Systemic lupus erythematosus (SLE) patients are vulnerable to infections. We aim to explore the approach to differentiate active infection from disease activity in pediatric SLE patients. Fifty pediatric SLE patients presenting with 185 clinical visits were collected. The associations between both clinical and laboratory parameters and the outcome groups were analyzed using generalized estimating equations (GEEs). These 185 visits were divided into 4 outcome groups: infected-active (n = 102), infected-inactive (n = 11), noninfected-active (n = 59), and noninfected-inactive (n = 13) visits. Multivariate GEE  analysis showed that SDI, SLEDAI-2K, neutrophil‐to‐lymphocyte ratio (NLR), hemoglobin, platelet, RDW-to-platelet ratio (RPR), and C3 are predictive of flare . Multivariate GEE analysis showed that SDI, fever temperature, CRP, procalcitonin (PCT), lymphocyte percentage, NLR, hemoglobin, and renal score in SLEDAI-2k are predictive of infection . We can simultaneously predict 4 different outcome with accuracy of 70.13% for infected-active group, 10% for infected-inactive group, 59.57% for noninfected-active group, and 84.62% for noninfected-inactive group, respectively. Combination of parameters from four different domains simultaneously, including inflammation , hematology , complement , and clinical status  is objective and effective to differentiate flares from infections in pediatric SLE patients. The presentation, disease course, and outcomes of SLE are unpredictable. Approximately 60–70% of patients exhibit relapsing–remitting and active disease patterns2. Pediatric lupus patients typically have a severe disease course. Additionally, in comparison to adults, a significantly higher percentage of children with SLE continue to have a status of high disease activity3. Several indices have been designed to assess disease activity. The most commonly used disease activity score is the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)4. SLE can also be complicated by chronic multiorgan damage. The Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) is a reliable instrument for the assessment of the degree of disease-related damage in children with SLE5.Systemic lupus erythematosus (SLE) is an autoimmune disease caused by autoreactive B cells in combination with T cell dysregulation and cytokine abnormalities6. Moreover, fever is a common symptom in pediatric SLE, and it is difficult to distinguish between an SLE flare and febrile infection7. Some infections may produce systemic manifestations mimicking SLE, either superimposed upon or triggering a flare8, making the diagnosis and therapeutic approach challenging. In one study, a delay in antimicrobial therapy of > 24 h reportedly increased the mortality of hospitalized SLE patients 12-fold; therefore, early identification and treatment of infections are essential9. The interaction between infection and SLE is complicated, as viral, bacterial, parasitic, and fungal pathogens can trigger SLE disease activity through molecular mimicry10. The establishment of a causative link between infection and autoimmunity has been studied in detail, confirming the role of infectious agents in the induction as well as the progression or exacerbation of SLE11. In general, clinicians have to make treatment decisions based on clinical judgment and laboratory parameters to distinguish between active disease and infection. Most such studies to date have been performed in adult populations, whereas data regarding pediatric SLE are lacking.SLE patients are highly susceptible to infections due to the combined effects of immunosuppressive therapy and immune system abnormalities. In Taiwan, infections are among the leading causes of death in pediatric SLE13. In addition, conventional biomarkers , procalcitonin [PCT])18 and new markers have been developed for the prediction of infection in SLE patients19. Although several recent studies have focused on markers for differentiating between disease flare and infection in febrile SLE patients22, most physicians agree that no single biomarker has sufficient predictive value for both events19. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are significantly higher in SLE patients than in healthy controls and correlate positively with the SLEDAI score23. However, the NLR might be a good additive marker for diagnosing infection in patients with SLE24. New scores, which include combinations of different biomarkers, may represent better solutions for differentiation19.There have been a number of studies on predictive biological markers of SLE flares, including anti-double-stranded DNA antibodies (anti-dsDNA Ab), the complement system, anti-extractable nuclear antigen antibodies (anti-ENA Ab), cytokines, and chemokinesOverall, it is possible that the use of only one biomarker would not be sufficient to distinguish infection from disease activity. We aimed to identify useful parameters for the differential diagnosis of disease flares and infections in pediatric-onset SLE patients and to develop predictive calculators that might assist in decision-making in daily clinical practice.Candida species and Pneumocystis jirovecii25. These 185 visits were divided into 4 groups: infected-active visits as group A , infected-inactive visits as group B , noninfected-active visits as group C , and noninfected-inactive visits as group D  , ANA level, anti-dsDNA level, antiphospholipid Ab level, and urine cast to be positive predictors of activity flare. Conversely, Hb levels (g/dL), platelet levels, C3 levels (mg/dL), C4 levels (mg/dL), and urine nitrate were negatively associated with the occurrence of disease activity , CRP level (mg/dL), PCT level (ng/mL), NLR, PLR, and renal score of SLEDAI 2 K but that lymphocyte percentage, Hb level (g/dL), and urine nitrate were negative predictors of infectious events  to establish multinomial logistic regression. The regression formula obtained by multinomial logistic regression is as follows:Multinomial logistic regression, which describes the probability of being in a specific group, was used to analyze the individual effects of covariates (independent variables) on discrete nominal outcomesA, πB, πC and πD. If the value obtained is larger, the probability of being classified into that nominal group is greater (the group divided into D is the reference group), and we will classify particular visits into that group . That is, if the calculated ln(πA/πD) is greater than 1 and is the largest number compared with others (ln(πB/πD) and ln(πC/πD)), the cases is categorized as in group A. If all three calculated numbers  are below 1, the case is categorized as in group D. With a combination of these ten parameters, we can simultaneously predict four groups with an accuracy of 70.13% for the infected-active group, 10% for the infected-inactive group, 59.57% for the noninfected-active group, and 84.62% for the noninfected-inactive group. From our multinomial logistic regression analysis, we identified SDI, SLEDAI 2K, fever temperature (°C), PCT, lymphocyte percentage, NLR, Hb (g/dL), PLT (K/μL), RPR, and C3 (mg/dL) as influencing factors for simultaneously differentiating activity flares from acute infections. By knowing the values of the observed parameters, we can predict the group classification of any specific visit for an individual patient.By inputting the value of the selected parameters into these three equations, we can obtain the ratio values of π38. On the other hand, for proteins that participate in both SLE disease inflammation and acute-phase inflammation, no significant difference in ESR (mm/h), C3 and C4, with or without infection, was observed.To observe whether there is an associated interaction between acute infection and activity flare, we sought to compare parameters from combined groups with or without infection Fig. . We foun28. Our trend analysis indicated that a higher initial ESR level (mm/h) might reflect the effect of both activity and infection in group A14. From the trend difference between groups A and C, we could differentiate noninfected-active SLE visits (group C) from infected-active SLE visits (group A) by the change patterns of ESR (mm/h), NLR, lymphocyte percentage, C3 (mg/dL), and C4 (mg/dL) over time.The results of the mean baseline level and changes per time interval of the different groups are shown in Supplement Table 39. Obtaining several parameters simultaneously remains necessary to differentiate flares from infections27. Attribution of clinical manifestations to SLE often requires a comprehensive, multidisciplinary approach to rule out mimics , taking into account the presence of risk factors , as well as other factors favoring alternative diagnoses 40. Cognitive bias might result in diagnostic errors41. It seemed that subspecialty expertise does not attenuate this bias. The use of our predictive models may aid in the diagnostic process. We hope to include more molecular biomarkers and genetic signatures in our model in the future, and the related mechanisms will be further explored.In contrast to previous reports, the most important parameters used to establish the predictive model in our study consisted of four domains under simultaneous evaluation: inflammation , hematology , the complement system C3, C4), and clinical status  . The management of pediatric SLE requires ongoing monitoring of patients, with the collection of data for many parameters/markers. We suggest that this composite predict score can be used in everyday clinical practice to improve the discrimination between activity flare and acute infection in pediatric patients with SLE, who have a greater risk of more fulminant SLE and/or more severe infection, and to take prompt interventions to improve clinical outcomes in pediatric SLE patients.This was a rare study focusing on distinguishing flares from infection in pediatric-onset SLE patients, and the study design closely approximated real-world clinical practice. Studies with designs similar to ours have been reported previously42. Previous studies have shown that the NLR is associated with rheumatoid arthritis and psoriasis. Recently, higher NLR and PLR both correlated positively with SLEDAI-2K score and disease activity43. On the other hand, the NLR is associated with infection, and it has been used as an indicator of bacteremia26. Our results showed that the NLR is associated with both disease flare and infection. In adult SLE cohorts, blood cell count ratios appeared to be more informative than blood cell counts per se because pancytopenia and thrombocytopenia tend to occur in SLE44. We observed the same tendency in our pediatric-onset SLE patients. Ideally, PLR values should be combined with values for NLR and other inflammatory markers to facilitate a more holistic determination44. Here, we demonstrate that the NLR and PLR are important complementary hematological