Laparoscopic cholecystectomy is now considered the first option for gallbladder surgery. However, 20% to 30% of cholecystectomies are completed as open operations often on elderly and fragile patients. The external validity of randomised trials comparing mini-laparotomy cholecystectomy and laparoscopic cholecystectomy has not been studied. The aim of this study is to analyse characteristics, procedures, and outcomes for all patients who underwent cholecystectomy without being included in such a trial.Characteristics , operation time, hospital stay, and mortality were compared for patients who underwent cholecystectomy outside and within a randomised controlled trial comparing mini-laparotomy and laparoscopic cholecystectomy.During the inclusion period 1719 patients underwent cholecystectomy. 726 patients were randomised and 724 of them completed the trial; 993 patients underwent cholecystectomy outside the trial. The non-randomised patients were older – and had more complications from gallstone disease, higher co-morbidity, and higher ASA – score when compared with trial patients. They were also more likely to undergo acute surgery and they had a longer postoperative hospital stay, with a median 3 versus 2 days . Standardised mortality ratio within 90 days of operation was 3.42 (mean) (95% CI 2.17 to 5.13) for non-randomised patients and 1.61 (mean) (95%CI 0.02 to 3.46) for trial patients. For non-randomised patients, operation time did not differ significantly between mini-laparotomy and open cholecystectomy in multivariate analysis. However, the operation for laparoscopic cholecystectomy lasted 20 minutes longer than open cholecystectomy. Hospital stay was significantly shorter for both mini-laparotomy and laparoscopic cholecystectomy compared to open cholecystectomy.Non-randomised patients were older and more sick than trial patients. The assignment of healthier patients to trials comparing mini-laparotomy cholecystectomy and laparoscopic cholecystectomy limits the external validity of conclusions reached in such trials. External validity of conclusions reached in a randomised trial depends upon the number and characteristics of eligible patients not included in the trial (excluded by patient or researcher) and number and characteristics of patients who do not meet the eligibility criteria . AccordiThe laparoscopic technique has been adopted as the method of choice for cholecystectomy, and epidemiological studies indicate that 70% to 80% of all cholecystectomies are now completed laparoscopically -5. HowevData for all patients over age 18 advised to undergo elective or acute cholecystectomy were prospectively recorded at four non-university hospitals and one university centre. Patients undergoing cholecystectomy as adjunct to other major abdominal operations such as pancreatic resection, hepatic resection and aneurysmectomy were excluded. The protocol included information on previous and present cholecystitis, pancreatitis, jaundice, and suspected or proven malignancy; American Society of Anaesthesiologists' (ASA) score; co-morbidity ; diagnostic investigations; and indication for surgery (elective/acute).Eligible patients were considered for inclusion in a randomised controlled trial comparing minilaparotomy and laparoscopic cholecystectomy. Patients were given verbal and written information about the trial and asked if they wished to participate. Exclusion criteria for the trial were: age under 18 years, jaundice, obesity (body mass index >45), pregnancy, cirrhosis of the liver, suspected or proven malignancy, and previous upper gastrointestinal surgery. Recruitment of patients, randomisation, blinding, sample size calculation, control panel and rules for stopping the trial, data collection, and processing have been described in previous reports ,8. DurinLaparoscopic cholecystectomy was performed according to routines at the participating units. Mini-laparotomy was defined as cholecystectomy performed through a laparotomy incision less than 8 cm. Trainees were encouraged to participate in surgery under supervision, as is routine practice. Complications were classified according to Clavien et al .Outcome of qualitative variables, for example acute cholecystitis (yes/no) or co-morbidity (no/at least one disease or item) was compared using chi-square analysis. The distribution of quantitative variables  was compared using the Kolmogorov-Smirnov non-parametric two-sample test. Mortality within 90 days and from 91 to 365 days of surgery was calculated as the standardised mortality ratio (SMR) i. e. ratio between the observed and expected number of deceased patients, taking into account sex and age of individuals. Mortality after cholecystectomy in a defined population is raised above that of the standard population until 90 days after operation  and therAmong non-randomised patients, operation time and