Neonicotinoids are a popular and widely-used class of insecticides whose heavy usage rates and purported negative impacts on bees and other beneficial insects has led to questions about their mobility and accumulation in the environment. Neonicotinoid compounds are currently registered for over 140 different crop uses in the United States, with commercial growers continuing to rely heavily on neonicotinoid insecticides for the control of key insect pests through a combination of in-ground and foliar applications. In 2008, the Wisconsin Department of Agriculture, Trade and Consumer Protection (DATCP) began testing for neonicotinoids in groundwater test wells in the state, reporting detections of one or more neonicotinoids in dozens of shallow groundwater test wells. In 2011, similar detection levels were confirmed in several high-capacity overhead center-pivot irrigation systems in central Wisconsin. The current study was initiated to investigate the spatial extent and magnitude of neonicotinoid contamination in groundwater in and around areas of irrigated commercial agriculture in central Wisconsin. From 2013–2015 a total of 317 samples were collected from 91 unique high-capacity irrigation wells and tested for the presence of thiamethoxam (TMX), a neonicotinoid, using enzyme-linked immunosorbent assays. 67% of all samples were positive for TMX at a concentration above the analytical limit of quantification (0.05 μg/L) and 78% of all wells tested positive at least once. Mean detection was 0.28 μg/L, with a maximum detection of 1.67 μg/L. Five wells had at least one detection exceeding 1.00 μg/L. Furthermore, an analysis of the spatial structure of these well detects suggests that contamination profiles vary across the landscape, with differences in mean detection levels observed from landscape (25 km), to farm (5 km), to individual well (500 m) scales. We also provide an update of DATCP’s neonicotinoid monitoring in Wisconsin’s shallow groundwater test wells and private potable wells for the years 2011–2017. Neonicotinoids are a popular and widely-used class of insecticides whose water-soluble nature and 20-year usage history has led to questions about their potential to accumulate in the environment and harm local ecosystems –6. When Most neonicotinoids are registered for application as seed treatments, foliar sprays, and in-furrow soil drenches, with seed treatments and soil applications constituting 60% of agricultural neonicotinoid usage . Seed anEmerging concern about neonicotinoid contamination has motivated the development of ecosystem- and regional-scale water quality surveys ,13–17. Cet al. suggest an ecological threshold for neonicotinoids be established at 0.2 μg/L long-term acute and 0.035 μg/L long-term chronic exposure limits. Similar aquatic invertebrate benchmarks of 0.385 μg/L acute exposure and 0.01 μg/L chronic exposure have been established by the US Environmental Protection Agency (EPA) as part of their registration review of IMD ) prior to analysis to satisfy assumptions of normality. Summary statistics specifically referencing “detections” do not include any values falling below the analytical limit of quantification. For certain figures and analyses where the inclusion of all kit results was warranted, values below the limit of quantification were left as determined by the kit rather than being re-coded as zeroes. Variance component estimates were calculated from a random effects model that included the three spatial  and three temporal factors  previously introduced. A fitted random effects model was generated using the function lme [varcomp [All statistical analyses were performed in R, version 3.4.1 . Functiotion lme . Varianc[varcomp . SpatialFig 1A. Individual detections for TMX, IMD, and CLO from 2011–2017 as well as the analytical limit of quantification is illustrated in Fig 1B.The Wisconsin DATCP has been testing select field-edge groundwater monitoring wells and private potable wells for neonicotinoid compounds since 2008. These samples were collected as part of the agency’s broader agrichemical contamination monitoring program and were also screened for a number of other contaminants, but here we cover only the results of their neonicotinoid assays. The location of all wells and location and magnitude of all positive neonicotinoid detections is illustrated in Table 2). Of the 527 total samples collected from monitoring wells, 150 (28%) tested positive for TMX, 162 (31%) tested positive for IMD, and 194 (37%) testing positive for CLO. Mean TMX detection was 0.90 μg/L, with a maximum detection of 3.89 μg/L recorded in Adams Co. in 2015. Mean IMD detection was 0.61 μg/L, with a maximum of 4.54 μg/L recorded in Waushara Co. in 2014. Mean CLO detection was 0.503 μg/L, with a maximum of 2.30 μg/L recorded in Dane Co. in 2017. In 2016 and 2017, DATCP also tested all monitoring well samples for the three less common neonicotinoids acetamiprid, dinotefuran, and thiacloprid. No monitoring well samples were positive for these three compounds at concentrations above the detection limit of 0.05 μg/L.From 2011 through 2017, 28 of the 53 monitoring well sites tested positive for at least one neonicotinoid, with 5 wells testing positive for two neonicotinoids, and 14 wells testing positive for IMD, CLO, and TMX during this seven-year period , with a mean of 0.52 μg/L and a maximum of 1.43 μg/L, recorded in Sauk Co. in 2011. IMD was detected in 40 samples (3%), with a mean of 0.47 μg/L and a maximum of 1.59 μg/L recorded in Waushara Co. in 2013. CLO was detected in 37 samples (3%), with a mean detection of 0.49 μg/L and a maximum of 3.88 μg/L recorded in Waushara Co. in 2013. All samples collected from private potable wells in 2016 and 2017 were also tested for the less common neonicotinoids acetamiprid, dinotefuran, and thiacloprid. One sample, from Junea Co. in 2017, tested positive for dinotefuran at 0.15 μg/L.Fig 2). Overall 78% of wells tested positive at least once for TMX at a concentration above the limit of quantification (0.05 μg/L) of the ELISA kits, with a maximum detection of 1.69 μg/L and a mean detection of 0.28 ± 0.29 μg/L. In 2013, 48 unique high-capacity wells were sampled once at the end of the vegetable growing season (late August to early September). In this survey year, 81% (39/48) of wells sampled returned a positive TMX detection (concentration > 0.05 μg/L), with a mean positive detection of 0.28 ± 0.28 μg/L and a maximum detection of 1.56 μg/L. In 2014, 79 samples were collected from 53 wells, of which 35 independent wells (66%) returned at least one positive detection. Mean detection was 0.26 ± 0.27 μg/L with a maximum detection of 1.21 μg/L. In 2014, the second year of our study, we divided sampling into mid-season (mid Jun–early Aug) and late-season (late Aug—Sept) portions of the growing season to better understand patterns of detection during the growing season. The detection rate of mid-season samples was 64% (34/53) with a maximum of 1.06 μg/L and mean of 0.25 ± 0.26 μg/L; the late-season detection rate was 73% (19/26) with a maximum of 1.21 μg/L and a mean of 0.28 ± 0.29 μg/L. In 2015, 56 wells were sampled approximately monthly from May through September  for a total of 190 samples. Three of these wells were sampled weekly rather than monthly during this same period. Of the 56 total wells sampled in 2015, 40 (71%) had at least one TMX detection above the limit of quantification. Maximum detection in 2015 was 1.69 μg/L, and mean detection was 0.29 ± 0.30 μg/L. Early season (May) detections averaged 0.32 ± 0.27 μg/L with a maximum of 0.89 μg/L and a detection rate of 62% (34/55 samples), mid-season (June-July) detections averaged 0.32 ± 0.37 μg/L with a maximum of 1.69 μg/L and a detection rate of 65% (54/83 samples), and late-season detections averaged 0.21 ± 0.17 μg/L with a maximum of 0.77 μg/L and a detection rate of 63% (33/52 samples).Over the entire three-year period of these investigations, 317 total samples were collected from 91 unique high-capacity irrigation wells . Wells on farms A, B, and E consistently possessed the highest average neonicotinoid concentrations across years, though mean concentration on most farms (except E) fell slightly each year. Note that Farm B only participated in 2015, and Farm D did not. While consistently higher detections on certain farms is grounds for further investigation, we cannot be sure which of the many human and physical factors that vary between farm locations may be responsible for these higher detections. However, farms C and D do overlap in space and TMX detections did not differ significantly between these farms for any year in which they were both sampled. Unless neonicotinoid usage rates differed significantly between farms, this does seem to suggest that physical and landscape characteristics may be playing an important role in the average contamination values in wells on these farms.Within each year of this study, we observed significant differences in mean TMX concentrations in high-capacity wells between farms . To evaluate the significance of farm and sampling week as explanatory factors of TMX concentration in these wells, a standard least-squares regression model including farm, farm*week, week, and week*week (starting May 1) was constructed. Results showed the farm main effect as a significant factor , as expected, considering the differences in mean concentration between farms noted previously. However, the week main effect term , farm*week interaction term , and week*week polynomial term  were not significant. Despite the lack of statistical significance of the temporal terms in this model, it is clear that concentrations vary from week to week at each well location, though not necessarily in a predictable way. More frequent sampling, taking several repeated samples and averaging the results, using polar organic chemical integrative samplers (POCIS), or sampling other wells with higher average detections could help improve these results in the future. Prior work has shown increased mobility of soil- and seed-applied neonicotinoids in potato fields later in the season after vine-kill and crop harvest [During 2015, our collaborators on farms A, C, and G agreed to sample one of their wells on a weekly rather than monthly basis to increase the temporal resolution of our sampling at select well locations. While most samples from Farms A and G were positive detections, no sample from Farm G was above the limit of quantification , we performed a variance component analysis on a nested random effects model containing these three spatial factors to determine the relative contribution of each factor in partially explaining the observed variation in TMX concentrations. While region and farm explained 11.4% and 13.6% of variance, respectively, the single most significant explanatory factor was well, explaining 51.2% of variance in TMX concentrations. The remaining 23.8% of variance is not explained by these factors. These results suggest that while differences exist in average TMX concentration and detection frequency between regions and farms, the most significant spatial factor in explaining both response variables is the individual well .One of the primary aims of this study was to determine the spatial scale at which differences in contamination levels can be observed. In order to quantify the relative importance of each of spatial scale . In the 47 wells we sampled that had more than two positive detections , the detection range was an average of 0.26 ppb, and in all four wells where the highest TMX detections were observed (>1.5 μg/L), we also observed detections below 0.5 μg/L. In fact, higher average detection in these resampled wells was strongly correlated with a wider range