Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention.Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate.Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves.Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19. Figure The Delta variant caused an increasing crisis in Indonesia and other nearby regions , 9 see , 2. By AIn Because of the limited data on COVID-19 prevalence in Indonesia, there was an investigation into SARS-COV-2, which causes COVID-19 disease. From June to December 2020, 1,819 participants aged 16 years or older were recruited from Surabaya city. An overall 11.4% prevalence of IgG antibodies against SARS-COV-2 was observed in the subjects see . Using aAs a part of the national public health response to COVID-19, PCR and serum tests were conducted on migrant workers residing in all dedicated hostels in Singapore between March 25 and July 25, 2020. This included 43 dormitories with a total population of 198,320 . PCR or serological results were positive in 111,280 inhabitants, with an overall infection rate of 56.1% (and 55.9–56.3% of 95% CI)  see Tab.Mathematical modeling has been successfully used to forecast COVID-19 trends and has assisted public health organizations in policy making. This article uses a simple compartmental epidemic model. Our model considers a time-dependent transmission rate since both changes inhuman behavior, and the use of control measures can alter the transmission rate of the disease. Other epidemiological parameters such as the latency period and the infection fatality rate (IFR) may vary as well. To simplify the model, we focus only on the time-varying transmission rate and assume that other parameters are constant.S), exposed (E), infectious (I), hospitalized or delayed (T), death (D), and recovered/immunized classes (R), respectively. Susceptible individuals can become exposed after getting contact with an infectious people. Exposed individuals can become infectious after the latency period. Infectious individuals can move to the hospitalized (or delayed) classes and recovered/immunized classes. Hospitalized (or delayed) individuals may die or survive and recover/be immunized. In addition, recovered/immunized individuals become susceptible after vaccine immunization failure. We choose the model and parameter values from  evenly cover the study period. Parameters σ, γ, and κ are rates at which the individual loses exposed status and infectious status, σ = 0.5 days, γ = 1/3 days, and κ = 1/12 days. Thus, we have a mean generation time (σ−1 + γ−1) of 5 days and a mean delay between loss of infectiousness to death 12 days that are well in line with estimates in previous studies (t) = v(s) represent the vaccination rate per capita among all population per day. When we incorporated this effective vaccination rate, we considered a delay of 2 weeks (τ) for the vaccine to take effect and uniform vaccine efficacy of η =85%. We note that this is the simplest model for this situation. Given that seven of the nine countries have a vaccination coverage (fully vaccinated) of less than 20%, the effect of the vaccination in these seven countries would be mild. In Malaysia and Singapore, especially in Singapore, the vaccination coverage is high, and we first fit the model with the vaccination, then we simulated our fitted model without vaccination. Thus, we showed the effects of the vaccination campaign. We used the famous R package POMP. A detailed description of the usage of this package on epidemiological models can be found here (nβ = 8), a lower vaccine efficacy (η =75%), and different model structure  and reduced IFR due to vaccination.Here, c spline –21 with  studies . The IFRund here –25. We pWe fit a unified model to the reported COVID-19 deaths in nine countries in this region. Our study period is from March 11, 2020 to August 17, 2021.We assume the transmission rate to be an exponential cubic spline function spanning the whole time series. This semi mechanistic approach of modeling multiple waves of infections has been successfully used in the previous epidemics and pandemics.Using data from the WHO dashboard, In Ever since the emergence of COVID-19 in 2019, it has spread worldwide (more than 200 countries and territories) rapidly. Countries in SA and SEA have implemented prevention and mitigation strategies to combat COVID-19 pandemic, including testing, contact tracing, and border control . In TablIt is well-recognized that because of the implementation of NPIs, COVID-19 pandemic in SA and SEA was kept under control in 2020 . Subsequvia a straightforward approach. The actual IFR and vaccination effect are much more challenging to model. Nevertheless, we have laid the groundwork for further improvement.This work has several limitations, including the assumption that all parameters are constant except for the transmission rate and that the model is for the whole country while we ignored the heterogeneity across age groups and regions. This study relies on the reported death data and adopted a non-mechanistic cubic spline type of transmission rate. Alternatively, one could consider explicitly incorporating all kinds of control measures (such as the Google mobility matrix) into their model and expect to get more insightful observations on the transmission status of COVID-19 in SA and SEA. This work should be treated as a simple conceptual modeling attempt to estimate the size of the epidemic (IAR) in this region and the associated large-scale trends. Our estimated IAR is essentially in line with serological studies, and we illustrate the effects of vaccination Before the vaccine coverage reaches sufficiently high level, public health department must establish and maintain strong public health infrastructure and NPI to respond effectively to combat COVID-19 pandemic in places where the IAR was low and vaccination coverage was high, vaccination saved a lot of lives.Publicly available datasets were analyzed in this study. The data are from World Health Organization website.DH, HS, and GF conceived the study, performed data analysis, and wrote the draft. DH, HS, YL, XW, and GF discussed the results, revised the manuscript critically, and approved it for publishing. All authors contributed to the article and approved the submitted version.This study described in this article was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU C7123-20G), the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (20200001), the National Natural Science Foundation of China (12171291), and the Key Research and Development Project in Shanxi Province (202003D31011/GZ).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.


Safflower is widely used in dying and in traditional medicine, and C-glucosylquinochalcones are the main metabolic species in the red color of safflower. Various safflower cultivars have flowers with different colors. However, the metabolic and transcriptional differences among safflower cultivars with different-colored flowers and the genes participating in C-glucosylquinochalcone biosynthesis are largely unknown. To provide insights on this issue, we performed integrated metabolomics and transcriptome analyses on the flavonoid biosynthesis of flowers of different colors in safflower . The metabolic analysis showed that flavonoid metabolites showed great differences among the different colors of safflower. More flavonoid metabolic species were detected in Y and W, while C-glucosylquinochalcones were not detected in W. The content of C-glucosylquinochalcones increased with increasing color. Transcriptional analysis showed that most of the annotated flavonoid biosynthesis genes were significantly increased in W. The expression of genes related to flavonoid biosynthesis decreased with increasing color. We analyzed the candidate genes associated with C-glucosylquinochalcones, and an integration of the metabolic and transcriptional analyses indicated that the differential expression of the chalcone synthase (CHS) gene is one of the main reasons for the difference in flavonoid species and content among the different colors of safflower. Combined with the expression pattern analysis, these results indicated that HH_035319, HH_032689, and HH_018025 are likely involved in C-glucosylquinochalcones biosynthesis. In addition, we found that their expression showed greatly increased after the methyl jasmonate (MeJA) treatment. Therefore, HH_035319, HH_032689, and HH_018025 might participate in C-glucosylquinochalcone biosynthesis, which ultimately leads to the red color in safflower. Carthamus tinctorius) is a member of the Asteraceae or Compositae family and represents an important commercial crop cultivated as an oilseed livestock feed or a dye source or for medicinal purpose (via the Silk Road). Thus, safflower has been cultivated and used in China for more than 2,000 years. Currently, safflower has been widely cultivated in Asia, Europe, Australia, and the Americas. According to the United Nations Food and Agriculture Organization (FAO)1, 717,900 hectares of safflower were sown globally, which produced 666,600 tons of safflower in 2018. The flowers of safflower are mainly used as dyes and cosmetics worldwide . Among them, peonidin is synthesized by methylation of cyanidin and malvidin and petunidin are formed under different degrees of delphinium methylation . PeonidiIn safflower, the chemical analysis showed that more than 200 compounds were isolated, including flavonoids, alkaloids, steroids, and some other compounds , and flaO-methyltransferase (AOMT) to form stable anthocyanins. Many reports have explained the flower color of plants at the molecular level based on the existing flavonoid metabolism pathway  and the oxidation of cytochrome P450 (P450) . Althouga glabra  and P450ne max L  and sweene max L , in whicIn this study, to elucidate the metabolic and transcriptional differences among safflower cultivars with different-colored flowers and the genes that responded to C-glucosylquinochalcone biosynthesis, four safflower cultivars with different flower colors  were used. Metabolic, transcriptional and integration analyses were carried out among the four different safflowers. At the same time, we analyzed the expression pattern of candidate genes involved in C-glucosylquinochalcone biosynthesis, including tissue specificity expression and to the different stages of flower development. At the same time, our previous study reported that flavonoid metabolic species of safflower, especially C-glucosylquinochalcones (such as safflomin A), significantly increased after treatment with MeJA . We alsoFour safflower cultivars with white (W), yellow (Y), light red (LR), and deep red (DR) flowers were used in this study . The secBased on the content of flavonoid metabolic species in the different colors of safflower, a heatmap was drawn . The flaIn addition, most flavonols and flavonoid carbonosides were more abundant in W than in the other three colors of safflowers. However, C-glucosylquinochalcones, such as safflomins A and C and their isomers, were not detected in W. Most flavonols and chalcone had a high content in Y, but the content of C-glucosylquinochalcones was lower than that in DR. With increasing color (from LR to DR), it can be clearly viewed that the content of C-glucosylquinochalcones increased. Anthocyanins were also detected in our research, and they mainly included glucosides of cyanidin, pelargonin, and delphinidin. However, there were no intersections among the detected anthocyanins. Combined with the content of C-glucosylquinochalcones, it