggers for loss of tolerance to self‐antigen, leading to antibody production and immune complex formation that can affect any part of the body and lead to different manifestations.2Our patient is a 33‐year‐old Sudanese male who presented 4 months ago complaining of dizziness and headache. No other symptoms at that time was diagnosed with malaria by blood film microscopic analysis and received antimalarial medications with no improvement. Then a few weeks later the patient develop depression and became in bad mood, seek for psychiatric consultation with little improvement.Two months later the patient develops painless abdominal distension progressive over weeks, not associated with shortness of breath. He went to a tropical hospital and was diagnosed with mild ascites by the abdominal US for further workup and paracentesis was done and no definitive cause was elicited at that time and the decision was made to treat him with anti‐tuberculosis medications for suspected tuberculous peritonitis, due to positive ascitic fluid adenosine deaminase. Fifteen days later his condition deteriorated. He developed jaundice, fever, dyspnea, and increased abdominal swelling, immediately anti‐TB drugs were stopped and he received antibiotics according to blood culture. Then the patient was referred to us at the rheumatology unit because of further workup revealed that the serology profile was positive for ANA (1:320 speckled pattern) and positive anti‐RNP/Sm, positive anti‐Sm. complements level were normal at the tropical hospital.Our medical interviews at the rheumatology unit revealed generalized weakness, fatigability, and weight loss. No joint pain and swelling, no morning stiffness, no skin rash, no photosensitive rash, no eyes symptoms nor sicca symptoms, and no Raynaud's symptoms. GIT revealed a 1‐year history of multiple attacks of abdominal pain and underwent upper GI endoscopy that showed gastritis.Any cardiopulmonary and genitourinary symptoms were not observed. The nervous system revealed mood changes and depression and he is on antidepressive medications. He is not known to have any allergies and with history of smoking for 10 years and quit smoking with this current illness. He takes over‐the‐counter medications for IBS and gastritis. No autoimmune diseases run in the family. There is a history of hypertension in the family.During admission, the patient's condition deteriorated and the mild ascites become massive with positive fluid thrill, and lower limb edema. Chest and cardiovascular examinations were normal. No oral ulcers. No joint deformities or evidence of synovitis or skin rash.3Klebsiella infection according to culture and sensitivity at a tropical hospital.Initial laboratory investigations were as follows: total white blood cell count, 2.9 cells/μL; hemoglobin, 15 g/dL; platelet count, 149 cells/μL; total bilirubin, 0.2 mg/dL; aspartate aminotransferase, 22 IU/L; alanine aminotransferase, 12 IU/L; serum total protein, 4.9 g/dL; serum albumin, 1.5 g/dL; serum globulin, 3.4 g/dL; then after starting antituberculous his tests show raising in serum bilirubin, 3.3 mg/dL; direct bilirubin, 2.5 mg/dL; indirect bilirubin, 0.8 mg/dL; ALK, 56μ/L; AST, 132μ/L; ALT, 29μ/L; blood urea nitrogen, 36 mg/dL serum creatinine, 1.0 mg/dL; Na, 128 mmoL/L; K, 3.7 mmoL/L; serum phosphate, 2.3 mg/dL; Ca, 6.9 mg/dL and after correction Ca was 8.9 mg/dL due to hypoalbuminemia; total cholesterol, 306 mg/dL; C‐reactive protein (CRP), 6.6 mg/dL; erythrocyte sedimentation rate, 30 mm/h. The viral screening was negative and urine analyses were normal. He received antibiotics (Meropenem 1 g IV every 8 h for 7 days) for Autoantibody profiles showed positive ANA (1:320 speckled pattern) anti‐RNP/Sm, and positive anti‐Sm, and the diagnosis of SLE was established based on the ACR and EULAR criteria for the classification of SLE (score 15 points).Chest x‐ray showed no abnormalities, echocardiography showed LVEF 57% and mild pericardial effusion. The US of the abdomen done early in admission showed normal liver architecture, mild ascites, diffuse gallbladder wall thickness, and mildly enlarged spleen but no evidence of portal hypertension. Later in admission, the patient condition deteriorated and ascites become massive with fluid thrill clinically detected, and lower limb edema.Paracentesis was performed and the fluid analysis showed pale yellow ascitic fluid; albumin, 2.4 g/dL; sugar content, 99 mg/dL; LDH, 249μ/L; with no nucleated cells or acellular deposition was seen. Ascitic AFB was negative, quantitative PCR for mycobacterium tuberculosis (MTB) was negative, and ascitic fluid gene Xpert for MTB was negative as well. ADA ascitic fluid was significant (66μ/L) and suggestive of tuberculosis, based on ADA ascitic fluid the patient started antituberculous with further deterioration then antituberculous was withdrawn and Ascites were considered to be caused by hypoproteinemia. The serum ascitic albumin gradient (SAAG) was 