hospital stay for open cholecystectomy, mini-laparotomy cholecystectomy, and laparoscopic cholecystectomy were compared using multiple regression analysis with open cholecystectomy as a reference. First, the influence of acute cholecystitis, pancreatitis, jaundice, malignancy, co-morbidity, ASA-score, common bile duct exploration, bilio-digestive shunt operation (choledocho-duodenostomy), concomitant operation, and acute operation was tested using univariate analyses. Adjustments were then made for these variables in a multiple regression analysis.Table The procedures used for non-randomised patients are shown in Table According to multiple regression analysis, operation time for mini-laparotomy cholecystectomy and open cholecystectomy in non-randomised patients did not differ significantly, whereas laparoscopic cholecystectomy took significantly longer (20 minutes) than open cholecystectomy Table . HospitaThe principal findings of present study are that non-randomised patients were older and more sick than trial patients and more likely to have an acute operation. Of all non-randomised patients 67.9% were operated with open methods, 52.4% with conventional open cholecystectomy, and 15.5% with mini-laparotomy cholecystectomy. In contrast to trial patients, non-randomised patients had a threefold excess death rate compared to the background population during the postoperative period. Multivariate analyses demonstrated that operation time for non-randomised patients did not differ between open cholecystectomy and mini-laparotomy, whereas laparoscopic cholecystectomy took 20 minutes longer. Postoperative hospital stay was shorter for both mini-laparotomy cholecystectomy and laparoscopic cholecystectomy compared to open cholecystectomy.The strength of the study is the completeness of the data. All cholecystectomies performed during the inclusion period are taken into account. The weaknesses of the study primarily relate to timing. Our study was conducted at a time when laparoscopic cholecystectomy had been in use for five years in Sweden and surgeons' experience with mini-laparotomy was limited. Cholecystectomy was not performed as day-case surgery at the time of the trial, but day-cases have since been introduced both for laparoscopic cholecystectomy and mini-laparotomy at the hospitals participating in the present trial.Cholecystectomy in elderly and vulnerable patients, who often have complications of gallstone disease, is associated with considerable mortality ,10. TherOur multiple regression analyses indicated that mini-laparotomy was a cost-effective alternative to laparoscopic cholecystectomy among non-randomised patients. This finding agrees with findings in single-blind randomised controlled trials ,14. and We found that non-randomised patients were more vulnerable than trial patients. Mini-laparotomy should replace conventional open cholecystectomy whenever possible. The versatility of mini-laparotomy has important implications for cost-effectiveness in gallbladder surgery. Low external validity of conventional randomised controlled trials as demonstrated in this report may to a great extent be overcome by expertise based randomised trials .The author(s) declare that they have no competing interests.AR, EN, and PC designed the study; AR was responsible for collection of clinical data; and PC, MR, and KB for data analyses. EN drafted the manuscript. All authors read and approved the final manuscript which has been approved by all authors.The pre-publication history for this paper can be accessed here:


Many studies have demonstrated that health related quality of life (HRQoL) improves, on average, after coronary artery bypass graft surgery (CABGS). However, this average improvement may not be realized for all patients, and it is possible that there are two or more distinctive groups with different, possibly non-linear, trajectories of change over time. Furthermore, little is known about the predictors that are associated with these possible HRQoL trajectories after CABGS.182 patients listed for elective CABGS at The Royal Melbourne Hospital completed a postal battery of questionnaires which included the Short-Form-36 (SF-36), Profile of Mood States (POMS) and the Everyday Functioning Questionnaire (EFQ). These data were collected on average a month before surgery, and at two months and six months after surgery. Socio-demographic and medical characteristics prior to surgery, as well as surgical and post-surgical complications and symptoms were also assessed. Growth curve and growth mixture modelling were used to identify trajectories of HRQoL.improvers and non-improvers). A series of logistic regression analyses identified different predictors of group membership for PCS and MCS trajectories. For the PCS the most significant predictors of non-improver membership were lower scores on POMS vigor-activity