indices that provide additional information about disease activity, the presence of neutrophilic inflammation, infectious complications, disease severity, and organ damage in SLE.The NLR and PLR have been used as prognostic indicators for malignancy and are associated with morbidity and mortality in chronic diseases45. The results from an adult lupus cohort study in Latin America also showed that increased disease activity and damage accumulation are predictive of infection31. One study demonstrated a positive correlation between SDI score and the number of recurrent major infections in pediatric-onset SLE46. Sit et al. also reported that disease damage was significantly associated with a greater number of episodes of major infection47. These findings are consistent with our study of the association between SDI and infection (P < 0.0001) using multivariate GEE. Our results also indicate that SDI and SLEDAI-2K renal scores can be used to accurately predict acute infection, consistent with previous reports.The relationship between infection and SLE disease damage is difficult to evaluate. Infection itself is reported to both potentially facilitate or protect against the development of SLE28. CRP levels reportedly increase during infection, arthritis, and serositis in SLE patients14. One study showed that the CRP level is more sensitive and specific in diagnosing bacterial infection than the PCT level33. Immune complexes induce severe inflammation via conventional pro-inflammatory pathways, including those for cytokines such as TNF-α and IL-6 (which leads to CRP production). On the other hand, the same immune complexes induce the production of type I IFNs and various immunoregulatory cytokines. The simple consequence is reduced production of CRP in active SLE (group B > group A), despite increased IL-6 levels, which are visible in concomitant infection28. PCT is a precursor peptide of calcitonin associated with invasive bacterial infections. Normally produced by parafollicular C cells, PCT is released in response to bacterial toxins and IL-1β8, and PCT has good specificity for distinguishing acute bacterial infection from disease flare in patients with autoimmune diseases, regardless of steroid use. The mechanism underlying PCT production after inflammation and its role are still not completely understood19. Furthermore, limited information is available regarding plasma PCT levels in patients with active SLE. Patients with active SLE may have slightly increased PCT levels18. However, Garvand et al. noted an unusual phenomenon, whereby PCT levels were high during macrophage activation syndrome (MAS) episodes in lupus flares48, and a systematic literature review suggested that PCT level and SLE disease activity do not correlate15. Our comparison of 4 groups showed that the active disease group had higher PCT levels , regardless of the presence or absence of infection, suggesting that activity flares are associated with elevated PCT levels.The utility of traditional markers  for detecting infection in SLE patients has been discussed for several decades. CRP is an acute-phase reactant synthesized by the liver during IL-6 regulation and is known as an inflammatory biomarker12. One reason for these inconsistent results may be because complement proteins participate in both autoimmune (SLE activity) and inflammation (infection) responses. As a consequence, inflammation due to infection increases, but immune complex consumption during disease activity decreases complement protein levels. Hence, levels of C3 and C4 are regulated by both mechanisms28. Interestingly, decreases in C3 or C4 levels are not detected in some patients. Instead, levels can increase relative to the baseline during flare visits49. Our results  could differentiate flares from infections in pediatric SLE patients. Clinicians could make appropriate judgement and treatment decisions based on the combination of parameters from four different domains simultaneously, including inflammation , hematology , complement C3, C4), and clinical status  in daily clinical practice . Patients who did not meet the 1997 ACR criteria for SLE diagnosis and those with overlapping autoimmune diseases or other chronic inflammatory diseases/infections or malignancies were excluded. This study was approved by the Institutional Review Board and Research Ethics Committee of the National Taiwan University Hospital and was conducted in compliance with the protocol for good clinical practices and the principles of the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians.This was a retrospective study conducted by reviewing the medical records of 50 pediatric-onset (≤ 18-year-old) SLE51, biomarkers of infection in SLE19, SLEDAI, SDI and markers based on previous reports52, were collected at each visit as a standardized clinical protocol to ensure that the resulting predicting model can be used in everyday clinical practice 53:Mild or moderate flares were defined as 1 or more of the following: (a) change in SELENA-SLEDAI instrument score of 3 points or more (but not to more than 12); (b) new or worsening discoid, photosensitive, or other rash attributable to lupus , nasopharyngeal ulcers, pleuritis, pericarditis, arthritis, or fever not attributable to infection; c) increase in prednisone but not to > 0.5 mg/kg/day; d) addition of nonsteroidal anti-inflammatory drugs (NSAIDs) or hydroxychloroquine for SLE activity; and (e) ≥ 1.0 increase in physician’s global assessment (PGA) score but not to more than 2.5.Severe flares were defined as 1 or more of the following: a) change in SELENA-SLEDAI instrument score to greater than 12; b) new or worsening central nervous system involvement, vasculitis, nephritis, myositis, thrombocytopenia (platelet count < 60 × 109 cells/L), or hemolytic anemia (hemoglobin level < 7 g/dL or decrease in hemoglobin level > 3 g/dL over a 2-week period), each requiring doubling of corticosteroid dosage to a final dosage greater than 0.5 mg/kg per day or hospitalization; (c) any SLE manifestation requiring an increase in dosage of prednisone or equivalent drug to greater than 0.5 mg/kg per day, or initiation of therapy with cyclophosphamide, azathioprine, mycophenolate mofetil, or methotrexate; (d) hospitalization for lupus activity; and (e) increase in PGA score to > 2.5. Acute infection was defined based on the following: (1) microbiological culture/isolation evidence that explained clinical symptoms; (2) clinical symptoms and/or inflammatory syndrome and/or laboratory/serological results that rapidly regressed after starting antimicrobial  therapy; (3) confirmation by radiological and/or imaging study; and/or (4) confirmation via an infectious specialist consultation35.Disease flare was defined according to the SELENA-SLEDAI Flare index (SFI)n) and percentages. The Mann–Whitney test was used instead of the t-test for the analysis of nonparametric data. Associations between parameters and the four outcome groups were analyzed using univariate and multivariate generalized estimating equations (GEEs) with an autoregressive (AR) model, as the GEE approach facilitates the analysis of data collected in longitudinal, nested, or repeated-measures designs and produces more efficient and unbiased regression estimates for analyzing measures with non-normal response variables54.The clinical and laboratory characteristics of the four groups were analyzed. Quantitative variables are presented as means ± standard deviations and ranges. Qualitative variables are shown as numbers 56. Through univariate analysis and multivariate logistic stepwise regression, the remaining parameters were designated as “significant effectors” of activity flare or acute infection and were used to derive a Predict Score equation. The estimates of individual significant effectors were combined to generate the Predict Score formula. After obtaining the Predict Score equation, we calculated the score for each visit of an individual patient according to the times of repeated measurements. At this time, the "estimated score" is regarded as x, and the "activity flare" or “acute infection” is regarded as y. We used logistic regression to find the best cutoff point at which the largest Youden Index  would be obtained.For both laboratory and clinical parameters, univariate logistic regressions were then fit to specific outcome models. A multivariate logistic stepwise regression was performed with variables that were significant according to both 57. Therefore, we assumed that discrete episodes were all conceptually independent. We selected parameters with significant P values in univariate analysis and excluded highly correlated parameters using Pearson pairwise correlation analysis. A total of 10 variables were used for modeling. We thus used a multinomial logistic regression (MLR) model for nominal outcomes by setting group D (noninfected-inactive group) as the reference group and developed three regression equations for simultaneous prediction36. The relationship between the other outcome  and any particular explanatory variable  was captured using particular parameters that define the log odds of response jumping from the reference outcome (group D) to the otherwise outcome 36.SLE is characterized by a relapsing–remitting course between at least two discrete clinical/laboratory episodes, separated by periods of clinical quiescenceTwenty-four hospitalized patients presented 29 admissions with serial measurement of parameters during admission. The evaluation at the first time point provided baseline data, and the data collected at each ensuing time point were assigned into one of the following 10 time periods: baseline/day 0 (assigned as time point 0), < 3 days (time point 1), 3–5 days (time point 2), 6–10 days (time point 3), 11–15 days (time point 4), 16–20 days (time point 5), 21–25 days (time point 6), 26–30 days (time point 7), 31–35 days (time point 8), and 36–40 days (time point 9). To identify potential markers, we analyzed group-specific parameter trends, i.e., baseline data and data from subsequent assessments in groups A vs C using a multivariate GEE with the AR model and the GLIMMIX procedure. To track trends in changes in laboratory and clinical parameters in groups A and C over time, we excluded the parameters that had estimates not statistically significant, and we utilized the GLIMMIX procedure using 13 variables , employing verification steps such as the type III tests of fixed effects to provide a solution for fixed effects. The default optimization technique for generalized linear mixed models is the quasi-Newton method. Dimensions included G-side covariance parameters. Fit statistics were calculated using generalized chi-square tests. Covariance parameter estimates and fixed-effect solutions were calculated. The analyses were performed using SAS version 9.1 .Supplementary Information.


