Understanding the characteristics of commenters on mental health–related online forums is vital for the development of effective psychological interventions in these communities. The way in which commenters interact can enhance our understanding of their characteristics.Using eating disorder–related (EDR) forums as an example, this study detailed a methodology that aimed to determine subtypes of mental health–related forums and profile their commenters based on the other forums to which they contributed.Reddit). A mixed-methods approach comprising network analysis with community detection, text mining, and manual review identified subtypes of EDR forums. For each subtype, another network analysis with community detection was conducted using the EDR forum commenter overlap between 50 forums on which the commenters also commented. The topics of forums in each detected community were then manually reviewed to identify the shared interests of each subtype of EDR forum commenters.The researchers identified all public EDR forums (with ≥500 contributing commenters between March 2017 and February 2018) on a large Web-based discussion platform  of commenters had also commented on pornographic forums and 16.66% (181/1086) had contributed to proeating disorder forums.The article exemplifies a methodology that provides insight into subtypes of mental health–related forums and the characteristics of their commenters. The findings have implications for future research and Web-based psychological interventions. With the publicly available data and code provided, researchers can easily reproduce the analyses or utilize the methodology to investigate other mental health–related forums. Compared with clinician-delivered psychological interventions, Web-based interventions targeting mental health conditions offer several benefits, such as cost-effectiveness , and potproeating disorder or prorecovery  were not included in this count and were excluded from all subsequent analyses.A list of search terms was created to identify EDR subreddits, which is provided in dit data . To focuA mixed-methods approach was then used to identify subtypes of the EDR subreddits. This approach comprised 3 techniques that are described below and were conducted in the order presented: (1) network analyses with community detection; (2) text mining; and (3) manual review of EDR subreddits’ focuses.subreddit A compared with subreddit B), the proportion of each subreddit’s commenters who had posted on the other subreddit was calculated, with the result ranging from 0 (no commenters overlap) to 1 . For example, a proportion of 0.4 (40/100) of subreddit A ’s commenters might post on subreddit B, whereas 0.8 (40/50) of subreddit B ’s commenters post on subreddit A. The mean of these 2 proportions  was calculated to account for the differences in the number of commenters on each subreddit. A matrix was then created using all these pairwise comparisons, where each cell within the matrix represented the mean commenter overlap between each pairing of the EDR subreddits.To conduct the network analyses with community detection, a list of commenters was compiled separately for each of the included EDR subreddits. For each pairwise comparison of the subreddits  network analysis was conducted using the  package  for R stsoftware . Many telgorithm  was usedlgorithm  and was lgorithm .VennDiagramR package  commenters). To focus this report on the largest subtypes of EDR subreddits, only subtypes with 1000 or more commenters were included in the analyses. The following analyses were repeated separately for each included subtype of EDR subreddits.For each of the subtypes of EDR subreddits identified through the previously described methods (Objective 1), a list was compiled of all the commenters who had contributed within the 1-year period to at least 1 subreddit within the subtype  in the 1-year period were identified. Any ancillary subreddits to which fewer than 1% of the EDR subtype’s commenters had contributed were excluded at this stage. This exclusion was made as ancillary subreddits with so few EDR commenters would not have been included in the final steps of the analysis (detailed below) and therefore represented unnecessary data to extract.AskReddit). At the same time, this step also avoided the inclusion of very small subreddits that had very high proportions of the EDR subtype’s commenters . As this threshold  was used solely to facilitate a clear interpretation of the results, extensions of this study could set different thresholds to explore the communities at varying levels of detail.The ancillary subreddits were then ranked separately in the descending order of (1) the number of the EDR subtype’s commenters who had contributed to each ancillary subreddit and (2) the proportion of each ancillary subreddit’s total commenters  that had also commented on at least 1 subreddit within the EDR subtype. For each ancillary subreddit, the mean of these 2 ranks was then calculated. Owing to the large number  of ancillary subreddits associated with each EDR subtype and to improve the interpretability of the results, the mean rank was used to identify the most representative ancillary subreddits for inclusion in the following analyses. Specifically, the 50 ancillary subreddits with the highest mean rank were included, resulting in the inclusion of ancillary subreddits that were both large in size  and included a large proportion of the EDR subtype’s commenters. This avoided the inclusion of ancillary subreddits that were very large in terms of the number of commenters but of which the EDR subtype’s commenters comprised a very small proportion  network analyses with community detection and (2) a manual review of the ancillary subreddits’ topics.The network and community detection analyses were conducted in the same way as detailed for Objective 1. The only difference was that the commenter overlaps (relating to both the ancillary subreddits and detected communities) were calculated using only the EDR subtype’s commenters .loseit was described as weight loss. All ancillary subreddits comprising the detected communities were then reviewed, and a label was produced to represent the general content of each community. For example, a community containing ancillary subreddits that related to eating behaviors and weight loss was labelled Eating/Body. To ensure transparency at every stage of this process, the name and labels of all included ancillary subreddits are presented in tables in the Results section, along with a summary of how each label for the detected communities was generated.A manual review of each ancillary subreddit’s name and brief description was undertaken to describe each subreddit’s general topic. For example, the general topic of the subreddit BingeEatingDisorder, bulimia, EatingDisorders, eating_disorders, fuckeatingdisorders, MyProAna, proED, ProEDmeme s, and thinspo. In total, 14,024 commenters posted on these 9 EDR subreddits. Of these commenters, 0.69%  included the term bot within their account name, with these commenters contributing a mean of 9 comments  to the EDR subreddits. The network analysis with community detection corresponding to the 9 EDR subreddits is presented in The search and inclusion strategy led to the identification of 50 EDR subreddits, a list of which is provided in MyProAna , proED , ProEDmemes , and thinspo . In contrast, community 2 (dark-gray circles) comprised the 5 EDR subreddits with the highest percentage of threads mentioning recovery: BingeEatingDisorder , bulimia , eating_disorders , EatingDisorders , and fuckeatingdisorders . These findings supported a conceptualization of community 1 comprising low recovery-focus EDR subreddits and community 2 comprising high recovery-focus EDR subreddits.only posted on subreddits within the low recovery-focus community, whereas 28.69%  only commented on subreddits within the high recovery-focus community. However, 5.34% commenters  posted on subreddits within both communities, indicating relatively little commenter overlap between the communities.Of the 14,024 commenters, 65.97%  EatingDisorders), a specific eating disorder diagnostic category , or online content associated with eating disorders . Each subreddit’s focus was therefore used to categorize the subreddits within each detected community. Consequently, the low recovery-focus community comprised 3 subtypes of EDR subreddits: pro-eating disorder, consisting of proED and ProEDmemes (8166 commenters); thinspiration, consisting of thinspo (1580 commenters); and pro-anorexia nervosa, consisting of MyProAna (731 commenters). As with the low recovery-focus community, the high recovery-focus community comprised 3 subtypes: pro-recovery eating disorder, consisting of EatingDisorders, eating_disorders, and fuckeatingdisorders (1986 commenters); pro-recovery binge eating disorder, consisting of BingeEatingDisorder (2520 commenters); and pro-recovery bulimia nervosa, consisting of bulimia (524 commenters).In addition to the degree of recovery focus, the EDR subreddits also differed in terms of whether they concerned eating disorders in general . Consequently, the analyses regarding Objective 2 were conducted for 4 subtypes of EDR subreddits: 2 that are conceptualized as low recovery focus  and 2 that are conceptualized as high recovery focus . To examine the networks in sufficient detail and owing to the particular importance of the proeating disorder communities [To focus this report on the largest subtypes of EDR subreddits, subtypes with fewer than 1000 commenters were excluded at this stage . 50 ancillary subreddits were identified, on which 61.95% (5059/8166) of the proeating disorder subtype’s commenters also posted. The network analysis with community detection is presented in In total, 974 ancillary subreddits had been contributed to by at least 1% (82) of the 8166 commenters associated with the Eating/Body as the ancillary subreddits were related to restrictive eating , weight loss or body transformations , or eating disorders . Community 2 (dark gray circles) was labelled Mental health as the subreddits mainly related to mental health conditions  or related issues . Community 3 (light-gray circles) was labelled Women/Appearance/Mixed as the subreddits related to women , appearance , or mixed topics .As shown in Of the 5059 proeating disorder commenters, 67.56% (3418) also posted on ancillary subreddits within the Women/Appearance/Mixed community, compared with 61.24% (3098) and 35.90% (1816) in the Eating/Body and Mental health communities, respectively. thinspiration subtype . A total of 50 ancillary subreddits were identified on which 68.73% (1086/1580) thinspiration subtype’s commenters also posted. The network analysis with community detection is presented in In total, 3932 ancillary subreddits had been contributed to by at least 1.01% (16) of the 1580 commenters associated with the Pornography: 1 as the ancillary subreddits were all pornographic in nature. Community 2 (dark-gray circles) was labelled Pornography: young/small as it mainly comprised pornographic subreddits that explicitly referred to women being young  or small . Community 3 (mid-gray circles) was labelled Pornography: 2 as all the subreddits were pornographic. Community 4 (light-gray circle) was labelled ProEDmemes as it comprised only 1 subreddit, ProEDmemes. Similarly, community 5 (white circle) was labelled proED, as it consisted of proED only.As shown in proED, and ProEDmemes communities, respectively. Of the 1086 thinspiration commenters, 67.59% (734) also posted on ancillary subreddits within the Pornography: 1 community, compared with 59.85% (650), 46.13% (501), 20.99% (228), and 10.87% (118) in the Pornography: young/small, Pornography: 2, ProEDmemes and proED). As such, the overlap between these 2 groups of communities was calculated. Of the 1086 commenters in the thinspiration commenter network, 78.18% (849/1086) only posted on ancillary subreddits within the pornography communities, whereas 16.67% (181/1086) only commented on subreddits within the proeating disorder communities. However, 5.16% (56/1086) of the commenters posted on subreddits within both groups of communities, indicating a small commenter overlap between these groups.As shown in Using the example of EDR subreddits, this study demonstrated a methodology