In older people, memory B cells are mostly high specialized antigen-selected CD27bright. Moreover, after in vitro stimulation with CpG, B cells from older individuals produced significantly fewer IgM and IgA antibodies compared to younger individuals. Aging is a complex process characterized by a functional decline in multiple physiological systems. The immune system of older people is well equipped to react to often encountered antigens but has a low ability to respond to new pathogens.High quality medical assistance and preventive strategies, including pursuing a healthy lifestyle, result in a progressively growing percentage of older people. The population and workforce is aging in all countries of the world. It is widely recognized that older individuals show an increased susceptibility to infections and a reduced response to vaccination suggesting that the aged immune system is less able to react and consequently protect the organism. The SARS-CoV-2 pandemic is dramatically showing us that the organism reacts to novel pathogens in an age-dependent manner. The decline of the immune system observed in aging remains unclear. We aimed to understand the role of B cells. We analyzed peripheral blood from children (4-18 years); young people (23-60 years) and elderly people (65-91 years) by flow cytometry. We also measured antibody secretion by ELISA following a T-independent stimulation. Here we show that the elderly have a significant reduction of CD27 The progressive deterioration of the immune system in aged individuals is termed “immunosenescence” . This prThe aging of workers is one of the most important issues for occupational health and safety in Europe. The EU’s demographic old-age dependency ratio  is projected to increase significantly in the coming decades. From about 29% in 2010, it is projected to rise to 59% in 2070 . This taWith aging, the organism experiences a progressive impairment of both the innate and adaptive arm of the immune system , where tOn the other hand, the role of B cells in immune aging remains unclear. Recent findings illustrate major shifts in B cell subsets in the elderly, suggesting that age-related changes in B cells may contribute to immunosenescence .Immunological memory is the ability of our immune system to remember previously encountered pathogens; it is acquired by experience and it changes throughout life affecting our susceptibility to infections. Neonates and children under 5 years are more susceptible to infections compared to adults, because they are continuously exposed to pathogens that they have never encountered before . InfectiStreptococcus pneumoniae) are the main cause of respiratory tract infections and a leading cause of mortality in people >65 years old  and are mostly of the IgM isotype. Instead, CD27bright MBCs are generated exclusively in the GC by a T cell-dependent mechanism and under a strong selective pressure by an antigen (Ag). For this reason, the frequency of somatic mutations (SMs) is low in CD27dull MBCs and significantly higher in CD27bright MBCs ; young people (23-60 years) and elderly people (65-91 years). Young and elderly individuals were health care workers from our hospital, the majority of whom were active, while others were already retired. Older individuals were screened by interview to ensure they did not have autoimmune diseases, tumors, or recent infections.At the time of blood sampling, none of the subjects had an acute infection or were taking any medication known to alter immune function (such as steroids or statins).Heparinized peripheral blood was collected and then centrifuged through a Histopaque gradient  for 25 min at 2400 rpm. PBMCs were collected from the interface. Next, PBMCs were spun down and washed twice with RPMI 1640 medium (Euroclone). Then PBMCs were frozen and stored in liquid nitrogen until use. The freezing medium contained 90% Fetal Bovine Serum (FBS) and 10% DMSO.6 cells/ml were resuspended in PBS (Euroclone) with 1% FBS (Gibco BRL) and loaded with 1μM CFSE for 20 min at 37°C. After washing, cells (at a concentration of 5x106 cells/ml) were stimulated with 0.35 μM of TLR9 agonist CpG-B ODN2006 (Hycult Biotech) in complete medium for 5 days at 37°C. Complete medium was prepared as follows: RPMI-1640 (Euroclone), 10% heat inactivated fetal bovine serum , 1% L-Glutammine (Gibco BRL); 1% Penicillin/Streptomicin 100X (Euroclone), 1% sodium pyruvate (Gibco BRL).PBMCs were loaded with Carboxyfluorescein succinimidyl ester . Briefly, 1x10Following CpG stimulation (5 days), we performed an ELISA to detect secreted immunoglobulin in the supernatant of the stimulated cells. Briefly, 96-well plates (Costar3590 EIA/RIA plate) were coated overnight with purified goat anti-human IgA+IgG+IgM . After washing with PBS containing 0.05% Tween and blocking with PBS containing 1% gelatin , plates were incubated for 1 h at 37°C with the supernatants of cultured cells. After removing the supernatants and washing, plates were incubated for 1 h with peroxidase-conjugated fragment goat anti-human IgA or IgG or IgM antibodies (Jackson ImmunoResearch Laboratories). The assay was developed with O-phenylendiamine tablets (Sigma-Aldrich) as a chromogenic substrate. Absorbance at 450 nm was measured, and immunoglobulin concentrations were calculated by interpolation with the standard curve.A total of 50 ul of total peripheral blood was used for enumeration of CD45+ cells using the DuraClone IM Count (Beckman Counter) following the manufacturer’s instructions. PBMCs were stained with the appropriate combination of fluorochrome-conjugated antibodies to identify B cell subsets according to standard techniques. Used antibodies were: CD19, CD21, CD24, CD27, CD38, IgG, IgA, and IgM Table S1Values were compared by the non-parametric Kruskal-Wallis test and, if significant, pairwise comparisons were evaluated by the Mann-Whitney U test nonparametric test. A level of p < 0.05 was considered statistically significant. * p < 0.05; **p < 0.01, and ***p < 0.001.dull MBCs. Peripheral blood mononuclear cells (PBMCs) were stained with antibodies to CD19, CD21, CD24, CD27, CD38, CD45, and IgM. The gating strategy used to identify B cell population is shown in We determined the frequencies and absolute numbers of B cell populations of children  and adults of different ages ;,  and over 70 years (n=9). We excluded infants from our analysis because their memory B cell pool is mainly represented by CD27Children have the highest frequency of B cells in the peripheral blood compared to adults of all ages, CD19+) (+ (19). BThe percentage of MBCs  is significantly increased in the young and older adult groups compared to children , 35. In dull and CD27bright MBCs, which are related but have distinct molecular signatures and functions , and secrete immunoglobulins  stimulation with CpG, a ligand of the toll-like receptor 9 (TLR9), B cells proliferate, differentiate into plasmablasts .FACS analysis of four representative samples is reported in in vitro-produced plasmablasts was similar between the four groups . To avoid health–related bias, we enrolled individuals in the study that did not have an associated co-morbidity or previous diseases of the immune or hematopoietic system.Besides a general age-related reduction in the percentage of CD19+ B cells, we found specific changes in B cell subpopulations. While naïve, transitional, and MBCs were reduced in either or both percentages and total numbers, we observed an increase in both the percentage and the absolute number of ATM B cells in older individuals compared to young. These cells have been identified in different contexts that include chronic infection, autoimmunity, and aging .dull MBCs in older people were significantly reduced both in percentage and absolute number, as well as the ratio between CD27dull and CD27bright. We have previously demonstrated that CD27dull MBCs are mostly of the IgM isotype and can be generated without T cells and GC (dull MBCs are the precursors of CD27bright MBCs. While CD27bright MBCs produce antibodies that are highly mutated, CD27dull MBCs are less mutated and may enter the GC reaction in competition with naïve B cells, having the advantage of a more suited transcriptome compared to naïve B cells (dull MBCs not only explains the reduction of IgM MBCs observed in the elderly (CD27s and GC . CD27dul B cells . Thus th elderly  but also elderly .dull MBCs in older individuals can explain the reduction of TI immune responses, such as those against Streptococcus pneumoniae (dull MBCs population is still scarce, reduced in the young and adult populations, where CD27dull MBCs are abundant, and increased again in the elderly (The dramatic reduction of CD27eumoniae . Interes elderly .dull MBCs also has an impact on sIgA synthesis. We have recently demonstrated that IgM MBCs are necessary for the production of sIgA at the mucosal surface (The significant reduction of CD27 surface . sIgA is surface . Interes surface .bright MBCs that are closer to the plasma cell stage than CD27dull MBCs (in vitro studies indicate an impairment of the B cells’ function following a TI stimulus and confirm that the most affected antibodies are IgM and IgA.B cells fight viruses and bacteria by producing antibodies following their differentiation into circulating plasmablasts. The percentage of plasmablasts increased with age and the ability of the B cells to proliferate and differentiate into plasmablasts seems to not be affected or even improved in older subjects because of the increase of CD27ull MBCs . HoweverThus, the observed plasmablasts number associated with a reduced level of secreted antibodies suggest an impaired capacity to exert their function with age.