Polychrus guturossus) is one of eight species belonging to the genus Polychrus, the only representative of the family Polychrotidae. Only a handful of studies have been reported concerning these lizard’s morphological variation, ecology, and natural history, probably because P. gutturosus is a canopy dweller and it can be difficult to locate individuals. It is believed that deforestation and habitat modification could pose a threat for this species, although to date no health assessment has been done. The aim of this study was to generate health baseline data on P. gutturosus. Forty Berthold’s bush anoles  were sampled at the Pacific versant in Costa Rica, where physical examination, skin and cloacal temperatures, and blood samples were obtained from individuals immediately after capture. Animals from the studied population were all healthy (body condition 2.5–3.0/5.0). No lesions or ectoparasites were detected, but hemoparasites were found in nine individuals. Hematological and biochemical values were obtained, and the morphology of leukocytes were found to be similar to other iguanians. A positive correlation was found between the tissue enzymes aspartate aminotransferase (AST) and creatinine kinase (CK) and a negative correlation was found between skin and cloacal temperatures and AST and CK. There were positive correlations between female weight and total protein, calcium, and the calcium and phosphorus ratio. No significant inter-sex differences were found in biochemical values, despite females being larger than males. This is the first health assessment performed on a free-ranging canopy dwelling lizard. These findings provide baseline data that may be useful for future monitoring if the species faces changes in health status due to anthropogenic causes or natural disturbances.Studies evaluating the health status and characteristics of free-ranging wildlife populations are scarce or absent for most species. Saurian health assessments are usually performed in species that have conservation issues or that are kept in captivity. The Berthold’s bush anole ( Population declines due to anthropogenic causes such as habitat fragmentation, pollution, invasive species, and global climate change are widespread . One wayArvicola terrestris) with better body condition and higher hematological values had greater survival probability when reintroduced into the wild.Health assessments are useful when baseline data on normal health parameter values from a clinically robust population are available . TherefoStudies evaluating the health status and characteristics of free-ranging populations are, however, scarce or absent for most species, especially those that are rarely seen in the wild . In lizaPolychrus guturossus) is one of eight species belonging to the genus Polychrus and the only representative of the family Polychrotidae in Middle America. This is a moderately large, diurnal lizard that is distinguished by its bright green body coloration and extremely long tail (over three times the length of the head and body)  . The spend body)  and femand body) . Despitend body) . Polychr species , althougBasiliscus plumifrons, P. gutturosus). The following baseline data was included: (1) body temperature and weight, (2) presence of ectoparasites and external abnormalities through physical examination, and (3) hematological and biochemical values. Most of our knowledge on P. gutturosus comes from museum specimens  under permit numbers SINAC-ACC-PI-R-102-2018 and SINAC-ACC-257-2018.A total of 40 adult individuals  were collected from October 2018 to May 2019, carrying out one field trip per month. Lizards were surveyed along a public, dirt road at El Rodeo , Ciudad ®, NUB8550AT model). The lizards were then hand-caught from shrubs or trees and taken to a workstation at the temporary mobile field laboratory approximately 5 m to 10 m from the collection site. A J/K/T/E thermocouple thermometer  was used to measure the lizard’s cloacal body temperature, which was taken by inserting the K probe into the cloaca, approximately 1–2 min after capture. A blood sample was taken after the temperature was measured. The process from catching the animal to collecting its temperature and blood lasted about 12 min.Once an individual was observed, the skin temperature at the resting site was measured using a digital laser infrared thermometer gun  with a Biomark® HPT12 radio frequency identification tag and released back where it was collected.The individual was placed in a cloth bag and weighed on a digital scale (to the nearest 0.1 g). Afterwards, the lizard was examined for obvious abnormalities or lesions. Physical examinations were performed according to ® tube. All samples were taken to the laboratory the same night and stored at 4 °C to be processed the following day. Red blood cell (RBC), white blood cell (WBC) and thrombocyte count (TC) were performed using the standard method of a Natt and Herrick solution (1/200) on a Boeco® Neubauer Improved chamber. Packed cell volume (PCV) was determined using high-speed centrifugation  of blood-filled microhematocrit tubes. Differential white blood cells were obtained by examining a peripheral smear stained with Diff-Quick® stain  using plasma from the microhematocrit tube. Biochemical parameters such as aspartate aminotransferase (AST), albumin (Alb), calcium (Ca), cholesterol (Chol), creatinine kinase (CK), glucose, phosphorus (P) and uric acid (UA) were measured with a Roche® analyzer (Cobas c111 model) following the company’s instructions.Total proteins were obtained by means of a clinical refractometer  ratio, WBC, weight and SVL and Scaled Mass Index (SMI) and sexes were determined using t-tests. A Pearson correlation was calculated to look at the association between body temperature , weight and SVL with all the hematologic and biochemistry values. To estimate body condition, the SMI was used. This index proved to be a better indicator of the relative size of energy reserves and other body components, SMI = Mi (Lo/Li) bSMA .The mean, standard deviation, range, and 95% confidence intervals for all blood parameters were calculated. Differences in weight, biochemistry and hematological values between the sexes were examined using Li) bSMA . The lenLi) bSMA . All staAll lizards appeared to be active and healthy. Female weight ranged from 27 g to 80 g (mean ± SD = 52.25 ± 13.56) and males weighed from 17 g to 52 g (mean ± SD = 37.30 ± 8.86). No evidence of lesions was detected during physical exams. No ectoparasites  were observed and none of the females had palpable oviductal eggs.t = 0.99, p = 0.33).The general body condition of all individuals was between 2.5 and 3.0 and a body mass index was also obtained. The SMI was 3.75 (±0.15) CI [3.70–3.79]. No significant differences were found between sexes , RBC, , WBC , H:L ratio , weight , SVL , and polychromasia .Heterophil to lymphocyte (H:L) ratios were calculated . Both shr = 0.795, p < 0.001). Skin temperature varied between 18.8 °C and 26.2 °C (mean ± SD = 22.31 ± 1.74) and cloacal temperature varied between 21.2 °C and 32.4 °C (mean ± SD = 25.22 ± 2.11). A negative correlation was found between skin temperature  and cloacal temperature  with AST  and cloacal temperature  with CK , total protein  and the calcium/phosphorus (Ca/P) ratio  with weight.Clinical biochemistry values are reported in with AST . The sam with CK . A positEven though hematological and biochemical information is not available for a number of iguanian families , some comparisons are still possible. For hematological parameters, WBC was found to be higher for Polychrotidae, Liolaemidae and Corytophanidae  compared to other families, while the number of lymphocytes is high in Polychrotidae and comparable with Iguanidae and Tropiduridae . For bioP. gutturosus.Health assessments provide baseline information that can be used to understand future changes in the health status of wildlife populations. Both physical examination and internal physiological data  can serve as valuable tools for evaluating and monitoring the health of wild populations , especiaPhysical examination of Berthold’s bush anoles showed no evident abnormalities, suggesting that all animals were apparently healthy. Body condition is assumed to influence an animal’s health and fitness  and althTiliqua rugosa) in agricultural environments had a regenerative anemia (low PCV and increased polychromatophils) compared to animals in non-agricultural areas.Packed cell volume and RBC counts were similar to closely related lizards , and polP. gutturosus, followed by heterophils and monocytes  had higher hemoglobin, PCV and glucose than females (Iguana iguana) had higher hemoglobin and PCV than males  ratio and weight in females but not in males. Ca and P homeostasis are directly interrelated because serum Ca interplays with serum P through the modulation of several hormones, such that serum concentration is approximately inversely related (a high Ca/P ratio means higher Ca than P) . CalciumFinally, intraerythrocytic parasites were found in nine individuals, but no differences were found between infected and non-infected animals with hematological values or physical measurements. The presence of hemoparasites in wild reptiles is common  and usuaP. gutturosus) is reported. Poorly studied species with populations occurring in altered or non-protected environments can be at greater risk from human activity. Since P. gutturosus depends on its arboreal habits, deforestation due to urban or agricultural activities can affect their survival, reduce its habitat and increase the transmission of diseases. Preventing species from becoming threatened requires conservation actions based on scientific knowledge. This includes health assessments of wild populations that can be used for future management and protective actions.Health assessments allow for evaluation of body condition, disease, stress levels, hydration status and temperature changes of wild populations to be detected  and thusHematological and biochemical values were obtained for the first time in this poorly studied arboreal lizard species. The morphology of leukocytes were similar to other iguanians. A positive correlation was found between aspartate aminotransferase (AST) and creatinine kinase (CK) and a negative correlation between skin and cloacal temperatures with AST and CK. There were positive correlations between female weight and total protein, calcium, and the calcium and phosphorus ratio. No significant inter-sex differences were found, despite females being larger than males. These findings provide baseline data that may be useful if this species faces changes in health status due to anthropogenic causes or natural disturbances in the future.10.7717/peerj.10649/supp-1Supplemental Information 1Saurian hematology and biochemistry for comparison.Click here for additional data file.10.7717/peerj.10649/supp-2Supplemental Information 2Click here for additional data file.


Excess nutrients on dairy farms can affect soil and animal health and have negative impacts on the environment. More nutrients are usually brought onto farms in animal feed than in fertilizer and, as dairy cows do not efficiently use feed nutrients to produce milk, most consumed nutrients are excreted in dung and urine. Estimating nutrients excreted by cows relies on measuring feed and nutrient intake. However, measuring pasture nutrients consumed by cows that graze on commercial farms is complicated. We modified the ‘Feeding Standards’ approach to estimate daily pasture dry matter and nutrient intake and nutrients excreted using data and samples readily available on commercial dairy farms. These data were collected on five visits in different seasons from 43 grazing system farms located in diverse climatic zones. Although these herds graze daily, the lactating cows only obtained slightly more than half their dry matter intake from pasture. Each day, on average, we estimated that a grazing cow excretes 433 g nitrogen, 61 g phosphorus, 341 g potassium, 44 g sulphur, 92 g calcium and 52 g magnesium on these farms. Using this approach to estimate nutrients excreted by grazing animals on dairy farms can assist farmers improve nutrient distribution and fertilizer requirements.Estimating excreted nutrients is important for farm nutrient management, but seldom occurs on commercial grazing system farms due to difficulties in quantifying pasture intake. Nitrogen (N), phosphorus (P), potassium (K), sulphur (S), calcium (Ca) and magnesium (Mg) intake, excretion and use efficiency of 43 commercial dairy herds grazing pasture were calculated to understand the range in nutrient intake and excretion in these systems. Milk production, feed (grazed and supplement), as well as farm and herd management data were collected quarterly on representative farms located in temperate, arid, subtropical and tropical regions of Australia. Lactating herd sizes on these farms averaged 267 (30 to 1350) cows, with an average daily milk yield of 22 (9 to 36) kg/cow per day and the herds walked from <0.01 to 4 km/day on a variety of terrains. The mean total metabolizable energy (ME) required by cows in the herds was estimated to be 195 (116 to 289) MJ/cow per day. Although these farms are considered grazing systems, feeding strategies ranged from total dependence on pasture to total mixed rations (TMRTMR) and consisted of a wide variety of nutrient and energy contents. Mean pasture dry matter intake (DMI)  was just over half of total DMI. Dietary concentration of crude protein, P, K, S, Ca and Mg concentrations were, on average, 19%, 0.45%, 2.1%, 0.29%, 0.65%, and 0.3%, respectively, for all herds and, except for N, supplement nutrient concentrations were always more variable than pasture. Approximately 72% and 88% of diets provided greater than recommended P and N intakes, respectively. Calculated mean N, P, K, S, Ca and Mg excretions were 433, 61, 341, 44, 92 and 52 g/cow per day, respectively. Of the farm characteristics examined, residual maximum likelihood (REML) analysis indicated that daily excreted N, P and S were significantly related to per ha milk production, and excreted P, K and Mg were related to percentage of herd DMI provided as supplement. Mean use efficiencies by cows of N, P, K, S, Ca and Mg were 21%, 25%, 9%, 16%, 23% and 