of detections at those same wells (R2 = 0.65).An analysis of detection ranges within individual wells confirms the results of our variance component analysis which identified individual well as the most significant explanatory factor among spatial factors. While we did find variation in mean TMX detection between farms, we also observed a high degree of variation in detections in individual wells at different time points . Within our weekly sampling data, we did note a pattern of rising and falling concentrations over the course of a single growing season , so even though we observed no trends in detection levels across years, contamination profiles do vary at individual wells within a season . This high degree of variability in detections strongly suggests that neonicotinoid contaminants are transient in these systems and a more frequent sampling regimen at additional wells would clarify some of the temporal dynamics at play. In addition, it has been reported that at least in surface water systems, grab sampling can result in variable contaminant detections that under-represent average contamination levels by 50% and maximum concentrations by 1 to 3 orders of magnitude [In this study, we noted no obvious long-term rising or falling trends in detection levels in this study . We are currently working on developing explanatory models for neonicotinoid concentrations reported in this paper based on factors such as cropping history in the vicinity of each well, physical well parameters, and hydrological factors including water table depth and proximity to watershed borders. We hope that identifying specific explanatory factors contributing to higher levels of groundwater contamination can help build a more complete picture of the processes and risk factors associated with the use of neonicotinoid insecticides in an agricultural context.We suspect that contaminant detections in groundwater samples taken from high-capacity wells are less variable than samples taken from static test wells or surface water grab samples because of the larger, deeper sources of water and non-point contamination sources, but in future work we would consider taking several samples at each well over a short period of time to potentially reduce variability. Despite high variance in TMX concentrations, certain farms, and certain individual wells within those farms, were more likely to have repeated above-average TMX concentrations in well water samples than others (Table 3) similar to, if slightly lower, than those reported in the same region by the Wisconsin DATCP over the same time period . This difference in concentration levels could be partially explained by the different sources of water: in this study, we drew water from high-capacity irrigation wells, which are usually installed with intake screens relatively deep within the aquifer (Table 1), while the Wisconsin DATCP drew water samples from shallow groundwater monitoring wells. Deeper sources of water would likely be less contaminated as they are farther from surface sources of neonicotinoid contaminants [We detected TMX in high-capacity irrigation wells at concentrations . In the Mekong Delta in Vietnam, TMX, among multiple other agrochemicals, was reported in groundwater and surface water used as sources of drinking water for the local populations [In addition to these environmental detections, several studies including this paper have reported detections of neonicotinoids in drinking water. Here we reported TMX detections in private potable wells in central Wisconsin at concentrations up to 1.43 μg/L, IMD detections up to 1.59 μg/L, and CLO detections up to 3.88 μg/L (ulations . Worryinulations . Neonicoulations  but the https://nassgeodata.gmu.edu/), reducing groundwater recharge routes that do not pass through a cultivated field surface, leaching agricultural contaminants into groundwater and subsequently into adjacent surface waters. Aquatic invertebrates living in these streams are highly sensitive to neonicotinoid insecticides and form a critical link in aquatic food chains [While most previous neonicotinoid surveys have focused on surface water contamination or direct field-edge runoff, we reported frequent detections of these compounds in both shallow and deep groundwater samples . Depending on local hydrology, up to 90% of streamflow may be attributed to groundwater discharge . In inted chains ,45. Any d chains ,6,46,47.Figs 3), suggesting neonicotinoid applications at individual fields may be responsible for elevated detections at nearby wells, rather than a more uniform detection profile indicative of a higher degree of groundwater mixing and dilution.One of the initial aims of this study was to quantify the spatial distribution of neonicotinoid contamination within Wisconsin’s Central Sands as prior detections had only been reported from a small number of wells spread widely across the region ,30,31. WFig 1), and suggesting that in the aggregate, agricultural activities in central Wisconsin are contributing to widespread detections of neonicotinoids in both deep and shallow groundwater. Considering that DATCP has tested samples from across the entire state, but detections were primarily reported in central Wisconsin, we suspect that there is an intersection of neonicotinoid usage rates and contamination risk factors in this part of the state that contribute to these frequent detections. While this study focused on central Wisconsin specifically, the intersection of neonicotinoid usage and groundwater systems vulnerable to contamination from agricultural activities covers a large part of the Midwestern United States and Great Lakes region and elsewhere [While concentrations varied from well to well, detection rates of TMX were overall relatively high in our study area, a finding confirmed by DATCP’s parallel groundwater monitoring activities (lsewhere ,41.The frequency of thiamethoxam detections in both shallow and deep wells throughout the study region underscores the need for growers to be judicious in the use of these chemicals when operating in areas at elevated risk of groundwater contamination. In combination, frequent neonicotinoid detections in shallow field-edge monitoring wells, deeper high-capacity irrigation wells, and private potable wells highlight the potential risk of agricultural contaminants to appear throughout an entire aquifer underlying an intensive agroecosystem. Neonicotinoids are popular and effective insecticides whose usage will likely continue to expand, absent new regulatory action or the commercialization of next-generation insecticides. Their usage is not currently considered a human health hazard, but it is becoming increasingly clear that neonicotinoids are easily mobilized into the environment after field applications ,48. Evid


Seminal plasma is a key biological fluid that modulates sperm function in the reproduction process. However, its role in sperm biotechnologies is scarce in poultry. The aims of the present study were to study the amino acids profile and total proteins of seminal plasma in 12 Spanish chicken breeds and to investigate the role of seminal plasma on cryoresistance of rooster sperm. To investigate the role of seminal plasma on cryoresistance, diluted pooled semen samples were cryopreserved in the presence and absence of seminal plasma. Glutamic acid was the most abundant free amino acid in seminal plasma, followed by alanine, serine, valine, and glycine. There was an influence of breed (P<0.05) on the percentage of viable sperm after freezing-thawing of samples with seminal plasma. Cluster analysis revealed that White Prat, Black Castellana, Blue Andaluza, Quail Castellana, and Red-Barred Vasca returned the best freezing-thawing response (good freezers). There was a positive correlation between seminal plasma concentrations of valine, isoleucine lysine, leucine and post thaw viability. The evaluation of fertilization capacity of frozen-thawed semen from the breeds White Prat (‘good freezer’) and Black-Red Andaluza (‘bad freezer’) showed that good freezer had higher fertility  compared to bad freezer breed , even if the difference was not significant (P = 0.08). The TUNEL assay revealed that freezing/thawing procedures in presence of seminal plasma provoked higher DNA fragmentation in most of the breeds, with a positive correlation between seminal alanine, valine, isoleucine, methionine, leucine, tyrosine, phenylalanine concentrations and DNA integrity. DNA fragmentation was lower in absence of seminal plasma and the breed effect on sperm viability was highly reduced. It is concluded that specific seminal plasma amino acids were associated with post-thaw percentage of viable sperm and DNA integrity. The removal of seminal plasma decreases the variability of the results and DNA fragmentation damages. The influence of seminal plasma on sperm storage may vary among species. Its removal is recommended in the majority of semen cryopreservation protocols of species such as caprine in order to ensure maximal sperm viability , but it +, K+, Ca+) [The components of rooster seminal plasma derive from the proximal efferent ducts, epididymides and deferent ducts . During K+, Ca+) , a charaK+, Ca+) ,14. The K+, Ca+) . The rolK+, Ca+) ,17, or oK+, Ca+)  or phospK+, Ca+) . MoreoveK+, Ca+) . SeveralK+, Ca+) –24. AlthK+, Ca+)  and protK+, Ca+) . CarpentK+, Ca+)  noted thThe seminal plasma amino acid profile may vary among genotypes , and dif2 sand-floor pens with partial roof cover at the El Encín Research Station . These birds were raised as part of the INIA’s genetic resources conservation program [The birds used in this study were of 12 Spanish chicken breeds . One hundred forty four roosters (12 of each breed), all of which were one year old at the beginning of the experiment were used for the collection of semen. In addition, hens of White Prat and Black-Red Andaluza breeds (30 hens per breed) were later used for insemination experiments. All animals were housed under natural photoperiod and temperature conditions in two 12 m program , 29. AllThe amino acid profile and total proteins in seminal plasma were studied within each breed. A pool of seminal plasma for each breed was obtained every month, from August to November, and amino acid and total protein analysed in them . Mean sperm concentrations were also evaluated within each breed.To investigate the role of seminal plasma on cryoresistance of rooster sperm, diluted pooled semen samples  collected from June to November, were divided into two aliquots. One aliquot was frozen with presence of seminal plasma, and the other one was frozen after removal of seminal plasma by centrifugation. Sperm variables were analysed before and after freezing-thawing.The fertilization capacity of frozen semen from one ‘good freezer’ and one ‘bad freezer’ breed, as judged from the obtained in vitro post-thaw sperm assessments, was estimated in an artificial insemination (AI) experiment from the percentage fertile eggs resulting from two consecutive intravaginal AI, three days apart, of a total of 60 hens (30 per breed). Hens of each breed were used for testing fertilizing ability of frozen-thawed semen of the two mentioned breeds; i.e. 15 hens belonging to good freezer breed were inseminated with semen of good freezer, and the remaining 15 with semen of bad freezer; the same criterion was used for the hens of bad freezer breed.2O (0.08 g), potassium acetate (0.5 g), polyvinylpyrrolidone  and 100 mL H2O . This diluted, pooled semen was then immediately placed at 5°C, transported to the laboratory, and sperm concentration and sperm variables  examined (within 45 min of collection). Afterward, each pool was divided into two aliquots. One aliquot, diluted as required with