-/- murine plasma cells show augmented endoplasmic reticulum stress and cell death, failing to give rise to an efficient antibody response and to form long-lived plasma cells in vivo (f/f CD19-Cre mice have reduced IgM and IgG responses in both TI and T-dependent immunization experiments, revealing a key adaptive role of autophagy in short-lived plasma cells and in antibodies responses (Aging is associated with the accumulation of damaged mitochondria, that are less efficient in ATP generation . Several in vivo . Atg5f/fesponses .Taken together these observations suggest that the defects in autophagy observed in the elderly may explain the functional impairment of immunoglobulin secretion by plasmablasts that we have demonstrated in our experiments.Understanding how the aging of the immune system works is an important step to protect one of the most sensitive groups of our population, especially in this present pandemic era. In some countries, including Italy, governments have issued special emergency regulations that oblige employers to implement special biohazard prevention measures for older workers . Among tThe number of subjects in the over 70 years old group is a limitation of our study. Nevertheless, the results are very homogenous and we have been extremely selective during the recruitment. Healthy elderly people represent <15% of the general older population  and therThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Bambino Gesù Children Hospital. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.RC and EP designed the study, analyzed data, and drafted the manuscript. SZ drafted the manuscript. MC, AF, CB, PP, EZ, MS, GF, and ST performed all experiments. RB, MV, and NM collected the samples. MC reviewed the manuscript. All authors contributed to the article and approved the submitted version.This work was funded by the RF2013-02358960 grant from the Italian Ministry of Health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


GSE133624), and could achieve accurate subtypes prediction for BC patients with high‐throughput sequencing. Taken together, we explored the immune molecular subtypes and their mechanisms of BC, and these results may provide guidance for the development of new BC immunotherapy strategies.The emergence of immunotherapy has provided an option of treatment methods for bladder cancer (BC). However, the beneficiaries of immunotherapy are still limited to small‐scale patients, and immunotherapy‐related adverse events often occur. It is a major challenge for clinical work to study the immune subtypes of BC and the molecular mechanism of immune escape, and identify the immune responders accurately. Here, we explore the immune molecular subtypes of bladder cancer and potential escape mechanisms. First, we screened the expression profiles of 303 differentially expressed immune‐related genes in BC patients from the Cancer Genome Atlas (TCGA) database, and successfully identified 4 molecular subtypes of BC. By comparing the clinical characteristics, immune cells infiltration, the expression of checkpoint genes, human leukocyte antigen (HLA) genes, and gene mutation status of different subtypes, we identified different clinical and immunological characteristics of 4 subtypes. Among 4 subtypes, Cluster 2 met the general characteristics of immunotherapy responders and responded well to immunotherapy, while Cluster 4 had the highest expression of immune characteristics, and is similar to the immune environment of normal bladder tissue. Then, the weighted gene co‐expression network analysis (WGCNA) of immune‐related genes revealed that brown module was positively correlated with subtypes. Pathway enrichment analysis explored the major pathways associated with subtypes, which are also associated with immune escape mechanisms. Moreover, the decision tree model, which was constructed by the principle of random forest screening factors, was also validated in internal validation set and external validation set from the Gene Expression Omnibus (GEO) cohort ( We successfully identified 4 molecular subtypes of BC via TCGA database. Moreover, the establishment of decision tree model, by the principle of random forest screening factors, can achieve accurate subtype prediction for clinical BC patients with high‐throughput sequencing. It is mainly represented by bladder urothelial carcinoma, which accounts for 90% of BC.The emergence of immunotherapy, especially immune checkpoint inhibitors, has revolutionized the treatment of many cancers. Currently, immune checkpoint inhibitors have been successfully used in the treatment of advanced bladder cancer and are increasingly being used in clinical work. Since April 2016, Food and Drug Administration (FDA) have approved atezolizumab, nivolumab, durvalumab, avelumab, and pembrolizumab, five PD‐1/PD‐L1 inhibitors for second‐line treatment options in patients with clinical advanced or chemotherapy resistant bladder cancer. Additionally, atezolizumab and pembrolizumab have replaced cisplatin as first‐line therapies for distant metastatic bladder cancer (stage Ⅳ). Many clinical studies have shown that bladder cancer immunotherapy is superior to conventional chemotherapy in terms of overall survival, progression‐free survival and objective remission rate.Most of the previous studies on immunotherapy about BC have focused on the identification of new immunotherapeutic targets and early clinical trials of immunotherapy efficacy for BC.This study aims to comprehensively explore the heterogeneous immune molecular phenotypes of BC and its clinical significance. We screened the expression profiles of 303 differentially expressed immune‐related genes in BC patients from the TCGA database, and successfully identified 4 molecular subtypes of BC. By comparing the clinical characteristics, immune cells infiltration, the expression of checkpoint genes and HLA genes, and gene mutation status of different subtypes, we identified different clinical and immunological characteristics of 4 subtypes. Among 4 subtypes, Cluster 2 has a decreased immune profile, while Cluster 4 has the highest expression of immune characteristics, and similar to the immune environment of normal bladder tissue. Moreover, the establishment of decision tree model, by the principle of random forest screening factors, can achieve accurate subtype prediction for clinical BC patients with high‐throughput sequencing. These findings prove the feasibility of predicting immune responders via immune molecular subtypes, and provide guidance for the development of new BC immunotherapy strategies.22.1https://cancergenome.nih.gov/). The gene expression value of mRNA‐seq was log2‐transformed for further exploration. The external validation cohort GSE133624 included 36 BC samples and 29 para‐cancer samples with high‐throughput sequencing data were also downloaded from GEO database (www.ncbi.nlm.nih.gov/gds/). Then 1830 immune‐related genes were obtained from an immune gene set in the IMMport database (https://www.immport.org/resources). And 225 immune checkpoint gens and 19 human HLA genes were retrieved and obtained from National Center for Biotechnology Information website (www.ncbi.nlm.nih.gov/gene/). All statistical extraction and analyses were performed using R 3.4.0  software.The mRNA‐seq data (counts format), simple nucleotide variation (SNV) data, clinical data of 409 BC patients were downloaded from the TCGA database  to distinguish different molecular subtypes. Moreover, Kaplan–Meier analysis and log‐rank test for overall survival were conducted for all upregulated immune genes in each subtype whose cutoff level was set at the median value of the expression value with the aid of survival package, and the most five prognosis‐related genes were displayed.The differentially expressed genes (DEGs) for 411 BC samples and 19 para‐cancer samples from TCGA were analyzed with the edgeR package, and |log2.3The relationship between clinical characteristics and immune subtypes was analyzed and visualized. Kaplan–Meier curves and log‐rank tests were used to compare the overall survival and disease free survival of the 4 subtypes. Chi‐square test and one‐way ANOVA were used for the comparison of other clinical features, which include age, sex, TNM stage, high‐ and low‐grade, and clinic pathological stage.2.4p < 0.05 were selected. The differences of prognostic immune checkpoint genes among 4 subtypes were demonstrated in the form of heatmap via pheatmap package. Then, Kruskal–Wallis test was conducted to compare the differences of the 4 clinical common immune checkpoint genes  and the 4 genes with the most significant differences among 4 subtypes , and ggplot2 package was used for the drawing of boxplot. Nineteen HLA genes were compared using heatmap and boxplot. The heatmap is drawn via pheatmap package, while the boxplot is drawn via ggpubr package.Univariate Cox regression analysis was performed on 225 immune checkpoint genes in BC patients, and genes with http://timer.cistrome.org/)Immune cells and immune‐related scores are important factors in the tumor immune microenvironment. We used MCPcounter2.5It is well known that the occurrence of cancer is the result of the accumulation of genetic mutations. TMB refers to a total number of coding errors, base substitutions, gene insertions, or deletions, etc. detected per million bases in somatic cells, and it is also considered to be a promising marker for predicting immune efficacy.2.6Weighted gene co‐expression network analysis (WGCNA)To explore the biological functions and pathways of gene modules, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed using clusterProfiler package and the cutoff set as false discovery rate (FDR) <0.05 and count ≥4. The main associated pathways of each module were visualized by ggplot2 package. The Cytoscape 3.7.12.7WGCNA analysis suggested that the brown module was the most related module to BC subtype. We set 411 BC sequencing samples as training set, and selected the expression matrix (log2‐transformed) of 79 genes in the brown module as the characteristic variables of the model. Through the algorithm of random forest with setting mtrys as 5 