4%, respectively. These estimates of nutrient excretion and feed nutrient use efficiencies can be used to improve nutrient management on grazing system commercial dairy farms. Nutrient accumulation that occurs on many dairy farms worldwide can lead to environmental pollution, through gaseous emissions and nutrient losses to waterways . In mostForty-three commercial dairy farms from a variety of grazing management systems in all climatic zones of Australia were selected, from an initial pool of 124 dairy farms  to a to a22])The ‘Feeding Standards’ approach  for quanwhere(i)2+] are the number of cows in their first and in their second or greater lactations, respectively;[NLactn1] and [NLactn(ii)[LiveWt] is the estimated liveweight of the cows and for the purposes of estimating daily energy requirements is assumed to be constant for the day of visit to the farm;(iii)[NMidLactn] and [NLateLactn] are the number of cows in mid and late lactation respectively, where the lactation length was 305 days, divided evenly into early (102), mid (103 to 204) and late (205+) lactation;(iv)2+];[MilkPerCow] is milk produced (L) per cow taking into consideration the number of heifers [NLactn1] and older cows [NLactn(v)[WalkDist] is the distance walked (km) by the herd from the dairy shed to the middle of each paddock;(vi)[Vert] is the steepness factor according to Heard et al. , based o(vii)m: 0.02 × [MEDiet] + 0.5;k(viii)c: 0.133;k(ix)l: 0.02 × [MEDiet] + 0.4;k(x)Diet: 10 MJ/cow per day (Heard pers comm).METhe equations for each calculation were as follows:Supplementfed) was summed for all supplements fed to the lactating herd, accounting for estimates of wastage, and based on the ME concentration of all supplements fed.The ME consumed in supplements  were obtained by subtracting total dietary nutrient intake from milk nutrients secreted. Animal nutrient use efficiencies were calculated as the proportion of nutrients secreted in milk to those consumed.Feed and milk samples were collected on all farms after each interview for subsequent chemical analysis. Approximately 400 mL of milk  was collected after thorough agitation of the vat (bulk tank), placed on ice, returned promptly to the laboratory and frozen (−20 °C) until chemical analysis. Separate grab samples of all dietary supplementary feeds  were collected, totaling 830 samples. The exact weights of non-forage supplements provided by farmers to herds in the dairy shed were confirmed by weighing (in triplicate) the amounts dispensed by feeders. Where necessary the amounts stated by farmers were corrected to reflect the actual amounts dispensed to the animals. A minimum of 20 grab samples of pasture (to post-grazing height) were collected and composited from pastures representative of paddocks recently grazed by the lactating herd on each farm.Supplementary feeds and pasture samples were oven-dried  then ground to pass a 2 mm screen. Subsamples were dried at 100 °C for 24 h for calculating percent DM. Feed and milk samples were analysed by George Weston Technologies, Sydney, currently operating as Weston Food Laboratories. Crude protein (CP) in feed and milk was measured according to AOAC methods  . Total PData collected at each interview visit as well as sample analytical data were entered into an Access  database and queries based on the equations above were used to calculate dietary and nutrient intakes, milk nutrient secretion, nutrient excretion and feed nutrient use efficiencies. Energy corrected milk was calculated according to Tyrrell and Reid. , where tFor 15 out of 227 records where milk fat and protein concentration data were not available, average concentrations were calculated using either data from the other interview samples for a farm, if data from more than three interviews were available (3 farms), and for three other farms using the average of the whole dataset.Statistical analyses were undertaken using Genstat 17  and RStudio© 0.99.891 (R version 3.2.3), and RStudio and S-plus 8.1  were used for graphical representation. Residual maximum likelihood  was used to analyse relationships between nutrient excretion and farm characteristics, such as herd size. The fixed model comprised an explanatory variate , or the explanatory variate crossed with interview date or season , and the random model consisted of the factor ‘farm’. In these analyses tests for fixed effects were determined by sequentially adding terms to the fixed model. Although interviews were conducted on five occasions on each farm, the herds at each interview consisted of different cows that were at different stages of lactation and which received different diets. Consequently, ‘interviews’ were not nested in ‘farm’ in the random model. Pearson correlation coefficient and linear regression analyses were performed in R using the cor and lm functions, respectively. Linear regression analyses investigated relationships between the explanatory variables total DMI or supplementary DMI and response variables ECM or FCE . For analyses of relationships at each interview visit, or different seasons, or for diets with low (L ≤ 20%) or high (H ≥ 20%) crude protein (CP), the data were subset )). Correlation coefficients and p-values were generated in a four column matrix using flattenSquareMatrix(cor.prob). The regressions and correlations were represented graphically or tabulated. All data met assumptions of normality and constant variance based on residual graphs of histograms, fitted value and quantile plots.The number of milking cows in each herd on these commercial farms for all interviews ranged from 30 to 1350, with approximately three times as many cows in their second or greater lactation as were in their first, and an estimated average age of the herds of 4 years old . Most heOf the calculated daily total ME , per cowDairy farms in this study, while considered grazing-based systems, utilised various feeding strategies, from complete dependence on pasture to total mixed rations, with diets containing a wide range of nutrient and energy contents . Cows grSupplement N, P, K, S, Ca and Mg intakes for all interview dates and farms averaged (range) 226 (7–667), 42 (2–155), 124 (3–511), 21 (1–72), 64 (2–216), and 28 (1–88) g/cow per day, respectively (data not presented). On average, supplement nutrient intake on these farms was 42%, 50%, 36%, 42%, 53% and 51%, respectively, of total nutrient intakes and ranged from 0 to 100% for all nutrients.Based on ME requirements of the cows and the total ME fed in supplements, calculated pasture DMI ranged from 0.04 to 22.5 kg DM/cow per day, excluding those animals that did not access pasture and therefore had no pasture intake . Mean an2 = 0.77, data not presented) and ECM (R2 = 0.75) were significantly (p < 0.008) related to total DMI at all interviews  in estimated total DMI (10 to 29 kg DMI/day) was observed for all farms and interviews (data not presented) compared to that for pasture (51%) and supplements (54%) DMI. Despite the variety of diets offered nationally, both milk yield  averaged (range) 226 (7−667), 42 (2−155), 124 (3−511), 21 (1−72), 64 (2−216), and 28 (1−88) g/cow per day, respectively (data not presented). Supplement nutrient intakes varied between interview dates for all farms and no farms ever had both maximum and minimum supplement intakes. On average, supplement nutrient intake was 42%, 50%, 36%, 42%, 53% and 51%, respectively, of total nutrient intakes—ranging from 0 to 100% for all nutrients.Calculated nutrient intakes (g/cow per day) were highly variable with pasture N intake having the lowest CV and pasture Mg intake the highest . SupplemPasture and supplement nutrient intakes were summed and divided by total DMI to give dietary CP, P, K, S, Ca and Mg concentrations, which, on average (range), were 19% (9−30), 0.45% (0.21−0.75), 2.1% (0.8−4.0), 0.29% (0.14−0.56), 0.65% (0.17−1.34), 0.3% (0.14−0.6), respectively. Except for N, supplement nutrient intake concentrations were always more variable than pasture nutrient intake concentrations. Maximum total N, K and S intakes were four times minimum intakes for these nutrients and six, eight and five times minimum dietary P, Ca and Mg  was positively related to farm size, while N and S excretion were strongly (p < 0.004) positively related to herd size, stocking rate and total milk produced on the farm. Nitrogen, P and Mg excretion increased with per cow milk production, while only N, P and S excretion were related to per hectare milk produced. Both P and Mg excretion increased (p < 0.001) as the percent of dietary ME that was imported increased. An indication for P excretion (p = 0.057) to be related to percent of the farm area that received irrigation was observed. Negative relationships were observed between K and Ca excretion and farm characteristics. Potassium excretion was significantly related to importation of supplementary ME (p = 0.007), and Ca excretion was related to herd size (p = 0.027) and stocking rate (p = 0.046). Highly significant season effects were observed for relationships between Mg excretion and per cow milk production (p = 0.007) with weaker relationships observed for the other characteristics (0.013 < p < 0.046). Neither season nor interview date influenced relationships between K or Ca excretion and the farm characteristics tested, but significantly (p < 0.001) influenced relationships between the other nutrients excreted and farm characteristics (data not presented).Residual maximum likelihood analysis  was undeMeasurement of animal intakes on commercial dairy farms is critical for assessing within-farm nutrient flows and how the latter influence nutrient deposition and accumulation; as a means of identifying nutrient management points of intervention. Due to difficulties in measuring nutrient intake by grazing animals, a number of methods have been compared for measuring pasture DM intake ,13,28, aThe ‘Feeding Standards’ approach was modified in this study to allow for calculation of daily rather than annual pasture intakes by grazing herds and therefore to account for seasonal differences on commercial farms. Not surprisingly, milk yield across the study farms varied, as they were selected to be representative of the systems that occur in a variety of climatic zones and regions nationally . The maxHeard et al.  observedEstimation of dietary ME is difficult to accommodate in studies on commercial dairy farms, with Reeves et al.  observinAverage pasture DMI was only just over a half of total DMI for all farms, despite the grazing-based nature of these systems, and considerably less than the average of 80% of ME intake that occurred on farms 15 years ago . As suchhttp://www.bom.gov.au/climate/data/), suggesting that some herds may have experienced temperature stress, although this may not have occurred near dates of farm visits.Estimates of DMI are known to be influenced by body weight changes that occur in early and late lactation . Losses p = 0.065; Auldist et al. [p < 0.001) correlations between FCE and ECM were observed . Calcult et al.  where tht et al. . In addit et al.  reportedt et al. . Despited , Ca (1.7–13.4 g/kg DMI) and Mg (1.4–6.0 g/kg DMI) estimated in this study spanned recommendations for lactating dairy cattle with different levels of milk production . Few incThe potential for N and P to be environmental contaminants ,45 resulMean dietary CP concentration 19%) for cows in this study was slightly higher than both Australian (16%)  and NRC % for cowMean N and P excretion (g/cow per day) calculated for these grazing system farms fell within ranges collated from a review of experimental literature . When reManagement factors can influence nutrient excretion, and as observed by Borsting et al. , N excreAnimal nutrient use efficiencies, based on dietary nutrients and nutrients secreted in milk, are often considered a useful means of comparing the potential for environmental impact. Estimating animal use efficiencies and excretion of macrominerals is both important for animal health and productivity as well as for minimizing salt loads to soils . On commThis paper addresses the requirement for technological solutions to improve nutrient management , and is The methodology to estimate dietary intake of grazing cows described in this paper can be used for calculating nutrient excretion and to develop within-farm nutrient budgets. The herd data  required for the daily ‘Feeding Standards’ approach are readily available on commercial dairy farms. Dates of data collection should represent major seasonal influences on diet and milk production, which for Australian grazing systems would be at least once in each season. However, as farmers seek to increase productivity, individualised feeding systems are recommended ,58 and tThis approach for calculating excreted nutrients can be used to identify nutrient management points of intervention on grazing system dairy farms. By combining excretion data with information about the locations within farms where animals are held , depositThis work demonstrated that the daily pasture DMI of lactating herds can be estimated with a modified energy requirement (‘Feeding Standards’) approach using data readily available on commercial dairy farms. The calculated intakes, which are comparable to those reported in the literature and are concordant with farmer estimates of feed provided, indicate the feasibility of this approach for estimating nutrients excreted by lactating cows in grazing systems. Using nutrient analyses of all feeds offered on farms instead of book values, including estimates of feed wastage, and ensuring accurate calibration of supplementary feeders are likely to have contributed to the agreement of calculated pasture DMI with farmer estimates of pasture consumed. More accurate liveweight data could improve DMI estimates, with sensitivity analysis indicating a less than 5% difference in calculated values. Using this approach for lactating herds grazing on commercial dairy farms, we showed that only approximately half the animals’ total DMI consisted of pasture, and calculated herd N and P intakes were greater than recommended in the literature. Daily excreted N, P, K, S, Ca and Mg were calculated for these grazing herds, with low feed N and P use efficiencies indicating the potential for nutrient losses that will need to be managed on commercial farms.