Lake and Ravie medium to a concentration of 1200 × 106 sperm/mL . In the other one  the seminal plasma was removed by dilution with Lake-Centri diluent (1:4 v/v) and centrifugation at 600 g during 20 min prior to freezing. Briefly, the Lake-Centri medium was composed of 1000 ml H2O, 1.28 g potassium citrate tribasic monohydrate, 19.2 g sodium-L-glutamate, 6.0 g D-fructose, 5.0 g TES, 5.1 g sodium acetate trihydrate, 0.8 g magnesium acetate tetrahydrate, and 5.2 ml of 1N sodium hydroxide . The pellet obtained was reconstituted with Lake-Ravie medium. Both aliquot with and without seminal plasma were diluted with Lake-Ravie medium to a final concentration of 1200 × 106 sperm/mL. Pure (≥99%) glycerol (GLY) was then added to the diluted samples, to leave a final 8% concentration (vol/vol), and equilibrated for 10 min at 5°C. After equilibration, the samples were loaded into 0.25 mL French straws and then frozen in two steps, i.e., from 5°C to −35°C at 7°C/min, and then from −35°C to −140°C at 60°C/min [Semen was collected twice weekly over the study period, in 15-mL graduated centrifuge tubes (Sterilin) using the massage technique described by Burrows and Quinn . Pools o60°C/min . FreezinSeminal plasma obtained for amino acid assay (see above) was immediately stored at -20°C until determination of the seminal plasma free amino acid composition. Separation and determination of amino acid was made by ion exchange column chromatography ,34. BrieSperm concentration and motility were assayed using a computer-aided sperm analyses (CASA) system coupled to a phase contrast microscope  and employing Sperm Class Analyzer  v.4.0. software  . For motPropidium iodide (PI) and SYBR-14 were used as fluorochromes in the examination of membrane integrity ; 200 cel6 spermatozoa/mL in 4% paraformaldehyde. Subsequently 10 μL of this dilution were placed on a glass slide and left to dry. Then, the spermatozoa were permeabilized with 0.1% of Triton X-100 in PBS. After a wash in PBS, fragmented DNA was nick end-labelled with tetramethylrhodamine-conjugated dUTP by adding 10 μL of the working solution provided by the kit–containing the substrates and the enzyme terminal transferase–on the sample. The reaction was conducted incubating the slides in a humid box for 1 h at 37°C. After a wash with PBS the nucleus were counterstained with Hoechst at 0.1mg/mL in PBS for 5 min in the dark. Following an additional wash with PBS the slides were mounted using Fluoromount  and observed under fluorescent microscopy . Percentages of positive TUNEL spermatozoa (TUNEL+) per sample were recorded by counting a minimum of 200 spermatozoa per microscopy preparation, using an epifluorescence microscope at 400× (wavelength: 510–560 nm).All sperm variables were measured again for each pool after their eventual thawing. In addition, DNA integrity was also assessed in fresh sperm and after freezing-thawing by terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL). For this, the kit “In Situ Cell Death Detection”  was used following manufacturer’s instructions with minor changes in order to adapt the technique to the analyses of rooster sperm. Briefly, each sperm sample was diluted to 12 x 10White Prat and Black-Red Andaluza breeds were chosen for the insemination trial as examples of ‘good freezers’ and ‘bad freezers’, respectively on the basis of the post-thaw in vitro sperm assessments. Glycerol was removed prior to AI. Straws of semen frozen were thawed, and the thawed semen was progressively diluted with four volume parts of Lake Centri medium at 5°C by successively adding 0.07, 0.18, 0.33, 0.6, 1.24, and 1.58 volumes of medium to one volume of semen . These samples were then centrifuged at 600xg for 10 min, the supernatant solution discarded, and the pellet resuspended  in Lake and Ravie medium . All inv value: The default value is 10, the minimum is 2, and the maximum is 999). The same type of analysis is then successively applied to the observations belonging to the v-1 folds (training sample), and the results of the analyses are applied to sample v (testing sample) to compute an index of "predictive validity". Variables with a skewed distribution were arcsine-transformed (sperm variables), log-transformed (proteins) or submitted to box-cox transformation (amino acids) before statistical analysis. The influence of breed on amino acid and total protein were analysed by one way ANOVA, following the statistical model xij = m + Ai + eij, where xij = the measured variable , m = the overall mean of x, Ai = the effect of breed (i = 1–12), and eij = the residual (j = 1–4). A Tukey post hoc analysis was performed to compare the differences between means of amino acids. Correlations between amino acids and sperm TUNEL+ and between amino acids and sperm viability were determined by the Spearman test; data of all breeds were included in the correlation analysis. The influence of breed and seminal plasma on frozen-thawed sperm variables were analysed by ANOVA, following the statistical model xijk = m + Ai + Bj + ABij + eijk, where xijk = the measured sperm variable, m = the overall mean of variable x, Ai = the effect of breed (i = 1–12), Bj = the effect of seminal plasma (j = 1–2), ABij = the interaction between A and B, and eijk = the residual (k = 1–14). A post hoc Newman-Keuls analysis was performed to compare the differences in mean sperm variable values between breeds and treatments . Comparisons between fresh and frozen-thawed sperm variables were made using a paired t-test. Identification of good and bad freezer was made by clustering  the differences between percentage of sperm viability before and after freezing of each breed. The association among fertility rate and semen from good and bad freezers was assessed using the Chi-squared test. Data were expressed as means ± S.E. All statistical calculations were made using TIBCO Statistica software v.13.3 (TIBCO Software Inc.).Clustering by the amino acid content in seminal plasma of each breed was performed using the iterative k-technique to classify the amino acids into three clusters. Statistica software  specifically uses Lloyd's method to implement the k-Means algorithm . The rig6 spermatozoa/mL) and Quail Silver Castellana (1515.8 x 106 spermatozoa/mL). The overall mean sperm concentration was 2204.9 (± 107.03) x 106 spermatozoa/mL. No correlation was found between plasma total protein concentration and sperm concentration.Seminal plasma content in amino acid in each breed is shown in There were no significant differences in fresh sperm viability between breeds. The removal of the seminal plasma by centrifugation reduced the sperm viability in seven of the 12 breeds  and VCL (P<0.05) in Black-Barred Andaluza, and total (P<0.05) and progressive motility (P<0.05) in Quail Silver Castellana; in contrast, the removal of seminal plasma significantly increased VSL (P<0.05) in Red Villafranquina . SignifiThe TUNEL assay revealed that fresh sperm from White-Faced Spanish had the highest degree of DNA fragmentation being statistically higher (P<0.05) than most of the breeds . We founIn fresh samples, cysteine was the only amino acid with a positive correlation with % TUNEL+ sperm . In frozen-thawed semen samples with plasma, there was a negative correlation between the values of post-thaw TUNEL+ and certain amino acids such as alanine, valine, isoleucine, methionine, leucine, tyrosine, phenylalanine, serine (i.e. a positive correlation of the concentration of these amino acids with integrity of DNA) .The evaluation of fertilization capacity of frozen-thawed semen from the breeds White Prat and Black-Red Andaluza, as examples of ‘good freezers’ and ‘bad freezers’, respectively, showed that the good freezer had higher fertility  compared to bad freezer breed , even if the difference was not significant (P = 0.08).In the present study, we showed that seminal plasma and breed differences in seminal amino acids contents affect the results of chicken sperm cryopreservation. There was a relationship between concentrations of some amino acids  and sperm viability and DNA integrity after freezing-thawing irrespective of the breed. Although the deleterious effect of freezing-thawing on the DNA integrity of chicken sperm has been previously reported , 40, 41,Differences between breeds were found with respect to seminal free amino acid concentrations, and some of these differences were relevant regarding sperm cryoprotection. Glutamic acid was the main amino acid in seminal plasma, accounting for 76–89% (on molar basis) of the total amino acid content. This is in agreement with earlier observations in cock semen . GlutamaThe use of glutamine as a component of the freezing medium resulted in a higher post-thaw motility in human  and roosWe found a positive association between the seminal plasma concentration of the hydrophobic amino acids valine, isoleucine and leucine and the charged lysine with membrane integrity as measured by the viability assay. It remains unknown whether these associations point to a casual effect, i.e. a cryoprotective effect of these amino acids. This could be tested in future experiments by including these amino acids in varying concentrations in the freezing medium.There were no significant differences between breeds in the percentage of sperm viability after freezing-thawing of samples without seminal plasma, whereas there was an influence of breed on the percentage of viable sperm after freezing-thawing of samples with seminal plasma. This suggests a role of seminal plasma to prevent or to favor cryoinjury during freezing-thawing process. The influence of factors that may vary between breeds other than seminal plasma amino acids should not be ruled out. For instance, Blesbois and de Reviers , reporteThe decreasing of sperm viability in samples frozen without plasma seems mainly due to damage during centrifugation. Interestingly, we found that, whereas the presence of seminal plasma did not seem to affect the sperm survival (% viable), as the relative decrease of viability, i.e. post-thaw compared with pre-freeze, was not different between semen frozen with and without plasma, the DNA was less damaged in the latter. This could reflect different mechanisms of damage; plasma membrane integrity may predominantly be damaged by temperature- and dehydration-dependent membrane phase transitions , along wNegative correlations were seen between post-thaw %TUNEL+ sperm frozen with seminal plasma and the concentrations in seminal plasma of specific amino acids: alanine, valine, isoleucine, methionine, leucine, tyrosine, phenylalanine, serine, most of which are of hydrophobic nature. Thus, it could be expected that incorporating some of these amino acids in freezing media could decrease DNA damage during freeze-thawing. It was indeed reported that the incorporation of methionine to bovine  and fishThe seminal plasma protein concentrations differed between breeds, in agreement with previous studies in other chicken breeds . While sIn conclusion, the results suggest that the decreasing of sperm viability in samples frozen without seminal plasma is largely due to damage during centrifugation. Our findings indicate that removal of seminal plasma did not seem to affect sperm survival during freezing and thawing, but did clearly reduce DNA damage of sperm. Specific amino acids were associated with post-thaw percentage of viable sperm and DNA integrity.S1 Dataset(PDF)Click here for additional data file.S2 Dataset(PDF)Click here for additional data file.S3 DatasetSperm viability of frozen sperm with plasma and without plasma of 12 Spanish rooster breeds.(PDF)Click here for additional data file.S4 Dataset(PDF)Click here for additional data file.S5 Dataset(PDF)Click here for additional data file.