that addressed 2 objectives: (1) determine subtypes of forums related to a similar mental health issue and (2) elucidate the characteristics  of the subtypes’ commenters by identifying other forums to which they contribute  and investigating the commenter overlap between these subreddits. These 2 objectives were achieved using mixed-methods approaches, comprising techniques that included a network analysis with community detection, text mining, and a manual review of the forums’ topics. Following the identification of 6 subtypes of EDR subreddits, the report focused on 2 specific subtypes—proeating disorder and thinspiration. The proeating disorder commenters also contributed to subreddits relating to the body and eating, mental health, and women, appearance, and mixed topics. Regarding the thinspiration subtype, 78% (849/1086) of the commenters also contributed to pornographic subreddits, whereas 17% (181/1086) also commented on proeating disorder subreddits.Twitter [MyProAna, proED, ProEDmemes, and thinspo) than more recovery-focused subreddits . Although the text-mining approach used textual data to assess the frequency of words’ occurrence in comment threads, the network analyses with community detection utilized behavioral data . As the results of the text-mining approach  clearly align with the detected communities , a strength of this mixed-methods approach is that the 2 distinct techniques appear to provide a degree of convergent validity to each other.Concerning the first objective, through the use of network analyses with community detection and a previously detailed text-mining technique , 2 commuTwitter . FurtherTwitter . The finfemalefashionadvice and TheGirlSurvivalGuide), the results also supported previous findings that suggest women are more likely to engage with pro-EDR online content [With regard to the second objective, the topics in which commenters on proeating disorder subreddits were interested are in line with other research ,11,16. S content ,9,30.legal or 18) or small . This finding is in line with previous research, which concluded that thinspiration images were typically sexually suggestive [In contrast to the proeating disorder results, the findings concerning thinspiration commenters were of great surprise. Namely, a clear majority of thinspiration commenters (78%) had also contributed to pornographic subreddits. Furthermore, a specific group of commenters contributed to pornographic subreddits that had names suggesting that women were young  to the current mixed-methods approach, its techniques can compensate for the methodological limitations of the previous studies. For example, survey-based studies concerning users of online forums are unlikely to have representative samples. As the current approach used data concerning all the commenters on public forums, it is not subject to this limitation. As a result, by consolidating the findings generated from these distinct methodological approaches, there can be greater confidence that results do not simply represent an artefact of one particular technique .Reddit data are unsolicited. Although this represents a strength of the data , this also results in a significant amount of noise in the data. Steps were taken to reduce this noise, such as only including 50 ancillary subreddits with a large number and proportion of EDR commenters in the network analyses. This approach identified the most representative subreddits by excluding very small subreddits  and very large subreddits (many of which had a large number but low proportion of EDR commenters). However, the effects of other sources of noise in the data are more difficult to mitigate. For example, the same person might have more than one Reddit username, which might lead to an underestimation of commenter overlap. Additionally, bots  exist that comment widely on subreddits and which might contribute to an overestimation of commenter overlap. Although strategies exist to identify bots [AutoModerator, a generic Reddit bot) were not excluded, to adopt a conservative approach to our analyses. To minimize the effects of these sources of noise, the conclusions are based on communities of subreddits, rather than individual subreddits. Although not necessarily a limitation of this study, caution should be exercised in generalizing these findings to people who have read, but not commented on, the online content. Exploring communities based on the content that users read would be important but ethically problematic, as this would likely require access to data that are not publicly available. Despite not being able to generalize to the readers of forums, Aardoom et al [The findings in this study must be considered in relation to the limitations of the data and methodology. First, it is important to note that the ify bots ,36, thesom et al  found thom et al . TherefoproED, fuckeatingdisorders, and EatingDisorders) [With regard to the approach used for the first objective, the findings in this study have implications for how mental health–related online communities should be conceptualized and investigated in future research. Specifically, when comparing multiple communities, the degree of user overlap between these should be acknowledged. For example, a previous study compared the frequency of fitness tracker mentions between 3 EDR subreddits  . By cons1200isplenty, loseit, and progresspics. As some mental health–related communities  might be unlikely to promote psychological interventions, the current approach could be utilized to identify the communities in which these users also tend to post. As a result, these communities could be approached to provide an alternative way in which to reach these people and to target prevention-focused interventions.Concerning the second objective’s methodological approach, the findings have implications for future research and the design of Web-based psychological interventions for mental health issues. With regard to future research, the approach presented here is entirely reproducible and can be used to explore similar questions in other groups of commenters of particular theoretical interest . The methodology could also be easily extended to explore longitudinal—and, therefore, causal—patterns of commenting. The approach is also useful for hypothesis generation and identifying new avenues of research. As detailed above, this study generated a surprising finding, in that over 3 quarters of thinspiration commenters also commented on pornography. As this relationship had not been identified before, it is a clear indicator of how this approach can be used to identify areas that require greater research attention. Concerning implications for psychological interventions, the current approach can identify other topics that are of interest to people commenting on mental health–related online discussion forums. For example, proeating disorder commenters were observed to be also interested in topics such as body, eating, mental health, and appearance. Consequently, these topics confirm the importance of existing EDR intervention focuses  . These fIn summary, this study has presented a reproducible and primarily data-driven methodology that can be used to (1) identify subtypes of mental health–related forums and (2) identify the interests of the commenters who post on the forums comprising these subtypes. This offers a powerful technique for hypothesis-generation and informing strategies for psychological intervention. Employing different methodologies to explore the same research question is vital to ensure that findings are not solely a result of a particular methodological design . This ap


Application of a coating on a mold surface is widely used in the foundry industry. Changes in coating change the heat transfer at the mold–melt interface, which influences the microstructure of the casting. In this study, the effect of boron nitride coating thickness on the interfacial heat transfer and slug microstructure in the Swirled Enthalpy Equilibration Device (SEED) process was investigated. The temperatures of the semi-solid slug and mold were measured, and the interfacial heat transfer coefficient and heat flux of the mold–slug interface was estimated based on these data. Microstructures of the quenched slugs were also examined. The results indicated that the interfacial heat transfer coefficient decreased with an increase in coating thickness and was sensitive to a coating thickness of less than 0.1 mm. The interfacial heat flux decreased sharply at the early stage, and then slowed down as the swirling time increased and the coating thickened. The coating thickness affected the temperature evolution of the slug at the early stage of the SEED process. As the coating thickness increased from near zero to 1.0 mm, the grain size of the slug increased by ~20 µm and the globular structure of the slug transformed into a dendritic structure. Aluminum parts, produced by efficient and low-cost casting processes, are widely used in transportation applications. Well-known casting processes include sand casting, permanent casting, and high-pressure die casting. All of these manufacturing routes have certain drawbacks, including porosity, air holes, and misruns of the casting. Semi-solid die casting, in which a semi-solid slug or slurry with fine and globular primary α-Al particles is pushed into the die cavity, was developed to eliminate these casting defects. For semi-solid die casting, preparation of the semi-solid slug is a key step. Dozens of processes have been developed to prepare such slugs with fine and globular primary α-Al particles. These processes can be classified into two categories: rheo routes and thixo routes. The rheo route has been widely adopted due to its low operating cost and high efficiency.The Swirled Enthalpy Equilibration Device (SEED) process is one such rheo route. The original version was developed by Doutre et al. from Alcan , and theInterfacial heat transfer at the melt–mold interface has been well researched in sand and permanent casting. Published research has shown that many factors affect the interfacial heat transfer coefficient (IHTC). Bouchard et al.  found thIn the present study, the temperatures of the slug and mold were measured for different coating thicknesses on the internal surface of the mold. The IHTC and heat flux on the slug–mold interface were estimated according to the temperature data. The temperature fields and microstructures of slugs produced with different thicknesses of coatings were characterized and analyzed.Commercial aluminum alloy 357  was melted in an electric resistance furnace and held at a temperature of 720 °C. Approximately 1.55 kg of melt at a temperature of 630 °C was poured into a cool tilted cylindrical steel mold of 78 mm inside diameter, 2.3 mm wall thickness, and 210 mm height. Eccentric rotation was then applied at a frequency of 180 rpm. ® Co., Ltd., Spokane, WA, USA) were applied. The thickness of the coating was controlled by adjusting the spray time. The detailed parameters are summarized in In the present study, four thicknesses of BN coating (provided by PyrotekA temperature acquisition system see  was desi4 + 200 mL H2O) under 20 V direct current for 90 s at room temperature. The anodized samples were observed using an Axiovert 200 MAT microscope  using polarized light. Five micrographs  were randomly selected from each sample for quantitative analysis. Particle size  is defined asP is the perimeter of the particle. For F = 1, the particle is a perfect circle; a particle with a more complex shape will correspond to a smaller value of F.The shape factor ) was used to describe the average shape factor of the particles:−2) on the mold–slug interface can be calculated by−3); −1·°C−1); −2·K−1); In the SEED process, the heat released by the melt is absorbed by the mold and ambient air, so the heat flux (h) (W·m−2·K−1) can be calculated by, location 4) (°C) and According to Newton’s law of cooling, the IHTC of the mold–slug interface . According to the heat transfer literature of Lienhard IV and Lienhard v [In this study, the temperature gradient within the mold could be ignored, so Tme) see a. The reenhard v , the tem, the parameters in According to Equations (4) and (5)−2 was reached. As the coating thickness increased, the largest heat flux decreased and its rate of reduction slowed down. The largest heat flux decreased from 800,000 W·m−2 to 180,000 W·m−2 when the coating thickness increased from 0.0 mm to 1.0 mm.