and ntrees as 600, the random forest model was constructed via random Forest R package. Then, the rpart package was used to construct a decision tree model for the most important five genes, so as to predict the subtypes of clinical patients with sequencing.2.8GSE133624, and highly consistent with the training set for its high‐throughput sequencing. The decision tree model was used to predict subtypes of 65 samples. TIMER website and estimate package were used to calculate the contents of 8 immune cells and immune‐related scores of the 36 tumor samples in GSE133624, and HLA genes expression profiles were also extracted. All of these features were shown in the form of heatmap via pheatmap R package, and compared with the immune characteristics of the original training set subtypes to test its predictive efficacy.We randomly selected 30% (123) samples from 411 BC mRNA expression profiles as an internal validation set. The confusion matrix of the predicted results is displayed and evaluated the internal stability of the decision tree model. There were 36 BC samples and 29 para‐cancer samples with high‐throughput sequencing data in 33.1Analysis of DEGs Figure  indicate3.2To explore the relationship between the BC clinical features and different subtypes, some clinical characteristics, including age, sex, TNM stage, high‐ and low‐grade, and clinic pathological stage, were analyzed and shown in Table 3.3p < 0.05 were selected for further study. Figure Immune checkpoint genes are currently the target genes in clinical immunotherapy. First, univariate Cox regression analysis was performed on the relationship between 225 immune checkpoint genes and prognosis of BC patients. The results were shown in Table HLA genes are important immune genes in human body, and mainly involved in the immune response as the presenting molecules of endogenous and exogenous antigens. Tumor‐induced immune escape could alter the expression of HLA genes, allowing the tumor to evade the immune system without being killed.As for immune cells and immune scores, MCPcounter, TIMER and estimate three algorithms were used to calculate and obtain immune cells and immune scores in BC samples from TCGA cohort. These features were shown as heat maps in Figure In summary, most of the immune signatures are downregulated in subtype Cluster 2 and upregulated in subtype Cluster 4 comparison with the other subtypes, which suggests that Cluster 2 had a decreased immune profile and Cluster 4 had an enhanced immune profile. It also suggests that the immune characteristics in bladder cancer patients are increased or decreased synchronously.3.4The higher the TMB, the higher the gene mutation frequency of tumor cells, and the more tumor antigens carried on the cell surface and vulnerable to attack by the body's immune system, and the better the efficacy of immunotherapy could be achieved.3.5To explore the potential biological pathways associated with BC immune subtypes and immune escape, WGCNA, and KEGG enrichment analysis were conducted. To construct a scale‐free network, the soft threshold power β was set as 2 Figure . Figure Subsequently, KEGG enrichment analysis of each module genes showed that the interaction pathways in each module Figure  were mai3.6In order to make the subtypes available for clinical work, we constructed a decision tree model using 79 genes in the brown module. According to Figure 3.7GSE133624, of which Cluster 1/2/3/4 has 9/22/2/3 samples, respectively (Table GSE133624 via the decision tree model, and 24 of them belonged to Cluster 4. For Cluster 4 has the highest expression of immune characteristics among all subtypes, it can be generally considered that normal bladder tissues have higher expression of immune characteristics. By comparing the immune characteristics of 4 subtypes, we believe that the immune subtype of normal bladder tissue is mostly Cluster 4, and different levels of immune escape and immune attenuation occur in the carcinogenesis of BC.Figure ly Table . By comply Table , it coul4BC is the most common tumor of the urinary system, and its treatment progress has been slow. In recent years, the introduction of immune checkpoint inhibitors has provided a treatment option for BC. The identification of BC immune subtypes can classify the heterogeneous tumor microenvironment of BC, which is helpful for the biological research of BC and individualized immunotherapy. The study analyzed the heterogeneous BC microenvironment subtypes and related clinical significance systematically using public data extracted from the TCGA cohort. Four immune subtypes were found to exhibit significantly different clinical characteristics, immune escape mechanisms, genomic alterations, and clinical outcomes. Finally, these subtypes can be well identified by the decision tree model and external validation in the GEO database. For clinical patients with bladder cancer, high‐throughput sequencing, and immune molecular subtypes prediction can be performed, and Cluster 2 patients can be treated with immunotherapy for a better immune response. This study provides new ideas and strategies for bladder cancer immunotherapy, and to some extent, proves the feasibility of predicting immune responders via immune molecular subtypes.GSE133624, we found that 24 of 29 para‐cancer samples belonged to Cluster 4 in the decision tree model prediction results. Although Cluster 4 had higher immune characteristics expression compared with other subtypes, it may be called the immune normal subtype, and Cluster 1, Cluster 2, and Cluster 3 occur varying degrees of immune attenuation. This indicates that there are multiple mechanisms of immune escape and transformation of multiple immune subtypes during the development of BC. Additionally, it also indicates that it may be unreasonable to call the subtype with high immune characteristics as the immune‐enhanced subtype in some subtypes‐related studies due to the lack of comparison with normal adjacent tissues.Previous studies have analyzed BC subtypes based on immune cell expression in the immune environment.The clinical characteristics of 4 immune subtypes were significantly different. For both overall survival and disease‐free survival, Cluster 2 patients have the best prognosis, while Cluster 4 subtypes had a poor prognosis. Compared with other subtypes, Cluster 2 patients tend to have lower age, early TNM stage, and clinicopathological stage. And most of the low‐grade BC patients are Cluster 2. The clinical characteristics of Cluster 4 are the opposite of Cluster 2. Cluster 2 patients have low‐expression immune characteristics and a better prognosis than other subtypes with higher immune characteristics. This is contrary to the prognosis of the immunodeficiency subtypes of ovarian cancer and hepatocellular carcinoma,To explore the immune‐related characteristics difference among 4 subtypes, we analyzed and compared differences in immune checkpoints, HLA genes, immune cells, and immune‐related scores among the 4 subtypes. These immune checkpoint genes and HLA genes are all low expressed in Cluster 2 and high expressed in Cluster 3 and Cluster 4. The algorithms MCPcounter, TIMER and estimate were used to estimate common immune cells and immune‐related scores. Heatmap comparison results show that Cluster 2 is an immune reduced subtype, while Cluster 4 was immune normal subtype. The occurrence of immune escape in BC is not only the change of immune checkpoint genes expression, but also the synchronous change of many immune characteristics. This characteristic is consistent with other solid tumors.BC is a tumor with a large number of gene mutations, particularly in chromatin regulatory genes, which occur more frequently than other common cancer.Cancer cells and immune system form a complex immune network between their struggle, and which is also the premise and guarantee of tumor immune escape. The maladjustment of immune checkpoint PD‐1 is one of the main mechanisms for tumor cells to achieve immune escape.This study has some limitations. First of all, the samples of each subtype are relatively small in the training and validation sets, and it was easy to miss some relatively rare immune subtypes of BC. Second, in order to explore the immune subtypes comprehensively, more clinical and demographic characteristics of BC patients should be included. In future studies on BC immune subtypes, a larger sample analysis should be carried out to find some rare clinical immune subtypes. Moreover, research on the immune escape‐driven mechanism and the transformation of immune subtypes during the carcinogenesis of BC may have more important findings.In conclusion, the current study suggests that the immune phenotypes of BC could be classified into 4 molecular subtypes with potential immune escape mechanisms in BC. Patients of different subtypes have significant differences in the immune checkpoint molecules, HLA genes, gene mutations, immune cells, and prognostic, etc. Specific functional pathways or gene mutations may drive the formation of microenvironment immune subtypes. In addition, through the construction of decision tree model, the subtype of BC patients with high‐throughput sequencing could be accurately predicted. These results may provide guidance for developing novel strategies of immunotherapy in BC, and to a certain extent, prove the feasibility of predicting immune response via immune molecular subtypes.The authors declare no financial conflict of interest.The study conception and design were performed by J.C. J.L. and JT. Material preparation, data collection, and analysis were performed by J.C., J.L., X.Y., P.L., and Z.Y. The first draft of the manuscript was written by J.C., D.H., and L.W. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.Bioinformatics analysis and publicly available datasets were used in this study, and ethical approval is not required.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.