Fecal occult blood tests (FOBTs) detect colorectal cancer (CRC) at high levels of sensitivity and specificity. However, the detection of early-stage cancers is highly important to reduce CRC mortality. We aimed to assess the sensitivity of a large number of different FOBTs according to various tumor characteristics. We observed among all FOBTs consistently lower sensitivities for UICC stage I cancers in comparison to more advanced cancer stages. An even stronger gradient was found according to T status, with substantially lower sensitivities for T1 than for T2–T4 cancers. Furthermore, sensitivities for T1 cancers were even lower than sensitivities for detection of multiple advanced adenomas. Further research should focus on improving the sensitivity of non-invasive tests for detection of UICC stage I and T1 cancers.n = 2043) and newly diagnosed CRC patients (n = 184) provided a stool sample before bowel preparation or CRC surgery. Fecal hemoglobin concentration was determined in parallel by nine different quantitative FITs among 94 CRC patients, 200 AA cases, and 300 participants free of advanced neoplasm. Sensitivities were calculated at original cutoffs and at adjusted cutoffs, yielding 93% specificity among all FITs. Results: At adjusted cutoffs, UICC stage I cancers yielded consistently lower sensitivities (range: 62–68%) compared to stage II–IV cancers (range: 73–89%). An even stronger gradient was observed according to T status, with substantially lower sensitivities for T1 (range: 39–57%) than for T2–T4 cancers (range: 71–100%). Sensitivities for the detection of participants with multiple AAs ranged from 55% to 64% and were by up to 25% points higher than sensitivities for T1 cancers. Conclusions: FITs detect stage I cancers and especially T1 cancers at substantially lower sensitivities than more advanced cancer stages. Participants with multiple AAs were detected with slightly lower sensitivities than stage I cancers and with even higher sensitivities than T1 cancers. Further research should focus on improving the detection of early-stage cancers.Background: Fecal immunochemical tests (FITs) are widely used for colorectal cancer (CRC) screening. The detection of early-stage cancer and advanced adenoma (AA), the most important premalignant lesion, is highly relevant to reducing CRC-related deaths. We aimed to assess sensitivity for the detection of CRC and AA stratified by tumor stage; number; size; histology of AA; and by location, age, sex, and body mass index (BMI). Methods: Participants of screening colonoscopy ( Fecal immunochemical tests (FITs) for hemoglobin are widely used for colorectal cancer (CRC) screening ,2,3. FITTwo recent publications ,10 reporIn this study, we simultaneously assessed the sensitivity of nine different FIT brands according to tumor stage (UICC stage or T status) and location; size; histology; and number of AAs; and sex, age, and body mass index (BMI) among participants of screening colonoscopy and newly diagnosed CRC cases.We followed the Standards for the Reporting of Diagnostic Accuracy Studies (STARD)  and the This study is based on the BLITZ and DACHSplus studies. Detailed information about both studies has been provided elsewhere ,14,15,16Written informed consent was obtained from each study participant. Both studies were approved by the ethics committee of the Medical Faculty Heidelberg of the University of Heidelberg (BLITZ study (178/2005): 13 June 2005 and DACHSplus study (310/2001): 27 March 2006) and by the ethics committees of the State Chambers of Physicians of Baden-Wuerttemberg, Rhineland-Palatinate and Hesse. Study participants recruited from 2005 through 2010 were asked to fill a sample cup (60 mL) with feces from a single bowel movement without any dietary or medicinal restrictions before starting bowel preparation for colonoscopy (BLITZ) or surgery (DACHSplus). Furthermore, they were instructed to store the stool-filled cups in a freezer and bring them to their colonoscopy appointment (BLITZ) or hospital admission (DACHSplus). After arrival, the samples were kept frozen at −20 °C and transported after an initial fecal hemoglobin measurement on dry ice to the German Cancer Research Center (DKFZ) for final storage at −80 °C.Colonoscopy and histology reports, as well as medical and histological surgery reports were collected, and relevant data were independently extracted by two medical data managers in a blinded manner (unaware of the FIT results).Study participants, who were recruited from 2005 through 2010 and who provided a stool sample were eligible for this study . After eMore than 95% of the FIT measurements were conducted in the context of a previously reported study . For theDetailed information about the FITs are shown in All quantitative FIT measurements were converted to the same and directly comparable unit of µg Hb/g feces .2; overweight: 25.0–29.9 kg/m2, obesity: ≥30 kg/m2). UICC stage definitions followed the AJCC Cancer Staging Manual (7th edition) and are provided in Sensitivities were calculated for CRC according to UICC stage, T status, tumor location , distal colon , and rectum), sex, age group , and BMI group  of sensitivities and specificities were calculated using the “exact” (Clopper–Pearson) method. The Cochran–Armitage Test for trend in proportions was used to evaluate the statistical significance of trends in sensitivities across T status, UICC stage, location, age, and BMI. Fisher’s exact test was used to test for differences in sensitivities between both sexes, AA size, architecture, dysplasia, and number of AAs. p-values below 0.05.Statistical analyses were performed using SAS Enterprise Guide, version 7.1 . Statistical significance was indicated by two-sided n = 15) and clinical (n = 79) CRC cases were combined. For two tests , the evaluation of CRC sensitivity is based on 65 CRC cases overall. Characteristics of the study population are shown in At original cutoff values, overall specificities ranged from 86% to 98% (median: 91%) across the nine FITs .p < 0.05) among all seven FITs that evaluated the larger sample size (n = 94 cases).Overall sensitivity for CRC ranged from 63% to 83% (median: 73%) . Sensitip < 0.05). Sensitivity of AA with high-grade dysplasia was detected at consistently higher sensitivities (by 7–16% points) than AAs without high-grade dysplasia; however, results were not statistically significant. Participants with multiple AAs (n = 33) yielded substantially higher sensitivities  than participants with only a single AA , and differences between both groups were statistically significant among all nine FITs . Interestingly, for 7 of the 9 FITs, sensitivity for the detection of multiple AA was by 7–25% points higher than sensitivity for the detection of T1 cancers. Overall sensitivity for AA ranged from 18% to 44% (median: 31%) . SensitiLooking at the tumor location , the higFurthermore, sensitivities for AA were consistently higher for males than for females, for older participants than for younger participants, and for obese participants than for over- or normal-weighted participants, even though most of these differences did not reach statistical significance .In general, overall and subgroup-specific sensitivities became very similar between the different FIT brands when cutoff values were adjusted to yield the same overall specificity . p < 0.05). Very similar to the results obtained at original cutoffs, strong variations of sensitivity of CRC detection by tumor stage were seen . Sensitip < 0.05). Furthermore, we found in an ancillary analysis that sensitivities for AA increased statistically significantly (p < 0.05) from <0.5 cm to ≥0.5–1 cm, ≥1–3 cm, and ≥3 cm (n = 167), AAs located in the distal colon showed higher sensitivities than AAs located in the rectum or proximal colon, but a statistically significant difference was not found. Again, sensitivities were much higher (by 28–41% points) among participants with multiple AAs than among participants with a single AA, and this difference was statistically significant for all eight FITs (p < 0.005). Additionally, sensitivities for multiple AAs were again higher (by up to 25% points) for 7 of the 8 FITs than for T1 cancers. Overall sensitivity for AA ranged from 27% to 32% (median: 30%) . Sensitind ≥3 cm . AA withIn line with results at original cutoffs, highest sensitivities were consistently observed for rectum cancers, whereas sensitivities were even slightly lower for cancers in the distal than for cancers in the proximal colon . Furtherp < 0.05).Furthermore, sensitivities for AA tended to be higher for men than for women even though differences did not reach statistical significance . SensitiIn this study, we evaluated for the first time the sensitivity for CRC and AA detection of a large number of different quantitative FITs according to tumor stage , histological characteristics of AA, location, sex, age, and BMI, using fecal samples of participants of screening colonoscopy enriched with newly diagnosed clinical CRC cases. Strong associations between more advanced tumor stage and higher sensitivity were observed. These associations were particularly strong and statistically significant by T status (differences between T1 and T2–T4 by up to ~60% points) but also notable among overall UICC stages (differences between stage I and II-IV by up to ~25% points). Participants with multiple AAs yielded only slightly lower sensitivities than those with UICC stage I cancer and by up to 25% points higher sensitivities than those with T1 cancers.The observed gradient in sensitivities with increasing UICC stage was even stronger than the gradient estimated in a previous meta-analysis  where poIn line with findings from previous studies ,26,27,28Our study has a number of strengths. To our knowledge, this is the first study to directly compare sensitivities of different FITs among the same study participants with additional consideration of tumor stage, AA characteristics, location, sex, age, and BMI. This increases comparability to other studies using only one FIT and precludes the potential influence of the different study designs, as an additional reason for varying sensitivities according to the different patient characteristics. Furthermore, we investigated the FITs at different cutoffs: the original cutoffs and at cutoffs adjusted to yield the same overall specificity of 93%. Sensitivities for all FITs were reported stratified by a range of potentially influential factors, such as tumor stage  and location; number; size; histology; and location of AA, by age, sex, and BMI. Ours is also the first study to our knowledge that compared the sensitivity of T1 and UICC stage I cancers with sensitivity of AA with various characteristics and found that sensitivities for multiple AAs were particularly high and exceeded sensitivity for T1 cancers by up to 25% points. Furthermore, several exclusion criteria were applied to the recruited study participants: Of the screening participants, we excluded those not in the relevant age for screening (<50 or >79 years), those at elevated risk (IBD  or histon > 5000), the limited number of CRC and AA cases resulted in wide confidence intervals of subgroup-specific sensitivities and limited power for detecting differences across subgroups. Furthermore, stool samples were frozen and thawed before analysis, which differs from the recommended procedure of sample processing . However, we found very similar diagnostic performance between both above-mentioned fecal sampling procedures [Our study also has limitations. Despite the large overall number of FIT measurements  and T1 cancers in particular.