−2·K−1 to ~800 W·m−2·K−1 during squeeze casting; Lau et al. [−2·K−1 to ~500 W·m−2·K−1 during permanent mold casting; Zhi-peng et al. [u et al.  showed tg et al.  found th−1  increased as the coating thickened  usually impede the growth of a dendritic microstructure and result in fine structure. This conclusion has been recognized by most researchers in this area. Zhu et al.  investigIn the present study, the observation that a thick coating resulted in a large dendritic structure can be explained by nucleation and grain growth. The analysis in −2·K−1 to ~1100 W·m−2·K−1, 800 W·m−2·K−1, and 500 W·m−2·K−1 when the coating increased in thickness from 0.0 mm to 0.1 mm, 0.5 mm, and 1.0 mm, respectively. The IHTC was sensitive to the BN coating thickness when this was less than 0.1 mm.The IHTC of the mold–slug interface in the SEED process was estimated. The IHTC fluctuated within a small range and decreased with an increase in coating thickness. The IHTC reduced from ~2700 W·m−2 to ~180,000 W·m−2 when the coating increased from 0.0 mm to 1.0 mm.The heat flux of the mold–slug interface decreased sharply from its highest value to a nearly constant low value during the SEED process. The reduction rate decreased as the BN coating thickness and swirling time increased. The highest value decreased from ~800,000 W·mThe temperature difference in the slug dropped off to a constant low value as the swirling time rose. According to this characteristic, the temperature evolution was divided into two stages: unstable and quasi-stable stages. As the coating thickened, the duration of the unstable stage increased and the highest temperature gradient in the slug decreased. The coating thickness had no obvious effect on the quasi-stable stage.The microstructures strongly depended on the BN coating thickness. When the coating thickened, the grain size enlarged and the grain morphology transformed from a globular shape to a dendritic structure. The grain size enlarged from 78 µm to 106 µm at the edge, from 80 µm to 98 µm at the middle, and from 83 µm to 100 µm at the center when the BN coating thickness increased from 0.0 mm to 1.0 mm.The present study showed that the coating thickness on the internal surface of a mold is very important for control of the microstructure of the semi-solid slug in the SEED process. The spray coating process should be well controlled during production to produce a thin and stable coating. In other semi-solid slug preparation methods, especially those based on extensive nucleation caused by cooling of the mold (such as the New Rheocasting (NRC) process patented by Adachi et al.  and the


Staphylococcus, Pseudomonas, and Escherichia coli) favors intestinal translocations with Gram negative bacteria or their endotoxins and could trigger sepsis, septic shock, secondary peritonitis, or various intestinal infections. Intestinal infections also induce epithelial lesions and perpetuate the risk of bacterial translocation and dysbiosis through epithelial ischemia and pro-inflammatory cytokines. Furthermore, the decline of protective anaerobic bacteria (Bifidobacterium and Lactobacillus) and inadequate release of immune modulators (such as butyrate) affects the release of antimicrobial peptides, de-represses microbial virulence factors and alters the innate immune response. As a result, intestinal germs modulate liver pathology and represent a common etiology of infections in HIV immunosuppressed patients. Antibiotic and antiretroviral treatments also promote intestinal dysbiosis, followed by the selection of resistant germs which could later become a source of infections. The current article addresses the strong correlations between the intestinal barrier and the microbiota and discusses the role of dysbiosis in destabilizing the intestinal barrier and promoting infectious diseases.The ecosystem of the gut microbiota consists of diverse intestinal species with multiple metabolic and immunologic activities and it is closely connected with the intestinal epithelia and mucosal immune response, with which it builds a complex barrier against intestinal pathogenic bacteria. The microbiota ensures the integrity of the gut barrier through multiple mechanisms, either by releasing antibacterial molecules (bacteriocins) and anti-inflammatory short-chain fatty acids or by activating essential cell receptors for the immune response. Experimental studies have confirmed the role of the intestinal microbiota in the epigenetic modulation of the gut barrier through posttranslational histone modifications and regulatory mechanisms induced by epithelial miRNA in the epithelial lumen. Any quantitative or functional changes of the intestinal microbiota, referred to as dysbiosis, alter the immune response, decrease epithelial permeability and destabilize intestinal homeostasis. Consequently, the overgrowth of pathobionts ( The intestinal barrier defines the morpho-functional unit responsible for the defense of the intestinal mucosa and consists of the intestinal microbiota, intestinal epithelial cells (IECs) and mucosal immunity tightly linked through a complex network of cytokines, antimicrobial peptides (AMPs), metabolic products, and numerous regulatory molecules . Given tThe intestinal microbiota represents the first defense line of the intestinal barrier. The microbiota entails millions of microogranisms colonizing the gastrointestinal tract most of which are bacteria. This large number of microorganisms withstand the unfavorable intestinal habitat thanks to their symbiotic relationships with the human organism. These symbiotic host-commensal relationships develop after birth and enable the metabolic, immune and antiinfectious processes through which the microbiota contributes to gut homeostasis . The strThe article discusses gut barrier defense mechanisms, the key anti-infectious role of microbiota inside of the gut barrier and the impact of dysbiosis in the life-threatening infections.Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria  for pre-existent intestinal niches (“niche competition”) as well as for intestinal nutrients (“nutritive competition”). This competition ensures the structural stability of the microbiota and is referred to as “colonization resistance.” Colonization resistance employs a network of specific molecules with a critical anti-infectious roles  with a broad spectrum of activity  such as Nod-like receptors (NLRs), toll-like receptors (TLRs) and other PRR families located on the cell membrane or in the cytoplasm which recognize specific microbial-associated molecular patterns (MAMP). Once the activation of these receptors induces cytoplasmic signal transduction cascades and further promotes the NF-kappaB (NF-kB) pathway along with other cellular transcription factors, inflammasome activation and pro-inflammatory cytokines  . The IECShort chain fatty acids and GPR43/109A stimulation also protects epithelial integrity via inflammasome activation and epigenetic immunomodulation of FoxP3+ regulatory T cells (Treg) proliferation as shown below .Dysbiosis induced by infectious or non-infectious causes  favors the distruption of the intestinal epitelium . The injThe intestinal epithelium represents the largest epithelial surface in the human body. It connects with the intestinal ecosystem containing commensal and pathogenic species. Commensal germs need to be immunologically accepted, whereas intestinal pathogens must be eliminated. This differentiation requires the activation of an extremely well-coordinated and efficient immune system. Considering that intestinal commensals are present in the human body ever since birth they represent, in fact, the first contact between the immune system and the exterior environment. As such, this large and diverse microbiota will serve as one of the body’s defense mechanisms against the invasion of pathogenic germs. At the same time, the microbiota develops mutually beneficial relationships with the organism and together with the intestinal epithelium and gut-associated lymphoid tissue (GALT) the microbiota forms a complex intestinal barrier against infectious threats.These symbiotic relationships are facilitated by the presence of PRRs and by the immunomodulatory capacity of the intestinal microbiota against the antigenic structures exposed by the intestinal microbiota.The intestinal epithelium expresses numerous types of receptors able to recognize different MAMPs expressed by microbial species and to convert them into gut signals for inflammatory cascades . Thus TL+Th1 response in mice and human experiments (Antigen-presenting cells (APC) belonging to GALT such as macrophages and dendritic cells (DCs) also exhibit cell receptors which regulate numerous genes and modulate the release of NF-κB transcription factor, immunomodulatory cytokines and AMP. Depending on the type of activated receptor DCs may generate a Th1/Th17 pro-inflammatory response. Thus, to exemplify, the NLR2 induced by commensal bacteria appears to play a central role in the downregulation of the GALT inflammatory activity and the ability of DCs to induce a polarized CD4eriments .+Th2 phenotype in the periphery , promotion of Treg cells and of CD4eriphery . The inderiphery . ConsequE. coli-derived LPS activates inflammatory mechanisms  whereas the stimulation with LPS derived from Poprhyromonas gingivalis induces IL-4 production and a Th2 response , intra-epithelial lymphocytes, macrophages, DCs, mesenteric ganglia, secretory IgA (sIgA) cells and lymphatics.Peyer’s patches localized in the mucosa and submucosa of the small intestine are covered by a “follicle-associated epithelium” containing specialized “M cells”. These cells engulf and transport antigens from the intestinal lumen to intestinal DCs where T cell lymphocytes (LT) are primed . LT subs+DCs involved in Foxp3 expression and intestinal conversion of naïve CD4+T cells into Treg cells. Commensal flora stimulates CD103+ DCs through the activation of specific receptors or through its own metabolic products such as SCFAs  inducing histone acetylation, and histone deacetylases (HDACs) inducing histone deacetylation. During acetylation, the chromatin structure loosens and can be accessed by transcription factors. HDAC inhibitors increase histone acetylation and subsequently regulate gene expression in numerous immune cells such as epithelial cells, neutrophils, APCs and T cells. On the contrary, deacetylation of histones by HDACs prevents gene transcription. Thus studies performed or HSP90 . The NF-or HSP90 , septic or HSP90 , or inflor HSP90 . Likewisor HSP90 . P53-medor HSP90 , while Hor HSP90 . Severalor HSP90  while otor HSP90 . It has or HSP90 .By inhibiting HDAC, SCFAs could therefore control numerous infectious and immune processes . AlthougE. coli (MicroRNAs (miRNAs) are small, evolutionary conserved non-coding RNAs of approximately 19-23 nucleotides involved in the post-transcriptional regulation of cellular mRNAs. The biogenesis of mature miRNA includes a two-step cleavage process from primary miRNAs (pri-miRNA). The mature miRNA is then loaded into the effector complex RNA-induced complex (RISC). RISC interacts with target mRNA and induce mRNA cleavage or translational repression hence controlling diverse metabolic or cellular pathways including cell cycle progression, differentiation, apoptosis, immune regulation or oncogenesis . miRNAs E. coli  act as pE. coli  and viruE. coli . The rolIntestinal miRNAs are released by IECs  and are Helicobacter pylori, Citrobacter rodentium, Listeria monocytogenes, Francisella tularensis, and Salmonella enterica) induce a different miRNA panel mainly represented by miR-155 and miR-146  , epithelH. pylori or Salmonella Typhimurium  , 2014, whimurium .Pathogenic bacteria also release membrane or outer-membrane vesicles containing sRNAs that modulate the host miRNA profile and gene expression . Some stVarious miRNAs have been correlated with intestinal oncogenesis according to human studies. In this respect miR-182,-503 and miR-17∼92 clusters modulate glycan production and correlate with the growth of certain bacteria which could potentially initiate the microenvironmental changes in colorectal cancer . Gut miREnteric