0.9. Lupus nephritis was unlikely in the absence of any significant abnormalities in kidney functions and clear urinalysis. Other causes of edema and ascites such as liver, heart failure, or thyroid dysfunction were also unlikely, the patient workup for evaluating the possibility of PLE was necessary. The patient was requested to do CT abdomen and endoscopy with biopsy to exclude other causes, he was offered counseling many times on how these investigations would help with the management unfortunately because of his financial difficulties and as we are in a resource‐limited country, moreover advanced tests were not available in the center, PLE was identified clinically as the cause of hypoproteinemia found in this patient.After the diagnosis of PLE as a presentation of SLE, corticosteroids were prescribed and the patient clinical improvement and was discharged with an outpatient prescription of prednisone 10 mg/day, azathioprine 50 mg twice/day and hydroxychloroquine 200 mg twice/day. The patient was followed up in the referred clinic and he responded to the treatment over the next few months as evidenced by the rise in serum albumin subsequently. He is well up to date and returns to his work with no significant complications. No further workup was done (like a CT abdomen and colonoscopy) as the patient's symptoms and conditions improved rapidly.4The present case describes a 33‐year‐old male, diagnosed clinically with Protein‐losing enteropathy as the first presentation of SLE.In low‐ and middle‐income countries, PLE is a diagnosis of exclusion and should be considered in the list of differential diagnoses of any unexplained ascites. The diagnosis of protein‐losing enteropathy required 24 h fecal a1 antitrypsin level, MR lymphangiography, and technetium 99 albumin scintigraphy.Pseudo‐pseudo Meigs syndrome (PPMS) should be in the differential diagnosis of ascites in SLE but in PPMS, there will be no gastrointestinal symptoms such as diarrhea and the ascites will be exudates in nature accompanied also by pleural effusion. Here in our case, we exclude PPMS by the prolonged history of diarrhea and gastrointestinal symptoms which support more protein‐losing enteropathy. Lupus peritonitis should be in the differential diagnosis of ascites in SLE but the abdomen examination will show tenderness and the serum protein will be normal, unlike our case with no tenderness and very low serum albumin which suggest PLE as the culprit cause in such a case. Also, hypercholesterolemia in our case supports our diagnosis of PLE.SLE is the most common autoimmune disease, and it has a wide range of clinical presentations with a remitting and relapsing course. SLE commonly affects the gastrointestinal tract and most cases can be life‐threatening. The most common cause is lupus mesenteric vasculitis, followed by protein‐losing enteropathy, intestinal pseudo‐obstruction, acute pancreatitis, and others such as celiac disease, inflammatory bowel diseases, etc.Our patient had protein‐losing enteropathy as an initial presentation of SLE that presented as subacute ascites. Serositis in SLE is associated with high morbidity, and its prevalence ranges between 11% and 54%.d‐dimer are significantly associated with serositis.Ascites were formerly considered to exclusively manifest in cases of nephrotic syndrome, Budd‐Chiari syndrome, and protein‐losing enteropathy.Although an association between SLE and protein‐losing enteropathy has been recognized, there have been less than 200 cases documented in world literatureSLE presenting as protein‐losing enteropathy has been reported even less commonly. Vasculitis of mesenteric vessels, inflammatory reactions along the digestive tract: mucosal edema, alterations in capillary permeability, and lymphangiectasia have been postulated as the pathogenic mechanisms.Zhen Chen 2014 reported that PLE in SLE had a significant association with greater frequencies of anti‐Sm, anti‐SSA, and hypocomplementemia, and higher cholesterol levels compared with the control group.Elham Abdalla: Conceptualization; data curation; investigation; methodology; project administration; writing – original draft; writing – review and editing. Noon Mohymeed: Conceptualization; data curation; methodology; writing – original draft; writing – review and editing. Abdelsalam M.A. Nail: Writing – original draft; writing – review and editing. Rayan Ali Tonga: Writing – original draft; writing – review and editing. Mohammed Alfatih: Conceptualization; project administration; writing – original draft; writing – review and editing. Mohannad Abdalfdeel Almahie Shaban: Writing – original draft; writing – review and editing. Hassan Eltoum: Conceptualization; project administration; supervision; writing – original draft; writing – review and editing.No funds.The authors have no conflict of interest to declare.Written informed consent was obtained from the patient to publish this report in accordance with the journal's patient consent policy.