The human gut is colonized by diverse fungi (mycobiota), and fungi have long been suspected in the pathogenesis of SpA. Our study unraveled a disease-specific interkingdom network alteration in AS, suggesting that fungi, or the interkingdom interactions between bacteria and fungi, may play an essential role in AS development. However, our study is limited by sample size, and in-depth mechanism studies and additional large-scale investigations characterizing the gut mycobiome in AS patients are needed to form a foundation for research into the relationship between mycobiota dysbiosis and AS development. Proteobacteria and decreased Bacteroidetes, which was contributed by enrichment of Escherichia-Shigella, Veillonella, Lachnospiraceae NK4A136 group, and reduction of Prevotella strain 9, Megamona, and Fusobacterium. The gut mycobiota of AS patients was characterized by higher levels of Ascomycota, especially the class of Dothideomycetes, and decreased abundance of Basidiomycota, which was mainly contributed by the decease of Agaricales. Compared to HCs, decreased ITS2/16S biodiversity ratios and altered bacterial-fungal interkingdom networks were observed in AS patients. Compared with nonsteroidal anti-inflammatory drugs (NSAIDs), treating AS patients with biological agents induced obvious changes in the gut mycobiota, and this result was highly associated with disease activity indexes, including AS disease activity index (ASDAS) C-reactive protein (asCRP), erythrocyte sedimentation rate (ESR), and Bath AS disease activity index (BASDAI). In addition, altered mycobiota in AS patients was also found associated with the degree of radiographic damage.Intestinal bacterial dysbiosis has been increasingly linked to ankylosing spondylitis (AS), which is a prototypic and best studied subtype of spondyloarthritis (SpA). Fungi and bacteria coexist in the human gut and interact with each other. Although they have been shown to contribute actively to health or disease, no studies have investigated whether the fungal microbiota in AS patients is perturbed. In this study, fecal samples from 22 AS patients, with clinical and radiographic assessments, and 16 healthy controls (HCs) were collected to systematically characterize the gut microbiota and mycobiota in AS patients by 16S rRNA gene- and ITS2-based DNA sequencing. Our results showed that the microbiota of AS patients was characterized by increased abundance of IMPORTANCE The human gut is colonized by diverse fungi (mycobiota), and fungi have long been suspected in the pathogenesis of SpA. Our study unraveled a disease-specific interkingdom network alteration in AS, suggesting that fungi, or the interkingdom interactions between bacteria and fungi, may play an essential role in AS development. However, our study is limited by sample size, and in-depth mechanism studies and additional large-scale investigations characterizing the gut mycobiome in AS patients are needed to form a foundation for research into the relationship between mycobiota dysbiosis and AS development. Spondyloarthritis (SpA) is a group of several related but phenotypically distinct disorders: psoriatic arthritis (PsA), arthritis related to inflammatory bowel disease (IBD), reactive arthritis, a subgroup of juvenile idiopathic arthritis, and ankylosing spondylitis (AS) . The exa3–Bacteroides  were found to be associated with intestinal inflammation in SpA  was also identified in fecal samples from IBD patients . Althougn in SpA . β-1,3-Gn in SpA , and thin in SpA . Dectin-n in SpA , 27. Hown in SpA , 28–32, In this study, we characterized both microbial and fungal microbiota in fecal samples from AS patients using high-throughput sequencing and analyzed the correlation between bacterial and fungal microbiota. We also compared the gut microbiomes of AS patients receiving different therapeutic regimens or with different disease activity parameters, including AS disease activity index (ASDAS) C-reactive protein (asCRP), erythrocyte sedimentation rate (ESR), and Bath AS disease activity index (BASDAI). Our study represented the first systematic analysis of the microbiome in AS patients, and data from this study provided a rationale to support the role of mycobiota dysbiosis in AS pathogenesis.n = 8) were newly diagnosed as having AS and 80.49% (n = 33) were patients that had the disease for different lengths of time and were treated by biological agents (BLs) or nonsteroidal anti-inflammatory drug (NSAIDs). Nineteen patients were excluded from this study due to medical histories of other diseases, and/or use of antibiotics, probiotics, prebiotics, or synbiotics before the collection of fecal samples. The 22 AS patients included in this study were all males with an average age of 34.86 years. Fourteen HCs were excluded for age and gender matching. The remaining 16 HCs were all males with an average age of 34.35 years . As expected, the majority of these patients were HLA-B27 positive (>85%) and with axial involvement (>94%). The disease activity parameters, including asCRP, ESR, and BASDAI were summarized in We included a total of 71 individuals in the current analysis, composed of 30 healthy controls (HCs) and 41 individuals with AS . The analysis based on weighted UniFrac showed a statistically significant increase in beta diversity in the AS group compared to the HC group (P = 0.0022), although the principal-coordinate analysis (PCoA) with weighted or unweighted UniFrac analysis did not exhibit an obvious separation between AS samples and HC samples  and 22 AS patients , with an average of 57,862 ± 5,400 reads aligned per sample. Compared with HCs, the observed species and alpha diversity (assessed using Shannon and Simpson indexes) of gut microbiota in AS patients was relatively increased, while there were no statistical differences among all indexes  and decreased Bacteroidetes (P = 0.0177) were found in AS patients compared to the HCs . At the genus level, enriched Escherichia-Shigella (Proteobacteria), Veillonella (Firmicutes), Faecalibacterium (Firmicutes), Eubacterium rectale group (Firmicutes), Streptococcus (Firmicutes), Lachnospiraceae NK4A136 group (Firmicutes), and reduced pattern of Prevotella strain 9 (Bacteroidetes), Megamonas (Firmicutes), and Fusobacterium (Fusobacteria) in AS patients were detected  and Proteobacteria . Download FIG S2, TIF file, 0.1 MB.Abundance of bacterial phyla in healthy control (HC) and ankylosing spondylitis (AS) groups. The Copyright © 2019 Li et al.2019Li et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Bacteroidetes phylum, especially the Bacterioidia class and the Bacteroidales order. In contrast, Enterobacteriales and Gammaproteobacteria were significantly more abundant in the AS group  tree showing significantly different bacterial groups between HC and AS groups. o, order; c, class; f, family. Download Copyright © 2019 Li et al.2019Li et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00176-18.4FIG S4FIG S4, TIF file, 0.2 MB.Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) in the gut microbiota of AS patients and HCs. Download Copyright © 2019 Li et al.2019Li et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the n = 5). The other patients were grouped by the therapeutic regimens received, including biologics (BL) (n = 8) and NSAID (NS) (n = 9)  compared to HCs. Among the AS patients, treatment with biologics resulted in reduction of alpha diversity of gut bacteria in contrast to the TN group, especially when tested by Simpson index (P = 0.0497). The principal-coordinate analysis (PCoA) by both weighted and unweighted UniFrac analysis showed that there was no obvious separation of groups  and the Veillonella genus were found enriched in AS patients treated with biologics compared with the patients without treatment (TN group), in which the species of Phascolarctobacterium faecium was significantly more abundant  (f groups . The varf groups  and D. Tabundant . HoweverP < 0.05). To explore the equilibrium between bacterial and fungal diversity in the gut, we determined the fungal-to-bacterial species ratio. This ratio was significantly decreased in AS samples , while the phylum Basidiomycota was dominant in HCs, which may primarily due to the abundance of Agaricales.By sequencing ITS2, we obtained a total of 2,201,454 high-quality filtered reads from the fecal mycobiota of 16 HCs  and 22 AS patients , with an average 57,933 ± 5,372 reads aligned per sample. The results showed that there were 354 OTUs unique to the HC group and 265 OTUs unique to the AS group, while 349 OTUs were shared by the two groups . In contr genera . The compatients , except 10.1128/mSystems.00176-18.5FIG S5FIG S5, TIF file, 2.9 MB.PCoA of gut mycobiota in AS patients and HCs with center of mass. Download Copyright © 2019 Li et al.2019Li et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P < 0.05; see also P = 0.027) and Etanercept (P = 0.046), compared with that of the HC group. The PCoA by both weighted and unweighted Unifrac analysis showed that the gut mycobiota of the BL group separately clearly from the HC, NS, and TN groups, indicating that the treatment of Etanercept had a profound influence on the fungal communities in AS patients  was used to establish the relationship between AS disease activity indexes  and the bacterial and fungal genera. As shown in patterns .10.1128/mSystems.00176-18.7FIG S7FIG S7, TIF file, 0.1 MB.Relative abundance of fungal genera in AS patients with different degrees of radiographic damage. 2, 3, 4, imaging classification of radiographic damage. 