and higher New York Heart Association dyspnoea class; for the MCS the most significant predictors of non-improver membership were higher scores on POMS depression-dejection and manual occupation.For both the physical component summary scale (PCS) and the mental component summary scale (MCS) of the SF-36, two groups of patients with distinct trajectories of HRQoL following surgery could be identified (It is incorrect to assume that HRQoL will improve in a linear fashion for all patients following CABGS. Nor was there support for a single response trajectory. It is important to identify characteristics of each patient, and those post-operative symptoms that could be possible targets for intervention to improve HRQoL outcomes. Coronary artery bypass graft surgery (CABGS) is aimed at alleviating patients' morbidity and prolonging their lives. Given the high success rate of such surgery in achieving these aims, it is clear why the assessment of health related quality of life (HRQoL) is of such importance. Longitudinal studies -3 have cMost studies that have investigated change over time in CABGS patients ,3,8-10 hWilson and Cleary  provide The biological/physiological or medical characteristics that have been consistently associated with poorer HRQoL outcomes after CABGS include pre-surgical cardiac functional status, such as the New York Heart Association (NYHA) classification of dyspnoea ,15,16, cIt is not surprising that symptoms of depression or anxiety have been associated with a marked alteration in mental HRQoL and worse outcomes after CABGS. Among patients scheduled for CABGS, the prevalence of depressive symptoms is high, with a recent Australian study finding that over half of bypass candidates were diagnosed as depressed . PreoperDecreased cognitive function has been recognized to be a major, although probably partly reversible, unintended outcome of CABGS . A growiAmong the individual characteristics that have been associated with poorer HRQoL outcomes after coronary interventions are age and gender. Younger patients ,25 have One possible environmental influence on HRQoL outcomes is cardiac rehabilitation (CR) program attendance. Despite its efforts to improve the psychological, as well as the physical, well-being of patients , CR atteThe aims of the present study were:a. to identify the general form of change of HRQoL over time, i.e. linear or non-linear, using growth curve modelling (GCM)b. to identify the different trajectories of HRQoL over a six month period for both physical health and mental health, using growth mixture modelling (GMM)c. to identify the socio-demographic, medical, psychological or cognitive variables that predict group membership of HRQoL trajectories.Eligible patients were adults on the waiting list for CABGS at The Royal Melbourne Hospital, Australia, between July 2001 and April 2004. Patients were excluded from the study if they were under the age of 18 or over 85 years of age; were subsequently assigned to a non-CABGS procedure; or refused consent. Patients could withdraw from the study at any stage between consent to participate and the final follow-up assessment at six months. The following were criteria for withdrawal: failure to return postal questionnaires, death, physical illness or frailty preventing participation, occurrence of a major medical or neurological illness that would independently affect cognitive outcome, refusal, and unavailability for follow-up for other reasons.2 = 10.6, df = 1, p =.001) or a positive family history of cardiovascular disease . There were no significant differences in all other medical variables, gender and age. Of the 183 returned questionnaires, HRQoL data were available for 182 patients.Of a consecutive sample of 444 patients to whom a questionnaire package was posted, 220 (49%) returned the questionnaires. The mean interval between completion of the baseline questionnaire and surgery was 33 days . Of the returned questionnaires, 37 were excluded because, based on medical records, it was ascertained that the patient had not undergone CABGS. In order to ascertain reasons for non-completion or non-return of questionnaires, a random sample of one in three patients (n = 78) was contacted by telephone. Amongst these patients, reasons for non-completion of the questionnaire were language difficulties (42%), refusal (38%), death (12%) or disability (8%). The 261 patients who were either excluded or did not return questionnaires were compared with the 183 included patients on all medical variables, gender and age. Excluded patients were less likely to have either high cholesterol (χn = 117) were compared with those of the patients who did not complete it at any of these time points (n = 65). There were no significant differences between dropouts and completers on any pre-operative medical or socio-demographic characteristics.To