pathogens alter the intestinal barrier, antagonize the intestinal microbiota, and trigger enteral infections through various mechanisms including increased intestinal inflammation, the release of bacteriocins and upregulation of AMPs and toxin delivery secretory systems  as well as the exploitation of nutrients or intestinal niches  and allow the overgrowth of species belonging to the Proteobacteria phylum   . Antibioamilies) .Bacteroides thuringiensis and the Lachnospiraceae and Ruminococcaceae families  from a healthy donor. The effectiveness of FMT in the management of t health .E. coli, a common gut resident  accompanied by a detrimental increase of intestinal pathobionts such as E. coli or Staphylococcus and Pseudomonas species (E. coli and Pseudomonas aeruginosa) acquire additional virulence genes during colonization or even change their morphotype after translocation to express virulence genes and to avoid host defense mechanisms giving rise to gut-derived sepsis  decreases epithelial apoptosis as well as the release of cytokines and bacterial translocations in experimental mice models  and further releases its own antibacterial products  involved in the intestinal homeostasis  and the dramatic decline of commensals such as Roseburia, Bifidobacterium, and Lactobacillales (Clostridia species (the clusters XI and XIVab), the Bacteroidales-Prevotella group and butyrate-producing Roseburia genus , antibiotics (Rifaximine), gut-derived hormones and even FMT for the manipulation of the gut-liver axis  initially arise due to the imbalance of the epithelial barrier turnover . In times circle . Thus dyBacteroides in favor of Proteobacteria  and the significant reduction of symbionts throughout the entire intestinal tract  could reactivate both unintegrated HIV-1 genomes and latent HIV proviruses through HDAC inhibition along with other transcription factors and concurrently explains HIV immunosuppression during ART (Intestinal dysbiosis occurs early during HIV and is aggravated by ART itself . Hence, activity . Otherwiring ART . The rolring ART . Consequring ART .Cl. perfringens expressing HIV-1 Gag protein (In conclusion, HIV pathogenesis involves the loss of the immunomodulatory gastrointestinal activity, bacterial translocations and intestinal dysbiosis, independent of ART. Current therapeutic strategies are modest and based on few studies in humans or macaques: the administration of probiotics , anti-in protein .The microbiota, the intestinal epithelia and mucosal immunity form an anti-infectious barrier rigorously regulated through complex mechanisms. The host-microbiota-pathogen interactions employ numerous cell receptors and molecules with antibacterial and anti-inflammatory roles, which also modulate the epigenetic and immune response. Together with epithelial and immune cells, these signaling molecules form a network that is essential for intestinal homeostasis and anti-infectious defense. Dysbiosis renders these defense mechanisms non-functional. It consequently aggravates gastrointestinal infections, favors bacterial and LPS translocations in sepsis, unbalances the immune defense in hepatitis viruses and HIV replications and controls the progression of infectious diseases to an unknown extent. A better knowledge on the interactions driving the antimicrobial response of the intestinal barrier is therefore crucial to improve the current anti-infectious armamentarium.Both authors contributed equally to the acquisition, analysis, and critical revision of the manuscript, gave their consent for the publication, and agreed to be responsible for the accuracy and integrity of the manuscript.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.


In vitro, astragalin inhibited the expression of matrix metalloproteinases  dose-dependently in TNF-α-induced MH7A cells, with no apparent cytotoxicity. Furthermore, astragalin suppressed the phosphorylation of p38, JNK, and the activation of c-Jun/AP-1 in TNF-α-induced MH7A cells. In conclusion, it has proven that astragalin could attenuate synovial inflammation and joint destruction in RA at least partially by restraining the phosphorylation of MAPKs and the activating of c-Jun/AP-1. Therefore, astragalin can be a potential therapeutic agent for RA.Astragalin, as a bioactive flavonoid with anti-inflammatory, antioxidant, and protective properties, provides a potential agent for rheumatoid arthritis (RA). In this study, its therapeutic efficacy and the underlying mechanisms were explored using DBA/1J mice with collagen-induced arthritis (CIA). It was demonstrated that astragalin could significantly attenuate inflammation of CIA mice. The effects were associated with decreased severity of arthritis (based on the arthritis index), joint swelling and reduced bone erosion and destruction. Furthermore, astragalin treatment suppressed the production of pro-inflammatory cytokines , and inhibited the expression of matrix metalloproteinases  in chondrocytes and synovial cells of CIA mice. Fibroblast-like synoviocytes derived from RA patients (MH7A cells) were applied to verify these effects.  Rheumatoid arthritis (RA) is a chronic autoimmune joint disease, which is characterized by inflammation of synovial tissue and the destruction of bone and cartilage in multiple peripheral joints . RA is eAstragalin, also known as kaempferol-3-O-glucoside, is a newly found flavonoid from leaves of persimmon and green tea seeds and has been used for treating many diseases for a long time . Severalin vivo and its underlying mechanisms in MH7A RA-derived FLSs in vitro.In this study, we investigated the role of astragalin in the regulation of synovial inflammation and joint destruction in a collagen-induced arthritis (CIA) mouse model ® 488) ; Actinomycin D (cat. # A1410), 4′,6-diamidino-2-phenylindole (cat. #D9564) from Sigma ; cell culture medium, fetal bovine serum and trypsin from Gibco .Astragalin  were provided by the Vital River company . Ten of these mice were assigned to the negative control group and thirty to the experimental group. This study was approved by the Medical Ethics Committee of Shanghai University of Traditional Chinese Medicine. The methods applied in this study were carried out in accordance with the approved guidelines and regulations.Collagen-induced arthritis model was established according to a previous protocol . BrieflyDBA/1J mice were randomly divided into four groups (10 mice/group). Group 1 was used the non-immunized mice (Control), whereas mice in group 2–4 were used the CIA mice. Group 2: mice treated with PBS, 0.2 ml/day/intraperitoneally (CIA-Veh); Group 3: mice treated with MTX, 0.1 mg/kg/3 day/intraperitoneally (CIA-MTX); Group 4: mice treated with astragalin, 5 mg/kg/day/intragastrically (CIA-Ast). All the mice from these groups received additional treatments between day 22 and day 50. The time diagram of the process of CIA induction and treatment is shown in Collagen-induced arthritis was considered to have successfully developed when swelling was observed in at least one digit or paw. The global assessment, arthritis index, swollen joints count, and hind paw thickness were scored and recorded every 5 days in a blinded manner as reported before. The severity of arthritis in each of the four paws was scored with a 0–4 scale by visual evaluation of each paw as follows: 0, no evidence of erythema and swelling; 1, erythema and mild swelling confined to the tarsals or ankle joint; 2, erythema and mild swelling extending from the ankle to the tarsals; 3, erythema and moderate swelling extending from ankle to metatarsal joints; 4, erythema and severe swelling encompass the ankle, foot, and digits, or ankylosis of the limb. The final score for each mouse was the sum of the four paws. Thickness of the ankle was measured with digital calipers placed across the ankle joint at the widest point.After 7 weeks of treatment, the knee and ankle joints of these mice were examined using the Vevo 2100 imaging system . Both B mode and color Doppler were scanned with the 550 scan head, 40–50 MHz probes, wall filter = 3 mm/s, scan speed = 2 mm/s, dynamic range = 65.0 dB, the pulses to radiofrequency cycle number = 2, the pulse repetition frequency = 6 kHz, after the 2-dimensional (2D) images were obtained in real time, the images were analyzed and measurements manually determined and calculated using the Vevo LAB software studio measurement package. The Vevo LAB software was then used to construct the scans into a 3-dimensional (3D) image, which allowed for accurate volume measurement and image sculpting creating a visual representation of the knee and ankle joints.On day 50, mice were sacrificed, the left knee and ankle joint tissues were collected and fixed in 4% paraformaldehyde, then decalcified in 10% EDTA for 20 days. Thereafter, the tissues were embedded in paraffin and sectioned using routine methods, and stained with hematoxylin and eosin (H&E). The joint sections were measured using a scale of 0–3 for grading the synovial inflammation, pannus formation, and destruction of bone and cartilage.On day 50, the mice were sacrificed, and their right legs were excised and fixed in 4% formalin for 1 day. A micro-CT (SCANCO μCT80) was used to estimate the structural status in the right knee and ankle joints. The X-ray tube voltage was 55 kV and, the current was 72 μA, with a voxel size of 10 μm. The segmented images were 3D-reconstructed using the SCANCO proprietary software. Joint bone radiological destruction was scored on a scale from 0 to 3: 0, no damage; 1, minor; 2, moderate; 3, severe.2O2) for 10 min. Next, the tissues were incubated with primary antibodies against MMP-1, MMP-3, MMP-13 at 4°C overnight. After washing the tissues with PBS for three times, secondary antibodies were added and incubated for another 1 h; then the signal was amplified with HRP-conjugated streptavidin using a Vectastain Elite ABC kit .The above-described paraffin-embedded knee and ankle joint tissues were deparaffinized with ethanol and xylene. After being hydrated in PBS, tissues were blocked with 0.3% hydrogen peroxide  was added to each well of the plate and incubated at 37°C for 1.5 h. The resulting optical density was detected at 450 nm by a microplate reader.Cell viability was determined using the CCK-8 assay as described previously . BrieflyΔΔCt method. The primer sequences (forward and reverse) were as follows: for MMP-1, 5′-CTCAATTTCACTTCTGTTTTCTG-3′ and 5′-CATCTCTGTCGGCAAATTCGT-3′; for MMP-3, 5′-GGCTTCAGTACCTTCCCAGG-3′ and 5′-GCAGCAACCAGGAATAGGTT-3′; for MMP-13, 5′-CAAGATGCGGGGTTCCTGAT-3′, 5′-AATGCCATCGTGAAGTCTGGT-3′.MH7A cells were pretreated with astragalin  for 2 h and then incubated for another 24 h in the presence or absence of TNF-α (10 ng/mL). Total RNA was extracted using Trizol reagent according to the manufacturer’s instructions and each sample was reverse transcribed using the cDNA synthesis kit according to the manufacturer’s protocol. Real-time PCR analysis was performed using SYBR Green PCR Premix Ex Taq II reagents on a CFX96 real-time system . Relative gene expression was calculated by the ∗106 cells/well) for 24 h, then pretreated with astragalin  for 2 h, then incubated for another 24 h in the presence or absence of TNF-α (10 ng/ml). Cell culture supernatants were collected and stored at -80°C until analysis. The concentrations of the cytokines in serum and culture supernatants were determined by ELISA using a commercial kit according to the manufacturer’s instructions.On day 50, the mice were sacrificed, serum samples were extracted from peripheral blood and stored at -80°C until analysis. MH7A cells were seeded into 6-well plates , and then exposure to TNF-α (10 ng/ml) for 0.5 h. The protein was collected, 30 μg of protein from each sample was separated by 15% SDS-PAGE and transferred to polyvinylidene fluoride membrane. After blocking with 5% bovine serum albumin in TBST at room temperature for 2 h, the membranes were incubated with the corresponding primary antibodies overnight at 4°C. After washing with TBST for three times, the membranes were incubated with the secondary antibodies. Proteins were scanned using the ECL detection system. The gray values of protein bands were analyzed using ImageJ software.MH7A cells were seeded on a round coverslips in 24-well plates for 24 h, and then pretreated with astragalin (200 μM) for 2 