0, the HCs without radiographic damage. Download Copyright © 2019 Li et al.2019Li et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the In this study, we explored a distinct mycobiota pattern and altered bacterial-fungal interactions in the guts of AS patients, which represented a novel research viewpoint of the gut microbiome dysbiosis in AS.Proteobacteria and 0.18-fold-decreased Bacteroidetes abundance were found in AS patients, which was shown in a previous study of the terminal ileum biopsy specimens of AS patients , patients receiving nonsteroidal anti-inflammatory drug (NSAID) (n = 18), and patients receiving biologics (BL) (n = 15). The samples of the healthy controls (HCs) (n = 30) were collected during routine physical examination at the Liaoning International Travel Health Care Center, Dalian, China.The fecal samples were collected in stool collection tubes , which were prefilled with stool DNA stabilizer  for collection, then frozen, and stored at −80°C for further use. All subjects were examined clinically before sampling and were subsequently divided into four groups according to different pharmacological therapies: treatment naive (TN)  for the study participants.The metagenomic DNA in 0.2 g of each fecal sample was extracted by the QIAamp DNA stool minikit . The purity and concentration of the metagenomic DNA were measured by using a NanoDrop 2000 spectrophotometer .The V3-V4 region of 16S rRNA genes (representing bacteria) and the internal transcribed spacer region 2 (ITS2) (representing fungi)  were ampSequences of the V3-V4 region of 16S rRNA genes and ITS2 were detected using an Illumina HiSeq PE250 platform . The Ribosomal Database Project (RDP) Classifier 2.8 was used for taxonomical assignment of all sequences at 50% confidence after the raw sequences were identified by their unique barcodes. OTUs present in 50% or more of the fecal samples were identified as core OTUs. The observed species and Shannon and Simpson indexes were calculated with QIIME (version 1.9.1). The abundance and diversity of the OTUs (beta diversity) were examined using principal-coordinate analysis (PCoA) with weighted or unweighted UniFrac analysis in R software. The linear discriminant analysis (LDA) effect size analysis (LEfSe) was used with the Kruskal-Wallis rank sum test to detect features with significantly different abundances between assigned taxa, and the linear discriminant analysis was performed to estimate the effect size of each feature. The bacterial groups with LDA score of ≧4.00 were shown as significantly abundant group in the indicated group. Canonical correlation analysis (CCA) was used to establish the relationship between environmental factors  and the bacterial community in fecal samples from AS patients as described previously . The cort test was performed with the assistance of GraphPad Prism 6 . Results were considered to be statistically significant with P < 0.05.All data were evaluated as means ± SEM. Statistical analysis of the quantitative multiple group comparisons was performed using one-way analysis of variance (and nonparametric), followed by Wilcox’s test; when two groups were compared, the nonparametric This study protocol was approved by the Ethics Committees of all participating hospitals, including Dalian Municipal Central Hospital and the Second Affiliated Hospital of Dalian Medical University, Dalian, China. All the procedures were performed in accordance with the guidelines approved by the Ethics Committee of Dalian Medical University, China. After receiving a written description of the aim of this study, all participants gave written informed consent prior to enrollment.PRJNA525366 and PRJNA525614.The sequence data were deposited in NCBI Sequence Read Archive (SRA) with accession numbers


This case-control study examines the association between sport-related concussion and levels of traumatic brain injury biomarkers in collegiate athletes. Is sport-related concussion associated with levels of traumatic brain injury biomarkers in collegiate athletes?In this case-control study of 504 collegiate athletes with concussion, contact sport control athletes, and non–contact sport athletes, the athletes with concussion had significant elevations in multiple traumatic brain injury biomarkers compared with preseason baseline and with 2 groups of control athletes without concussion during the acute postinjury period.These results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in sport-related concussion. There is potential scientific and clinical value in validation of objective biomarkers for sport-related concussion (SRC).To investigate the association of acute-phase blood biomarker levels with SRC in collegiate athletes.This multicenter, prospective, case-control study was conducted by the National Collegiate Athletic Association (NCAA) and the US Department of Defense Concussion Assessment, Research, and Education (CARE) Consortium from February 20, 2015, to May 31, 2018, at 6 CARE Advanced Research Core sites. A total of 504 collegiate athletes with concussion, contact sport control athletes, and non–contact sport control athletes completed clinical testing and blood collection at preseason baseline, the acute postinjury period, 24 to 48 hours after injury, the point of reporting being asymptomatic, and 7 days after return to play. Data analysis was conducted from March 1 to November 30, 2019.Glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament light chain, and tau were quantified using the Quanterix Simoa multiplex assay. Clinical outcome measures included the Sport Concussion Assessment Tool–Third Edition (SCAT-3) symptom evaluation, Standardized Assessment of Concussion, Balance Error Scoring System, and Brief Symptom Inventory 18.P < .001), UCH-L1 , and tau levels  at the acute postinjury time point compared with preseason baseline. Longitudinally, a significant interaction (group × visit) was found for GFAP , UCH-L1 , and tau ; the interaction for neurofilament light chain was not significant . The area under the curve for the combination of GFAP and UCH-L1 in differentiating athletes with concussion from contact sport controls at the acute postinjury period was 0.71 ; the acute postinjury area under the curve for all 4 biomarkers combined was 0.72 . Beyond SCAT-3 symptom score, GFAP at the acute postinjury time point was associated with the classification of athletes with concussion from contact controls  and non–contact sport controls . Athletes with concussion with loss of consciousness or posttraumatic amnesia had significantly higher levels of GFAP than athletes with concussion with neither loss of consciousness nor posttraumatic amnesia at the acute postinjury time point .A total of 264 athletes with concussion , 138 contact sport controls , and 102 non–contact sport controls  were included in the study. Athletes with concussion had significant elevation in GFAP (mean difference, 0.430 pg/mL; 95% CI, 0.339-0.521 pg/mL; The results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in SRC. Early work has identified select biomarkers with favorable performance in the context of mild TBI, including glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament light chain (NF-L), and S100B.11 As in the broader mild TBI spectrum, diagnostic biomarkers have potential to objectively assess SRC presence and severity and to monitor recovery while also advancing our understanding of the underlying pathophysiological mechanisms of concussion. A previous systematic review12 found several studies that reported alterations in various biomarkers during the acute phase after SRC, but the strength and generalizability of their findings are limited by small sample sizes, varied methods, and other factors. That review12 concluded that there was early, but still limited, evidence that biomarkers have significant promise as research tools for studying SRC but require larger-scale validation to determine their ultimate clinical utility.Sport-related concussion (SRC) represents a sizeable subset of mild TBI, affecting millions of athletes per year. Within the spectrum of TBI, SRC reflects the most subtle and mild form of mild TBI; consequently, diagnosing SRC is often challenging because of the need to rely on subjective self-reported symptoms that are not specific to mild TBI and the limited diagnostic utility of common clinical tests.13 The current study investigated the association of acute SRC with blood biomarkers in collegiate varsity athletes compared with 2 closely matched athlete control groups without SRC. We hypothesized that targeted biomarkers would have elevated serum levels in athletes with concussion during the acute phase compared with preseason baseline levels and with levels in control athletes without concussion.The National Collegiate Athletic Association (NCAA) and US Department of Defense (DoD) Concussion Assessment, Research, and Education (CARE) Consortium is a large-scale investigation of the acute effects of SRC in NCAA student athletes and military service academy cadets.13 This study was approved by the Medical College of Wisconsin institutional review board and the human research protection office. Written informed consent was obtained from all participants. All data were deidentified. Data analysis was conducted from March 1 to November 30, 2019. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.14This multicenter, prospective, case-control study was conducted by the NCAA-DoD CARE Consortium from February 20, 2015, to May 31, 2018, at 6 CARE Advanced Research Core (ARC) sites.15 Both contact sport controls and non–contact sport controls were matched to athletes with concussion by institution, sex, race/ethnicity, and baseline score on the Wechsler Adult Reading Test.16 In addition, contact sport controls were matched to athletes with concussion based on sport, position, years of participation, and concussion history. Non–contact sport controls were recruited from sports with similar exertional requirements but without head impact exposure, including baseball or softball, basketball, and track and field. Both control groups underwent the same clinical protocol for baseline and serial assessment. Only athletes with concussion and contact sport control athletes had blood samples collected at baseline.A total of 1760 athletes completed baseline clinical assessments and blood sample collection. Of these, 504 athletes with concussion, contact sport control athletes, and non–contact sport control athletes also completed clinical testing and blood sample collection at the acute postinjury period, 24 to 48 hours after injury, the point of reporting being asymptomatic, and 7 days after return to play. Concussion was defined according to the consensus definition from the DoD evidence-based guidelines.The CARE ARC protocol involves preseason baseline clinical testing and blood biospecimen collection in contact sport athletes from football, soccer, lacrosse, ice hockey, rugby, and wrestling. The CARE ARC postinjury protocol includes follow-up clinical testing and blood collection in athletes with concussion at several time points: (1) acute postinjury time point, (2) 24 to 48 hours after injury, (3) the point of reporting being asymptomatic and the start of the return-to-play (RTP) protocol, (4) 7 days after unrestricted RTP, and (5) 6 months after injury. Not all participants completed all visits. Sample sizes for blood sample collection at each visit are presented in eTable 4 in the Athletes with concussion and controls were administered the Sport Concussion Assessment Tool–Third Edition (SCAT-3) symptom evaluation, the Standardized Assessment of Concussion (SAC), and the Balance Error Scoring System (BESS) at all time points. The Brief Symptom Inventory 18 (BSI-18) was administered at all time points except the acute postinjury visit.g, and then aliquoted into cryovials. The cryovials were stored upright in a −27 °C freezer until shipped on dry ice to the CARE Consortium biorepository at Indiana University School of Medicine for long-term storage and remained frozen until analysis.Nonfasting blood samples were collected by venipuncture at baseline and all postinjury time points. A 10-mL red-top tube for serum was collected at each time point, centrifuged within 60 minutes of collection for 15 minutes at 1500Single molecular array technology  was used to measure serum biomarker levels. Multiplex technology simultaneously quantified UCH-L1, tau, NF-L, and GFAP. Assays were batched to minimize variability, with each batch run with appropriate standards and controls to ensure reliability. To reduce potential batch effects, longitudinal samples from the same individual were run on the same plate, and the 3 groups were distributed randomly across plates. All samples were analyzed in