determine whether there was any systematic bias to the analyses due to participant dropout over the six months, the socio-demographic and pre-operative medical characteristics of patients who completed the HRQoL measure at all three time points . Very high PCS scores indicate no physical limitations, disabilities or decrements in well-being as well as a high energy level. Very low PCS scores indicate substantial limitations in self-care, physical, social and role activities; severe bodily pain; or frequent tiredness. Very high MCS scores indicate frequent positive affect, absence of both psychological distress and limitations in usual social/role activities due to emotional problems, while very low MCS scores indicate frequent psychological distress and substantial social and role disability due to emotional problems. A comprehensive literature review [The (Short-Form) SF-36 . The PCSe review  of the pe review .Patient mood was assessed by the Profile of Mood States (POMS) . The POMA modified 28-item version of the Everyday Functioning Questionnaire (EFQ)  was usedA range of pre-surgical, surgical and post-surgical variables was obtained from hospital medical records. Many of these variables were identified in the published literature as risk variables for mortality and health outcomes after CABGS ,8-10,44.For each SF-36 summary scale, modelling was carried out in two stages. At Stage 1 GCM was undertaken. With GCM, individuals are assumed to come from one population and a mean growth curve can be estimated using all individuals. The latent variables representing the intercept (i.e. estimated baseline scores) and trajectory (i.e. estimated change over time) for each sub-scale were derived from the sub-scale scores obtained at the three time points.2 value and Comparative Fit Index (CFI) [2 values and higher CFI scores are indicative of a better fit of the model to the data. More specifically, CFI values greater than .900 indicate a good model fit [Three separate trajectory shapes were tested: linear, square root and quadratic. While a linear function assumes constant change, a square root function assumes more rapid change in the early months, and a quadratic function assumes more rapid change in the later months. These functions were selected from a larger number of possible functions because they represented the most likely trajectory shapes. Selection of the best trajectory shape for each summary scale was based on two commonly used measures of model fit: χex (CFI) . Lower χodel fit .In order to examine the extent to which the intercept and slope were related to the medical socio-demographic and emotional variables the two model parameters were regressed on the 16 potential predictors. A significant coefficient for the term "Intercept regressed on predictor" indicates that the baseline  HRQoL varies according to the level of the predictor. A significant coefficient for the term "Slope regressed on predictor" indicates that the change over time in HRQoL varies according to the levels of the predictor.The second stage of analysis involved GMM. Unlike GCM, GMM calculates fit statistics for sub-groups of individuals called "mixtures", each described by a different growth curve. Hence, GMM utilises more fully the heterogeneity that results from variation in sub-scale scores over time . Lower BIn order to determine the predictors of PCS and MCS sub-groups, a screening process and a series of logistic regression analyses were undertaken. The screening process aimed to optimize the number of potential predictors to be included in the multivariate analysis in order to reach a satisfactory compromise between making Type I and Type II errors. Two screening strategies were adopted. First, dichotomous variables were excluded from further consideration if there were fewer than 10 cases endorsed in any of the two response categories. Second, Chi-square and one-way analysis of variance were used to explore differences between identified sub-groups for variables measured using nominal and interval scales, respectively. Removing those variables that had p > .10 resulted in a reduction of pool predictors from 48 to 12 for PCS and from 48 to 17 for MCS, thereby reducing the probability for type I errors. Using a p-value of > .10 rather than >.05 reduced the likelihood of type II errors.The remaining independent variables associated with the outcome variables with a p-value of = .10 were then included in a series of logistic regression analyses. For each SF-36 summary scale logistic regression analyses were run separately for socio-demographic variables, medical variables, psychosocial scores and cognitive symptoms. The significant predictors from these analyses were included in two final logistic regression analyses; one for PCS and one for MCS. A backwards regression approach (p < .05 to remain in model) was used for all