Fibroblast growth factor 19 (FGF19), fibroblast growth factor 21 (FGF21) and Klotho are regulators of energy homeostasis. However, in the pediatric population, the relationships between obesity, metabolic disorders and the aforementioned factors have not been clearly investigated. We analyzed the role of FGF19, FGF21 and Klotho protein in children with normal body weight as well as in overweight and obese subjects and explored their associations with insulin resistance (IR) and metabolic syndrome (MS) and its components.This was a cross-sectional study conducted in a group of hospitalized children and adolescents. Laboratory investigations included serum analysis of FGF19, FGF21, and Klotho with ELISA kits as well as the analysis of the lipid profile and ALT serum concentrations. Moreover, each subject underwent an oral glucose tolerance test (OGTT) with fasting insulinemia measurement to detect glucose tolerance abnormalities and calculate the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index. Furthermore, the clinical analysis included blood pressure measurement, body fat percentage estimation and assessment of the prevalence of MS and its components.N = 48), obesity (N = 92) and overweight (N = 34). Klotho concentration was significantly higher in the obese children [median 168.6 pg/ml (90.2 to 375.9)]) than in the overweight [131.3 pg/ml (78.0 to 313.0)] and normal-body-weight subjects [116.6 pg/ml (38.5 to 163.9)] (p = 0.0334) and was also significantly higher in insulin-resistant children than in insulin-sensitive children . FGF21 was elevated in patients with MS compared to the FGF21 levels in other subjects . The multivariable model showed that FGF19 was an independent predictor of IR after adjusting for pubertal stage and BMI Z-score.The study was conducted with 174 children/adolescents aged 6–17 years with normal body weight (Klotho levels were associated with body weight status in children and adolescents. Moreover, Klotho, FGF19 and FGF21 concentrations correlated with IR status and/or components of MS. Overweight and obesity in children and adolescents have become a worldwide problem , 2. ExceParticular interest has been given to the fibroblast growth factor subfamily 19, which includes fibroblast growth factor 19 (FGF19) and fibroblast growth factor 21 (FGF21). These hormones have been reported to regulate energy homeostasis in the prolonged response to nutritional status after insulin and glucagon action. FGF19 is mainly secreted from the small intestine in response to food intake and exerts insulin-like effects: it promotes glycogen synthesis and inhibits gluconeogenesis. FGF21, on the other hand, is released from the liver in response to starvation and exhibits glucagon-like properties: it promotes lipolysis, thermogenesis and gluconeogenesis –7.It was previously shown that adult patients with obesity and metabolic diseases present reduced serum FGF19 levels with compensatory increases in FGF21 concentrations –10. HoweThe biological action of molecules from the FGF19 subfamily is mediated by a transmembrane Klotho protein, which promotes their binding to specific receptors . FurtherThe goal of this study was to investigate FGF19, FGF21 and Klotho serum concentrations in children and adolescents in relation to body weight status. We also aimed to evaluate the association between the factors mentioned above and the occurrence of MS and its components. Finally, we assessed the relationship between the concentrations of the measured proteins and IR.This was a cross-sectional observational study based on patients aged 6–17 years who were hospitalized between 2015 and 2019 in the Department of Gastroenterology, Allergology and Pediatrics, Polish Mother’s Memorial Hospital – Research Institute in Lodz, Poland due to gastrointestinal tract symptoms. Patients with confirmed organic causes of symptoms were excluded from the study. All obese and overweight subjects (identified by ICD code) were invited to participate in the study. Nonobese children and adolescents were included as a convenience sample (with a guardian’s consent and lack of contraindications to participate). Exclusion criteria included admission due to acute conditions , chronic inflammatory diseases, chronic kidney diseases, endocrine disorders , malignancy and/or current use of antibiotics or other medications that might influence body composition or glucose and lipid metabolism . Only children born at term and with adequate birth mass were included in the study. During hospitalization, the participants received a standard diet containing 1500–2000 kcal/day . The energy supply varied according to differences in patient age, sex and body weight , height [cm] (Radwag WPT 60/150 OW) and waist circumference [cm], as well as subscapular and triceps skinfold thickness [mm] (MSD Skin Fold Meter). Body mass index (BMI) was calculated according to the formula weight/heighth charts , 22. We h charts . Howeverh charts . The diah charts .Venous blood samples were collected after 12 h of fasting into standard vacuum tubes on the second day of hospitalization. Low-density lipoprotein cholesterol (LDL-C) was measured directly by a two-step reaction. Triglycerides (TGs), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) were analyzed using enzymatic colorimetric assays. The enzymatic activity of alanine aminotransferase (ALT) was measured by the akinetic method, and plasma glucose was measured by the oxidase method. All these assays were performed using the Vitros 5.1FS or 4600 platforms . Electrochemiluminescence was used to measure serum insulin levels .Furthermore, each child underwent a standard 2-h oral glucose tolerance test (OGTT) with 1.75 g glucose/kg (max. 75 g). Two hours after ingestion, plasma glucose between 7.8 mmol/L (140 mg/dl) and 11.1 mmol/L (200 mg/dl) were interpreted as impaired glucose tolerance.Serum samples for FGF19, FGF21 and Klotho analysis were immediately stored at − 80 °C until analysis. They were thawed at room temperature only once for the measurement. We measured FGF19 and FGF21 concentrations with Human FGF19 and FGF21 ELISA Kits  according to the manufacturer’s instructions with an ELISA reader iMARK™ (Bio-Rad) at a wavelength of 450 nm. The manufacturer reported no observed cross-reactivity with human FGF19, FGF21 and FGF23. The limits of detection for FGF19 and FGF21 were 4.8 pg/ml and 7.0 pg/ml, respectively.We used the double-antibody sandwich ELISA Kit to determine serum Klotho concentrations . The analysis was performed as instructed by the manufacturer, with the ELISA reader iMARK™ (Bio-Rad) at a wavelength of 450 nm. The manufacturer reported no significant cross-reactivity or interference between Klotho and analogs. The detection range was 15.6–1000 pg/ml.We described the levels of FGF19, FGF21 and Klotho below the detection ranges as 0.IR was evaluated by calculating the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index according to the following formula: fasting insulinemia (μU/ml) × fasting glycemia (mmol/l)/22.5. Excessive IR was diagnosed when HOMA-IR exceeded 2.67 in boys and 2.22 in girls in the prepubertal period and 5.22 in boys and 3.82 in girls in the pubertal period . MS diagWe compared the normal weight, overweight and obese groups in terms of clinical characteristics and concentrations of FGF19, FGF21 and Klotho proteins with Kruskal-Wallis ANOVA with post hoc Dunn tests. The data are presented as medians and 25–75% ranges. The relationships between continuous variables and concentrations of FGF19, FGF21 and Klotho proteins were assessed by Spearman’s R coefficients. Given low variability of continuous variables in our cohort, we decided to interpret Spearman’s correlation coefficients < 0.3 as weak associations. We noted the frequencies of important metabolic outcomes  in each group and compared them  using odds ratios with 95% confidence intervals (95% CI). We then compared the concentrations of the investigated proteins in patients with and without specific conditions with Mann-Whitney U tests.2 values. All calculations were performed with Statistica 13.1 (Statsoft) software.The relationship between the measured protein concentrations and IR was evaluated using multivariate linear regression with HOMA-IR (log-transformed with base 10) as a continuous outcome. The initial predictors included sex, age, Tanner stage, BMI Z-score, BF% and FGF19, FGF21 and Klotho serum concentrations. After the univariate assessment, we discarded BF% due to its high correlation with BMI Z-score. FGF21 and Klotho were also eliminated due to nonsignificant associations with HOMA-IR. Age and sex were retained in the model despite no significant association with the outcome. For the sake of clarity, physical development (Tanner stage) was recoded as 1 for stage III and 0 for all other stages. We constructed the final model using stepwise forward regression and expressed its performance in predicting HOMA-IR with adjusted RN = 48, 35.4% boys), obesity  and overweight . The sex distribution was similar in all three subgroups (p = 0.2525).Among the 5058 subjects aged 6–17 years who were hospitalized in the study period, 174 children/adolescents (45.4% boys) with a median age of 12.10 years were enrolled in the study after taking into account the exclusion criteria and consent to participate. The study included 49 (28.1%) children under 10, the youngest being 6.15 years old. Based on the 85th and 95th BMI percentile cut-offs, the group was divided into participants with normal body weight (p = 0.3812) and pubertal stage (p = 0.8710). However, they differed significantly in terms of metabolic conditions. MS was diagnosed in 18 patients (10.3%). The components of MS as well as other abnormalities were more prevalent in overweight and obese patients than in those with normal weight  vs 197.7 pg/ml (123.5 to 279.3), p = 0.1125; FGF21 – males 85.1 pg/ml (42.2 to 160.9) vs 89.3 pg/ml (42.8 to 174.2), p = 0.5915] or pubertal stage . The studied proteins were also not associated with one another (data not shown).The protein concentrations did not correlate with the age of the children  Table  and werep = 0.0334). The discrepancy was greatest among those with obesity [median concentration 168.6 pg/ml (90.2 to 375.9)] and normal body weight [median 116.6 pg/ml (38.5 to 163.9)] (post hoc p = 0.0282). The differences between overweight and obese patients (post hoc p = 1.0000) as well as overweight subjects and subjects with normal body weight (post hoc p = 0.3633) were not significant. Furthermore, there were several significant associations of the studied protein concentrations with lipid profiles as well as ALT levels  or no association FGF21) with the body mass of the children. Weak associations were also detected between adiposity and the concentrations of studied markers  . The model, however, managed to explain only a small portion of the overall HOMA-IR variation (R2 = 30%).Finally, those with MS presented an elevated concentration of FGF21 [136.2 pg/ml vs 82.6 pg/ml, In our study, we examined the concentrations of circulating FGF19, FGF21 and Klotho proteins among normal weight, obese and overweight children and adolescents and their relationships with metabolic parameters. The results complement the existing reports that thus far lack pediatric-specific data.We noted increased FGF21 concentrations in children and adolescents with MS compared to the concentrations in other subjects. Moreover, FGF21 levels correlated with both the clinical  and biochemical  features of MS. Despite the role of FGF21 in metabolism regulation, reports on its usefulness as a biomarker for obesity and abnormalities associated with MS are conflicting , 28–30. To our knowledge, this is the first study in children and adolescents to show that FGF19, in addition to pubertal stage and BMI Z-score, is an independent predictor of IR. Given that current reports on the relationship between FGF19 levels and metabolic parameters (including IR) are conflicting , 38, ourFGF19 is released from the small intestine in