h, stimulated with TNF-α for 0.5 h, fixed with 4% paraformaldehyde for 15 min and then permeated with 0.2% Triton X for 20 min. After blocking with 5% bovine serum albumin at room temperature for 0.5 h, cells were incubated with the anti-phospho-c-Jun antibody overnight at 4°C and then incubated with the goat anti-rabbit IgG H&L secondary antibody for 1 h at room temperature. The nuclei were visualized using 4′,6-diamidino-2-phenylindole (DAP). The coverslips were mounted onto glass slides and images were recorded by an Olympus BX-51 microscope.t-test were used to determine differences between groups. As to three groups treated at different times points, two-way ANOVA comparison was performed. p < 0.05 was considered statistically significant.The data were reported as mean ± standard error of the mean (SEM). Significant differences were assessed using GraphPad Prism 7.0 software . One-way ANOVA followed by Dunnett’s in vivo, we established CIA mouse model. Treating mice with astragalin initiated at 22 days after the first immunization. Assessment of the severity of the arthritis was observed every 5 days after the booster injection. Compared with the negative control group, the CIA model mice showed a slight body weight loss  in combination with the Doppler technique has already been used to quantify synovial inflammation by measuring the increased joint space volume and blood flow in CIA mice . The volA histopathological evaluation of the knee and ankle joints was performed to examine the degrees of arthritic damage. Severe synovial hyperplasia, infiltration of inflammatory cells into synovial tissues, pannus formation, and cartilage and bone destruction were observed in the CIA mice. As expected, MTX significantly attenuated the pathological symptom of CIA in the knee and ankle joints. Consistently, astragalin also alleviated the histopathological arthritic damage in the CIA joints . The 3D Various cytokines regulate the pathogenesis of RA. The production of pro-inflammatory cytokines, particularly TNF-α, IL-1β, IL-6, and IL-8, may cause synovial inflammation and pain. MMP-1, MMP-3, and MMP-13 are essential factors for the degradation of joint cartilages. In the present experiment, the production of pro-inflammatory cytokines in the serum of CIA mice was determined using the ELISA kit, and the expression of MMPs in knee and ankle joint tissues were measured by immune-histochemical staining. As shown in in vitro increases MMPs production. To assess the potential cytotoxicity of astragalin, cell viability was evaluated by the CCK-8 assay. As shown in MMPs are mainly secreted by FLSs, which play a critical role in the destruction of joint cartilage . StimulaIn addition, the expression of MMPs is affected by transcriptional regulation and mRNA stability. To clarify the effect of astragalin on the mRNA stability of MMPs, we performed mRNA stability assays. MH7A cells were pretreated with astragalin (200 μM) or not for 2 h, then stimulated with TNF-α for 6 h. Actinomycin D (4 μg/mL) was added to the cells and then mRNA was isolated at time point 0, 2, 4, 6, and 8 h. The levels of MMPs mRNA stability were detected by RT-PCR. We found that astragalin did not alter the mRNA stability of MMPs –G.Previous studies have shown that the MAPK and AP-1 mediated pathways can regulate the expression of MMPs synthesis and inflammatory cytokines . To furtAstragalin is an extract separated from leaves of persimmon and green tea seeds, which possess potent biological effects including anti-inflammatory, antioxidant and anticarcinogenic activities . It showin vitro and in vivo experiments. In the present study, we found that CIA mice treated with astragalin had lower histopathology scores, inflammation, synovial hyperplasia, articular cartilage, and bone destruction. At the same time, astragalin significantly down-regulated the expression levels of MMP-1, MMP-3, and MMP-13 in cartilages and synovial tissues in CIA mice. Consistent with our in vivo findings, astragalin also profoundly inhibited TNFα-induced production of MMP-1, MMP-3, and MMP-13 in cultured MH7A cells. Taken together, these findings suggested that the joint-protective properties of astragalin may be mainly attributed to the down-regulation of MMPs expression.Destruction in articular cartilage and bone hallmarks the presence of RA . Osteoclin vitro increases MMPs production via transcriptional activation (Stimulation of RA-FLSs with TNF-α or IL-1 tivation . AP-1 bitivation . c-Jun ativation . Recent tivation , thus vaAP-1 belongs to the class of basic leucine zipper transcription factors. Its activity can be markedly increased in synoviocytes as a response to the stimulation of cytokines, such as IL-1β and TNF-α, through MAPK, AKT, and other signaling pathways . ActivatThe MAPK pathway is central to many host responses and is one of the major signaling pathway-transmitting signals to immediate early genes implicated in the regulation of cytokine responses . RegulatIn summary, our study demonstrated that astragalin markedly ameliorated synovial inflammation and joint destruction in CIA mice. The joint-protective properties of astragalin could be mainly attributed to the down-regulation of MMPs expression by inhibiting the JNK/p38/AP-1 pathways. Taken together, astragalin may serve as an effective therapeutic drug for RA.QL, QS, and YW conceived and designed the experiments. QJ and TW performed the research. HX analyzed the result. XW and YL wrote the manuscript draft. All authors have read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


GreenPhylDB is available since 2007, after the release of the Arabidopsis thaliana and Oryza sativa genomes and has undergone multiple releases. With the number of plant genomes currently available, it becomes challenging to select a single reference for comparative genomics studies but there is still a lack of databases taking advantage several genomes by species for orthology detection. GreenPhylDBv5 introduces the concept of comparative pangenomics by harnessing multiple genome sequences by species. We created 19 pangenes and processed them with other species still relying on one genome. In total, 46 plant species were considered to build gene families and predict their homologous relationships through phylogenetic-based analyses. In addition, since the previous publication, we rejuvenated the website and included a new set of original tools including protein-domain combination, tree topologies searches and a section for users to store their own results in order to support community curation efforts.Comparative genomics is the analysis of genomic relationships among different species and serves as a significant base for evolutionary and functional genomic studies. GreenPhylDB ( Plant comparative genomics resources usually compare reference genomes to compute homology sequences and enable functional annotation transfer ,2,3,4,5.Until now, comparative genomics databases have not fully taken advantage of these new datasets. Here, we present an updated version of the GreenPhylDB, a database that features multiple genomes for 19 species  as well as 27 other species with single reference genomes, for a total of 46 genomes. Publicly available genomes were processed to generate representative pangenes (i.e. a set of representative or consensus sequences) for species that were used in multi-species sequence clustering. Resulting gene families were functionally annotated and analysed with orthology detection methods.We retrieved 132 publicly available datasets (coding DNA sequence and protein-coding genes) for 46  and asseBrachypodium distachyon dataset , protein sequences were aligned using MAFFT v7.313  paramet. For eacFor multi-copy clusters (multiple sequences per genome), we applied the same procedure as for single-gene copy clusters but added a preliminary step to select a representative sequence by cluster. Multiple sequence alignments were used to generate a distance matrix using distmat (Jukes-Cantor correction method) from EMBOSS v6.6 Figure . The matFor genotype-specific clusters , we generated a distance matrix between all sequences and the sequence with the lowest average distance (min(d/sum(d))) of all sequences was putatively considered as the most representative sequence. Those sequences were added to the pangene.e-value on the protein-coding genes of all other species genomes to predict their putative prediction accuracy. Sequences with a minimum of one hit in at least two species were added to the pangene; otherwise sequences were excluded.Finally, singletons (cluster of one sequence) were searched for similarity using DIAMOND  with a dMusa acuminata).As a unique identifier was required for each pangene, we defined a nomenclature with a prefix composed of the [5-letter UniProt taxonomy database code]_pan, followed by p (for protein) and an auto-increment of 6-digits , defining 4 levels of stringency (from 1 to 4) to take into account potential sub-classification and we obtained 9419, 18 805, 23 409 and 29 345 clusters, respectively.Pangenes and protein-coding genes of reference genomes (without pangenes) were searched all against all using DIAMOND. We then performed a clustering using TribeMCL   which enabled us to predict ∼17.8 million of orthologs and ∼1.8 million of in-paralogs  relationships. The pipeline was complemented by a Reciprocal Best Hits (RBH) method—computed between all pairs of genomes—that resulted in more than ∼12.1 million orthologous relationships.The previous phylogenetic-based methodology that we applied in the previous version has been conserved but uses a larger set of genomes to update our automated pipeline. The pipeline uses MAFFT for the multiple alignment step. FastTree 2 (v2.1.11)  was prefWith this updated version, the website has received a face-lift. It now takes advantage of the bootstrap and D3.js frameworks to improve the user experience and to be more responsive. Alternatively, it can also be accessed programmatically using Resource Description Framework (RDF) as implemented in AgroLD, a knowledge-based system relying on semantic web technologies .All cluster (or gene family) pages present the same type of information divided into several tabs :Gene family composition: a bar chart allows users to visualize at a glance the composition of the gene family by species  and PhyD3 . It is possible to filter and select only a subset of species of interest.The new page type for pangene sequences is a central and unique concept in this version as they were used for the clustering and homology predictions instead of all individual sequences that compose it. When browsing these pages, users can quickly see which genes are present or missing by looking at the status: core or dispensable compartments. Then, information related to the sequence composition is reported. Users can access information about pangene classification, the consensus sequence (except for singletons) with the multiple sequence alignments used to create it as well as related homology predictions Figure . In the The database can still be searched via keyword searches or by entering a query sequence for similarity search using DIAMOND (which replaces BLAST for faster processing), enhanced by new tools to further explore those datasets.A new interface has been designed to retrieve in a comprehensive and concise way all the information associated with gene family names, sequence annotation and annotations from InterPro and UniProt mappings.It is now possible to search sequences and associated clusters based on a combination of InterPro domain signatures. This can be particularly helpful when searching for transcription factors for which sequences must contain some domains but not others  as the MA tree search can be done by filtering on gene tree topologies . Users cWhile automatic clustering is a relevant and efficient starting point, sometimes limitations  are present and prevent access to ready-to-use datasets, justifying a deeper characterization that will eventually lead to a refinement of the automatic clustering. As a result, knowledge generated on individual gene families is often available only in publications and their supplementary information as PDFs. To encourage knowledge capture, we developed a section for advanced users to create and share their own gene families. Two methods are