logistic regression analyses.Descriptive statistics including mean, standard deviations, frequencies and percentages were used to summarize and present the data. These analyses were performed using SPSS version 13.0 .Pre-operative characteristics of the study population are detailed in Table Table 2 values and CFIs well above the acceptable level of .900, indicating very good model fit. For both sub-scales the intercept and slope parameters were highly significant.For the PCS, a linear trajectory was found to provide the best fit of the data. Combined with a positive slope parameter, this suggests that PCS scores improved at a steady rate (1.37 units per month) over the six months. For the MCS, a square root function provided the best model fit. Again, combined with the positive slope parameter this indicates that MCS scores improved more rapidly in the first two months followed by relatively less improvement between two and six months. As shown in Table Table For the PCS, lower baseline HRQoL was associated with poorer NYHA functional class; peripheral vascular disease; higher POMS tension-anxiety, depression-dejection, fatigue-inertia, confusion-bewilderment; and lower vigor-activity. No variables were significantly associated with change in physical HRQoL over time.For the MCS, lower baseline HRQoL was associated with previous cardiac surgery; higher POMS tension-anxiety, depression-dejection, anger-hostility, fatigue-inertia, confusion-bewilderment; and lower vigor-activity. Greater improvement in mental HRQoL over time was associated with having a partner and higher scores on POMS tension-anxiety, depression-dejection, anger-hostility, and fatigue-inertia.Table Figure The predicted and actual group trajectories for the two-group model for the MCS are presented in figure non-improver group was the presence of atrial fibrillation (p < .10), having chronic heart failure (p < .05), higher BMI (p < .10), having a previous MI (p < .05), higher NYHA functional status (p < .001), having cardiac arrhythmia after surgery (p < .05), and higher pulmonary pressure recorded during surgery (p < .05). For the socio-demographic variables non-improver status was associated with being female (p < .10), being born outside of Australia (p < .05), and not being in the workforce (p < .05). For the psychosocial variable only lower scores on POMS vigor-activity (p < .001) and higher scores on POMS fatigue-inertia (p < .05) were associated with non-improver status. No cognitive variables distinguished improvers from non-improvers for the PCS.The screening process found that three categories of variables were significant bivariate predictors of PCS group status. For the medical variables, a greater likelihood of being in the non-improver membership. Of the three socio-demographic variables, being not in the workforce was a significant unique predictor of non-improvement. From the psychosocial variables, both POMS (lower) vigor-activity and (higher) fatigue-inertia, remained significant predictors in the third multivariate analysis.When the seven medical variables were included in a logistic regression to predict PCS group, three  were significant unique predictors of non-improvers) were: non-participation in the workforce, lower POMS vigor-activity scores, higher NYHA dyspnoea class, experiencing a new cardiac arrhythmia during or following the operation, and higher pulmonary pressure recorded during the procedureTable non-improver group was the presence of atrial fibrillation (p < .10), having off-pump surgery (p < .10), having had previous cardiac surgery (p < .001), having a previous MI (p < .10), and non-attendance at CR (p < .05). For the socio-demographic variables non-improver status was predicted by not having a current partner (p < .10) and having a manual occupation (p < .05). For the psychosocial variables non-improver status was associated with POMS tension-anxiety anger-hostility, vigor-activity, fatigue-inertia, confusion-bewilderment and depression-dejection . For the cognitive variables non-improver status was associated with higher EFQ memory (p < .05), organisation (p < .001), communication (p < .05), and concentration (p < .001).The screening process found that there were significant bivariate predictors of MCS group status from all four categories of variables. For the medical variables, a greater likelihood of being in the The four separate logistic regressions identified the following variables as significant unique predictors of non-improvement: previous cardiac surgery, previous MI, lower CR attendance, manual occupation, higher (poorer) scores on POMS depression-dejection, and higher (poorer) scores on EFQ concentration.non-improvers) included: previous cardiac surgery, manual occupation, higher scores on POMS depression-dejection and higher scores on EFQ concentration.Table The present study supports