can be concluded that the color difference among the four safflower cultivars is mainly caused by C-glucosylquinochalcones but not anthocyanins.PRJNA738310). Through gene annotation, a total of 22,432 genes were annotated in Trembl, 19,108 genes were annotated in GO, 18,075 genes were annotated in KEGG, and 22,643 genes were annotated in NR  and two homologs to BAR73279.1  showed major expression in flowers. For the P450 homologous genes, HH_009388, HH_032521, and HH_005676 showed no expression in the flowers. HH_010713, HH_010718, HH_015117, and HH_014824 showed little expression in the flowers, three genes homologous to tr| M1KVN4| MENPI  and three homologous to cytochrome P450 71D9  showed major expression in flowers. The heatmap of the gene expression is shown in Based on our previous research reports  and the CHS is a rate-limiting enzyme in the biosynthesis of flavonoids. From the results, it can be clearly viewed that the expression of different expressed CHS genes was the highest in W, and the higher in LR. Considering that both the number and content of flavonoid metabolic species were higher in W, this may be caused by the high expression of CHS genes. In addition, CHI is another key enzyme in the biosynthesis of flavonoids. The expression of CHI (HH-028799) in DR was relatively high. Combined with the metabolomics results, the high content of the metabolic species detected in DR  may be due to this gene. During the biosynthesis of isoflavones, the expression levels of HH-015436 and HH-010949 were relatively high. There were several flavonoid metabolic species, especially those detected in W, and were also detected in other flowers and colors, whose content in W was relatively high, which may be due to the genes of HH-015436 and HH-010949. To the biosynthesis of C-glucosylquinochalcones, we can see that several genes, such as HH_034447 and HH_025963 for P450 and HH_011181 HH_000131 and HH_011181 for CGT, had an expression in DR and LR. Combined with the detected C-glucosylquinochalcones, it is possible that these genes are involved in the biosynthesis of C-glucosylquinochalcones in red flowers. There is also much more information to be mined from the integrated metabolic schematic diagram, which provides clues for screening the genes responsible for the biosynthesis of flavonoids in each color flower.To further screen candidate genes involved in C-glucosylquinochalcone biosynthesis, we first analyzed the expression pattern of the candidate genes that had expression in flowers . It is kP = 0.01). HH_011181 and HH_003238 also significantly increased during MeJA treatment (P = 0.01). Overall, the results indicated that HH_035319, HH_032689, and HH_018025 might participate in the biosynthesis of C-glucosylquinochalcones, which ultimately leads to the red color in safflower.At the same time, our previous study reported that flavonoid metabolic species of safflower, especially C-glucosylquinochalcones (such as safflomin A), significantly increased after treatment with MeJA . TherefoFlavonoids are the main metabolic species in safflower, and a total of 277 metabolic species were detected and annotated as flavonoids by KEGG, which is far more than our previous research, in which 209 flavonoid metabolites were detected based on a triple quadrupole-linear ion trap mass spectrometer (Q TRAP), API 6500 QTRAP LC/MS/MS system equipped with an ESI Turbo ion-spray interface . AlthougThere are six main anthocyanins in plants: cyanidin, delphinidin, pelargonin, peonidin, malvidin, and petunidin, which can change the color of plants from pink to blue violet . AnthocyCHS is the key gene in flavonoid biosynthesis, Expression analysis show that it was more highly expressed in W and LR than in other flowers and colors, which may explain the high flavonoid types and content in W and LR. At the same time, there was no one-to-one correlation between some genes and flavonoid metabolic species from the comparison of the existing flavonoid metabolic species and the annotated genes, which may be related to the lack of identification of a number of genes involved in flavonoid biosynthesis in safflower. This subject is worthy of further study. Of course, when mining the genes participating in flavonoid biosynthesis, our results can be favored, as we provided a lot of data about the candidate flavonoid biosynthesis genes and the related flavonoid species.The combination of different omics techniques provides a deeper understanding of several crucial genes involved in plant growth, development, and different stress responses . In saffThe chemical structure of C-glucosylquinochalcones indicates that it is formed by the glycosylation of naringin chalcone by C-glycosyltransferase (CGT) and the oxidation of cytochrome P450 (P450). At present, there are also a few reports on glycosyltransferases in safflower , althougFour kinds of safflower materials with different colors, white (W), yellow (Y), light red (LR), and deep red (DR), were used in this study. They were cultivated at the medicinal botanical garden on the Wenjiang Campus of Chengdu University of Traditional Chinese Medicine. For RNA sequencing, five inflorescences of safflower were mixed as a sample and three replicates for RNA sequencing were carried out. For the metabolism analysis, ten inflorescences of safflower plants were mixed as one sample.TM UltiMate 3000 UHPLC and Q Exactive benchtop Orbitrap mass spectrometer were used for data acquisition It has been widely used for metabolic species detection . The mobile phase was eluted with 0.1% formic acid water (A) to 0.1% acetonitrile (B) by gradient elution , the flow rate was 0.2 mL⋅min–1, the column temperature was 35°C, and the injection volume was 3 μL. An electrospray ion source (ESI) was used for positive ion detection. The spray voltage was 3.2 kV, the ion source temperature was 350°C, the sheath gas flow rate was 35 arb, the auxiliary gas flow rate was 10 arb, and the ion transport tube temperature was 320°C. The scanning mode was full MS/DD-MS2, with a first-level resolution of 35,000 and a second-level resolution of 17,500. The scanning range was m/z 100–1,500, and the impact energy gradient was 20, 40, and 60 eV.The freeze-dried sample was crushed using a mixer mill  with a zirconia bead for 1.5 min at 30 Hz. One hundred milligrams of powder was weighed and extracted overnight at 4°C with 1.2 mL 70% aqueous methanol. Following centrifugation at 12 000 rpm for 10 min, the extracts were filtered and used for liquid chromatography and mass spectrometry analyses. A Thermo Scientificetection . The chr2, and annotated metabolic species were then mapped to the KEGG Pathway database3. We focused on the analysis of flavonoids. The metabolic species annotated through KEGG (KEGG number: ko00941 to ko00944) were treated as flavonoids. In addition, as C-glucosylquinochalcone was not annotated in KEGG, C-glucosylquinochalcones were selected separately, together with the notes of flavonoids, as the analysis object.The raw data were imported to Compound Discoverer 3.0 software through template wizard settings and methods to establish the unknown compound identification process. Peak alignment and peak extraction were performed on the original data. The possible molecular formula can be fitted by the molecular ion chromatographic peak and isotope peak obtained from the extraction. The measured spectrum of secondary fragments was matched with the MZCloud network database and local TCM component database OTCML. The filtering parameters for matching results were set as follows: peak area threshold 80,000, the quality of primary and secondary deviation 5 ppm. The matching score was higher than 80. Compounds were analyzed and identified by comparing the filtered ions with the compound information in the database, reference substances, and related literature. The identified metabolic species were annotated using the KEGG Compound database4. The data were unit variance-scaled before unsupervised PCA. The HCA  results of the samples and metabolites are presented as heatmaps with dendrograms, while the Pearson correlation coefficients (PCCs) between samples were calculated by the cor function in R and presented as only heatmaps. Both HCA and PCC were carried out by R package pheatmap. For HCA, the normalized signal intensities of metabolites  were visualized as a color spectrum. Significantly regulated metabolites between groups were determined by VIP = 1 and absolute Log2 FC (fold change) = 1. VIP values were extracted from the OPLS-DA results, which also contained score plots and permutation plots and were generated using the R package MetaboAnalyst R. The data were log transformed (log2) and mean centered before OPLS-DA. To avoid overfitting, a permutation test (200 permutations) was performed.Unsupervised PCA  was performed by statistical analysis within RRNA isolation and purification and cDNA library construction and sequencing were performed as previously described . All tisP-value) with the Benjamini-Hochberg procedure to obtain the false discovery rate (FDR) when | log2–fold change| ≥ 1 and FDR < 0.05.DESeq2  was used28S rRNA expression as internal control. F:GGGTCCTTTCACGTTTCTGA and R: GGCCTGACTTATCGGTAGCA used as forward and reverse primers for 28S. The relative transcription levels were calculated using the ΔCt method. We compared the gene expression of RNAseq data (based on FPKM) with qPCR data. The accuracy of RNAseq was judged by analyzing the expression similarity between the two data. The details can be viewed in The results of RNAseq should be validated by qPCR to see if there is a good correlation between the expression obtained by both techniques. In this study, five genes  were randomly selected from RNAseq to make the validation. Firstly, the primers for the qPCR were designed quantitative primers using Primer 5, F: ACACCGCAAGAACAACAA and R:CACCTGAGCCGTAAGAAA were used as forward and reverse primers for HH_000029. F:GCTAATTTGCTAAT CCGTAC and R:GTGCCTCAGAAGACACTC were used as forward and reverse primers for HH_000060. F:TGCTGCTTTCTCCTCACA and R:ATTAGGCTCGC TCCATAC were used as forward and reverse primers for HH_000086. F:TCTTGTGGCTGTTGTGATTG and R:AGAGGCATTTCGGGTTTT were used as forward and reverse primers for HH_000142. F:AGCCATTGCTCCTTCTGA and R:AACCCTTCCTTGGTCTGC were used as forward and reverse primers for HH_000164. The qPCR analysis was performed using Bio-Rad CFX96 system  in a total reaction volume of 25 μL of SYBR Green PCR Master Mix (TaKaRa). The Ct value was determined using the instrument’s software. The relative quantification of gene expression was monitored after normalization to The treatment was primarily applied according to our previous report with some modifications . A 100 μ28S coding region were used as an internal reference gene. The primer details are listed in Real-time PCR analysis was performed as previously described . Total RPRJNA738310.The original contributions presented in the study are publicly available. This data can be found here: National Center for Biotechnology Information (NCBI) BioProject database under accession number JC and QW conceived and designed the experiments and wrote the manuscript. RW, SD, CR, and BX performed the experiments. CC contributed to the material planting and sample collection. JW and JP contributed to the data analysis. All authors discussed the results, commented on the manuscript, and read and approved the final 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.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.