possible: users can either start from scratch and upload their data or use existing clusters and take advantage of multiples operations implemented in the ‘MyList’ features: such feature was indeed developed to facilitate intersecting, combining clusters. This new tool can be valuable during the review process by providing a unique identifier to referees—and eventually to users—to explore the structure and composition of the submitted gene family.In this section, we describe three possible uses that are enabled by this new GreenPhylDB version. Concrete examples related to each of them are further documented in Supplementary Data.You want to analyse a gene family with focus on a specific species sampling.search the gene family by keyword(s), locus gene ids or sequences using dedicated search families (toolbox menu).browse the family structure and explore sub clusters at level 2, 3 or 4.browse the family composition of the cluster (or a specific sub cluster) to list all the sequences and pangenes selecting one or several bars in the diagram. Export the selected sequences of interest using proposed file formats. if the gene family sequences are characterized by several protein domains, check possible additional sequences in the database using IPR2genomes (toolbox menu). For individual protein domains, its specificity is indicated in each gene family page . In case of additional analyses (e.g. addition of sequences from a new sequences genomes), you can create a ‘custom family’ by uploading the sequences on the website and share the link in a manuscript, with collaborators or reviewers during the review process for a user-friendly exploration of the dataset.You are interested in finding genes that are linked to a specific evolutionary scenario (e.g. duplicated genes in one species but not in another)retrieve full list of gene trees and related gene families using TreePattern (toolbox menu).browse examples gene families to see patterns (highlighted with dashed lines in the tree).click on the sequence name to access the family.browse the family composition of the cluster (or a specific sub cluster) to list all the sequences and pangenes selecting one or several bars in the diagram. Export the selected sequences of interest using proposed file formatsArabidopsis).You have a candidate gene in rice, maize or banana (or any of the 19 pangenes) and want to retrieve the related sequences in other genomes of the same species and then find orthologs in other species . check the multiple gene alignment to see level of divergence.go to the gene family and explore (or download) the gene tree.retrieve predicted orthologs . Alternatively, use the homologous sequence search directly (toolbox menu).This new version of GreenPhylDB provides a unique way to scale up plant comparative genomics studies across multiple plants species by leveraging pangenomic datasets. This release paves the way to the transition from reference-based genomics to pangenome-based systems and tools. In this context, the website includes new powerful search interfaces to explore the content of the gene family collection. Advanced users can also deposit the results of their expert gene family curation for further use and reference. GreenPhylDB is an important resource to understand the genetic basis of genome diversity among plant species and has the potential to accelerate gene discovery to support crop improvement.https://www.greenphyl.org/cgi-bin/downloads.cgi.All datasets produced by our automatic analyses are accessible via GreenPhylDB user interfaces or can be downloaded at gkaa1068_Supplemental_FileClick here for additional data file.


There is a need for a quick assessment of severely ill patients presenting to the hospital. The objectives of this study were to identify clinical, laboratory and imaging parameters that could differentiate between influenza and COVID-19 and to assess the frequency and impact of early bacterial co-infection. A prospective observational cohort study was performed between February 2019 and April 2020. A retrospective cohort was studied early in the COVID-19 pandemic. Patients suspected of sepsis with PCR-confirmed influenza or SARS-CoV-2 were included. A multivariable logistic regression model was built to differentiate COVID-19 from influenza. In total, 103 patients tested positive for influenza and 110 patients for SARS-CoV-2, respectively. Hypertension (OR 6.550), both unilateral (OR 4.764) and bilateral (OR 7.916), chest X-ray abnormalities, lower temperature (OR 0.535), lower absolute leukocyte count (OR 0.857), lower AST levels (OR 0.946), higher LDH (OR 1.008), higher ALT (OR 1.044) and higher ferritin (OR 1.001) were predictive of COVID-19. Early bacterial co-infection was more frequent in patients with influenza (10.7% vs. 2.7%). Empiric antibiotic usage was high (76.7% vs. 84.5%). Several factors determined at presentation to the hospital can differentiate between influenza and COVID-19. In the future, this could help in triage, diagnosis and early management. Clinicaltrial.gov Identifier: NCT03841162 Seasonal influenza, caused by the influenza virus, causes 4–50 million symptomatic cases in EU/EEA and approximately 500,000 cases in Belgium per year , 2. MortThe coronavirus disease 2019 (COVID-19) pandemic, caused by the novel coronavirus SARS-CoV-2, resulted in 9,063,320 confirmed cases in the EU/EEA until 8th November 2020. Currently, 1,250,275 patients have died in the EU/EEA . On the The severity of both respiratory virus infections is partly dependent on host factors. The elderly and those with the underlying disease have higher chances for a severe disease course and are more frequently hospitalized , 8. SeveThe primary objective of this study is to compare risk factors and identify clinical, laboratory and imaging characteristics that can differentiate between influenza and COVID-19 at clinical presentation with sepsis and that could aid in the early triage and management of these patients. The secondary objective is to assess the frequency of early bacterial co-infection and its impact on the prognosis of patients with influenza and COVID-19.clinicaltrial.gov identifier NCT03841162). Adult patients presenting at the ED with suspected sepsis, defined as patients with symptoms of systemic infection for whom blood cultures were drawn, were asked to participate in the study. Patients who tested positive for influenza by PCR were included in this analysis.A prospective observational cohort study, as part of the Fast Assay for Pathogen Identification and Characterization (FAPIC) project, was performed between February 2019 and April 2020 at Jessa Hospital, Hasselt  were included for this analysis.In Jessa Hospital, for all patients presenting at the ED with suspected sepsis, blood cultures are performed to exclude bacteraemia. During the yearly influenza season (December–April), nasopharyngeal swabs for the detection of influenza were taken based on clinical suspicion. Since the emergence of COVID-19, SARS-Cov-2 PCR was performed according to the national case definition which was based on the WHO case definition guidance –16. AfteWritten informed consent was obtained from all participants with influenza. No informed consent was needed for the retrospective cohort study of COVID-19 patients, according to Belgian legislation. Documented approval for both studies was obtained from the Ethics committees of Hasselt University and Jessa Hospital.Nasopharyngeal swabs for influenza detection were analysed with real-time PCR, either with the ARIES® Flu A/B & RSV assay on the ARIES instrument (Luminex Corporation) or with an in-house multiplex PCR on Quantstudie 7 flex (ThermoFisher) for the simultaneous detection of 23 respiratory pathogens. If available, lower respiratory tract samples, such as sputum, bronchial aspirates or broncho-alveolar lavage fluid, were preferred.E-gene on the ARIES analyser (Luminex Corporation). In patients with a clinical suspicion based on history, laboratory and/or chest X-ray results, and a negative initial PCR test, a second nasopharyngeal swab or, if possible, a lower respiratory tract sample such as sputum, bronchial aspirates or broncho-alveolar lavage fluid, were collected after 24–48 h ). At a cutoff value of 0.49, the sensitivity was 83.5% and the specificity was 81.6%. The PPV and NPV of the logistic regression model were 82.6% and 82.5% respectively.In Table p = .011). Urinary antigen test for Streptococcus pneumoniae and Legionella pneumophila was performed in 59 patients with COVID-19 (53.6%) and in 81 patients with influenza (78.6%). Legionella pneumophila antigen was negative in all patients. Streptococcus pneumoniae antigen was not detected in any patient with COVID-19 but was positive in six patients with influenza. In Table p = .147).In Table This study shows the clinical, laboratory and imaging differences between patients with influenza and patients with COVID-19 who were suspected of sepsis at clinical presentation to the hospital. More patients with COVID-19 had cardiac comorbidities and hypertension than patients with influenza. Overall, COVID-19 was associated with worse clinical outcomes, a higher risk of ICU admissions, longer hospital stay, higher mortality and more discharge to a rehabilitation centre. Furthermore, abnormalities on chest X-ray were more often found in patients with COVID-19, compared to patients with influenza. Early bacterial co-infections were diagnosed in less than 10% of patients, although more frequently in patients with influenza compared to patients with COVID-19. Empiric antibiotics were prescribed to three-fourths of all patients.Cardiac comorbidities and hypertension were reported as risk factors for mortality in patients with COVID-19  while thUnivariate analyses in our study confirm the findings in the Chinese ARDS patients study, showing higher body temperature in patients with influenza and higher levels of CRP, LDH and AST in patients with COVID-19 .The finding that patients with COVID-19 with abnormalities on chest X-ray had more often bilateral abnormalities is in accordance with previous studies [In the best-fitted multivariable logistic regression model, the presence of hypertension, unilateral and bilateral chest X-ray abnormalities, lower body temperature, absolute leukocyte count and AST, and higher LDH, ALT and ferritin were able to predict COVID-19 and differentiate the infection from influenza. The model could very accurately predict COVID-19 with a sensitivity of 83.5% and a specificity of 81.6%. Luo et al. described a diagnostic model combining 18 routine biochemical tests. The model could distinguish between influenza and COVID-19 with an accuracy of 69.6% . The higLastly, our finding that bacterial co-infections are less frequent in patients with COVID-19 is supported by a meta-analysis performed by Lansbury et al. . The preOne of the strengths of this analysis is the availability of two large datasets. Both patient groups were similarly defined, and this resulted in a high number of severely ill patients included. The prospective data collection resulted in data of high quality and both cohorts had a low number of missing values. This study has some limitations. Patients with influenza were included during two consecutive seasons, while the patients with COVID-19 were included in 1 month. Second, we did not collect data on all features and risk factors that are known to be associated with COVID-19 such as obesity and coagulation abnormalities. Early in the pandemic, coagulation tests were not routinely performed in the absence of symptoms suggesting thrombotic events. Obesity is a risk factor for both severe influenza and severe COVID-19 , 24. ThiIn conclusion, differences in clinical, laboratory and imaging characteristics between patients with influenza and patients with COVID-19 presenting with suspected sepsis were identified. Early bacterial co-infection in COVID-19 seems to be rare. Empirical antibiotics should be reserved for patients in whom a bacterial infection is suspected. A multivariable logistic regression model was used to define factors that can differentiate between influenza and COVID-19 in this population. In the future, these early features could help in rapidly triaging patients during the influenza season. Future studies should validate our results in larger cohorts.