previous findings -3,7,50 tFor the total sample there were significant medical and psychosocial predictors of baseline physical HRQoL including poorer NYHA functional class, having peripheral vascular disease and poorer emotional state. Baseline mental HRQoL was predicted by only previous cardiac surgery among the medical variables and poorer scores on all emotional scales. No cognitive variables significantly predicted baseline HRQoL. For the total sample there were no significant predictors of change in physical HRQoL over time. The apparent contradictory findings that poorer emotional scores predicted greater improvement over time in mental HRQoL may be explained by the fact that these patients had much lower scores at baseline and had greater capacity to change over time. It does appear that having a partner was beneficial for recovery in mental HRQoL and this finding is consistent with previous studies ,51.It is important to note that these results only relate to the trajectory of the whole sample. These overall trends, may mask the true picture of recovery following CABGS. The findings of the present study support the hypothesis that identifiable sub-groups of patients exists, each described by a different growth curve, and that these different groups may have different outcomes. Indeed, in this sample, there are two distinct groups of patients for both the physical and mental components of HRQoL. The GMM has identified a larger proportion of the patient population who experience rapid and continued improvement in PCS and MCS scores over time. Of serious clinical relevance, however, the analysis has also identified a smaller group of patients who experience little or no improvement during the first six months following surgery.non-improver membership for the MCS. Characteristics of the individual, namely not being in the workforce and having a manual occupation, were predictive of non-improver membership for the PCS and the MCS respectively. These findings are consistent with those of past studies [The findings of this study lend support to investigation of a range of biopsychosocial predictors, such as that advocated by Wilson and Cleary , in expl studies ,52. It i studies . These f studies , may expnon-improver membership for the PCS is also supported by previous studies [Symptom status was also highly predictive of trajectory classification. The finding that higher NYHA dyspnoea class was predictive of  studies . Presuma studies  resultinnon-improver membership of the PCS, and that higher scores on the POMS depresssion-dejection scale predicted non-improver membership for the MCS. These findings are consistent with two recent studies that have also found higher pre-operative depression scores to predict lower HRQoL six months after surgery [It is also not surprising that lower scores on the POMS vigor-activity scale indicated  surgery ,56. It i surgery , are les surgery , and mor surgery , all facnon-improver membership for the MCS. This finding is supported by a previous study which showed that perceived cognitive function, reflecting ability to concentrate, was a major determinant of HRQoL outcomes in a cross-sectional study of Swedish CABGS patients [This study adds weight to the growing body of evidence that patients' perceived cognitive abilities may influence HRQoL outcomes. Lower scores on EFQ concentration were predictive of patients . It has patients . Furtherpatients  which reimprover group when medical variables were analysed separately, but was not significant in the presence of competing variables. This finding is consistent with other studies that have found little or no difference between CR attenders and non-attenders in HRQoL measured by the SF-36 [The present study also improved upon previous studies by assessing the impact of operational and post-surgical variables. It has been argued that HRQoL in the months after CABGS may be affected by environmental factors such as processes and structures of care, complications of the surgery, or interim life change or health events ,14. The he SF-36 ,32. It hhe SF-36 . CR was he SF-36 .The main limitations of the present study concern the fact that it relied on postal collection of data with over half of the questionnaires not being returned. This limitation raises the possibility that the results may not be generalisable to all CABGS patients. Those who did not return the questionnaire had lower rates of high cholesterol and were less likely to have a positive family history of heart disease. However, in all other medical variables there were no significant differences between questionnaire returners and non-returners. Moreover, there were no differences with regard to gender and age. Other socio-demographic measures such as education level and employment status, were not investigated in excluded patients, so the possibility that