response to food intake and reaches its peak serum level 3 h after a meal compared with 1 h for insulin . In liveKlotho gene were shown to have less detectable adipose tissue than wild-type animals [Klotho gene are resistant to obesity induced by a high-fat diet [In our study, we noted that children and adolescents affected by obesity showed higher serum Klotho concentrations than those with normal body weight. This finding is in contrast with other studies on the matter. Amitani et al. showed markedly lower plasma Klotho levels in patients with obesity and anorexia nervosa than in the control group, which suggests that Klotho may reflect normal nutritional status . On the  animals . Finallyfat diet , 43.Interestingly, we noted differences in Klotho levels between patients with IR and those with normal insulin sensitivity. A possible explanation is that Klotho participates in the enzymatic modification of N-glycans in insulin and IGF-1 receptors and thus inhibits the intracellular insulin/IGF-1 signaling pathway. As a result, insulin-stimulated glucose uptake becomes blocked, which contributes to IR development , 44. ImpThere are potential limitations of our study. First, we studied only peripheral hormone levels and did not assess local  expression levels, which was out of scope for this study. Second, we relied on BMI Z-score to recognize overweight and obesity without body content assessment by dual-energy X-ray absorptiometry (DXA), as it was not available. Estimated BF% could not be translated into sex- and age-independent Z-scores or percentiles due to the lack of modern pediatric charts for the Polish population. Moreover, serum Klotho concentrations may depend on vitamin D and calcium-phosphate homeostasis, which we did not examine in the studied subjects. However, the abovementioned dependence is observed mostly in patients with chronic kidney diseases, who were excluded from this study. Our multivariate model for HOMA-IR explained only a small fraction of patient-to-patient variability. This demonstrates that there are likely other factors that might be associated with IR in a stronger and more direct way. The subjects were also enrolled in the study in a hospital setting, which might be a potential limitation. However, the inpatient conditions assured a similar exposure to potential confounding factors such as diet, physical activity and ambient temperature. Finally, the samples were taken from the local population, which prohibits us from generalizing the results to Polish or European children.In the studied pediatric group, increased serum Klotho concentrations were associated with obesity and IR. This may suggest the existence of a mutual regulation of hormones in the insulin/IGF-1 signaling pathway. We also found a negative association between HOMA-IR and FGF19 concentrations, which may result from the compensatory effect of the interaction between insulin and FGF19. The increased FGF21 concentrations observed in children and adolescents with MS may be an effect of the FGF21 resistance observed in subjects with central obesity.


Syphilis and HIV among gay, bisexual and other men who have sex with men (MSM) are syndemic suggesting current prevention strategies are not effective. Sex partner meeting places and their networks may yield effective and optimal interventions. From 2009 to 2017, 57 unique venues were reported by > 1 MSM and 7.0% (n = 4), 21.1% (n = 12) and 71.9% (n = 41) were classified as syphilis, HIV or co-diagnosed venues, respectively. Forty-nine venues were connected in one main network component with four online, co-diagnosis venues representing 51.6% of reports and the highest degree and eigenvector centralities. In a sub-analysis during a local syphilis epidemic, the proportion of venues connected in the main component increased 38.7% (61.5% to 86.4%); suggesting increasing overlap in syphilis and HIV transmission and density of the venue network structure over time. This network analysis may identify the optimal set of venues for tailored interventions. It also suggests increasing difficulty of interrupting network transmission through fragmentation. The current syphilis epidemic in the United States (U.S.) among gay, bisexual and other men who have sex with men (MSM) is closely linked to the HIV epidemic. In 2017, MSM accounted for 79.6% of all primary and secondary (P&S) syphilis cases among males and 57.9% of all P&S syphilis cases overall .The syndemic is likely due in part to the fact that syphilis infection can facilitate the transmission and acquisition of HIV infection –6. ThereTraditional methods of syphilis  control were designed to address network transmission. Local health departments have traced the partners of patients with syphilis, a process called contact tracing, since the 1930s for the purpose of treatment and prevention , or mistAn alternative or complement to contact tracing is to collect information on sex partner meeting places. Sex partner meeting places are venues such as a bar, club, internet application (app), website, market, park, school, or street corner where individuals meet sex partners forming sexual dyads and larger sexual networks. The rationale is that sex partner meeting places are less subject to the limitations of contact tracing and may be an effective means to access priority populations for interventions such as providing access to pre-exposure prophylaxis (PrEP), antiretroviral therapy (ART), testing and linkage to care/treatment for STIs and HIV, and messaging for undetectable equals untransmissable (U = U) campaigns and safer sex practices , 19, 20.The use of specific sex partner meeting places to disrupt network transmission is not new. Bathhouses were an important meeting place to find anonymous sex partners among MSM in San Francisco in the 1980s and were used to create sexual networks in which the first HIV/AIDS cases were traced and mapped . Venues In our recent work, we have taken an innovative approach to analyzing the sex partner meeting places of newly HIV diagnosed MSM , an apprThe overall goal of this work is to inform innovative, data-driven public health control strategies for reducing STIs and HIV. Among MSM with early syphilis and/or a new HIV diagnosis, the objectives are to (1) characterize the connectivity of sex partner meeting places to assess venue co-affiliation network structure and examine transmission overlap; (2) in a sub-analysis, describe temporal changes in the co-affiliation network structure over a 5 year period when early syphilis cases increased by 40% among MSM. We hypothesize that a majority of venues will be nominated by both early syphilis and/or newly HIV diagnosed individuals, and that a small number of venues with the greatest centrality measures will reveal a discrete set of venues that may be useful for public health control activities and/or interventions. We also hypothesize that the venue co-affiliation networks of early syphilis and/or newly HIV diagnosed MSM will become increasingly dense over the 5-year period during an increase in early syphilis cases among MSM.Baltimore City, Maryland ranks among U.S. cities with the highest incidence of syphilis and HIV among MSM. In 2017, the rate of P&S syphilis was 3.6-fold higher than the national rate  . In 2016This work was conducted through a public health-academic partnership between the STI/HIV prevention and control program of the Baltimore City Health Department (BCHD) and the Johns Hopkins University Center for Child and Community Health Research (CCHR). Reporting of syphilis and HIV to state and local health departments is legally mandated in Maryland. The BCHD refers all early syphilis and/or newly HV diagnosed individuals with a primary residential address within Baltimore City for partner services. An early syphilis case is defined as an individual with primary, secondary, or early latent syphilis as staged by a clinical provider or a BCHD disease intervention specialist (DIS). A new HIV diagnosed case is defined as an individual without a prior record of an HIV infection reported in the U.S. During routine partner services, in addition to collecting demographic, risk behavior, and sex partner information, the BCHD routinely collects information on sex partner meeting places . The information is elicited by a question to the individual, “Where did you meet your sex partners in the past 12 months?”.For this study, we used public health surveillance data from MSM with an early syphilis and/or new HIV diagnosis and reporting at least one sex partner meeting place from 2009 to 2017. Male cases were considered to be MSM if during the partner services interview, they self-identified as gay or bisexual or reported having sex with men. The recall for sexual risk behaviors  and drug use during partner services interviews was determined by the critical period for the diagnosis: 12 months for HIV diagnoses and 3, 6, and 12 months for primary, secondary, and early latent syphilis diagnoses, respectively. In this analysis, drug use was defined as self-report of injection drug use or any method of consumption of the following drugs—cocaine, heroin, and/or methamphetamines.A specific sex partner meeting place was defined as a physical location that could be placed on a map or an online location that was a specific application or website. Reported places that were non-specific venues or regions, such as “bars,” “online” and “West Baltimore”, were not included. In order to build the venue co-affiliation networks, only venues reported by more than one individual were included in the analysis. This study used routine public health surveillance information and was considered exempt from human subjects research by the Johns Hopkins Medicine IRB.Summary statistics were generated to describe and compare the study population overall and by diagnosis  using chi-squared tests or t-tests, as appropriate. Descriptive statistics included demographics , sexual risk behavior (e.g. number of sex partners), and number and type of venues reported. In addition, we compared individuals who reported a sex partner meeting place (i.e. the study population) to those who did not report a sex partner meeting place to identify potential biases in the study population.Venues were classified by typology, diagnoses, and sexual risk behaviors. Venues were classified into six typologies: internet/app (i.e. online), bar/club, street/park/neighborhood, market/mall, or other . Venues To describe the co-affiliation network, we calculated the proportion of venues by diagnoses and sexual risk behaviors  of the individuals who nominated the venue and by venue typology.Two measures of venue centrality, degree centrality and eigenvector centrality, were calculated to evaluate the relative prominence of specific venues in the venue co-affiliation network for objective one. Degree centrality was calculated as the number of ties (i.e. a tie is where two venues share a patron) the venue had to all other venues within the network . A venueEigenvector centrality was calculated as the number of ties the venue had within the network  weighted by the degree centrality of each of the venues’ adjacent venues in the network . For exaTwo measures of network density, connectedness and compactness, were calculated to assess changes in the venue co-affiliation network density over time for objective two. Connectedness was calculated as the proportion of venue pairs that could reach each other by a path of any length to all possible venue pairs in the network (i.e. the proportion of nodes in the main component) . A netwoTo characterize the connectivity of sex partner meeting places, we created a venue co-affiliation network of sex partner meeting places reported by at least two individuals over the entire study time period (objective one) from 2009 to 2017 and in a sub analysis, by each year (objective two) from 2010 to 2014. Venues were connected in the network, i.e. shared a tie, when they shared at least one patron.To determine syphilis and HIV transmission overlap (objective