duplicate. In the rare instances in which coefficients of variance exceeded 20%, samples were rerun. Data were not used if intra-assay or interassay performance was above 20%. Samples with concentrations below the level of detection were excluded from analysis. The mean coefficients of variance for each protein were the following for data included in the analyses: 9.02% for UCH-L1, 7.92% for tau, 4.59% for NF-L, and 3.07% for GFAP.2 tests. Biomarker levels were natural log transformed to decrease the skewness of their distributions. Linear mixed-effects (LME) models were used to evaluate changes in clinical measures and biomarker levels within athletes over time as a function of group, with visit modeled as a repeated factor , group , and the group × visit interaction. Sensitivity analyses were conducted to test whether acute injury severity differed systematically between athletes with and without biomarker data , thereby possibly biasing the biomarker results. The SCAT-3 symptom severity was used as an index of acute injury severity. Sensitivity analyses with the covariates of sex, body mass index, and number of prior concussions indicated that these covariates were not significantly associated with the LME results for biomarkers. The LME models assessed biomarker changes over time and between groups in athletes with concussion with and without loss of consciousness (LOC) and/or posttraumatic amnesia (PTA) compared with both control groups. Bonferroni correction to protect the familywise error at α = .05 was conducted for post hoc tests when indicated .Statistical analyses were performed using SPSS Statistics, version 24.0 (IBM Corp). Statistically significant results were declared at the nominal significance level of α = .05. Group comparisons of demographic characteristics and clinical outcomes were evaluated using 1-way analyses of variance and χP < .05 was considered to be statistically significant.Receiver operating characteristic curves and area under the curve (AUC) with 95% CIs were used to quantify the ability of markers to discriminate athletes with concussion from contact sport controls or non–contact sport controls at the acute postinjury time point and 24 to 48 hours after injury. Logistic regression analyses were conducted to assess whether the inclusion of blood biomarker levels provided additional information to aid in the classification of athletes with concussion and controls than that provided by self-reported symptoms alone . A 2-sided A total of 264 athletes  sustained a concussion during the study period and met the study criteria. Similar visits were completed by 138 matched contact sport controls  and 102 matched non–contact sport controls . The P < .001; concussed vs non–contact sport control mean difference, 25.60; 95% CI, 22.76 to 28.43; P < .001; BESS: concussed vs contact sport control mean difference, 5.63; 95% CI, 3.86 to 7.41; P < .001; concussed vs non–contact sport control mean difference, 4.66; 95% CI, 2.72 to 6.59; P < .001; SAC: concussed vs contact sport control mean difference, −1.64; 95% CI, −2.17 to −1.12; P < .001; concussed vs non–contact sport control mean difference, −1.67; 95% CI, −2.25 to −1.09; P < .001) and 24 to 48 hours after injury  . There were no significant differences in the concussion group compared with baseline or controls on the SCAT-3, BESS, SAC, or BSI-18 at the asymptomatic and 7-day post-RTP time points. All significant pairwise comparisons of clinical outcome assessments are presented in eTables 1-3 in the At 24 to 48 hours after injury, the BSI-18 score was also elevated in athletes with concussion compared with baseline and both control groups  had blood specimens collected at baseline and acute postinjury time points, and 206 athletes with concussion (78.0%) had blood samples collected at 1 or both of the acute postinjury and 24 to 48 hours after injury time points. Sensitivity analysis demonstrated that athletes with and without blood samples at the acute postinjury time point did not differ in acute injury severity based on SCAT-3 symptom severity score at the acute postinjury or 24- to 48-hour time point. Median and interquartile ranges (IQRs) for time from injury to clinical testing and blood sample collection at each of the postinjury time points were as follows: acute postinjury time point, 3.42 hours ; 24 to 48 hours after injury, 41.25 hours ; point of reporting being asymptomatic, 6.96 days ; and 7 days after RTP, 21.41 days .At preseason baseline, no differences were found between the concussion and contact sport control group in GFAP, UCH-L1, NF-L, or tau. Furthermore, contact sport and non–contact sport controls had stable levels of all biomarkers across all time points, with no significant main effect for visit in either control group and no differences between the 2 control groups on any biomarker at any time point.F7,1507.36 = 16.18, P < .001), UCH-L1 , and tau ; the interaction for NF-L was not significant .P < .001; 24-48 hours after injury: mean difference, 0.255 pg/mL; 95% CI, 0.183-0.328 pg/mL; P < .001; point of reporting being asymptomatic: mean difference, 0.124 pg/mL; 95% CI, 0.056-0.191 pg/mL; P < .001; and 7 days after RTP: mean difference, 0.092 pg/mL; 95% CI, 0.022-0.162 pg/mL; P = .002) and significantly higher levels of GFAP compared with contact sport and non–contact sport controls at the acute postinjury time point  and 24 to 48 hours after injury . There were no significant group differences in GFAP levels at the asymptomatic time point or 7 days after RTP.Post hoc analysis revealed that athletes with concussion had a significant elevation in GFAP levels at all postinjury time points compared with baseline  and significantly higher UCH-L1 levels than both control groups at the acute postinjury time point . There were no elevations from baseline or group differences in UCH-L1 levels at 24 to 48 hours after injury or 7 days after RTP. Athletes with concussion had significantly lower UCH-L1 levels at the asymptomatic time point compared with baseline  and compared with non–contact sport controls .Athletes with concussion had a significant elevation in UCH-L1 levels compared with baseline  and significantly higher tau levels than both control groups at the acute postinjury time point . At 24 to 48 hours after injury, tau levels in athletes with concussion were significantly lower than baseline levels  and levels in both control groups . No group differences were evident at other postinjury time points. All significant pairwise comparisons of biomarkers are presented in eTable 5 and eTable 6 in the For tau level, athletes with concussion had significant elevation from baseline  and UCH-L1 . The AUC for the combination of GFAP and UCH-L1 at the acute postinjury time point ranged from 0.70  to 0.71 , whereas the AUC for all 4 biomarkers combined was 0.72 . The AUC for SCAT-3 at the acute postinjury time point ranged from 0.94  to 0.95 . In comparison, the AUC was 0.65  for SAC and ranged from 0.64  to 0.68  for BESS at the acute postinjury time point.P = .001) and non–contact sport controls  than SCAT-3 symptom severity score alone at the acute postinjury time point.Logistic regression analysis revealed that inclusion of GFAP was associated with greater classification of athletes with concussion from both contact sport controls  had neither LOC nor PTA associated with their injury (no LOC-PTA); 57 athletes (21.6%) had PTA or LOC, including 40 athletes (15.2%) who experienced PTA without LOC and 17 (6.4%) who had both LOC and PTA. Athletes with either LOC or PTA (LOC-PTA) were combined in our analysis because of the low number of athletes with LOC. F11,1509.57 = 16.14, P < .001; UCH-L1: F11,1154.18 = 4.77, P < .001; NF-L: F11,1511.84 = 4.71, P < .001; tau: F11,1486.61 = 4.46, P < .001). There were no significant differences between the LOC and PTA subgroups and contact sport controls at baseline on any biomarkers. The no LOC-PTA group had higher GFAP levels compared with both control groups  and UCH-L1  compared with contact sport controls at the acute postinjury time point.The LME analysis revealed a significant interaction of group and visit across all biomarkers (GFAP: P < .001), 24 to 48 hours after injury , and at the asymptomatic time point . The GFAP levels in the LOC-PTA group were also significantly higher than those in the no LOC-PTA, contact sport control, and non–contact sport control groups at the acute postinjury time point , 24 to 48 hours after injury , and asymptomatic time point .The LOC-PTA group had significantly elevated GFAP levels compared with baseline at the acute postinjury time point  and the contact sport control group  at the asymptomatic time point. This elevation in NF-L levels in the LOC-PTA group persisted through 7 days after RTP compared with the no LOC-PTA group  and both control groups . All significant pairwise comparisons of biomarkers in athletes with and without LOC and PTA are presented in eTable 9 and eTable 10 in the The secondary analysis rendered markedly different results for NF-L. In the LOC-PTA group, there was an increase in NF-L levels over time after injury. The LOC-PTA group had significantly higher NF-L levels than the no LOC-PTA group  at the acute postinjury time point was equivalent to the AUC that differentiated all athletes with concussion from both control groups . In differentiating the LOC-PTA group from the control athletes without concussion at the acute postinjury time point, the AUC was 0.81 for GFAP  and ranged from 0.73  to 0.74  for UCH-L1. The AUC for the combination of GFAP and UCH-L1 at the acute postinjury time point was 0.84 , and the AUC for all 4 biomarkers combined ranged from 0.83  to 0.85 . At the acute postinjury time point, the SCAT-3 symptom score was not different between the athletes with LOC-PTA and no LOC-PTA .In this prospective study of blood biomarker levels after SRC, athletes with concussion had significant elevations in GFAP, UCH-L1, and tau levels compared with preseason baseline levels and compared with 2 groups of control athletes without concussion during the acute postinjury period. Elevations acute postinjury time point were most evident in GFAP and UCH-L1, 2 biomarkers recently granted US Food and Drug Administration clearance for clinical use in identifying the presence of underlying intracranial injury after more severe TBI. We also saw incrementally higher elevations in GFAP, UCH-L1, and NF-L levels after more severe SRC that involved LOC and PTA. The GFAP level remained elevated in athletes with concussion up to 24 to 48 hours after injury, with even more extended elevations in GFAP and NF-L levels for several days after concussion with LOC and PTA. Overall, these findings provide further support for the role of biomarkers in the study of concussion as well as the eventual prospect of potential clinical utility.17 Although significantly higher than baseline and in the 2 control groups, biomarker levels in this study were considerably below levels reported in studies of moderate and severe TBI. The findings of higher levels of GFAP and UCH-L1 as well as increasing levels of NF-L after SRC with LOC and PTA are closer to the pattern observed