Malnutrition is recognized as a risk factor for poor outcome in patients with gastric cancer (GC). In 2018, the Global Leadership Initiative on Malnutrition (GLIM) published standardized criteria for the diagnosis of malnutrition. Our aim was to investigate whether any of the components of the GLIM diagnostic criteria were related to worse clinical outcomes in patients with GC.This study analyzed patients with GC who underwent radical gastrectomy in our hospital between 2014 and 2019. A preoperative nutritional assessment was performed for each patient. Matching was based on the presence of three GLIM components: high weight loss (WL), low body mass index (BMI), and low skeletal muscle index (SMI).The analysis included 1,188 patients, including 241 (20.3%) with high WL, 156 (13.1%) with low BMI, and 355 (29.9%) with low SMI. Before matching, patients who met the GLIM component criteria were mostly associated with older age, low nutritional reserves, and late tumor progression. After matching, the clinical characteristics of the three cohorts were balanced. In the matched queue, the survival prognosis of the high WL group was worse than that of the non-WL group, and the postoperative complication rate was higher in the low SMI group than in the normal SMI group (P <0.05). In addition, the clinical outcomes in the low and normal BMI groups were similar (P >0.05).Of the GLIM criteria, high WL and low SMI may be associated with poor clinical outcomes in patients with GC, while a low BMI may not be associated with outcome. In 2020, the estimated number of new global cases of gastric cancer (GC) was more than one million, ranking fifth of all newly diagnosed cancers . As a maMany methods for nutritional screening and evaluation have been used to determine the nutritional status of patients, but there is no current consensus on the diagnostic criteria for malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) has published a new definition of adult malnutrition to establish a global consensus concerning the diagnostic criteria of malnutrition in clinical diagnosis and treatment . After rA total of 1,433 patients with GC who underwent radical gastrectomy between July 2014 and February 2019 at the First Affiliated Hospital of Wenzhou Medical University were recruited for this retrospective cohort study. The inclusion criteria were as follows: (a) pathologically diagnosed primary gastric adenocarcinoma before surgery; and (b) planned radical gastrectomy. The exclusion criteria were as follows: (a) patients with metastatic cancer or remnant stomach cancer; (b) patients who underwent neoadjuvant chemotherapy; (c) patients with other malignant tumors; and (d) patients with incomplete clinical records. Ultimately, 1,188 patients were included in the analysis. The management of GC was mainly based on the Japanese Gastric Cancer Treatment Guidelines 2010 . RelevanThe following data were collected and analyzed for all patients: (1) preoperative basic information, including sex, age, height, weight, hemoglobin concentration, albumin concentration, and comorbidities; (2) nutritional data, including weight loss (WL), body mass index (BMI), and skeletal muscle index (SMI); (3) postoperative pathology information, including tumor size, pathologic tumor–node–metastasis (pTNM) stage, and tumor differentiation degree; and (4) prognostic information, including postoperative complications (PC), postoperative length of stay (LOS), readmission rate within 1 month, and long-term survival information. Complications were classified in accordance with the Clavien–Dindo classification . Major PEach patient underwent an outpatient follow-up examination in the first month after surgery. Afterwards, additional follow-ups were performed by phone or outpatient examination every 3–6 months. Each follow-up visit included a physical examination, laboratory examination, and necessary imaging examinations, such as computed tomography (CT), ultrasound, and endoscopy. Overall survival (OS) time was defined as the time from the date of surgery to death from any cause or the last follow-up visit. Disease-free survival (DFS) time was defined as the date of surgery to the date of tumor recurrence or death from any cause. The last follow-up was conducted in July 2021.The GLIM criteria have five components, including three phenotypic criteria  and two etiological criteria  . As a tu2/m2 for males and ≤34.9 cm2/m2 for females according to the prospective study  or median  according to the data distribution, and the differences between groups were compared using Student’s t-test or Mann-Whitney U test. Categorical data were expressed as counts and percentages and were compared using the chi-square test.P <0.05 in the univariate analysis, were included in the multivariate analysis. Differences were considered statistically significant at P <0.05. All data were analyzed using SPSS Statistics  and R version 4.1.0 (http://www.r-project.org).To reduce the influence of selection bias and potential confounding factors on the prognostic analysis, we performed propensity score matching (PSM) analysis to obtain matched data. Three different matches were used to explore the correlation between the three phenotypic criteria and prognoses of patients with GC. Propensity scores were calculated based on the following patient data: age, Charlson score, hemoglobin, albumin, tumor size, TNM stage, and two phenotypic indicators . Three PSMs were conducted at a ratio of 1:1 using a caliper set to 0.20. Then, three sets of matched cohorts were obtained: the high and non-WL groups, the low and normal BMI groups, and the low and normal SMI groups. The Kaplan–Meier method was used to analyze the OS and DFS rates. The log-rank test was used to compare survival differences between the groups. Univariate and multivariate logistic and Cox regression analyses were used to analyze the risk factors of PCs and survival rates in the entire cohort. Factors with A total of 1,188 patients were included in the analysis, of which 867 (73.1%) were male. Baseline characteristics before matching are shown in P <0.05, The incidence of major PC in the entire cohort was 25% , of which 9.7%  had severe PC. Univariate and multivariate logistic regression analyses for the risk factors of major and severe PC were conducted in the entire cohort. The results showed that age, low SMI, albumin level, and Charlson score ≥2 were independent risk factors for major and severe PC , while there was no statistical difference in the matched WL and BMI cohorts  months. The three-year OS and DFS rates in the entire cohort were 67.5% and 64.2%, respectively. Univariate and multivariate Cox regression analyses for the risk factors of OS and DFS rates were performed in the entire cohort. The analysis results showed that age, high WL, tumor size, and TNM stage were independent risk factors for OS and DFS rates . Resistance exercise contributes to the synthesis of muscle proteins , and theThe current study has some limitations. First, this study was limited by the retrospective single-center design despite its large sample size, and further multi-regional validation studies are necessary. Second, despite the rigorous PSM analysis, our conclusions were limited by other variables that could not be measured. For example, we did not include preoperative and postoperative nutritional support data in the analysis due to incomplete data collection. Third, this study only analyzed the data of GC patients who underwent radical resection, so the conclusions drawn from this study cannot be generalized to GC patients receiving neoadjuvant chemotherapy or metastatic patients.In conclusion, high WL was associated with poor OS and DFS rates, and low SMI was associated with high PC rates. However, a low BMI may not be associated with poor clinical outcomes. Of the GLIM criteria, high WL and low SMI may be more valuable in predicting prognosis than low BMI in order to optimize clinical intervention.The 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 the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.Y-SH and X-DC contributed to conception and design of the study. L-BX, Y-QC, and W-JC acquired the data. L-BX, S-XZ, and LW analyzed and interpreted data and carried out the statistical analysis. L-BX and T-TM wrote the first draft of the manuscript. All authors contributed to manuscript revision, 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.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.