While several studies have described the clinical course of patients with coronavirus disease 2019 (COVID-19), direct comparisons with patients with seasonal influenza are scarce. We compared 166 patients with COVID-19 diagnosed between February 27 and June 14, 2020, and 255 patients with seasonal influenza diagnosed during the 2017–18 season at the same hospital to describe common features and differences in clinical characteristics and course of disease. Patients with COVID-19 were younger  and had fewer comorbidities at baseline with a lower mean overall age-adjusted Charlson Comorbidity Index  than patients with seasonal influenza. COVID-19 patients had a longer duration of hospitalization , a more frequent need for oxygen therapy  and invasive ventilation  and were more frequently admitted to the intensive care unit  than seasonal influenza patients. Among immunocompromised patients, those in the COVID-19 group had a higher hospital mortality compared to those in the seasonal influenza group . In conclusion, we show that COVID-19 patients were younger and had fewer baseline comorbidities than seasonal influenza patients but were at increased risk for severe illness. The high mortality observed in immunocompromised COVID-19 patients emphasizes the importance of protecting these patient groups from SARS-CoV-2 infection. While the strain that COVID-19 places on healthcare systems, economies, and societies worldwide is unprecedented, comparisons have often been drawn with the 1918 influenza pandemic4. Also, the pandemic preparedness plans currently used in many countries are largely based on the experience of several influenza pandemics during the last decades5. Thus, a detailed understanding of common features and differences of patients with SARS-CoV-2 and influenza virus infections in the hospital setting can help to plan resources in this ongoing outbreak. COVID-19 and seasonal influenza are viral respiratory infections that are primarily transmitted from person-to-person via respiratory droplets or aerosols among close contacts8. The clinical presentation of both infections is highly variable and ranges from only mildly symptomatic cases to acute respiratory distress syndrome (ARDS) and death9. However, there remains substantial uncertainty regarding the differences between COVID-19 and seasonal influenza with respect to high-risk-populations, clinical course, and case-fatality rates. Most previous studies describing the clinical course of COVID-19 lack a control group13, and data comparing patients with SARS-CoV-2 infection to patients with other viral respiratory infections such as seasonal influenza in the hospital setting are limited19. This single-center observational study aimed to systematically compare demographics, clinical characteristics, and disease outcomes of two non-selected cohorts of consecutive patients with SARS-CoV-2 infection and influenza virus infection, respectively, who were all treated at the University Medical Center Hamburg-Eppendorf.On March 11, 2020, the WHO declared the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a pandemici.all adult patients with SARS-CoV-2 infection confirmed by RT-PCR between February 27 and June 14, 2020, treated at the University Medical Center Hamburg-Eppendorf.ii.21.all adult patients with seasonal influenza virus infections confirmed by RT-PCR treated at the University Medical Center Hamburg-Eppendorf between December 25, 2017, and April 8, 2018, which is the definition of the influenza season by the Robert Koch Institute, Germany's national public health institute22.The study was reviewed and approved by the Ethics Committee of the Medical Council of Hamburg (WF-017/18 and WF-052/20). Informed consent was waived by the same ethics committee since only anonymous data were analyzed and published. All experiments were performed in accordance with relevant guidelines and regulations. We did not include psychiatric patients and patients younger than 18 years in the study. Also, employees who were diagnosed with COVID-19 by our institution’s screening algorithm but did not require hospitalization were not included in the studyWe conducted a retrospective observational study at the University Medical Center Hamburg-Eppendorf to compare two patient cohorts:23 throughout the influenza season. After the first case of COVID-19 was confirmed in Germany in January 202024, all symptomatic patients were additionally screened for SARS-CoV-2 infection by RT-PCR using either a laboratory developed test for the NeuMoDx 96 system 25, a Cobas6800 IVD , a GeneXpert Xpress System  or a Cobas6800-based UCT 26. Only patients with influenza or SARS-CoV-2 infection confirmed by RT-PCR were included in this analysis.According to our hospital standards, all patients admitted with fever or respiratory symptoms were screened for influenza infection by RT-PCR using either the GeneXpert Xpress System  or a laboratory-developed assay21. For the current study, COVID-19 and influenza patients were stratified according to whether they were treated as outpatients in our emergency department, were admitted to regular wards of our hospital, or had to be admitted to the intensive care unit (ICU). The age-adjusted Charlson Comorbidity Index (ACCI) was applied to assess comorbidities28. Patients were classified as immunocompromised if they were allogeneic stem cell transplant recipients, had a hematopoietic malignancy known to cause immune dysfunction or if they received systemic immunosuppressive therapy, chemotherapy, or immunomodulatory agents .Antiviral treatment was defined as administration of a specific antiviral agent against influenza and COVID-19, respectively. Patients who participated in placebo-controlled, double-blind treatment studies were assigned to the treatment group for this analysis. Immunotherapy was defined as administration of convalescent plasma, the anti-IL-6 antibody tocilizumab, or the anti-adrenomedullin antibody adrecizumabContinuous variables were expressed as mean and standard deviation (SD) or median and interquartile range (IQR) and compared with Student’s t-test. Categorical variables were expressed as number (%) and compared by Fisher's exact test. A log-rank test was used to compare the proportion of patients discharged alive during the first 90 days of hospitalization. P values less than 0.05 were considered statistically significant. Figures were designed using GraphPad Prism version 8 for macOS . All other analyses were performed using SPSS, version 21.0 .21. The baseline characteristics of both groups are listed in Table Between February 27 and June 14, 2020, a total of 166 patients with SARS-CoV-2 infection were treated at the University Medical Center Hamburg-Eppendorf. During the 2017–18 influenza season, 255 patients with laboratory-confirmed seasonal influenza were treated at our centerAmong COVID-19 patients, the proportion of men was higher than in influenza patients . Patients with SARS-CoV-2 infection were younger  and had a lower overall ACCI  than patients with seasonal influenza  and renal replacement therapy  than influenza patients. The rate of patients receiving extracorporeal membrane oxygenation (ECMO) did not significantly differ between the two groups. A total of 27 (16.3%) patients with COVID-19 and 62 (24.3%) patients with seasonal influenza received specific antiviral therapy (P = 0.05). In addition, 15 (9.0%) patients in the COVID-19 group received immunotherapy with convalescent plasma, tocilizumab, or adrecizumab. There was no statistically significant difference between the two groups in the rate of patients receiving antibiotic therapy during the hospitalization  , bacterial  and fungal  co-infections was similar in both groups and also the overall spectrum of pathogens did not significantly differ between COVID-19 patients and influenza patients . Among those patients admitted to the ICU, no differences in mortality were observed between the two groups . Among all immunocompromised patients, those in the COVID-19 group had a higher mortality compared to those in the seasonal influenza group . Among patients with SARS-CoV-2 infection, mortality was significantly increased in immunocompromised compared to immunocompetent individuals . In contrast, no significant difference in mortality was observed between immunocompromised and immunocompetent patients with seasonal influenza .Notably, all five allogeneic stem cell transplant recipients in the COVID-19 group and one in the seasonal influenza group (14.3%) died  and 176 were infected with influenza B virus (IBV). While the hospital mortality did not differ between patients with IAV and IBV infections , the rate of patients requiring mechanical ventilation was significantly higher in patients with IAV virus infections  compared to patients with IBV infections .We further compared demographic information, comorbidities, and immune status of patients treated as outpatients, those admitted to regular wards and those admitted to the ICU between the COVID-19 group and the seasonal influenza group (see Supplementary Table In this current study, we directly compared a cohort of 166 consecutive patients with COVID-19 with all 255 patients diagnosed with seasonal influenza during the 2017–18 season treated at the University Medical Center Hamburg-Eppendorf. This comparative approach allowed us to identify common features and differences between those two respiratory infections in the identical hospital setting and provide indications of the strain the COVID-19 pandemic places on hospital resources and capacities.32. Even though currently available data indicate similar overall COVID-19 case numbers in men and women33, male sex has been linked to a generally more severe course of disease with more frequent need for hospitalization and an excess in case fatality rates35. While the detailed mechanisms underlying those observed sex-differences remain to be established, our study contributes to the growing body of evidence suggesting that male sex is a major risk factor for severe COVID-1918.An important finding to emerge from this comparison is that the majority of COVID-19 and influenza patients were men and that this male predominance was even more pronounced in COVID-19 patients. These observations are consistent with previous studies that have demonstrated a higher overall incidence of seasonal influenza and higher disease severity in male compared to female patients37 and with a recent cohort study analyzing electronic healthcare databases of the US Department of Veterans Affairs that observed a mean age of 69 years19. However, our observation that patients with SARS-CoV-2 infection are generally younger than seasonal influenza patients is consistent with previously published data18. Interestingly, among patients that were admitted to the ICU, age