non participants differed in these characteristics has not been examined.The present study relied upon self-report for psychosocial measures and HRQoL rather than formal diagnostic criteria. Although the questionnaire order was varied, the possibility of a self-report bias exists. It has been shown that patients with depressive symptoms may over-report negative aspects of HRQoL and a spurious exposure-outcome association may be generated . The incAnother possible limitation of the study is the reported lack of responsiveness of the SF-36. Hawkes and colleagues argue that the SF-36 may not be sufficiently responsive as an outcome measure with CR patients . Devon  comparedThe findings of the present study highlight the probability that sub-groups of patients exist within the CABGS population, each with its own trajectory of HRQoL following surgery, which may not necessarily be linear. The results further imply that particular socio-demographic, medical and symptom-related variables are useful in predicting membership of these sub-groups. When considering these predictors of HRQoL outcomes, it is important to consider a comprehensive model that incorporates pre-operative, operative and post-operative variables, psychosocial and cognitive symptoms, and characteristics of the individual and the environment. Furthermore, development of interventions to improve HRQoL outcomes and enhanced clinical decision making may result from improved identification of predictors of HRQoL after CABGS.CABGS Coronary artery bypass graft surgeryHRQoL Health related quality of lifeNYHA New York Heart AssociationGCM Growth curve modellingGMM Growth mixture modellingSF-36 The (Short-Form) SF-36PCS Physical component summaryMCS Mental component summaryPOMS Profile of Mood StatesEFQ Functioning QuestionnaireBMI Body mass indexCR Cardiac rehabilitationCFI Comparative Fit IndexBIC Bayesian Information CriterionMI Myocardial infarctionThe author(s) declare that they have no competing interests.MLG drafted the manuscript and analysed the data using SPSS. PE supervised the data analysis, assisted with the interpretation of findings, and analysed the data using M-Plus. MW, BM and AJG participated in the design of the study. CE and RH participated in the coordination of the study. All authors read and approved the final manuscript.


Upper Cretaceous amber from the Raritan Formation  has been investigated by Pyrolysis-GC-MS and Pyrolysis-GC-matrix isolation FTIR-MS. Results establish the existence of two distinct forms of amber in this deposit. Both forms are Class Ib ambers, but they are unambiguously differentiated on the basis of their (intact) diterpenoid composition. The presence of callitrisate in both forms, and cupraene in samples designated form 1, strongly suggest that both derive from related-but-distinct species within the Cupressaceae.In addition to callitrisate, dehydroabietate and analogous 17-nor-, 16,17-dinor- and 15,16,17-trinor- analogues of these compounds are also observed. The distributions of these products in multiple samples suggest that they are the result of biological emplacement, rather than diagenetic modification of the parent compounds. This indicates that the distributions of diterpenes observed in these samples are representative of the original bioterpenoids and, hence, are useful for chemotaxonomic analyses. Amber, the fossil form of plant resins, occurs in terrestrial sediments throughout the geologic record from about the early-middle Mesozoic forward and is relatively common in Cenozoic sediments . Older fGeologic classification/nomenclature of ambers is of little value. Ambers from separate deposits are known by a plethora of names which have no relationship to structural properties or composition. Even within a single deposit, ambers of different clarity, color, friability and/or other properties may be referred to by discrete names, which can result in serious confusion and misclassification. Geochemically, ambers are classified on the basis of their structural properties into groups with comparable structural characteristics ,5-7. ClaNote: Chemical structures referred to in the text are illustrated in Appendix regular" stereochemistry. That is, structures including and related to communic acid, communol etc  that are preserved within it. Fossiliferous material within amber is often preserved to a higher degree of fidelity than perhaps any other medium. Grimaldi  has evenAmber is found in numerous locales in North America, including several locations on the Atlantic Coastal Plain. Perhaps the largest and best known of these  deposits occurs in the Raritan formation, which extends from Staten Island across parts of New Jersey. Amber from this area was first reported in 1821  and sincAraucariaceous origin. Grimaldi, Wampler and Shedrinski [Pinaceous (or Taxodiaceous) origin, based on the results of pyrolysis-gas chromatography (Py-GC) and pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) analyses. However, the high pyrolysis temperatures  used for these analyses tends to favor the formation of secondary products [Langenheim and Beck and subseedrinski  subsequeproducts .