With the advent of highly sensitive real-time PCR, multiple pathogens have been identified from nasopharyngeal swabs of patients with acute respiratory infections (ARIs). However, the detection of microorganisms in the upper respiratory tract does not necessarily indicate disease causation. We conducted a matched case–control study, nested within a broader fever aetiology project, to facilitate determination of the aetiology of ARIs in hospitalised patients in Northeastern Laos. Consenting febrile patients of any age admitted to Xiengkhuang Provincial Hospital were included if they met the inclusion criteria for ARI presentation . One healthy control for each patient, matched by sex, age, and village of residence, was recruited for the study. Nasopharyngeal swabs were collected from participants and tested for 33 pathogens by probe-based multiplex real-time RT-PCR (FastTrack Diagnostics Respiratory pathogen 33 kit). Attributable fraction of illness for a given microorganism was calculated by comparing results between patients and controls (= 100 * [OR − 1]/OR) (OR = odds ratio). Between 24th June 2019 and 24th June 2020, 205 consenting ARI patients and 205 matching controls were recruited. After excluding eight pairs due to age mismatch, 197 pairs were included in the analysis. Males were predominant with sex ratio 1.2:1 and children < 5 years old accounted for 59% of participants. At least one potential pathogen was detected in 173 (88%) patients and 175 (89%) controls. ARI in admitted patients were attributed to influenza B virus, influenza A virus, human metapneumovirus (HMPV), and respiratory syncytial virus (RSV) in 17.8%, 17.2%, 7.5%, and 6.5% of participants, respectively. SARS-CoV-2 was not detected in any cases or controls. Determining ARI aetiology in individual patients remains challenging. Among hospitalised patients with ARI symptoms presenting to a provincial hospital in Northeastern Laos, half were determined to be caused by one of several respiratory viruses, in particular influenza A virus, influenza B virus, HMPV, and RSV. Establishing which respiratory pathogens cause ARIs is important for targeting prevention and treatment, avoiding unnecessary use of antibiotics, and developing specific public health strategy2. Diagnosing the cause of pneumonia is challenging since few patients are bacteraemic, and sampling the lower respiratory tract is too invasive for routine investigation. Diagnosis frequently relies on sampling of the upper respiratory tract. The advent of molecular techniques permits identification of a broad range of viral and bacterial respiratory microorganisms4. PCR testing of nasopharyngeal swabs has been shown to be highly sensitive5, but the microorganism detected may not necessarily be clinically significant and could simply represent nasopharyngeal carriage. Increasing nasopharyngeal pneumococcal colonization density has been shown to be associated with severe pneumonia in children < 5 years with ARIs6; however, this association is still controversial7. One approach to aid in causal determination of ARIs is to recruit controls from the same population to characterize the nasopharyngeal microbiome and estimate the fraction of ARI cases attributable to the detected microorganism. The Pneumonia Etiology Research for Child Health (PERCH) study recruited 4232 patients aged < 5 years admitted with severe pneumonia, and 5119 community controls at nine sites in seven low-or middle-income countries (LMICs) in Africa and Asia between 2011 and 2014. Viruses accounted for most pneumonia (61% of 1769 cases) overall, with 31% of all cases attributable to respiratory syncytial virus (RSV)8. In the PERCH study, to limit biases in the estimation of pneumonia fraction attributable to the detected microorganism, participants presenting with respiratory symptoms were not excluded from acting as controls, as long as they did not meet the definition of a case9.Acute respiratory infections (ARIs) were classified as the fourth most common cause of death worldwide in 2016, with children under 5 years old being the most affected1. The 13-valent pneumococcal conjugate vaccine (PCV13) was introduced in late 2013 with limited catch-up vaccination per the national immunization schedule10. Post-vaccine observational studies in the capital Vientiane showed a reduction in nasopharyngeal carriage of PCV-13 serotypes following the vaccine’s introduction11. In another study of 383 children < 5 years of age hospitalized with confirmed ARI at Mahosot Hospital in Vientiane (2014), the most common virus detected was RSV in 41% of tested children12. Several other studies conducted elsewhere in the country have described viral pathogens detected in patients with respiratory infection16.In Laos, the probability of dying before 5 years of age is 47/1000 live births, and the death rate due to acute lower respiratory infections was estimated as 10/1000 live births in 2017The aim of our study was to understand which pathogens potentially caused ARIs in patients at Xiengkhuang Provincial Hospital in Northeastern Laos, and to describe bacteria and viruses colonizing the nasopharynx in controls in the community.This case-control study was conducted at Xiengkhuang Provincial Hospital , Laos, from 24th June 2019 to 24th June 2020 with case to control ratio of 1:1.Cases were febrile patients with acute respiratory symptoms admitted to Xiengkhuang Provincial Hospital, and recruited as part of an ongoing larger study into the epidemiology of fever (EFS). Criteria for patient inclusion in EFS were: all patients of any age admitted at hospital with fever (tympanic temperature ≥ 37.5 °C) of any duration, or history of fever in the last 72 h. Participants were screened and recruited from Monday to Friday. Patients were recruited in the current study if they had, in addition to EFS criteria, at least one of the following symptoms: cough, runny nose, nasal congestion, sore throat, difficulty breathing, and/or abnormal chest auscultation. Physical examination was performed by ward doctors and clinical, socio-demographic, medical history, and living environment data were collected using a standardized case record form.After each case recruitment, the study team went to the patient’s village to include a control with similar age  and same sex as the patient, within two weeks of case enrolment. The matched control could not be selected randomly as initially planned as there were no reliable records about the age of residents. We therefore selected the control who was present at the time when the team went to the village following suggestions given either by cases, cases’ parents, or village authorities.Controls were recruited if they had no history of hospital admission in the last 30 days, and did not meet the definition of ARI, which meant they did not present concomitant fever and respiratory symptoms but they could have either fever or respiratory symptoms, and if they consented to participate in the study. Physical examination was performed and data collected on a case record form. The controls were followed-up after seven days by phone to see whether they developed fever with respiratory symptoms. If they did, they were excluded from the study. If the date was still within two weeks of case recruitment, another control was recruited.® 516CS01, COPAN Diagnostics Inc., Brescia, Italy) which were inserted gently into the nose at an angle parallel to the roof of the mouth about one-half to two-thirds the distance from the nostril to ear lobe and rotated, then the swabs were put into the tube containing virus transport medium, Sigma Virocult® , then transferred in a cool box to the laboratory of the provincial hospital. On arrival, they were aliquoted into two tubes of 500 μL and kept at − 20 °C before being shipped on dry ice to the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit on a monthly basis. 400 µL of each swab were extracted using EZ1 virus mini kit , elution volume of 120 µL, following the manufacturer’s instructions. Then, 10 µL of extract was submitted to multiplex probe based real-time RT-PCR, using the Fast Track Diagnostics Respiratory pathogen 33 kit , following the manufacturer’s instruction for: influenza viruses: A (with identification of H1N1), B, C; human rhinoviruses (HRV); human coronaviruses (HCoV): NL63, 229E, OC43, HKU1; human parainfluenza viruses 1, 2, 3 and 4 (PIV 1–4); human metapneumoviruses (HMPV); human bocaviruses (BocaV); human respiratory syncytial viruses (RSV); adenoviruses (ADV); enteroviruses (EV), human parechoviruses (HPeV); Mycoplasma pneumoniae; Chlamydophila pneumoniae; Staphylococcus aureus; Streptococcus pneumoniae; Haemophilus influenzae; Moraxella catarrhalis; Klebsiella pneumoniae; Legionella spp.; Pneumocystis jirovecii; Bordetella pertussis; Salmonella spp.. From January 2020 specimens were also tested for SARS-CoV-2 by probe based real-time RT-PCR using E-gene system as described previously17. The qRT-PCR assays were considered as positive if the Cq value was < 40.Nasopharyngeal swabs were collected by the trained study team from cases and controls using flocked swabs (FLOQSwabs18. The attributable fraction (AF), estimation of the proportion of patients attributable to a given microorganism, was calculated by multiplying AFE by prevalence in patients of the given organism. AFE and AF can be calculated only for microorganism with OR > 1. Taking influenza B virus as an example with OR = 18.5 and proportion in patients = 18.8% , which allows one to take into account matching, was used to assess the association between living environment factors and outcomes which were coded 0 for controls and 1 for ARI cases. Multivariable regression for independent association was not performed as univariate analysis did not show any association apart from ethnicity variable. The same logistic regression was also used to test the association between each binary exposure (organism) and the case–control status. The proportion of individuals found positive by qRT-PCR for a given microorganism was calculated in controls and in patients, and then compared by calculating OR. The probability of the microorganism detected in patient to be the cause of the disease was estimated by calculating the attributable fraction among the exposed (AFE = [OR − 1]/OR)8% Table ” sectionhttp://www.laohrp.com/)  and the Oxford Tropical Ethics Committee (OxTREC)  .Ethical approval for this study was granted by the Lao National Ethics Committee for Health Research  patients met the definition of ARI and were recruited as cases . Children < 15 years old accounted for 175/197 (88.8%) participants, 116/197 (59%) were ≤ 5 years old . Antibiotics were prescribed in 193 ARI patients (98%). Ceftriaxone was the most prescribed in 96 cases (50%), followed by ampicillin in 56 cases (29%), gentamicin in 52 cases (27%), and penicillin in 34 cases (18%). Two or more antibiotics were provided in combination in 59 patients (31%).M. catarrhalis, H. influenzae and S. pneumoniae . The bacteria that co-existed together the most were M. catarrhalis and S. pneumoniae (co-detected in 30%). More than one virus per patient was found only rarely, with HRV and EV detected in 2.9%  ARI cases. A single organism was detected in 46/173 (27%). Of which, a single bacterial genus was detected in 11% of patients and a single virus in 16%. Influenza virus was the main virus detected alone, in 10% of positive patients. Detection of multiple organisms was common (73%) with a maximum of 6 microorganisms found in four ARI cases , S. pneumoniae (41.1%), H. influenzae (33.5%), HRV (12.2%), K. pneumoniae (8.6%) and S. aureus (7.1%), and ADV (6.1%); however, they were significantly more frequently detected in controls  positively associated with ARIs presentation compared to other ethnic groups with OR (95% CI) being 2.77 (1.29–5.95)  Table .Table 3E19. Two of those had completed PCV-13 vaccination. The microorganisms detected in the nasopharynx of those patients are shown in Table Ten of 116 (8.6%) children (≤ 5 year old) patients met the WHO definition of pneumonia Table 120. Feikin et al. using a similar approach, in an investigation of nasopharyngeal samples from 766 ARI cases and 273 healthy controls in Kenya, 2007–2010, showed that influenza A virus was the most common virus (22%) and S. pneumoniae the most common bacteria (16%) detected in patients21. They also showed that influenza virus, RSV and HMPV were associated with illness whereas ADV, parainfluenza viruses, HRV/EV, HPeV, and M. pneumoniae were not. In a study conducted in Vietnam, 2007–2010, on 1992 ARI patients and 350 healthy children. HRV (24%), RSV (20%) and influenza A virus (12%) were the main viruses detected in ARI cases. RSV and influenza A virus were associated with increased risk of hospitalisation22. In rural Thailand, 2005–2010, among 7388 hospitalized children with acute lower respiratory tract infections (ALRI), the main viruses detected were RSV (20%), HRV (18.7%), bocavirus (12.8%) and influenza viruses (8%). The inclusion of 667 controls permitted to show that RSV, influenza, ADV, HMPV and parainfluenza viruses 1 and 3 were associated with ALRI23. Similarly, the PERCH multi-country case–control study with the aim to find the causes of severe pneumonia in children found the same respiratory viruses were commonly associated with the cases. Additionally, S. pneumoniae and H. influenzae type B were identified as causal and more common in severe pneumonia8. Despite the fact the viruses were likely the predominant cause of ARI, nearly all (98%) of patients were treated with antibiotics. As the results show, by June 2020 Laos had avoided widespread community transmission of SARS-CoV-2 with no cases detected in this study. Evidence from other countries suggests that SARS-CoV-2 has displaced other respiratory viruses that usually circulate, with physical distancing and other lockdown measures thought to have acted to reduce transmission25.Our study showed that half of the febrile patients admitted to Xiengkhuang Provincial Hospital between June 2019 and June 2020 and recruited to the EFS study presented with signs and/or symptoms of acute respiratory infections, and the majority of them were children aged less than 15 years old (89%); 59% were aged less than five years. Sixty-six percent (66%) were recruited between May and October. All patients were discharged alive. No risk factor for ARI presentation could be identified. Among patients ≤ 5 years old, 9% (10/116) had pneumonia by WHO criteria, and six of them (60%) had severe pneumonia. The ARIs were attributable to influenza in 36% of cases, human metapneumovirus in 7.5% and respiratory syncytial virus in 6.5% cases. These findings are in accordance with similar studies in literature as reported in the metanalysis from Shi et al. showing that RSV, influenza virus and HMPV were strongly associated with ALRI  in children less than five years oldAlthough microorganisms were detected in nearly 90% of patients, their potential aetiological role could not be established for most of them. Many respiratory viruses known to cause ARIs were detected in our study, there was no evidence of association of detection of parainfluenza viruses, enterovirus, bocavirus, coronavirus with acute respiratory illness. Moreover, it is noted that adenovirus, and rhinovirus were detected more frequently in controls who had no history of ARIs within the last 14 days before recruitment and did not develop any respiratory symptoms seven days after recruitment based on the report of participants or family. The fact that those microorganisms were found more frequently in healthy individuals does not mean that those microorganisms were definitely not the cause of ARI in some cases.26. Moraxella catarrhalis, Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae, and Haemophilus spp., which were found in our study associated with controls, are well known frequent contributors to the respiratory microbiota. In the PERCH study, S. pneumoniae was, as in our study, significantly more frequently detected in controls than in patients8. However, Ho Man et al. showed in a matched case–control study in a paediatric intensive care unit that S. pneumoniae and H. influenzae were strongly associated with lower respiratory tract infection27. The composition of the upper respiratory tract is dynamic with lifelong evolution of organism diversity and quantity. Recent interest in studying the role of the microbiome has provided evidence on the complexity of the factors influencing microbiome changes, and of the role that the microbiome could have in development of disease. Schippa et al. found that there was a shift in the content of nasal microbiota with significantly lower biodiversity in children with bronchiolitis due to RSV28. In our study, antibiotic treatment, as well as viral infection, in ARI patients may have caused changes in the microbiome. However, Ramos-Sevillano et al. found only minor perturbation of the nasopharyngeal microbiome during influenza infection29. Although, Moraxella catarrhalis, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae are reported as causes of community acquired pneumonia, their presence in samples collected from the upper respiratory tract is difficult to interpret. Over the last two decades, the introduction of vaccines against Haemophilus influenzae type B and S. pneumoniae have led to a substantial reduction in incidence and severity of childhood pneumonia30. In Laos, the PCV 13 coverage is heterogeneous. The median PCV 13 coverage increased from 4.5% in 2014 to 37.5% in 2017 among children less than five years and the PCV 13 coverage had indirect effects on pneumococcal vaccine-type carriage10.The nasopharynx is colonized by a variety of different microbial species. Most research focuses on bacteria, and the role of viruses in the composition of the respiratory microbiota is likely to be underestimated32. The development of syndromic commercial kits, as FTD or Film Array, permit easy implementation of microorganism screening in diagnostic laboratories. The counterpart of highly sensitive detection of microorganism using real-time PCR is a decrease in specificity due to the detection of microorganisms naturally carried in upper respiratory tract at low density (URT). Conventionally, pneumonia aetiology is identified by detecting microorganisms in lung or in normally sterile body fluids such as blood. However, investigation of lung tissue is invasive and usually not available, and detection of microorganisms in blood has low sensitivity. Hammit et al., discussed these challenges in determining the aetiology of pneumonia and some strategies to overcome them18. The case–control study approach permits estimation of the attributable fraction for each tested microorganism, facilitating the identification of the main aetiologies at the population level. However, this does not permit to establish the aetiology at individual level. Indeed, the attributable fraction only provides a probability for the detected microorganism to be the cause of the disease. Moreover, the attributable fraction cannot be calculated for the microorganisms which are more frequently detected in controls than in cases, whereas their causal role cannot be excluded. In addition, our findings show that this strategy is not applicable for many respiratory pathogens which are part of the normal commensal flora. In the PERCH study, an integrated analysis method using a Bayesian approach was used. This method integrated results for multiple tests and permitted estimation of aetiology at individual level for a large panel of microorganisms. This approach requires the use of a combination of various tests from different samples, and the assumption for key parameters, such as aetiology fraction, sensitivity of all tests used and uncertainty range. This is difficult to implement in low-income countries where sampling from lower respiratory tract is rarely performed.This study highlights the difficulty in making a definitive etiological diagnosis of ARI. The technological breakthrough of molecular assays, more specifically probe based real-time PCR, in the past decades have greatly helped to understand the diversity of pathogens causing respiratory tract illnessMicroorganism detection in URT showed low specificity for most microorganisms- only influenza A virus, influenza B virus, metapneumovirus and RSV had an attributable fraction among the exposed of over 80%. In term of diagnosis, this means that the positive predictive value of the detection of microorganisms in the URT is elevated for HRSV, HMPV and influenza virus.34. In our study, 30 (40%) of influenza positive patients were less than five-years-old, and three of them did not have severe pneumonia. RSV is the number one cause of lower respiratory tract infection in young children, frequently causing bronchiolitis and pneumonia. In our study, there were only ten children aged less than five years who met the WHO pneumonia definition and RSV was found in two of them. However, all were judged to require hospitalisation by the assessing clinicians. HMPV is also an important cause of respiratory infection in children, causing spectrum of illness from mild disease to bronchiolitis and pneumonia. HMPV infection is usually less severe than influenza virus and RSV infections35. In our study, only one child with severe pneumonia had HMPV infection.Seasonal influenza, a common cause of acute respiratory illness, is usually self-limiting in healthy people. However, younger children and elderly people are at risk of severe disease with secondary bacterial infection resulting from perturbation of the microbiome in the upper respiratory tract and the translocation of colonized bacteria to the lower respiratory tract36. Finally, some ARI patients who were admitted during the weekend were not included to the study. This might cause selection bias. However, patients admitted during the weekend were in general not many compared to work days and patients admitted on Sunday could be recruited on Monday if they met the inclusion criteria.Our study has important limitations. First, the sample size was small- a larger sample size with severe disease might give better understanding of the roles of these organisms and permit analysis stratified by age. Second, we could not determine whether some detected organisms were the causes of acute respiratory infection based only on nasopharyngeal swab and AFE since those organisms were more commonly found in healthy controls. Third, the number of patients with ARI presented to hospital was not representative of the true epidemiology due to the COVID-19 pandemic resulting in travel restrictions, patients fear of coming to hospital during the second 6 months of the study. The first COVID-19 control measures in Laos were implemented quite early in February 2020 by national recommendation for wearing face masks, hand hygiene and respiratory etiquette. The first COVID-19 case was reported on March 24, 2020, leading the government to strengthen the measures with suspension of internal and international travels followed by a nationwide lockdown from 1st April to 3rd May 2020In conclusion, ascertaining which pathogens detected from nasopharyngeal swabs are the cause of ARI is still challenging and complex due to the limitation of analytic methods. By using the AFE estimate, we showed the importance of influenza A virus, influenza B virus, human metapneumovirus, and respiratory syncytial virus infections as causes of ARI in hospitalized patients in Northeastern Laos. This is supported by data from other studies and suggests that a modified testing approach targeting these organisms might be useful to facilitate appropriate antibiotic use and for surveillance in Laos. URT swabbing is an easy and reliable technique to identify cases caused by those viruses, enabling implementation of extended surveillance which would permit to better understand the burden of those infections at country level.