SerumCovid, a COVID-19 serological database focused on the diagnosis of health professionals, providing a preliminary analysis to contribute to the understanding of the antibody response to the SARS-CoV-2. The study population comprises 321 samples from 236 healthcare and frontline workers fighting COVID-19 in Vitória de Santo Antão, Brazil. Samples were collected from at least six days of symptoms to more than 100 days. The used immunoenzymatic assays were Euroimmun Anti-SARS-CoV-2 ELISA IgG and IgA. The most common gender in SerumCovid is female, while the most common age group is between 30 and 39 years old. However, no statistical differences were observed in either genders or age categories. The most reported symptoms were fatigue, headaches, and myalgia. Still, some subjects presented positive results for IgA after 130 days. Based on a temporal analysis, we have not identified general patterns as subjects presented high and low values of IgA and IgG with different evolution trends. Unexpectedly, for subjects with both serological tests, the outcome of IgA and IgG tests were the same (either positive or negative) for more than 80% of the samples. Therefore, SerumCovid helps better understand how COVID-19 affected healthcare and frontline workers, which increases knowledge about the infection and enables direct prevention actions.Serological databases represent an important source of information to perceive COVID-19 impact on health professionals involved in combating the disease. This paper describes  The global health crisis established in March 2020 by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) mobilized the entire scientific community to better understand and combat the pandemic. In the severe acute respiratory syndrome (SARS) epidemic in 2002–2003, the healthcare workers (HCW) represented the high-risk group of infection with rates around 20% . NowadayCoronaviruses are a group of enveloped viruses with non-segmented, single-stranded, and positive-sense RNA genomes . Severalet al. [The serological test for the presence of antibodies against SARS-CoV-2 might provide a more accurate estimate of the cumulative prevalence of SARS-CoV-2 infection in a population compared to the viral test, as the antibodies against the virus, in particular IgG, are likely to persist for a more extended period after the viral infection . Gómez-Oet al.  recentlyIn this context, databases in health are defined as a collection of organized and stored, accessible, and retrievable data. Then, building and providing an open database with data of age group, gender, diagnosis, professions and extracting information from them in the pandemic scenario offers relevant support to elaborate preventive and monitoring actions .SerumCovid database, which allows comparison between several HCW subgroups. This paper describes a database gathered in a northeastern city of Brazil from June 2020 to January 2021 focused on diagnosing HCW using tests for IgA and/or IgG antibodies. Subjects who presented suggestive clinical symptoms of respiratory syndromes and underwent clinical evaluation were invited to the study. Additionally, each participant was asked to answer a questionnaire including clinical data.Here, we present the descriptive and preliminary analysis performed on SerumCovid dataset used in this paper is provided in the supporting information . VSA, a municipality situated 40 km from Recife , has 139,583 inhabitants and registered 2,550 confirmed COVID-19 cases in 2020 . We consormation .The samples were initially collected in the Vitória Specialty Center, and then, the whole blood was processed in the Central Laboratory of Vitória. Finally, the frozen serum was sent to the Academic Center of Vitória/UFPE for subsequent serological analysis.The Enzyme-Linked Immunosorbent Assays (ELISA) were the Euroimmun  Anti-SARS-CoV-2 ELISA IgG and IgA assays following the manufacturer’s guidelines with all samples taken in duplicates. The kit targets spike recombinant proteins, Subunit S1, tested in different research groups with varied cohorts and populations. It demonstrated good sensitivity (90%) and specificity (92.5%) to IgA and excellent specificity when combining IgG and IgA (100%) in samples collected after only four days of symptoms  despite The initial step in the analysis process is to adjust the entire dataset to handle better a precise statistical analysis . FirstlySerumCovid includes information on 236 different subjects and a total of 321 samples. Each subject presents two groups of data: input information and questionnaires. The input information group refers to personal information  as well as objective medical information . The questionnaires, to which subjects can choose not to respond, include: ‘presence of symptoms’, ‘date of first symptoms’, ‘number of days with symptoms’. Some subjects do not have all fields filled, even for the input information.Additionally, as IgA and/or IgG antibodies may have been analyzed, the respective results are presented separated for each antibody. To summarize the individual results in one metric, we consider a positive diagnosis if at least one of the two tests (IgA and IgG) is positive. A negative diagnosis is defined if both tests are negative or if one is negative and the other is undefined . The undefined case, in turn, is when subjects present undefined results in both tests. All possible combinations of IgA and IgG results, and the respective diagnosis, are depicted in We provided descriptive statistical analyses on the complete database and its stratifications to characterize the profile of subjects. For visualization, we present doughnut charts, pyramid bar charts, bar plots, and violin plots . SpecifiThe results presented in this paper compose a larger study with approval of the Ethics Committee on Human Research UFPE—CAV 4.244.984; all participants provided written informed consent.SerumCovid enable evaluating participants’ serological structure against COVID-19. In this case, the mean and standard deviation of IgA and IgG levels were similar, as shown in Descriptive statistics of IgA and IgG antibodies with From the total of 321 samples, the IgA test was performed in 142 (44.2%) samples, while the IgG test was performed in 316 (98.4%) samples. The intersection comprising 137 (42.7%) samples has IgA and IgG tests information. It is possible to verify the consistency between results . In this case, more than 80% of the IgA and IgG tests were either both positives or negatives. Also, around 10.2% of the samples present only one defined output (positive or negative) while the other is inconclusive. Finally, about 9.5% of data are inconsistent regarding the antibody tests’ results, as IgA and IgG levels indicated opposed outputs .Adopting the one metric summarization as presented in SerumCovid  are associated with an affirmative answer. However, around 40% of these do not depict specific symptoms due to missing filling. For the remaining samples with at least one specified symptom , the most frequent for a positive diagnosis were fatigue/asthenia followed by headaches and myalgia .We also detail the work profile of the subjects analyzed in SerumCovid based on the one metric diagnosis  and IgG .Compared to the male group, the female group has a higher mean value and standard deviation for both IgA and IgG . We perfp-value: 0.483) and IgG .In this case, most of the data are in the range of 30–49 for both IgA and IgG. The first group (under 30 years) presented the smallest mean and standard deviation for both tests, while a greater value is found for both IgA and IgG for the 30–49 group . In termp-value of 0.396, there is no statistical evidence for shifts in the locations (medians) for IgA values for time since the first symptoms either < or ≥ 60 days. For IgG values, we had the same statistical conclusion . However, apart from an extreme observation (IgG: 11.48) in the < 60 days set, we do not observe a variability discrepancy between groups as in the IgA case.Most of the available data (60 for IgA and 79 for IgG) relates to symptoms in the previous two months . Only 12 IgA and 22 IgG samples relate to the first symptoms prior to that . Based on the violin plots from p-value: 0.491). IgG medians, in turn, are visually considerably different in the symptomatic and asymptomatic groups. Still, the statistical test provided p = 0.05, which is the usual threshold to decide against the equality of medians, possibly due to the small asymptomatic set size.Finally, we compared IgA and IgG values related to positive results on symptomatic and asymptomatic groups . For IgAWe also performed an analysis of the temporal serological evolution in subjects diagnosed with COVID-19, in accordance with the reasoning depicted in For a richer analysis, we considered subjects with at least three collections . This approach resulted in a different number of sequential samples for IgA and/or IgG. However, as previously mentioned, not all samples underwent both IgA and IgG tests. Also, we analyzed the temporal serological evolution after the subject presented a positive result. Therefore, filtering those cases, only seven patients maintained at least three collections starting in a positive one. The exams related to those seven patients are presented separately in The expected behavior for IgA is to drop sometime after the infection is cleared. However, some subjects have high values of IgA, or even increasing IgA trend, after two months of the positive result . Indeed, for IDs 66, 105, and 224, IgA increased, despite an initial decreasing behavior. For IgG, in turn, an opposite behavior is expected: IgG level rises over some months after infection ends. Yet, IgG decreased for four of the seven subjects, even though an initial increase is evidenced for one of them (subject 105). Therefore, we do not identify general patterns, with subjects presenting high and low values of IgA and IgG and with different evolution trends.SerumCovid is a database concerning HCW and frontline workers fighting the COVID-19 pandemic in 2020 who had suspicious symptoms, clinical or positive laboratory humoral immune response results for viral infection. Indeed, it has been intensely reported that antibody response to SARS-CoV-2, considering its high relevance for understanding the virus’ clearance, has a crucial role in preventing viral reinfection [The availability of descriptive data directly impacts health and frontline professionals involved in fighting COVID-19 as it permits elaborating care and prevention policies for health conditions monitoring. et al. [SerumCovid has shown that women would be the most exposed to the risk of infection, which is aligned to results reported by Buonafine et al. [Different from Galanis et al.  who indie et al. . Also, oe et al. .SerumCovid, the stratification with the most positive cases was from women aged between 30 and 49 years, similar to the results of Medeiros et al. [et al. [et al. [In s et al. . Indeed, [et al. . Surpris [et al. .et al. [et al. [As we used the IgA and IgG values to define a given test result , there is a clear distinction in the mean value and standard deviation when comparing positive and negative samples for IgA and IgG . This iset al. . Interes [et al.  presenteet al. [SerumCovid, the proportion of symptomatic concerning IgA positivity was 86.5%. In comparison, 82.7% was the IgG counterpart. These high figures are expected because an inclusion criterion for the database was subjects with suspect symptoms or positive laboratory humoral immune response results for viral infection.The observation of most frequent symptoms as fatigue/asthenia, headaches, and myalgia, is in agreement with the results of Buonafine et al. . These aet al. . In Seruet al. [SerumCovid, even without fitting in severe or even moderate cases, the subject classified as mild and even asymptomatic had detectable specific antibodies and continuity of detection of these values. Other researches confirm such information [Here, although the serology analysis concerning days since first symptoms showed that IgA persists for more than three weeks, like the results of Dan et al. , the meaormation ,22,26.SerumCovid contains data from serial collections for specific subjects, ranging from two to six samples per subject. The data suggest the absence or difficulty in identifying a pattern as both IgA and IgG values presented increasing and/or decreasing behavior. It evidences the urgency of monitoring subjects naturally infected or not that may have undergone active immunological sensitization by vaccines [We have also performed a temporal analysis as vaccines .The data presented here are of great relevance. They provide municipal and state management with valuable information to guide policies for monitoring HCW and frontline on their return to work. Indeed, our analysis is relevant as only clinical evaluation in suspected cases, and rapid tests for antibodies’ detection were previously offered to HCW and frontline of VSA municipality. The possibility of performing serological tests of high specificity and sensitivity allowed a more assertive descriptive profile and follow-up of the serological level of COVID-19 infection. It also aided the identification of risks and the necessary measures to mitigate when caring for COVID-19 patients in the basic health units and centers of high complexity in VSA. Our constant dialogue with the municipal authorities favors the development and implementation of policies based on serological evidence of the local HCW and frontline professionals.Additionally, the collected data serve as a basis for other studies, some already underway by our research group. Our efforts are directed to (i) the monitoring of infection rates among HCW and frontline workers, (ii) the understanding of immunological performance after vaccination, (iii) the analysis of patient life quality after COVID-19 infection, and (iv) the identification of which variables in this database would be more impactful to develop the disease by computational modeling.The findings in this report are subject to at least three limitations. First, it was not possible to test subjects with molecular RNA SARS-CoV-2, which would be more reliable tests for diagnosis . Second,SerumCovid is being used as a data source for monitoring a group of subjects for a longer time, including serological dosage after vaccination. These results will be released as soon as the minimum follow-up time is completed. It may provide data that will be important for targeting research and political decision-making to impact the population’s health positively.S1 File(XLSX)Click here for additional data file.