in a previous study9 of complicated mild or moderate TBI. Collectively, our findings in athletes with LOC and PTA, often considered an indicator of more severe injury, suggest a dose response of TBI severity to candidate TBI biomarker levels, which could be particularly valuable for triaging patients with TBI in critical care settings and stratifying patients for TBI clinical trials.Data from the current study potentially extend the lower threshold of the TBI biomarker sensitivity by detecting a signal on GFAP and UCH-L1 after acute SRC, arguably the mildest of injuries along the TBI spectrum. These findings are particularly relevant to a subset of patients with mild TBI who are not typically treated at level I trauma centers, such as athletes affected by SRC and military service members with mild TBI. The potential role of blood biomarkers in SRC is accentuated by the fact that neuroimaging study results are normal in most athletes with acute concussion.From a clinical standpoint, candidate biomarkers in the current study had a modest ability to differentiate athletes with concussion from control athletes in the acute phase. The AUC at the initial acute postinjury time point was 0.70 to 0.71 for the combination of GFAP and UCH-L1 and 0.72 for the 4-plex of biomarkers, which was slightly higher than SAC  and BESS . Perhaps more important in terms of both research and clinical utility is the finding that, beyond SCAT-3 symptom severity score, GFAP at the acute postinjury time point was associated with greater classification of athletes with concussion, contact sport controls, and non–contact sport controls. This finding suggests that biomarkers may provide an objective and independent means to increase diagnostic certainty without the dilemma of clinical diagnosis based on subjective, self-reported symptoms that are not specific to concussion. The potential prognostic utility of biomarkers in assessing recovery after SRC also warrants further investigation.Several methodologic attributes of the current study strengthen our results. The availability of preinjury baseline blood and serial postinjury blood sample collection is a unique advantage of the SRC research model that strengthens the interpretation of findings from the current study. The collection of blood biomarker levels soon after injury  was also critical to biomarker signal detection in our study, whereas longitudinal follow-up of athletes with concussion and control athletes was critical to understanding the trajectory and time course of dynamic biomarker abnormalities during clinical and neurobiological recovery after SRC. An important finding that has relevance to the broader landscape of TBI biomarker research is that contact sport and non–contact sport controls in our study had stable levels of all biomarkers, with no association of time or contact sport exposure with any biomarker at any time point.11 of biomarkers in high school athletes reported similar results. As is common in longitudinal studies with several data collection points, we encountered some degree of missingness across visits in all 3 groups. Sensitivity analysis demonstrated, however, that athletes with and without blood samples did not differ in acute injury severity, suggesting that our primary biomarker results were not systematically biased by the effects of missing data . Also, our group comparisons of biomarker results do not equate to diagnostic certainty at the individual case level, as would be required to support any recommendations for clinical use of biomarkers in concussion assessment and management.This study has limitations. Our sample of athletes with concussion was confined to collegiate athletes participating in high-contact sports, such as football, soccer, lacrosse, ice hockey, rugby, and wrestling. Further study of concussion biomarkers in other sports would be beneficial. Our study did not include athletes at different developmental stages or competitive levels , although a studyThis study’s findings provide support for the utility of blood biomarkers as research tools to inform the underlying pathophysiological mechanisms of concussion, but there is still not sufficient evidence to support broad implementation of biomarkers for clinical diagnosis, evaluation, and management of SRC. The goal of biomarkers should not be to replace signs and symptoms in clinical diagnosis of concussion but rather to augment clinical specificity and confidence with more objective indicators of brain injury. Further research is required to optimize and validate a multiplex of biomarkers that would meet accepted standards for clinical adoption. In addition, if supported by the evidence of clinical use, there is a critical need for a translational pathway for biomarker implementation in acute TBI and concussion care, including point-of-care testing of patients with concussion or TBI.


Studies on the reliability of the MTS and its predictive power for hospitalisation and mortality in the older population have demonstrated mixed results. The objective is to evaluate the performance of the Manchester Triage System (MTS) in older patients (≥65 years) by assessing the predictive ability of the MTS for emergency department resource utilisation, emergency department length of stay (ED-LOS), hospitalisation, and in-hospital mortality rate. The secondary goal was to evaluate the performance of the MTS in older surgical versus medical patients.A retrospective cohort study was conducted of all emergency department visits by patients ≥65 years between 01 and 09-2011 and 31-08-2012. Performance of the MTS was assessed by comparing the association of the MTS with emergency department resource utilisation, ED-LOS, hospital admission, and in-hospital mortality in older patients and the reference group (18–64 years), and by estimating the area under the receiver operating characteristics curves.Data on 7108 emergency department visits by older patients and 13,767 emergency department visits by patients aged 18–64 years were included. In both patient groups, a higher emergency department resource utilisation was associated with a higher MTS urgency. The AUC for the MTS and hospitalisation was 0.74 (95%CI 0.73–0.75) in older patients and 0.76 (95%CI 0.76–0.77) in patients aged 18–64 years. Comparison of the predictive ability of the MTS for in-hospital mortality in older patients with patients aged 18–64 years revealed an AUC of 0.71 (95%CI 0.68–0.74) versus 0.79 (95%CI 0.72–0.85). The majority of older patients (54.8%) were evaluated by a medical specialty and 45.2% by a surgical specialty. The predictive ability of the MTS for hospitalisation and in-hospital mortality was higher in older surgical patients than in medical patients .The performance of the MTS appeared inferior in older patients than younger patients, illustrated by a worse predictive ability of the MTS for in-hospital mortality in older patients. The MTS demonstrated a better performance in older surgical patients than older medical patients regarding hospitalisation and in-hospital mortality.The online version of this article (10.1186/s12873-018-0217-y) contains supplementary material, which is available to authorized users. Over the past few decades the number of Emergency Department (ED) visits has increased substantially, resulting in ED crowding , 2. ThisOlder patients more often than younger patients present with atypical signs, symptoms and multi-morbidity, they therefore represent a complex population at the ED , 11. EDsEDs require a valid and reliable triage system to rapidly prioritise patients presenting in the ED based on their clinical urgency, as to efficiently plan available resources and time. Since 2003 the Dutch EDs use the five-level Manchester Triage System (MTS) to determine treatment priority . StudiesThe objective of this study was to evaluate the performance of the MTS in older patients (≥65 years) by assessing the predictive ability of the MTS for ED resource utilisation, ED-LOS, hospitalisation, and in-hospital mortality. The secondary objective was to compare the performance properties of the MTS between older surgical patients and older medical patients.A retrospective cohort study was conducted at a teaching hospital in the Netherlands . ApproxiData on all consecutive ED visits between September 1st 2011 and August 31st 2012 were extracted from electronic patient records by one investigator using a standard data collection form. Multiple visits per patient were possible and numbered accordingly in the database. Patients referred for cardiology are predominantly cared for in the emergency cardiac care unit, and therefore are not part of this study. Additionally, the majority of patients with gynaecologic and obstetric emergencies are cared for elsewhere in the hospital and do not present to the ED. Exclusion criteria were patients aged < 18 years old, visits with a missing triage level and patients directly transferred to another department. Patients aged 18–64 years were included as a reference group to the older patients. Exemption of ethical approval by the Institutional Review Board of Máxima Medical Centre was acquired.Triage at the ED presentation is performed using the MTS . This fiThe primary outcome of interest was the performance of the MTS as assessed by the predictive ability for ED resource utilisation, ED-LOS, hospitalisation and in-hospital mortality of older ED patients (aged ≥65 years old). ED resource utilisation comprised of the number of diagnostic tests, medical procedures, medication administration and the number of specialty consultations. Diagnostic tests performed on the ED consisted of laboratory tests, urine tests, cultures, electrocardiograms, X-rays, ultrasonography, computed tomography scans, and magnetic resonance imaging. Placement of intravenous access, intubation, placement of urinary catheter or gastric tube, cardiac rhythm monitoring, wound, eye or compressive bandage, plaster cast, sling, and tetanus vaccination were considered as medical procedures. Final disposition was categorised into discharge home without follow-up, discharge home with follow-up by a GP or in an outpatient clinic, admission to the acute medical unit, admission to a high care unit, or admission to another hospital ward, died on ED and left without been seen by a physician (LWBS). Intensive care unit (ICU), medium care unit (MCU), stroke care unit (SCU) and cardiac care unit (CCU) were considered high care units. Data on admission to a high care unit was extracted manually from electronic patient records and only collected for the older patients. The primary medical specialty involved on the ED was divided into surgical  and medical . The time of presentation was divided into day (8 am – 5 pm), evening (5 pm – 12 pm) and night (12 pm – 8 am). The mode of referral was categorised as referral by GP, ambulance or medical specialist and self-referral. ED recording times (in minutes) were sectioned into 1) time in waiting room: time from ED arrival to ED bed placement, 2) treatment time: time from ED bed placement to end treatment time and 3) ED-LOS: time between ED arrival and ED discharge or hospital admission .Statistical analyses were performed using SPSS . The unit of analysis for this study was the ED visit thereby assuming independence of multiple visits by the same patient. Comparisons of the patient characteristics per triage category were tested using the Chi-square test for categorical variables. The Mann-Whitney U test, the Kruskal-Wallis test, the T-test, or ANOVA  were used for the comparison of continuous variables depending on the number of groups and distribution. Continuous variables were described by means of standard deviation (SD) or medians with interquartile range (IQR) as appropriate.The association between the MTS category and dichotomous outcome variables was described by odds ratios (OR) and corresponding 95% confidence intervals (CI) as calculated by an univariable logistic regression analysis. The association between MTS category and ED-LOS was expressed as a regression coefficient and the corresponding 95% CI as calculated using linear regression analysis. A poisson regression analysis was performed to assess the association between the MTS category and the number of diagnostic tests and medical procedures performed. Results were expressed as incidence density ratios (IDR) with a 95% CI. The MTS category green was considered as the reference category in the regression analyses. Multivariable regression analysis was not performed as we were interested in the predictive ability of the MTS only. Patients that died on the ED were considered as not admitted to the hospital and excluded from the analyses on admission, and in-hospital mortality.To compare the predictive ability of the MTS between older patients and the reference group (i.e. aged 18–64 years), we performed regression analyses with the MTS category as the independent variable stratified by age category. In addition, to compare the performance of the MTS in older patients assessed by a surgical versus medical specialty, a regression analysis was used stratified by the primary specialty on the ED.p-value < 0.05 was considered significant. The Bonferroni test was performed to correct for multiple comparisons. A p-value < 0.0036 was considered significant after Bonferroni correction.The ability of the MTS categories to predict hospitalisation, admission to a high care unit, and in-hospital mortality was evaluated by estimating the area under the receiver operating characteristics (ROC) curves. A higher area under the curve (AUC) indicates a better accuracy; an AUC of 1.0 indicates an excellent performance and an AUC of 0.5 indicates a 50% chance of an accurate score. A two-sided n = 7108) out of 20,875 eligible ED visits and were considered for the study population. In addition, 13,767 ED visits by patients aged 18–64 years old were included as the reference population  ED visits were excluded because they consisted of a paediatric population (19.4%), a missing triage level (2.5%) or direct transfers to other departments (10.2%). Older patients (aged ≥65 years) accounted for 34.1% (% ED visip < 0.001 compared with older patients).The majority of older patients were assigned to the MTS category yellow (44.5%), followed by green (39.7%), orange (15.0%), and red (0.9%). In the group aged 18–64 years old, patients were predominantly assigned to the MTS category green (59.7%), followed by yellow (32.0%), orange (7.9%) and red (0.4%) (p < 0.001). In both groups, male patients had higher urgency levels than female patients. The primary mode of referral for ED visits by older patients was by GP (57.5%), especially in categories orange, yellow and green . In category red, both older patients and patients aged 18–64 years predominantly arrived by ambulance (55.7 and 60%). In 3215 ED visits (45.2%), older patients were primarily treated by a surgical specialty and in 3893 ED visits (54.8%) by medical specialties (Table The mean age of the entire older study population was 77.0 years (SD 7.6 years). 45.8% were male compared with 55.6% males in the group aged 18–64 years (p < 0.001). In both groups, the time in the waiting room increased as the MTS urgency decreased, with the longest time in category green  (p < 0.001). Overall, the median ED-LOS was 136 min (IQR 94–180) in older patients, and 99 min (IQR 61–146) in patients aged 18–64 years (p < 0.001). In ED visits by older patients, the median ED-LOS was longest in category yellow , while the median ED-LOS was longest in category orange in patients aged 18–64 years  (Table The median time in the waiting room was 3 min IQR 0–11 min) in older patients and 7 min in patients aged 18–64 years IQR 2–24 min) . In older patients, the mean number of medical procedures on the ED was 1.4 (SD 2.2) compared with 1.0 (SD 1.0) in patients aged 18–64 years (p < 0.001). Older patients assigned to category orange received the highest number of diagnostic tests   in older patients and 1.2 (SD 1.3) in the reference group . In older patients the median hospital length of stay decreased significantly with a lower MTS urgency from 7.0 days (IQR 2–14) in category red to 5.0 days (IQR 2.0–10.0) in categories yellow and green (p = 0.012).In total, 13.0% of the older patients were discharged home without a follow-up, 27.3% with a follow-up and 14 patients (0.2%) died on the ED  higher risk of hospitalisation than the green category, in patients 18–64 years the risk increase in yellow versus the green category was significantly higher  (Table –5.3 highp < 0.001). The mortality risk increased with increasing MTS category in older patients from 1.7% in the green category to 34% in the red category  (Table p = 0.259).The majority of older patients assigned to the MTS category red and orange were treated by a medical specialty  (Table p < 0.001). Overall, the median ED-LOS was considerably shorter in older surgical patients  compared with older medical patients   than medical patients  Table . The ass1) Table . In 11.71) Table .Table 4Ep < 0.001). The AUC for the MTS and admission to a high care unit was 0.84 (95% CI 0.77–0.92) in older surgical patients, and 0.73 (95% CI 0.70–0.75) in older medical patients.Hospital admission was more frequent in older medical patients (79.9%) than older surgical patients (35.0%) Table . The prep < 0.001). The in-hospital mortality in older medical patients was higher in every MTS category as compared with older surgical patients (p < 0.001) . However, the performance of the MTS as a predictor of ED resource utilisation and in-hospital mortality in older patients was not as good as in patients aged 18–64 years (i.e. reference population). Stratifying the performance of the MTS by specialty revealed a better predictive ability of the MTS for hospitalisation and in-hospital mortality in older surgical patients than older medical patients.Research on the validity of triage tools is complicated by the lack of a gold standard for true patient acuity. We based the performance of the MTS on its ability to predict hospitalisation and in-hospital mortality as a surrogate for acuity. In addition, we used the ED resource utilisation and the ED-LOS as a proxy for workload. These outcome measures are clinically relevant and correspond to the objective of the MTS, which is to prioritise patients based on clinical acuity in a setting with limited resources and persistent time pressure .Our study confirms previous findings of worse performance of the MTS in older patients compared with younger patients , 22. TheConsistent with previous studies, the ED-LOS increased across MTS categories green to orange, and was shortest in category red in older patients . The assThe predictive ability of the MTS for hospitalisation in our cohort of older patients AUC 0.74) was fair, which is consistent with a previous study in the general population . The adm4 was faiIn both older patients and patients aged 18–64 years, the in-hospital mortality increased with an increasing MTS urgency, which is consistent with existing literature , 33. HowOur study demonstrated a difference in the performance of the MTS in older surgical patients compared with older medical patients. The ED resource utilisation was higher in older medical patients compared with surgical patients. Furthermore, the admission rate in medical patients was considerably higher than in surgical patients (80% versus 35%), which might be elucidated by a higher percentage of elective admission for surgical patients. Moreover, in-hospital mortality in older patients categorised yellow and green was significantly higher in medical specialties than surgical specialties . In contrast with previous research, the MTS performed better in older surgical patients than medical patients regarding the number of diagnostic tests performed, and the ED-LOS . In addiOur results may have been influenced by several limitations. Firstly, owing to the retrospective observational design of the study, there is a risk of bias. The perception of acuity may differ from the real urgency of a patient’s condition, which is difficult to identify based on administrative data. Secondly, our findings may be less generalisable to other hospitals and countries, because of the single-centre setting and distinct health care organisation in the Netherlands. Therefore, the organisation of emergency care in other countries should be considered when interpreting our findings. The majority of patients presenting with emergency cardiac and gynecological complaints were not included in this study, therefore our results may be less applicable to these patients. Thirdly, the impact of comorbidity on the difference performance of the MTS in older and younger patients was not taken into account in our study. Fourthly, only including admission to a high care unit or the need for immediate lifesaving intervention in the assessment of the performance of the MTS might be insufficient in older patients to identity actual acuity. This might result in an overestimation of the performance of the MTS, because of possible confounding by treatment constraints, such as a do not resuscitate order or a no ICU admission order. Last, the chance of a type 1 error was larger than 0.05 due to multiple testing.In our retrospective cohort, the MTS appeared to perform worse in older patients (≥ 65 years old) as compared with younger patients (18–64 years old). Although, the MTS was associated with ED resource use, ED-LOS, hospitalisation and in-hospital mortality in older patients, the predictive ability of the MTS for in-hospital mortality was worse in older patients than in patients aged 18–64 years. The MTS demonstrated a better performance in older surgical patients than older medical patients regarding hospitalisation and in-hospital mortality. These findings emphasise the need for an increased awareness of the higher risk of adverse outcome in older emergency department patients, particularly, in older medical patients.Additional file 1:Table S1. Characteristics of emergency department visits by patients aged 18–64 years per MTS category. Comparison of the following characteristics in patients aged 18–64 years old per MTS category: age, mode of referral, specialty, median ED-LOS, ED resources utilisation, disposition and in-hospital mortality (DOCX 17 kb)