Penicillium, while metabarcoding suggest a higher abundance of Alternaria. This discrepancy might reside on the targeted portion of the community  or in the specific features of each technique. Thus, this study shows that commercial wheat flour hosts a wide fungal diversity with several taxa potentially representing concerns for consumers, aspects that need more attention throughout the food production chain.Consumers are increasingly demanding higher quality and safety standards for the products they consume, and one of this is wheat flour, the basis of a wide variety of processed products. This major component in the diet of many communities can be contaminated by microorganisms before the grain harvest, or during the grain storage right before processing. These microorganisms include several fungal species, many of which produce mycotoxins, secondary metabolites that can cause severe acute and chronic disorders. Yet, we still know little about the overall composition of fungal communities associated with wheat flour. In this study, we contribute to fill this gap by characterizing the fungal microbiome of different types of wheat flour using culture-dependent and -independent techniques. Qualitatively, these approaches suggested similar results, highlighting the presence of several fungal taxa able to produce mycotoxins. In-vitro isolation of fungal species suggest a higher frequency of  Alternaria, Cladosporium, Fusarium, and Helmintosporium have been reported as contaminants of grains in the field (moisture content of 18–30%), while Aspergillus, Penicillium, Eurotium, and Mucor are mainly reported to contaminate grains in storage conditions (moisture content of 14–16%) [Wheat flour is an essential ingredient for the human diet on a global scale. However, the final quality and safety of flour-based products can be greatly influenced by fungal contaminations that can occur before and/or after harvest ,2,3. For 14–16%) ,6 and, m 14–16%) . Althoug 14–16%) ,10, few The presence of mycotoxin-producing fungi in grains influences all the downstream production chain as the milling process does not destroy fungi, and wheat flour may carry a significant mycological and mycotoxigenic load that can contaminate the food products at the end of the production chain ,12. FurtIn this study, we explored the fungal diversity associated with different types of wheat flour  using both a culture-dependent (in vitro isolation of fungi) and a culture-independent method , showing that these food products host a wide diversity of fungi, several of which potentially producing mycotoxins.We analyzed the diversity of the fungal microbiome associated with different types of wheat flours using two different methods. Wheat flour samples were collected from commercial mills located in Calabria  during 2018. Samples included flour of type “00”, “0”, and wholemeal (3 samples each). All samples were collected as three subsamples of ∼1 kg from uniform bulks of flour. Flour samples were kept in sterile plastic bags at 5 °C and analysed within 5 days after sampling. Samples were then used to isolate fungi in vitro (culture-dependent method) and processed to prepare ITS-amplicon libraries for metabarcoding analyses (culture-independent method).Alive fungal contaminants were isolated from our flour samples by plating them on potato dextrose agar (PDA) plates . From each individual sample, we collected three sub-samples of ∼10 g, and suspended them in 20 mL of sterile water:agar solution (0.1%). Each suspension was serially diluted up to a 1:1000 ratio in sterile water:agar solution (0.1%). Then, we plated 0.1 mL from each suspension on PDA plates amended with ampicillin and streptomycin  to prevent bacterial growth. Plates were incubated at 20 ± 2 °C for 5 days, and then inspected to count the number of CFU/g of sample. Each colony was then isolated on PDA plate, and isolates were grouped according to the morphology of colonies on PDA and according to microscopic features , yielding 34 representative isolates.These representative isolates were identified using molecular barcoding. Each isolate was grown on Potato Dextrose Broth (PDB)  at 22 °C for 5 to 7 days according to the growth speed of each different isolate. The mycelium was collected by centrifugation, washed twice with sterile distilled water and lyophilized. DNA was then extracted from 20 mg of lyophilized tissue using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions, and concentration and quality were measured using Nanodrop 2000 spectrophotometer .2) using a Mastercycler Ep Gradient S  set at 94 °C for 3 min; 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 45 s repeated 35 times; and ending with 10 min of extension at 72 °C. PCR products were purified with Amicon Ultra-0.5 mL 100 k 96 PK centrifugal filters  and prepared for Sanger sequencing using a BigDye Terminator v3.1 Cycle Sequencing Kit  according to the manufacturer’s instructions. Libraries were then sequenced on a 3500 Genetic Analyzer  in both directions.PCR reactions were performed using primers ITS4 and ITS5  in 50 µLSequences were merged and manually curated using ChromasPro version 1.7.6, and grouped in bins containing identical sequences defined as sequence types (STs) . Each STWe characterized the composition of the whole fungal community in our flour samples using metabarcoding. DNA was extracted from ∼25 mg of flour using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions, quantified using a Nanodrop 2000 spectrophotometer , and normalized to 50 ng/µL using ultrapure water. The ITS2 region from the fungal rRNA was amplified using the primers ITS3_KYO2 and ITS4  modifiedDemultiplexed forward and reverse reads were merged using the PEAR 0.9.1 . Data haPenicillium was the most diverse of all investigated samples  or to two or more reference species with identical or very similar ITS sequences , followed by type “00” (80–173 CFU/g) and type “0” (53–80 CFU/g). The genus  samples  with 17 equences . FurtherAspergillus was isolated from all samples of flour type “00” with a concentration ranging from 7 and 20 CFU/g. The analysis of sequences enabled the identification of two STs associated with five different Aspergillus species  in all investigated samples (Mycosphaerella (8.4%), and Fusarium (3.6%). Overall, all genera detected using the culturing method were also detected in metabarcoding analyses although data about the relative abundance were not consistent between the two methods , 417 (type “0”), and 301 OTUs . Members of the phylum Ascomycota dominated in all samples and accounted for 94.8% of the total number of detected sequences followed by Basidiomycota (3.3%) and unidentified fungi (1.4%). Within the phylum Ascomycota, Dothideomycetes (52.5%), and Sordariomycetes (39.4%) were found to be the most representative classes. At the level of genus, more than 130 different taxa were identified across all investigated samples. Among these, 17 had a relative abundance ≥1%. The genus  samples . This ge methods . In partanalyses , with thIn this study, we used two different approaches (culture-dependent and -independent) to characterize the fungal microbiome associated with different types of wheat flours. Our results suggest the presence of a wide diversity of fungal species that can be isolated from these products, with several taxa being potential producers of mycotoxins. In addition, we were able to isolate a higher number of CFU from wholemeal flour (527–1840 CFU/g) compared to “00” (80–240 CFU/g) and “0” (53–80 CFU/g) types. This might be the result of the product transformation, as wholemeal flour contains grain elements that are removed in the other two flour types, and this might reduce the whole microbial load.Aspergillus were the dominant members of the community, followed by Penicillium spp. Similar results were observed in wheat flour [Alternaria, Fusarium and Epicoccum resulted the most common members of the fungal community [Fusarium was the most abundant fungal genus in grains of durum wheat. These examples show that the fungal microbiome associated with grains and flours is quite diverse and variable, even when we look only at the portion that can be isolated and cultivated in vitro.While in our study we focused on studying the whole fungal community of different types of wheat flour, previous studies mostly focused on identifying mycotoxigenic fungal species ,38. For at flour , cerealsat flour , maize fat flour , and peaat flour . On fresommunity , while Community  found thPenicillium was the most abundant genus within the flour-associated mycobiome, followed by Cladosporium, Aspergillus and Alternaria. Similar results were obtained on whole wheat and corn flour [Alternaria, Cladosporium, Fusarium, and Helmintosporium are more common to contaminate grains in the field, while Aspergillus, Penicillium, Mucor, and Eurotium, are more common contaminants during the post-harvest phase [Penicillium in our samples may indicate that the flour products we used were contaminated by potential mycotoxigenic fungi during the postharvest storage.Our results suggest that rn flour , althougrn flour ,43,44,45st phase . Thus, tPenicillium, for example, was the most abundant in our samples with also the highest diversity of STs, and species of this genus have