A lower MNA score was significantly associated with the risk of frailty , depression , and poor quality of life , and sarcopenia  after adjusting for potential confounders. In conclusion, patients with dementia were at risk of malnutrition, and nutritional status was associated to the risk of sarcopenia, frailty, depression, and quality of life.The purpose of this study was to investigate the nutritional status of dementia patients and examine the correlation with sarcopenia, frailty, depression, and quality of life. We enrolled patients aged 60 years and over with Mini Mental State Examination (MMSE) scores ≤ 26 (Taiwan), and dementia diagnosed by a neurologist or psychiatrist. Nutritional status was assessed with the Mini Nutritional Assessment (MNA). Muscle mass was measured by dual-energy X-ray absorptiometry. Muscle strength and endurance were evaluated by handgrip, leg-back strength, dumbbell curls, sit to stand test, and gait speed. Quality of life, frailty, and depression status were measured by questionnaires. Patients with moderate dementia (MMSE ≤ 20) had a significantly lower MNA score, muscle function, and quality of life than patients with mild dementia ( The number of deaths from dementia has increased globally. The latest report of the World Health Organization (WHO) indicates that dementia was ranked as the 7th leading cause of death in 2019  indicateDementia is a term used for a group of symptoms with deterioration in cognition, memory, thinking, emotion, and the ability to perform daily activities . DementiSarcopenia and frailty are also aging-related conditions ,9. IndivThis study was a cross-sectional study. The subjects were recruited in outpatient from Chung Shan Medical University Hospital between August 2019 and July 2021. Eligible subjects were ≥60 years old with a Mini Mental State Examination (MMSE) score ≤26 and dementia diagnosed by a neurologist or psychiatrist. The cutoff point for MMSE score set at 26 was according to the definition of the Dementia Diagnostic Manual of Taiwan Ministry of Health and Welfare . This st® duo control, Heidenheim, Germany). Body mass index (BMI) was calculated by height and weight. Waist circumference and calf circumference (CC) were measured by a tape. We used a dual-energy X-ray absorptiometry machine  to measure whole body lean soft tissue, appendicular skeletal muscle mass index (ASMI), and body fat percentage. The definition of obesity was based on the value of body fat percentage ≥25% for males or ≥30% for females [A questionnaire was used to collect the demographic data of the subjects, including age, gender, education, lifestyle habits, and family history of dementia, which were performed by the research assistant from asked the subjects or their caregiver. Blood pressure was measured by a digital electronic sphygmomanometer  were used to collect fasting venous blood specimens. Serum samples were prepared after centrifugation at 4 °C and 3000 rpm for 15 min. Hematological data, such as albumin, creatinine, glutamic pyruvic transaminase, glucose, and total cholesterol, were measured by an automated chemistry analyzer .The Mini Nutritional Assessment (MNA) was used to estimate the nutritional status of subjects. The MNA questionnaire consisted of 18 questions, including appetite, weight loss, mobility, physical and mental status, and dietary intake. The maximum total score of the screening tool is 30, and a score lower than 24 indicates malnutrition or risk of malnutrition . The SOFTests of handgrip and leg-back strength were used to evaluate the upper and lower limb muscle strength, respectively. Handgrip strength was assessed by a handgrip dynamometer . Subjects were instructed to squeeze the device with the dominant hand as hard as they could. Leg-back strength was assessed by a back dynamometer . For the test, subjects stood on the base of the back dynamometer; knees and hips flexed slightly while the lower back was maintained upright, and grasped a bar attached to a chain and dynamometer. Then, subjects were asked to lift up the bar with maximal effort. Two measurements were taken with handgrip and leg-back strength tests, and the maximum measurement value was recorded for the analysis.2 for males or <5.4 kg/m2 for females defined as low ASMI, the handgrip strength <28 kg for males or <18 kg for females defined as low handgrip, five-time sit to stand test ≥12 s defined as poor sit to stand test, the gait speed <1.0 m/s defined as low gait speed, and the SPPB score ≤9 defined as low SPPB. Sarcopenic obesity was defined as the subject meets the diagnosis of sarcopenia and obesity .The dumbbell curls, sit to stand test, and gait speed were used to assess the upper and lower limb muscle endurance according to the Senior Fitness Test Manual . Subject2 value, which was examined by using IBM SPSS Statistics for Windows . The statistical significance level was set at a p value <  0.05.SigmaPlot software  was used to examine all statistical tests in the present study. The descriptive statistics are presented as the means  ±  standard deviation (medians) or numbers of participants (percentages). We used the Shapiro–Wilk test to examine the normality of the distribution of the data. The independent t-test or Mann–Whitney rank sum test was used to compare the difference in continuous variables between the mild dementia and moderate dementia groups. A chi-square test or Fisher’s exact test was used to examine the difference in categorical variables between the mild dementia and moderate dementia groups. The correlations between nutritional status, frailty, depression, quality of life, and muscle function were examined by Pearson correlation (a parametric test) or Spearman’s rank order correlation (a non-parametric test) coefficient based on the normal or non-normal distribution of the data. Multiple logistic regression was used to examine the associations between MNA scores and frailty, depression, quality of life, and sarcopenia after adjusting for age, gender, and exercise habit. The logistic regression model’s goodness of fit was evaluated by the value of Hosmer-Lemeshow test and calculate Nagelkerke Rp < 0.01); however, there was no significant difference in age, sex, blood pressure, biochemical data, education level, tobacco use, alcohol use, or exercise habits between patients with mild dementia and patients with moderate dementia. Although there was no significant difference in anthropometric data between the mild and moderate dementia groups, the values for waist circumferences in both genders and body fat percentage in females were higher than the reference values . Additionally, there was a higher proportion of obesity in the moderate dementia group than in the mild dementia group (p = 0.09), which was slightly significant.The mean age of the participants was 76.5 ± 6.3 years, and the proportion of females was 72.5%. p < 0.01) and quality of life (p < 0.01) than those with mild dementia. In addition, the proportion of malnutrition  was higher in the patients with moderate dementia than in those with mild dementia. Regarding muscle mass, strength, and endurance, patients with moderate dementia had significantly lower values of CC (p < 0.01), whole body muscle mass (p = 0.01), ASMI (p = 0.01), handgrip strength (p < 0.01), dumbbell curls (p = 0.04), leg-back strength (p = 0.02), and SPPB (p = 0.02) than those with mild dementia.p = 0.04) and low SPPB scores  than patients with mild dementia. The proportion of low handgrip strength  and low CC  was slightly higher in the patients with moderate dementia than in those with mild dementia.r = 0.42; p < 0.01) and quality of life  and significantly negatively correlated with frailty  and depression .The correlations between MNA score and the severity of dementia, frailty, depression, and quality of life in patients with dementia are shown in p < 0.05). The frailty score was significantly correlated with muscle strength and endurance (p < 0.05). The depression and quality of life scores were significantly correlated with muscle endurance (p < 0.05).p < 0.01), depression , poor quality of life , low CC  and low handgrip . Additionally, after adjusting for potential confounders, a lower MNA score was significantly associated with an increased risk of low ASMI  and sarcopenia  in patients with dementia. In the adjusted regression models, the Hosmer and Lemeshow tests were all above 0.05, indicating that it was a goodness of fit test for logistic regression, and the values of Nagelkerke R2 for frailty, poor quality of life, low CC, low ASMI, and sarcopenia were 22.1%, 24.9%, 18.6%, 27.9%, and 29.8%, respectively. p = 0.13, A total of twenty-four patients with sarcopenia in the present study. We further calculated the proportion of sarcopenic obesity in patients with dementia . Fifty-e2, and a lower intake of daily food such as dairy products, vegetables, and fruits  to compare, and we cannot clarify the causal relationship between nutritional status and dementia in this cross-sectional study. Second, the critical cause of dementia including Alzheimer’s disease or vascular dementia had not been confirmed. Although the small sample size was one of the limitations in the present study, we have tried to perform the post hoc tests to calculate the statistical power of the sample size and found the statistical power for correlations between MNA score and these outcomes such as muscle mass, frailty, depression, and quality of life have reached 0.89–0.99. Large-scale and case-control studies are needed to detect the differences in nutritional status between patients with dementia and non-dementia. Third, although we did not examine the reliability of the questionnaires , these questionnaires were well developed and validated in clinical practice; besides, all questionnaires were performed by the research assistant from asked the subjects or their caregiver carefully to reduce the reported error. Regarding the reliability of the questionnaires, these questionnaires detected similar domains, such as body weight changes, activity, and mood states. We attempted to examine the consistency of MNA, SOF, GDS, and, QOL on similar questions and we found the consistency for the response status of the cases were high (>90%). So, the response status of our cases in the present was consistent. In patients with dementia, the risk of malnutrition was significantly correlated with muscle function, as well as the risk of sarcopenia, frailty, depression, and decreased quality of life, all of which worsen with the severity of the disease. Thus, we suggest that in patients with dementia, nutritional status should be monitored regularly to prevent the risk of sarcopenia and improve quality of life along with slowing the progression of dementia. Lifestyle interventions, including caloric restriction, protein supplementation, and physical activity consisting of aerobic and resistance exercise, have been considered the cornerstone of the treatment of sarcopenic obesity . Thus, f


Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs. The concept of artificial crossings to generate experimental plant populations was introduced scientifically in the historical work of Mendel  and becaApomixis is a type of asexual reproduction through seeds that produces a progeny which is genetically identical to the maternal plant  markers have been shown to be an excellent tool for genomic studies in function of their high-throughput nature, low error rates, and abundance in eukaryote genomes , syn. Panicum maximum Jacq. (B. K. Simon & S. W. L. Jacobs), Urochloa decumbens (Stapf), syn. Brachiaria decumbens Stapf (R. D. Webster) and Urochloa humidicola (Rendle), syn. Brachiaria humidicola  . Then, different biparental crossings are simulated (2.4). Finally, contaminant identification methodologies are applied to the simulated and real data .The following sections describe the steps involved in the generation of real and simulated data and their use to propose a methodology for contaminant identification in biparental crosses. First, the genotyping and allele dosage estimation for biparental F1 progenies of Urochloa humidicola , Urochloa decumbens , and Megathyrsus maximus , three important species of tropical forage grasses used in the pastures of tropical and subtropical areas. All these intraspecific crossings were performed by the Brazilian Agricultural Research Corporation (Embrapa) Gado de Corte, located in Campo Grande, Mato Grosso do Sul, Brazil , and are part of the breeding programs of this research institution. Details about the crossing were described by Deo et al.  and the apomictic cultivar U. humidicola cv. BRS Tupi, as described by Vigna et al.  and the apomict U. decumbens cv. Basilisk generated a progeny with 239 hybrids  .Genotyping-by-sequencing (GBS) libraries of the a et al.  and Deo a et al. , respecta et al. , and thea et al. , containUrochloa spp. and M. maximus against the reference genomes of Setaria viridis v1.0 and Panicum virgatum v1.0, respectively, since the reference genomes are not available for the species under study. In this stage, a limit of 20 dynamic programming problems (D), a maximum of four times to align a read (R), and a very-sensitive-local argument were considered. Both genomes used as references were retrieved from the Phytozome database  was set at six different values  for M. maximus and U. decumbens, aiming to compare the rates of the tetraploid dosages in the parents and assess the influence of the number and quality of the markers in further analysis. For the hexaploid population of U. humidicola, prop_mis was set at 0.2.For quality purposes, the SNPs were submitted to a filtering procedure using VCFtools  were created, and the genotypes of these parents were simulated based on the rate of allele dosages of parents genotyped in real biparental progenies: P1 and P2 from M), and all subsequent manipulations were performed using R software  apomictic clones (ACs), or (2) self-fertilization progeny of one of the parents (SPs), resulting in segregating individuals. The last two scenarios represent (3) cross-contamination, that is, when fertilization occurs by foreign pollen, resulting in half-siblings (HSs), or (4) when physical mixtures occur during seed handling, resulting in full contaminants (FCs). In each of the four possible scenarios, the size of the base population was 200 hybrids (HPs), and the HPs were progressively replaced by contaminants until 25% of the samples were contaminants. In addition, to investigate a joint scenario with four parents  with AC and SP contamination, a population of 1,200 individuals  was created. These described populations were constructed to investigate how contaminants influence principal component analysis (PCA) scatter plot dispersion patterns.n as the total simulated markers, the groups were composed of: n/2, n/4, n/8, n/16, n/32, and n/64 markers. For each population, the subset of markers used was randomly sampled from the total simulated markers. Furthermore, a biparental population with 200 individuals  was also simulated to exemplify the use of GA and CA in the contaminant identification.For the evaluation of the proposed contaminant identification method, 6,000 populations were simulated. Each one was composed of 200 individuals with a random number of contaminants, ranging between 1 and 50 and distributed per contaminant type considering random probabilities between 0.1 and 0.8. The populations were divided into six equal size groups according to the number of genotyped markers. Considering m,n representing the n random variables (herein SNPs) across m individuals, this analysis transforms X by multiplying it by the orthogonal eigenvectors, generating a matrix Xm,p of new p variables [the principal components (PCs)] with specific mathematical properties  is employed here to refer to an analysis that evaluates all the samples of a progeny considering what is genotypically expected for a contaminant. Three different measures were created for evaluating the samples: GA-I for AC identification and GA-II for SP identification, both accounting for a similarity rate between the sample and one of the parents (computed separately for each), and GA-III, accounting for a rate of unexpected genotypes in each sample considering the genotypes of both the parents, enabling the identification of half-siblings (HSs) and full contaminants (FCs) in the progeny.x is an AC of a parent p, the GA-I scores were calculated using the marker matrix M with n rows  and m columns (markers). Then, the similarity between x and p was the proportion of allele dosages in Mx,i that satisfied the condition, Mx,i = Mp,i with 1 ≤ i ≥m. This measure is based on the presumption that, given Mendel's law, each individual inherits genetic material from its parents  that satisfied Mx,i = Mp,i = 0. If a parent reproduces through self-fertilization, Mendelian segregation should be observed. Using a tetraploid species as an example, a parent with the genotype AABB at a specific locus, after self-fertilization, would generate a progeny with genotypes in all possible doses   for a marker and the other is simplex (AAAB), the gametes produced by the nulliplex are all AA, and for the simplex, they can be AA or AB  performed using an average linkage hierarchical clustering approach with R software  individuals within a cluster having the greatest GA-I values for one parent were considered ACs when the median of these measures was >0.75; (2) individuals within a cluster with the median GA-II values for one parent >0.75 and not belonging to Group (1) were considered SPs; and (3) individuals not belonging to Groups (1) and (2) and with a within-group minimum GA-III value greater than the maximum measure of the group with the minimum GA-III median were considered HSs/FCs. Therefore, in a simplified and automated process with only the threshold of GA measures as an The method was evaluated on a set of 1,000 simulated populations, computing the following metrics considering the most indicated clustering scheme: mean rate of hybrids correctly identified (MRH), mean rate of contaminants correctly identified (MRC), mean rate of apomictic clones correctly identified (MRAC), mean rate of SPs correctly identified (MRSP), and mean rate of cross-contaminants (HSs/FCs) correctly identified (MRCC). Furthermore, the same metrics were computed considering the three most indicated clustering schemes; in this situation, the highest rate among the three schemes for each simulated population was used to calculate the mean.Combining GA, CA, and PCA, we established a four-step contaminant identification methodology as follows, and applied it to the real populations  from one of the parents can be artificially added to the population, changing the dispersion pattern of individuals and inducing contaminant separation;Calculation of five different GA measures for each individual . GAI and GAII were calculated in the same way for all ploidy, but for hexaploid progeny, GAIII was adapted considering its respective segregation;Performance of CA using the GA data to identify clusters in the population;Visual inspection of the histograms, to classify the clusters according to the GA value differences described in section Clustering analysis. This step is done in a sequential process, in which the first ACs are identified and removed, then SPs, and lastly, HSs/FCs are identified and removed.Recalculation of PCA to confirm in the biplot the expected dispersion pattern of a population with no contaminants.https://cran.r-project.org/web/packages/shinydashboard/index.html), and DT (https://cran.r-project.org/web/packages/DT/index.html). polyCID is an R-Shiny Web graphical user interface (GUI) that combines all the described analyses in a simple way and provides a user-friendly tool, fully available and documented at https://github.com/lagmunicamp/polycid.All these procedures were unified in polyCID Shiny app, created using R software together with the libraries shiny . Next, the application of principal component analysis (PCA) to the simulated data is shown (3.2). Then, the use of GA and CA in contaminant identification in the simulated data is described (3.3), and finally, the results obtained from the contaminant identification in real data are presented (3.4). Furthermore, for simulated and real populations, P1/Parent 1 is the female parent and P2/Parent 2 is the male parent.Urochloa humidicola, 8,036 for Urochloa decumbens, and 6,337 for Megathyrsus maximus. Three individuals  of the U. humidicola progeny were removed because of the high content of missing data (>44%).After SNP calling using the Tassel- genotyping-by-sequencing (GBS) pipeline  , self-feConsidering that the analysis of the first two PCs through a PCA scatter plot could not reveal contaminants at low frequencies, biparental populations with 199 HPs and one contaminant were simulated, and 50 ACs (25%) of one of the parents were included. This unique contaminant may be an SP (Scenario 2), AC (Scenario 3), or FC (Scenario 4). As a result, we observed that the inclusion of these simulated clones, which occurs in real populations, changed the sums of variance in the first two PCs to a value of ~10.3% and increased the dispersion pattern in the PCA scatter plot, leading to the formation of different subgroups that allowed for the visualization of SP or HS contaminants , 12. On Finally, when simulating an open pollination population with four different possible parents, the biplot of the PCs was able to provide visual separation of the different progenies. It was possible to identify each cross since HPs formed a subgroup between their respective parents. In addition, the AC and SP contaminants grouped together with their parents .To look for the patterns in contaminant genotype analyses (GA) measures, the three described GAs were calculated in a simulated population of 200 samples composed of 150 hybrids (HPs) and 50 contaminants ; thus, five different values for each putative hybrid were generated. We analyzed how GA histograms behave for each type of contamination. In By using the idea underlying these visual histogram inspections, we implemented on GA measures a clustering-based approach for automatic contaminant identification. The established methodology employs a single hierarchical clustering algorithm on a different range of cluster numbers, defining the best clustering scheme with 27 clustering indexes . EmployiThe mean rate of hybrids (MRHs) correctly identified was 100% for all sets of markers, except for the smallest one (86 markers), which had a slight reduction. On the other hand, the mean rate of contaminants (MRC) was around 90% for the three largest sets , which started decreasing, reaching the value of 48% in the smallest one . It was In the function of these modest values, we also evaluated the method efficiency on the second and third best clustering configurations identified by the calculated indexes. Considering the best group separation within these three possible configurations also noticed in GA histograms, we achieved a performance improvement in all set markers, reaching an approximate accuracy of 100% in the three largest sets for all types of samples. Next, we observed a slight reduction  in the set of 344 markers, and more prominent reductions in the two smallest sets, reaching the values of 49% (MRAC), 85% (MRSP), and 40% (MRCC) . These f1 progenies of tropical forage grasses. For the progeny of M. maximus, the PCA plots with different values of prop_mis showed similar sample dispersion patterns and a reduction in variance explained by the first two PCs from 10.1 to 7.5% as the number of markers increased. Therefore, the dataset obtained with the default value of prop_mis = 0.20 was used in the further analysis. Even though there was no clear group formation in the PCA scatter plot, the pattern of parents on the opposite sides and HPs grouped between them provided evidence for the presence of contaminants (M. maximus cv. Mombaça)  .M. maximus progeny, with 134 and two samples. The GA-I histogram for Parent 2 (M. maximus cv. Mombaça) showed that the cluster with two samples had high scores and must be considered putative ACs of Parent 2 (M. maximus cv. Mombaça)  with GA revealed two clusters in the Mombaça) . On the  progeny . The exc progeny .U. decumbens, the PCA biplots for the different values of prop_mis showed different sample dispersion patterns (data not shown). As this is a very intuitive measure for the quality of SNPs when estimating allele dosage , providing evidence that those samples were putative ACs of this parent  showed that the cluster with 65 samples had scores close to 1.0  that provides all previously described analyses in a user-friendly tool that allows users to identify contaminants in biparental progeny in a simple way. The polyCID is completely R based, easy to install and presents a graphical interface designed for non-expert users, with several functions for interactive visualization of the results. The package accepts SNP data in the form of marker matrices with allele dosage information, loads this information, and performs the four-step contaminant identification methodology, as described in section Contaminant identification in real data. The Shiny-based GUI is included in the package as a standalone application, available at Experimental populations used in the breeding programs are usually derived from a controlled cross between two or more parents, but depending on the field experiment, the species analyzed, and its reproductive biology, individuals may be generated from the mixtures of seeds, foreign pollination during open pollinated crosses, self-fertilization, or apomixis by one of the parents during the crosses. The non-identification of these contaminant individuals can interfere not only in the selection cycles of breeding programs  is a multivariate data technique used to represent a dataset as orthogonal variables named principal components (PCs). Aiming at reducing the dimensionality of a set of variables through linear combinations, repeated information can be removed while the maximum variance–covariance structure of these variables is maintained  form different distributions with specific parameters. Although there are other approaches for parentage estimation already discussed in the literature, such as Identity by Descent (IBD) or pairwise relatedness (r)  can assist the breeder to better understand the reproductive biology of the species or genotype. On the other hand, identifying HS or FC contaminants highlights the need for greater control during the field experiment, avoiding foreign pollen or seed mixtures. Interestingly, we noticed that each cluster captured a distinct pattern in the GA measures, a phenomenon that can be leveraged to decipher the contaminant origin of the individuals. Importantly, by using the proposed approach, we did not find any configuration in which true hybrids were discarded, which is of great value for real applications.In summary, our proposal is a unique methodology that brings together all types of contamination in a single identification pipeline, representing an important resource for breeders, who need specific tools to deal with such contamination. Instead of relying solely on the putative population structure revealed by PCA methodologies, genotypic analysis (GA) indexes are calculated, taking into account the genetics behind the origin of the contaminants. Compared to the exclusive use of PCA, this pipeline identifies one or a few contaminating individuals with more confidence. This increased confidence makes this methodology ideal for situations in the field that lead to mixtures of seeds or foreign pollen during fertilization, which usually occurs at low rates.Megathyrsus maximus, Urochloa decumbens, and Urochloa humidicola F1 progenies led to the conclusion that these real progenies had AC contaminants  corroborated the findings of Deo et al.  were identified as clones of the male parent. It is possible that these clones were inserted into these two progenies during seed collection. Additionally, the male parent was used as a control in the field experiments, and the plants may have produced seeds and/or seedlings that became mixed with the real progeny. As the female parent of these populations was entirely sexual, the absence of SPs suggests the predominance of allogamy in these plants and self-incompatibility as the main mechanism to guarantee this mode of reproduction.Principal component analysis, GA, and CA using genotypic data from the aminants . For M. o et al. , while fU. humidicola (2n = 6x = 36). GA-I and GA-II were performed in the same way as for tetraploid species, but GA-III was adapted considering the segregation and possible combination of gametes in hexaploid species. For the progeny of U. humidicola, the combined PC and GA-I histogram analysis suggested the presence of 61 clones of the female parent (21.8% of the population). This result suggests that the genotype H031 (CIAT 26146) also reproduces through facultative apomixis, even though it has been widely cited in the literature as a unique obligate sexual genotype of U. humidicola  , which have been experiencing considerable cheapening in the recent years. PCA, GA, and CA were combined in a simple and semi-automated pipeline, and the coupling of a low-cost genotyping with such pipeline thus allows for a more precise and efficient detection of incompatibilities between a group of putative hybrids and the identification of contaminants in biparental crosses of tetraploid and hexaploid species.The implementation of this simple approach in the identification of contaminants in biparental progenies of polyploid species can increase the efficiency of breeding programs. In this context, the polyCID Shiny app was designed to enhance the ability of breeders to use our methodology, even with no bioinformatics expertise. Great advances in sequencing technologies and genotyping tools have enabled us to explore vast amounts of genetic data in a more cost-effective and faster way; however, the ability to handle and apply this genome information to breeding remains a significant barrier for most breeders and experimental researchers. Therefore, we designed the polyCID Shiny app as an interactive and user-friendly application that is completely R based and easy to install, incorporating the analysis in a single environment and enabling the users to extract information on contaminant individuals without requiring knowledge of a programming language.a priori, taking into account how the number and quality of the markers affect the final results.Finally, although our analyses were performed with real and simulated progenies of tropical forage grasses, this methodology can be extended to any biparental progeny of tetraploid or hexaploid species. It can be applied in the early stages of genomic studies with GBS in biparental polyploid progenies, such as genetic linkage map construction and genomic prediction, to identify possible contaminants. However, as the price of SNP genotyping is constantly decreasing and other polyploid genotyping tools are emerging, the application of our methodology even in experiments that do not involve SNPs may be possible, mainly in the intermediate and final stages of the breeding program to confirm the absence of contamination in the final stages and cultivar release. In the case of genotyping with a lower number of molecular markers, it is suggested that simulation studies be carried out https://www.ncbi.nlm.nih.gov/, PRJNA703438, https://www.ncbi.nlm.nih.gov/, SRP148665, https://www.ncbi.nlm.nih.gov/, PRJNA563938.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: FM, AM, AA, BV, and AS conceived the study. LC, RS, SB, MS, LJ, and CV conducted the field experiments. AM, RF, and BV performed the laboratory experiments. FM, AM, and AA analyzed the data. FM, AM, AA, RF, and BV wrote the manuscript. AA and FM implemented the Shiny web app. All authors contributed to the article and approved the submitted version.This work was supported by grants from the Fundação de Amparo à Pesquisa de do Estado de São Paulo (FAPESP), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Embrapa, and the Associação para o Fomento à Pesquisa de Melhoramento de Forrageiras (UNIPASTO). FM received a Ph.D. fellowship from CAPES (88882.329502/2019-01); AA received a Ph.D. fellowship from FAPESP (2019/03232-6); RF received a PD fellowship from FAPESP (2018/19219-6); SB, LJ, and AS received research fellowships from CNPq .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.