one), we first described the connectivity of syphilis only and HIV only venues (i.e. single diagnosis venues) vs. co-diagnosis venues and then we compared the median degree centrality and median eigenvector centrality of single diagnosis venues vs. co-diagnosis venues using Mann–Whitney tests (i.e. a nonparametric test of difference).To assess changes in density and transmission overlap over time (objective two), we calculated the median degree centrality and median eigenvector centrality of the co-affiliation network for each year. The percent change in median degree centrality and median eigenvector centrality of the co-affiliation network in 2010 was compared to 2014 using Mann–Whitney tests. We also calculated two additional network measures, connectedness and compactness, by year to assess changes in network density over time. Changes in connectedness and compactness were described using percent changes from 2010 to 2014.Descriptive analyses and network analyses were performed using Stata Version 14 and UCINET Version 6.657. Network visualizations were built in R version 3.5.0 using the iGraph package . For alldata not shown).Between January 2009 and December 2017, 6175 individuals diagnosed with early syphilis and/or a new HIV diagnosis were reported to the BCHD. Among these 6175 individuals, 77.1% (n = 4758) were males, among whom 61.4% (n = 2921) were MSM. Among MSM, 88.5% (n = 2585) received partner services interviews, of whom 24.8% (n = 641) reported at least one specific venue and were included in this analysis. MSM who reported a venue were not statistically different by age, race, ethnicity or diagnoses compared to MSM who did not report a venue (data not shown).Among these 641 individuals, 31.8% (n = 204) were diagnosed with early syphilis only, 42.3% (n = 271) were diagnosed with HIV only, and 25.9% (n = 166) were co-diagnosed with early syphilis and new HIV  11.00], 79.1% were Black (n = 507), and 5.0% were Latino (n = 32). The median number of sex partners reported was 2 (IQR 50.00), 7.2% (n = 46) of individuals reported engagement in commercial sex work, 8.7% (n = 56) reported any drug use, 53.8% (n = 345) reported anonymous sex, and 95.0% (n = 609) reported sex without a condom. The 641 individuals nominated 979 venues, of which 85.4% (n = 836) were nominations of venues reported by more than one MSM and were included in this analysis. The median number of venue reports per individual was 1 (IQR 7.00).Objective one: characterize the connectivity of sex partner meeting places to assess venue co-affiliation network structure and examine syphilis and HIV transmission overlap.In the venue co-affiliation network of venues from 2009 to 2017, there were 57 unique venues and among these, 7.0% (n = 4) were classified as syphilis venues, 21.1% (n = 12) as HIV venues, and 71.9% (n = 41) as co-diagnosed venues. A majority of the venues were online venues  followed by bars or clubs . The remaining venues were streets, parks, or neighborhoods , markets or malls , or other . Almost half of all venues  had at least one report of commercial sex work, and almost half of all venues  had at least one report of drug use. The percent of reports of commercial sex work at each venue ranged from 0 to 100% . The percent of reports of any drug use at each venue ranged from 0 to 50%  A total of 94.7% (n = 54) of venues had at least one report of anonymous sex, and 100.0% (n = 57) of venues had at least one report of condomless sex. The percent of reports of anonymous sex at each venue ranged from 0 to 100% , and the percent of reports of condomless sex at each venue ranged from 50 to 100% .Overall, the median degree centrality was 4 (IQR 54.00). Four venues, Facebook, Adam4Adam, Jack’d, and Instagram, represented 51.6% of all unique venue reports. All four are online venues and were classified as co-diagnosed venues. These four venues had the highest degree centralities of 152, 101, 64, and 54, respectively. These four venues also had the highest eigenvector centralities of 1.00, 0.50, 0.27, and 0.22, respectively.Of the 57 unique venues, 86.0% (n = 49) were connected in one main network component of which 6.1% of venues (n = 3) were syphilis venues, 20.4% (n = 10) were HIV venues, and 73.5% (n = 36) were co-diagnosed venues. To assess transmission overlap, connectivity of single diagnosis venues (i.e. syphilis only and HIV only venues) and co-diagnoses venues were described, and centrality measures were compared between single and co-diagnosis venues. Among single diagnosis venues, 69.2% (n = 9) were directly connected to at least one co-diagnosis venue, 7.7% (n = 1) were indirectly connected (i.e. connected through another single-diagnosis venue) to a co-diagnosis venue, and 23.1% (n = 3) were not connected to any other venue (i.e. an isolate). Among co-diagnosis venues, 100% (n = 36), were directly connected to another co-diagnosis venue, and 2 co-diagnosed venues formed the only dyad (i.e. pair of venues disconnected from the main network component). 33% (n = 12) of co-diagnosis venues were directly connected to at least 1 single diagnosis venue, 52.8% (n = 19) were indirectly connected to a single diagnosis venue, and 8.3% (n = 3) were isolates.Single diagnosis venues had significantly lower median degree centralities compared to co-diagnosed venues . The median eigenvector centrality was not significantly different between the two groups [0.00 (IQR 0.01), 0.01 (IQR 0.03), Mann–Whitney Fishers p = 0.15, respectively, Fig. Objective two: describe temporal changes in the co-affiliation network structure from 2010 to 2014.Over the 5-year period from 2010 to 2014 when early syphilis cases increased 40% among MSM, 408 MSM nominated 54 unique venues.data not shown).The median degree centrality increased 33% from 3 (IQR 4.00) in 2010 to 4 (IQR 13.00) in 2014 (Mann–Whitney Fishers p = 0.49), and the median eigenvector centrality decreased 78% from 0.14 (IQR 0.22) in 2010 to 0.03 (IQR 0.09) in 2014 , and 79.1% were Black. These demographics are notable because in Baltimore City, as well as other cities across the U.S., young Black MSM are a population that has continued to experience a high incidence and, in some places, an increasing incidence of syphilis and HIV 1.00, and. TargeteThe venue co-affiliation networks of early syphilis and/or newly HIV diagnosed MSM suggest a high degree of overlap in the potential transmission of both syphilis and HIV transmission networks. Approximately 72% of venues were co-diagnosed venues. When comparing the median degree centrality of the venues, single diagnosed venues had a lower median degree than co-diagnosed venues demonstrating that these co-diagnosed venues are overall more central indicating that they have more shared patrons with other venues than single diagnosis venues. In addition, almost half of the venues were reported by individuals also reporting high sexual risk behaviors such as commercial sex work (41%) and drug use (41%). Four venues, Facebook, Adam4Adam, Jack’d, and Instagram, represented 51.6% of all unique venue reports and were all online, co-diagnosed venues. These venues also had the highest degree centrality and eigenvector centralities. These network measures suggest that these four venues had the most nominations and shared the most ties with other venues in the network, i.e. they were connected to the most venues that also had a high number of other venue ties. Other venue-based studies have identified a similar pattern of a dense core of venues, although most have identified a core with both physical and online venues , 24.The four online core venues we identified may represent the optimal set for tailored interventions. Interventions, for example, could include offering HIV/STI testing, sexual risk reduction behavioral interventions and/or PrEP through messaging on the apps. The data also suggest, however, that an intervention targeting each of the four venues may be redundant, and optimal interventions may be able to target one or two of the highly central venues and reach a majority of the venues and individuals. The broader network of venues also suggests that it may be important to additionally target venues that are less closely tied to this highly connected set of four venues.In addition, it is notable that the majority of nominated sex partner meeting places (53.1%) were online venues. Our prior work showed increasing trends in nominations of online sex partner meeting places, but also suggested that physical places remained important and highly connected to online spaces , 34. OurOur analysis of temporal changes of the venue connectivity over a 5-year period of increasing early syphilis cases among MSM suggests increasing nomination, density and connectivity of the co-affiliation network structure over the time period. The network structure changes over time may also suggest higher transmission rates and increased difficulty of interrupting network transmission through fragmentation. The findings suggest a 69.2% increase in the number of venues that were reported by at least 2 MSM. The proportion of venues connected in the main component as measured by connectedness increased 57% over the 5 years, suggesting an increasing density of venue connection over time and potentially increasing transmission potential over time. The increase in compactness of 28% over the 5 years is also crucial because it suggests that the paths between venues are shorter and therefore, transmission may be more efficient.These results may reflect either an increasing number or use of venues for MSM to meet sex partners, or alternatively a condensing of venues for MSM to meet sex partners, therefore increasing the likelihood that a venue is nominated by more than one individual. These two scenarios—increasing number or use versus condensing of venues—are very different phenomena and each has different implications for transmission and intervention. The former scenario may result in greater challenges in zeroing in on the transmission sources. The latter scenario in contrast, may present fewer challenges with fewer venues to choose from.There are important limitations to this study. Data were limited to venues reported by MSM with an early syphilis and/or new HIV diagnosis. The men reporting venues may be different than those not reporting venues, although our statistical testing did not suggest differences. In addition, the data reported on venues may be subject to recall bias and may not represent all venues MSM frequented to meet sex partners in the past 12 months. Also, because co-affiliation network analysis is a tool to identify opportunities for social and sexual connection, these data are not necessarily a reflection of where direct connections occurred or where the infection was acquired. Missing information in the surveillance data did not allow for a thorough examination of individual risk information such as drug use, commercial sex work, unprotected sex, and co-infection, which if incorporated may have helped to further elucidate the transmission potential of venues and assist in tailoring control strategies. In addition, we did not interview non-infected individuals to assess how their networks may differ. Another limitation was that we simplified the analyses by comparing single vs. co-diagnosis venues and this may have minimized some nuances related to the connectivity of the venues. Finally, there are different interpretations of what it means to be central in an affiliation network, and a related challenging aspect is that we assume one-mode relationships of patron co-memberships without having important information about the pattern of affiliation ties .In summary, this approach is innovative as it harnesses the potential power of routinely collected public health surveillance data, may be more reliable than traditional contact tracing sexual network data, and importantly, focuses on the inherent networks of sex partner meeting places to identify transmission networks and overlapping transmission networks of syphilis and/or HIV . Leverag