been previously reported to produce the mycotoxins patulin and ochratoxin A [Penicillium verrucosum, which is one of the most important ochratoxigenic species [Penicillium griseofulvum known to produce patulin [Penicillium citrinum, Penicillium commune, and Penicillium chrysogenum, potentially producers of citrinin, cyclopiazonic acid and roquefortine C, respectively, [Aspergillus are known to produce mycotoxins such as aflatoxins, ochratoxin A and fumonisins [Aspergillus, but the resolution of the marker we used did not allow to distinguish between Aspergillus oryzae, Aspergillus kamarensis and Aspergillus fasciculatus. Although these species are not known to produce mycotoxins [Aspergillus flavus that is an important aflatoxin producer [Alternaria  found in our study in low abundance can produce mycotoxins like alternariol or tenuazonic acid [Fusarium, Cladosporium, and Mycosphaerella, which are known pathogens/endophytes of wheat plants, and none of them has been previously reported to produce mycotoxins. Thus, the fact we were able to isolate them can be just the result of plant-microbe interaction occurring in the field, with no influence on the quality/safety of food products.Interestingly, most of the fungal genera isolated in our study contain species that can produce mycotoxins . Penicilatoxin A . Indeed, species , Penicil patulin . We alsoctively, . Similarmonisins . In our cotoxins , the ideproducer . Additionic acid ,52, but nic acid ,54. ThusThese results obtained using classic methods for fungal isolation and cultivation in vitro largely matched those obtained using metabarcoding. Qualitatively, all the fungal genera identified through in vitro cultivation were also found in the metabarcoding dataset. However, the region amplified during metabarcoding library preparation is much shorter compared to the one we used to identify fungal isolates. Thus, we were not able to accurately identify all the OTUs to species level, and we preferred to group them at the genus level to avoid providing incorrect information on their identity. In addition, the metabarcoding dataset uncovered a wider diversity of fungal taxa associated with wheat flours, and this can be the results of two main factors. First, the in vitro isolation has the limit that not all organisms can be cultivated, either because they are difficult to cultivate using standard media or they are not cultivable at all ,56, whilThis study provides a comprehensive picture of the fungal diversity of the three most used typologies of wheat flour. Using both culture-dependent and -independent techniques, we found the presence of several fungal taxa that can cause harm to consumers. Future research can further expand our results, by testing a wider range of producers, sampling timeframes, production areas, and comparing the mycobiome of wheat grains and flour to understand the source of potential mycotoxigenic fungi. A better understanding of the mycobiome of wheat flours increases our knowledge on the frequency and distribution of mycotoxin producers, with a positive impact on food safety. Consumers are increasingly demanding food with higher quality and safety standards, so it is essential to integrate these state-of-the-art tools into the quality control procedures so that unsuitable food products are quickly identified and removed from the production chain.


This study investigated incidence and risk factors for venous thromboembolism (VTE) in patients with cervical cancer. We selected 49,514 patients newly diagnosed with cervical cancer from the Korean Health Insurance Review and Assessment Service databases. During the total follow-up period and first 6 months after initiation of primary treatments, incidence of VTE, and association of risk factors with VTE occurrence were evaluated according to primary treatments or no treatment, surgery, radiotherapy, and chemotherapy. VTE occurred in 1.15% of patients with cervical cancer. Regardless of the period after initiation of primary treatments, and of VTE, the incidence of thromboembolism was highest in chemotherapy. During the first 12 months, monthly incidence of VTE was highest in chemotherapy and decreased with time in all primary treatments. Compared with no treatment, VTE risk significantly increased for all primary treatments    and for chemotherapy during the first 6 months . In this cohort study, incidence and risk of VTE in patients with cervical cancer were the highest when chemotherapy was the primary cancer treatment, and incidence of VTE decreased with time. Although a predisposition to hypercoagulability in patients with cancer induces VTE, VTE can also be caused by a combination of acquired factors, including age, immobility, surgery, trauma, smoking, obesity, medical comorbidity, history of VTE, chemotherapy, and hormone therapy5.Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), aggravates morbidity and mortality and increases economic burden in patients with cancer6. Current guidelines recommend pharmacologic and mechanical prophylaxis and treatment of VTE in patients with cancer10. However, current guidelines do not specify different risks and managements for VTE in different cancer types. Pharmacologic thromboprophylaxis prior to chemotherapy is recommended in ambulatory cancer patients with intermediate or high risk for VTE (Khorana score 2 or higher)8. Using this score, gynecologic cancers as a group are considered high risk, although different gynecologic cancer types have heterogeneous risks for VTE11.Incidence of VTE in patients with cancer has been reported to be 20%27. Although VTE is a common complication in gynecologic cancer that induces high morbidity and mortality, studies evaluating their relationship were relatively uncommon1. Therefore, current understanding and management of VTE in gynecologic cancers is mainly based on studies of solid cancers10.Incidence of VTE in patients with gynecologic cancer has been reported to be 0%-41.5% because of heterogeneity in the population, diagnostic methods, and study designsStudy of the incidence of VTE related to various risk factors in different types of gynecologic cancers evaluated using big data may guide prophylaxis and treatment of VTE according to different gynecologic cancer types. Therefore, this study aimed to investigate the incidence and risk factors for VTE in patients with cervical cancer using Korean Health Insurance Review & Assessment Service (HIRA) data.Data from 83,508 women who had two or more diagnostic codes for cervical cancer between 2007 and 2018 were extracted. Of these, 49,514 women newly diagnosed with cervical cancer since 2009 were selected  and decreased after 60 years with surgery. The highest incidence occurred between 50 and 55 years for chemotherapy, between 50 and 60 years for radiotherapy, and at more than 75 years for no treatment   . Risk of VTE also significantly increased according to increased age, CCI, and year of cervical cancer diagnosis   . Low SES was not a risk factor for VTE  1.492; 95% confidence interval (CI) 1.186–1.877; P < 0.001). Risk of VTE significantly increased according to increased age and years of cervical cancer diagnosis  . Low SES and CCI were not risk factors of VTE  and 95.2% received prophylactic anticoagulants (95.7% of DVT and 95.4% of PE). Incidence of VTE was 0.1% (0% of DVT and 0% of PE) in patients did not receive prophylactic anticoagulants and 8.3% (4.8% of DVT and 4.2% of PE) in patients who received prophylactic anticoagulants (Supplementary Table Prescribed prophylactic anticoagulants were UFH (77.8%), DOAC (17.4%), warfarin (17.2%), LMWH (9.7%), and fondaparinux (0.0%). DOAC was the most frequently used therapeutic anticoagulant (52.6%), and 6.7% of patients with VTE used an inferior vena cava filter anticoagulants  decreased with time after cancer diagnosis31. Corresponding to these findings, our study demonstrated that incidence of VTE decreases with time for all types of primary cancer treatments, including surgery.In two cohort studies that evaluated incidence of VTE within 30 days (n = 175) and 90 days  from gynecologic cancer surgery, the incidence of VTE decreased with time22. In a prospective study performed in patients with gynecologic cancer (n = 411), prior pelvic radiation therapy was a risk factor for postoperative DVT32. Moreover, in patients receiving gynecologic brachytherapy (n = 329), incidence of VTE was 1.2%12. The claims database analysis of one cancer cohort  showed that, in patients with common solid tumors including ovarian cancer treated with chemotherapy, the incidence of VTE was 12.6% in the 12 months after initiation of chemotherapy  to those with cancer)33. Among all chemotherapeutic agents, the risk of VTE was highest in patients receiving cisplatin and bevacizumab 33. A retrospective study reported that in patients with cervical cancer (n = 798), chemotherapy was a risk factor for VTE25. In our study, incidences and risks of VTE were high in the order of chemotherapy, radiotherapy, surgery and no treatment regardless of the time period after initiation of primary cancer treatments, VTE, DVT and PE. Patients who receive chemotherapy as initial treatment for cervical cancer have more advanced disease such as locally advanced disease and distant metastasis, and patients with