Multivariate analysis showed that T, fT, and the fT/C ratio were inversely correlated with the CRP, AAG, and FER concentrations independently of age and blood lipids. When adjustment for BMI was made, T, fT, and the fT/C ratio were negatively correlated with the AAG concentrations only. In addition, it was demonstrated that gonadal androgens were positively correlated with physical activity level (p < 0.01). We have concluded that a lowered serum T concentration may promote inflammatory processes independently of adipose tissue and age through a reduced inhibition of inflammatory cytokine synthesis, which leads to enhanced acute phase protein production. Therefore, a low serum T concentration appears to be an independent risk factor in the development of atherosclerosis and cardiovascular diseases. Moreover, the positive correlation between testosterone and physical activity level suggests that exercise training attenuates the age-related decrease in gonadal androgens and, in this way, may reduce the enhancement of systemic low-grade inflammation in aging men.The negative relationship between testosterone and inflammatory cytokines has been reported for decades, although the exact mechanisms of their interactions are still not clear. At the same time, little is known about the relation between androgens and acute phase proteins. Therefore, in this investigation, we aimed to study the relationship between androgen status and inflammatory acute phase reactants in a group of men using multi-linear regression analysis. Venous blood samples were taken from 149 men ranging in age from 18 to 77 years. Gonadal androgens [testosterone (T) and free testosterone (fT)], acute phase reactants , cortisol (C), and lipid profile concentrations were determined. It was demonstrated that the markers of T and fT were negatively correlated with all acute phase proteins (CRP, FER, and AAG;  Age-related upregulation of the inflammatory response (described as “inflamm-aging”)  and the In recent years, connections between testosterone and the inflammatory process have been widely studied , althouThe interactions between androgens and inflammation may be influenced by adipose tissue because it is well known that the inflammatory process results from an imbalance between the pro- and antioxidant systems often related to dysfunctional adipose tissue . We haveThe possible influence of body fat content on the relationship between androgen and inflammatory status also indicates that the level of physical activity could be of paramount importance in this connection. There is a fair amount of data showing that regular exercise training may attenuate inflammation , 15, eveStudies concerning the relationship between androgens and inflammation have so far mainly focused on the inflammatory cytokines such as interleukin-1 (IL-1), IL-6, and TNF-α . Far lesn = 94) involved in this investigation participated in the study by Majerczak et al. .Overnight blood samples were taken from the antecubital vein at rest between 7:30 and 8:30 a.m. from all participants. Blood for serum CRP, AAG, FER, IL-6, total cholesterol (TC), triglycerides (TG), low- and high-density lipoproteins , total T, cortisol (C), and sex hormone-binding globulin (SHBG) concentrations was collected into plain tubes and left to clot for a minimum of 30 min at room temperature and then centrifuged at 4,000 rpm for 5 min. Blood for plasma IL-6 was collected in plain tubes containing EDTA and then centrifuged at 653 × −1 and the intra- and inter-assay coefficients of variation (CVs) were <8% and 10%, respectively.The CRP and AAG concentrations were measured using the Siemens-Dade Behring BN ProSpec nephelometer . Serum TC, TG, and LDL and HDL concentrations were determined with an enzymatic colorimetric method according to the manufacturer’s protocol using the Cobas c501 analyzer . Moreover, non-HDL concentration was calculated by subtracting the HDL cholesterol value from TC. Serum FER was measured with the chemiluminescence method using the Architect i1000SR analyzer . Plasma IL-6 concentration was determined by enzyme-linked immunosorbent assay (ELISA) according to the manufacturer’s instruction . The detection limit for this measurement was 0.039 ng L−1 for T, C, and SHBG, respectively. The intra- and inter-assay CVs for these assays were 3.4% and 5.9%, 1.2% and 1.6%, and 2.4% and 3.7% for T, C, and SHBG, respectively. The measurement method for T and C was standardized against the isotope dilution gas chromatography–mass spectrometry (ID GC/MS) reference method. SHBG measurement was standardized against the 1st International Standard for SHBG from the National Institute for Biological Standards and Control . Moreover, fT was calculated using the assumption-free empirical equations  with detection limits of 0.09, 1.0, and 0.8 nmol Lquations , as it wquations .f = 0.1  with five predictors, a significance level of 0.05, and power of 0.90. Non-normally distributed variables were log-transformed prior to analysis in order to reduce skewness of the data. The data points that deviated from the group means by more than 3 SDs were treated as outliers and excluded from further analysis. Then, the assumptions of the multiple regression analysis were verified and were not violated. The significance level was set at p ≤ 0.05, and all data are presented as the mean ± SD. The analyses were performed using STATISTICA software, version 10 .In this study, the bivariate correlations were evaluated using Spearman’s correlation coefficient because of the non-normal distribution of the inflammatory and lipid profile variables and the time spent on physical activity. Multiple regression analysis was performed to evaluate the independent contribution of androgens to inflammatory markers, adjusting for age, BMI, and blood lipids. We have calculated that a sample size of 134 individuals would be needed to obtain an effect size of The means for the basic anthropometric, hematological, blood biochemical, and hormonal parameters of the studied group of subjects were all in the range of the reference values ].p = 0.06) (p > 0.05).It was demonstrated that androgens (T and fT concentrations) and the fT/C ratio were significantly inversely correlated with the blood lipid profile in all studied men .Simultaneously, age and BMI correlated positively with most of the inflammatory markers  and the lipid profile variables  and negatively with the androgen profile parameters  and HDL concentration. The only exceptions were a non-significant correlation between age and the AAG concentration and between age and the HDL concentration .Moreover, we also determined the relationship between T, fT, and the fT/C ratio and physical activity level. It was demonstrated that there were significant positive correlations between all the androgen profile variables and the time spent on physical activity and sports  and blood lipid profile  and between the markers of androgen status and the inflammatory markers  , suggesting a connection between these two parameters that is unrelated to adipose tissue–testosterone interactions. The exact mechanism by which androgens may influence the AAG level is unclear, but based on the above-mentioned effects of androgens on the inflammatory process, it may be suggested that testosterone may diminish AAG production through its inhibitory action on inflammatory cytokine synthesis. The negative association between T concentration and the inflammatory markers, which is frequently reported , a higher BMI, and a lower androgen profile (see Results), which, together, represent a hallmark of the inflammatory state, especially in the absence of other medical conditions. It seems that the role of AAG in inflammatory progression needs to be further investigated, also in view of the recent data that demonstrated the importance of IL-6 and IL-6R (interleukin-6 receptor) in cardiovascular diseases  , there was a significant negative correlation between age and the androgen status, especially the fT concentration, i.e., the biologically active form of gonadal androgens  . Howeverctively)  reportedgens see , that thThe data regarding the effect of exercise training on gonadal androgens in aging males are, however, inconsistent , 53. Nev2 peak decline during aging (p > 0.05), it should be noted that exercise training may induce an enhanced HDL concentration. One may speculate that a moderately higher level of physical activity affects positively both the testosterone and HDL concentrations, however, recent data have indicated that a more important risk factor for cardiovascular events is HDL cholesterol efflux capacity 2 peak deng aging  and posing aging  and thatng aging . Althougcapacity , which ccapacity .Based on the above literature data, it may be inferred that the correlation between androgens and inflammatory markers observed in this study is not accidental. Simultaneously, we are aware of the limitations of our study, especially that related to the cross-sectional design of the research and the limited number of studied men, which impeded us from establishing a firm causality between the androgen status and the inflammatory markers and blood lipid profile. We also acknowledge that measurements of the apolipoproteins and oxLDL (oxidized LDL) and determination of anti-inflammatory markers would reinforce our results. Nevertheless, the multiple regression analysis, including androgen, inflammatory, and blood lipid profile, in this moderately large group of men see  and suggSumming up, we have shown that chronic low-grade inflammation might be linked to lowered androgen levels in men. Based on the results of this study and on previously published data, we have concluded that a lowered androgen profile is related to a reduced inhibition of inflammatory cytokine synthesis, which leads to an enhanced production of acute phase proteins. Accordingly, we postulate that a low serum T concentration should be considered as an independent risk factor in the development of atherosclerosis and cardiovascular diseases. Moreover, the positive correlation between testosterone and physical activity level suggests that exercise training may reduce the age-related decrease in gonadal androgens, which seems to be one of the main beneficial effects (anti-inflammatory one) of physical activity in aging men.The 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 the Local Ethical Committee at the Regional Medical Chamber in Krakow, Poland (opinion no. 48/KBL/OIL/2009). The participants provided written informed consent to participate in this study.MG and JAZ contributed to the conception and design of the study. MG, JM, JZ-B, KD, JKK, and JAZ performed the experimental procedures and data analysis. MG performed the statistical analysis and wrote the first draft of the manuscript. MG, JM, JKK, and JAZ wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the European Union from the resources of the European Regional Development Fund under the Innovative Economy Programme (grant no. POIG.01.01.02-00-069/09) and by funds from the University School of Physical Education in Krakow, Poland, for the statutory research in 2015–2018 awarded to JAZ (grant no. 120/BS/KFiB/2017) and to MG (grant nos. 75/BS/KFiB/2015 and 121/BS/KFiB/2017).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.