did not significantly differ between the two groups. Also, patients requiring ICU admission were significantly older than those treated on regular wards or as outpatients in the COVID-19 group but not in the seasonal influenza group. These findings suggest that age may potentially be a more important driver of disease severity for COVID-19 than for seasonal influenza37.Another remarkable observation is that patients with SARS-CoV-2 infection were significantly younger than those with seasonal influenza. The median age of COVID-19 patients in our study was lower than in recent multi-center cohort studies of patients hospitalized with SARS-CoV-2 infection in Germany that observed a mean age of 68–70 years18. In our study, COVID-19 patients had a significantly lower ACCI than influenza patients, reflecting a lower overall prevalence of comorbidities. Interestingly, among patients admitted to the ICU, mean ACCI did not differ between the two groups, suggesting that comorbidities may be stronger determinants of disease severity in COVID-19 patients than in seasonal influenza patients. This is further supported by the fact that in the seasonal influenza group, patients admitted to the ICU even had a lower mean ACCI than seasonal influenza patients treated on regular wards. These results may imply that COVID-19 more frequently causes severe disease requiring hospitalization in relatively healthy individuals compared to seasonal influenza but that patients at highest risk for critical disease requiring admission to the ICU are primarily older individuals with comorbidities for both respiratory infections. Remarkably, we observed a significantly lower prevalence of chronic respiratory diseases in COVID-19 patients than influenza patients in the subgroups admitted to regular wards as well in those admitted to the ICU. While underlying respiratory diseases are associated with more severe outcomes of both viral respiratory infections39, our observations suggest that chronic respiratory conditions may have an even higher impact on the severity of seasonal influenza than on the severity of COVID-19.Assessing the prevalence of specific comorbidities in COVID-19 patients is paramount to identify and protect patient groups at high risk for severe disease. However, many studies characterizing COVID-19 patients are hampered by the lack of control groups, and the comparability of patient cohorts from different countries or different clinical settings is limited. For example, a large cohort study based on electronic health records and claims data showed that compared to influenza patients, COVID-19 patients had fewer comorbidities in the USA but more comorbidities in South Korea40 and 5–8% for seasonal influenza42. Of note, a number of patients with acute leukemia with SARS-CoV-2 infection experienced a severe clinical course with high hospital mortality and contributed to the relatively high overall mortality in the COVID-19 group. Indeed, while the attributable risk of many immunocompromising conditions and immunosuppressive therapies on severity and outcome of COVID-19 yet needs to be established, there is some evidence that patients with hematological malignancies are at increased risk of severe COVID-1945. Likewise, there is evidence that immunocompromised individuals have a higher risk for influenza-associated complications46. However, no significant effect of immunosuppression and immunodeficiency on overall mortality was observed for influenza patients in our cohort. This might either result from a generally lower severity of immunocompromised state in patients with seasonal influenza compared with COVID-19 patients or reflect a lower impact of immunocompromised state on disease outcome of seasonal influenza compared to COVID-19. Notably, amongst immunocompetent patients, those with COVID-19 had a more severe overall course of disease than those with seasonal influenza, but mortality did not significantly differ between the two groups.Even though COVID-19 patients were generally younger and healthier than influenza patients, the overall course of disease was more severe, with fewer patients treated as outpatients, longer overall duration of hospitalization, more frequent admission to the ICU, more frequent need for oxygen therapy, and invasive ventilation. The overall in-hospital mortality in our cohort was 15.9% in COVID-19 patients and 9.0% in influenza patients. This significantly higher case fatality rate among patients hospitalized with SARS-CoV-2 infection is generally in line with large multi-center observational studies, which have shown an overall in-hospital mortality of 15–22% for COVID-1947, HFNC has been suggested to benefit COVID-19 patients with acute hypoxemic respiratory failure without this increased risk of SARS-CoV-2 transmission to patients and healthcare workers49.Remarkably, eight patients with SARS-CoV-2 infection were successfully treated with HFNC and did, therefore, not require tracheal intubation. While NIV is associated with an increased risk of aerosolization and nosocomial transmission50. More recently, several studies have reported a high prevalence of invasive pulmonary aspergillosis in COVID-19 patients admitted to the ICU54, suggesting that those patients might be at equally high risk to develop co-infections with Aspergillus. Our observations emphasize that clinicians need to be aware of this complication in order to conduct prompt and comprehensive analysis in high-risk patients55.In our cohort, the prevalence of invasive pulmonary aspergillosis was similar among critically ill influenza and COVID-19 patients, even in the absence of underlying immunocompromising conditions and was associated with significant mortality. While invasive pulmonary aspergillosis typically occurs in severely immunocompromised hosts, it has also been shown to be a frequent complication in immunocompetent but critically ill patients with influenza infectionsWhile bacterial co-infections in general and the different causative bacterial pathogens, in particular, were equally common in seasonal influenza patients and COVID-19 patients, the latter were significantly less frequently treated with carbapenems and glycopeptides. The reason for this less frequent use of these broad-spectrum antibiotic classes may be a more established antibiotic stewardship program at our hospital in the 2020 outbreak versus the 2017–2018 season.21. Notably, the rate of patients requiring mechanical ventilation was significantly higher in patients with IAV infections compared to patients with IBV infections. These findings demonstrate that extrapolation of disease outcomes related to other influenza seasons should be performed with caution. Fourthly, the screening and treatment algorithm at our center was different for seasonal influenza and COVID-19. While generally only symptomatic patients are tested for influenza infections, all patients admitted to our hospital were screened for SARS-CoV-2 infection from April 20 onwards, which may contribute to sampling bias and result in a higher number of COVID-19 patients with mild or subclinical infections. COVID-19 patients were treated strictly according to a hospital-wide algorithm and, therefore, may, for example, have received low-flow oxygen at higher blood oxygen saturation levels than seasonal influenza patients. Lastly, it is important to note that the significantly lower age and fewer comorbidities in COVID-19 patients compared with influenza patients admitted to regular wards may also, to some extent, reflect a lower threshold for admitting patients with SARS-CoV-2 infection to the hospital due to the challenges of self-quarantine from household members and less data on reliable predictors for disease severity. Likewise, duration of hospitalization in COVID-19 patients may be prolonged due to PCR positivity and not due to clinical necessity, which may place additional strain on healthcare systems.Our study is subject to a number of limitations. Firstly, patients diagnosed at and referred to our tertiary care center may have generally more baseline comorbidities and a more severe course of disease than patients in different clinical settings, so our results may not apply to all other patient cohorts. Secondly, while COVID-19 and seasonal influenza are both viral respiratory infections that share a common transmission route and cause similar symptoms, they are subject to several important differences that considerably limit the comparability of our cohorts. While most humans have pre-existing immunity to seasonal influenza virus strains and vaccines are available for high-risk populations, the COVID-19 pandemic has demonstrated the potential impact of a novel pathogen on an immunologically naive population. Remarkably, only four of the 23 deceased seasonal influenza patients in our study cohort were vaccinated against seasonal influenza. Thirdly, given relatively low influenza case numbers throughout the 2019–2020 season, we compared COVID-19 patients to patients diagnosed with seasonal influenza throughout the 2017–18 season, when most laboratory-confirmed influenza cases were attributed to influenza B virus, and mismatch between the trivalent vaccine and the circulating strains occurredDespite those limitations, our study can provide some relevant insights into common features and differences in clinical characteristics and required hospital resources between patients with COVID-19 during the early phase of the pandemic and those with seasonal influenza. Prospective multi-center studies using standardized admission, treatment, and outcome protocols are needed to confirm the current findings and to assess the changes in morbidity and mortality of COVID-19 as well as of seasonal influenza throughout this evolving pandemic.In conclusion, we demonstrate that patients treated with SARS-CoV-2 infection during the early phase of the pandemic were younger and healthier than those with seasonal influenza infections. However, patients with SARS-CoV-2 infection had a generally more severe course of disease. These results suggest that this phase of the COVID-19 epidemic was associated with a higher demand for both critical care and regular care hospital beds than seasonal influenza epidemics with similar patient numbers. Importantly, the high mortality in COVID-19 patients with hematological malignancies and older patients emphasizes the importance of protecting these patient groups from SARS-CoV-2 infection. While patients hospitalized with SARS-CoV-2 infection in the future will likely differ from those in our cohort in clinical characteristics and disease outcome due to changes in disease epidemiology and novel therapeutic agents and strategies, our observations provide important insights for the future course of the COVID-19 pandemic.Supplementary Information.