advanced-stage cancer appear to be at a greater risk of VTE occurrence34. Therefore, in our study, chemotherapy as the primary therapeutic modality with the highest incidence and risk for VTE might be attributed to the advanced stage. However, determination of risk of VTE in patients by type of primary treatment might provide significant information for patients with cervical cancer.Some studies reported that in patients with cervical cancer who underwent radical hysterectomy  and/or pelvic and/or para-aortic lymphadenectomy, incidence of VTE ranged between 2.7 and 9.5%31. In our study, regardless of time period after initiation of primary treatments and/or VTE (DVT and PE), patients with VTE were older than patients without VTE. In addition, risk of VTE increased with increasing age.In one cervical cancer cohort (n = 272), patients older than 60 years had a fourfold increased risk of VTE compared to patients younger than 60 years (10.4% vs. 2.6%). In another cervical cancer cohort (n = 704), the absolute rate of VTE increased in patients over 60 years (12 per 1000 person years vs. 30 per 1000 person years)In our study, we assume that patients with low SES receive adequate cancer management under the Korean national health insurance system because low SES was not a risk factor for VTE. The risk of VTE increased in patients diagnosed with cervical cancer more recently, which might be attributed to developments in diagnostic techniques for VTE and improved accessibility under the Korean national health insurance system.During the total follow-up period after initiation of primary treatments, 95.2% of patients with VTE received prophylactic anticoagulants in our study. The incidence of VTE was higher in patients who received prophylactic anticoagulants than in patients who did not receive them (8.3% vs 0.1%). We presume that, in our study, patients exposed to known risk factors of VTE, such as old age, high CCI, radical hysterectomy, radiotherapy, and chemotherapy, receive prophylactic anticoagulants more frequently compared to those without such risk factors.The significance of this study was the investigation of the incidence and risk factors for VTE VTE in patients with cervical cancer using a large scale cohort. To our knowledge, this is the first report that identifies incidence and risk of VTE according to primary cancer treatments in patients with cervical cancer. This study has the following limitations based on the use of claims data. First, because diseases in this study were indirectly defined based on diagnostic and prescription codes without reviewing medical records, some patients may have been incorrectly diagnosed by erroneous coding. However, based on our inclusion criteria, this study might have included patients who were incorrectly diagnosed. Second, patients who did not have a prescription code or only had one prescription code for anticoagulants were not considered VTE patients. Under the Korean national health insurance system, patients diagnosed with VTE, regardless of symptoms, are usually treated for VTE. Therefore, there should have been few patients who truly did not receive treatment for VTE. Third, this study excluded patients with VTE before cervical cancer diagnosis, and patients with VTE before initiation of primary cancer treatments were not considered VTE patients. It is difficult to define eligible patients because the order of cervical cancer or VTE diagnosis and primary cancer treatment may be altered if they occur over a short time. However, criteria were used to clarify relationships between VTE and cervical cancer or primary cancer treatment. Finally, this study could not investigate relationships between VTE and body mass index or stages and/or histologic types of cervical cancer because the HIRA dataset does not provide this information. However, each primary treatment may represent a risk of VTE in patients with cervical cancer compared with a no treatment group comprised of a large population in any cancer stage, although the analysis was not adjusted for stage.Based on a large Korean cohort study, we evaluated the incidence and risk factors of VTE and time to VTE occurrence in patients with cervical cancer. Incidence and risk of VTE were highest in patients who received chemotherapy as a primary cancer treatment, and incidence of VTE decreased with time for all primary cancer treatment types. The results of our study contribute to management of patients with cervical cancer.36. The HIRA shares most National Health Insurance Service data. Diagnosis codes for cancer among medical information have high accuracy because patients with cancer receive additional medical payment discounts. The claims data of the HIRA represent 23 million women per year35. This is a retrospective cohort study using the entire insurance data from the HIRA between January 1, 2007 and December 31, 2018.South Korea has a universal health coverage system, the National Health Insurance, which covers approximately 98% of the overall Korean populationThe 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), the Health Insurance Medical Care Expenses (2017 and 2018 version), and the HIRA Drug Ingredients Codes were used for diagnostic codes, surgery codes, and prescription codes to select eligible patients. Patients with cervical cancer were defined as patients who had two or more diagnostic codes for cervical cancer (ICD-10: C53x) between 2007 and 2018. Among patients diagnosed with cervical cancer, patients who had diagnostic codes for VTE  before the first C53x codes were excluded. Patients who had C53x codes between 2007 and 2008 (wash out period) were also excluded to select only patients with newly diagnosed cervical cancer. Patients with VTE were defined as patients who have prescription codes for anticoagulants more than 2 times simultaneously with diagnostic codes for VTE after initiation of primary cancer treatment (surgery or radiotherapy or chemotherapy). If patients did not get primary cancer treatment (no treatment group), patients with VTE were defined as patients who have prescription codes for anticoagulants more than 2 times simultaneously with diagnostic codes for VTE from the date of the first diagnostic code for cervical cancer. Deep vein thrombosis (DVT) and pulmonary embolism (PE) were defined as patients who have prescription codes for anticoagulants more than 2 times simultaneously with diagnostic codes of I80.2 or I80.3 and I26, respectively.37. Primary treatments  were defined as treatments performed first after diagnosis of cervical cancer. No treatment was defined as lack of prescription code for surgery, radiotherapy, and chemotherapy. Surgery was defined by surgery codes for total hysterectomy, radical hysterectomy, and trachelectomy  simultaneously with diagnostic codes of cervical cancer. If two or more surgeries were performed, the first surgery was used as the primary treatment. Neoadjuvant chemotherapy followed by laparotomy or laparoscopy was considered as surgery. Radiotherapy was defined as prescription codes for radiotherapy simultaneously with diagnostic codes for cervical cancer. Chemotherapy was defined as prescription codes for chemotherapy  simultaneously with the diagnostic code for cervical cancer. Pharmacologic thromboprophylaxis for VTE was defined as prescription for anticoagulants more than 2 times without diagnostic codes for VTE after initiation of primary treatment or from date of cervical cancer diagnosis (if treatment was not performed). Prophylactic anticoagulants comprised unfractionated heparin (UFH), low molecular weight heparin (LMWH), warfarin, fondaparinux, and direct oral anticoagulants (DOAC). Therapeutic anticoagulants comprised UFH, LMWH, warfarin, aspirin, fondaparinux, and DOAC.Age was categorized in intervals of 5 years. Low socioeconomic status (SES) was defined as patients with medicaid as National Health Insurance. The Charlson Comorbidity Index (CCI) was calculated according to Quan's method using data between 365 days and 1 day before the first diagnostic date of cervical cancerData from the total follow-up period and first 6 months after initiation of primary treatments were analyzed.P values < 0.05 were considered statistically significant. When there was a missing value, the mean imputation method was used.All statistical analyses in this study were performed using SAS Enterprise Guide version 6.1  and R version 3.3.2 . Continuous variables were analyzed using the independent t-test and Mann–Whitney U test, whereas categorical variables were analyzed using the Chi-square test and Fisher’s exact test. In addition, associations between variables and VTE occurrence were analyzed using the Cox Proportional Hazard Regression model with or without adjusting for confounding factors such as age per 5 years, low SES, CCI, year of cervical cancer diagnosis, and types of primary treatment. All statistical analyses were performed using two-tailed tests and Based on the South Korea’s Bioethics and Safety Act, because the HIRA dataset uses anonymous identification codes to protect personal information, approval of this study was waived by the Institutional Review Board of Inha University Hospital (No. 2019-11-007) on November 25, 2019 and informed consent was not required.Supplementary Information.