- hence products .) Furtheproducts . As a reIn the present study, samples of Raritan amber have been analyzed by Py-GC-MS and Py-GC-matrix isolation FTIR-MS (Py-GC-mi FTIR-MS) with in situ methylation of acidic products by tetramethyl ammonium hydroxide (TMAH). The observed distribution of products is discussed both with respect to chemotaxonomic significance and also in relation to potential diagenetic modification of the original diterpene distributions.Samples were collected from the Raritan formation , Sayerville, New Jersey, by Mr. Allen Graffham and donated for analysis. Samples were water-washed and unpolished. A total of five individual blebs were used for these analyses. All were visually comparable, being generally clear, yellow-orange in color and irregular but rounded in shape and ranging in size from ~0.7 cm to 2 cm Figure . Each blinitial = 40°C (4 minutes); ramp = 4°C/min to Tfinal = 280°C. Temperature was then held isothermally for at least 16 minutes.Py-GC-MS analyses were carried out as previously described , except Five discrete samples from this deposit were analyzed by Py-GC-MS. The resulting data clearly indicate the existence of two related-but-distinct forms of amber. These two materials are visually indistinguishable, but are clearly differentiable on the basis of the compounds observed in their pyrolysates. This finding contradicts the earlier finding that ambers from this site are chemically uniform .The total ion chromatogram (TIC) obtained from Raritan amber form 1 is illustrated in Figure Polymer-derived bicyclic products observed are dominated almost entirely by carboxylic acids derived from communic acid (IVa-VIIa). A-ring defunctionalized analogues (IVe/f-VIIe/f) of these characteristic bicyclic acids  are also14/C15  Data for the second form are also indicative a Class Ib amber, and give comparable CThe distribution of the major intact diterpenoids observed in the pyrolysates of Raritan form 1 amber is illustrated in Figure Grimaldi et al concludeCallitris columellaris (Cupressaceae) by Carman and Deeth [Callitris [Juniperis (Cupressaceae) species [Salvia [Caleolaria [Rabdosia [Pinus [Callitrisic acid nd Deeth . Subsequallitris , various species -30, and  [Salvia ,  indicates that the paleoenvironment at this site was dominated by ostrobus  but ambeudolarix , which wudolarix .Grimaldi et al  report tCupressinoxylon bibbinsi"). Resinous cone scales with characteristics similar to Juniperus also occur in association with these ambers structural studies of (resinous) fossil woods and cone scales associated with ambers from the Atlantic Coastal Plain are reviewed and summarized by Grimaldi et al ,19. In tIn addition to dehydroabietate  and call+. and M-15+ ions are in part determined by instrumental parameters.) However, confirmation of the A-ring stereochemistry is readily apparent from IR spectra, which exhibit strong and characteristic differences due to differences in the configuration of the A-ring carboxyl group  [Methyl dehydroabietate  and meth55 cm-1) ,47 as ilTo further investigate this, the distribution of various nor-methyl and dehydro analogues of dehydroabietate and callitrisate were examined. Reconstructed ion chromatograms (RIC) (m/z = 239 + 237+ 225 + 211 + 197) for samples of form 1 and 2 are illustrated in Figures The presence of the various analogues of both abietate and callitrisate suggests the possibility of a either a diagenetic degradation pathway from dehydroabietate and a parallel pathway from callitrisate, or a biological relationship emplaced during biosynthesis of the original resin. In this study, the availability of multiple samples collected from the same deposit  and the existence of two distinct forms with differing compositions, allows definitive differentiation of these possibilities. If the relationship between these compounds were derived from a diagenetic  degradation, then it can be assumed that alteration pathway will be the same in all samples. Hence for any given initial composition, the relative ratios of the parent compounds and their daughter products should be comparable in all cases. This is not observed. From the data illustrated in Figure This result establishes two things: Firstly, it establishes that diagenetic pathway(s) for this group of compounds need to be interpreted with care because of the potential for superimposition of biological signatures over potential diagenetic modifications. Secondly, it establishes that the distributions of compounds observed in these samples reflects the original resin composition and is, therefore, useful for chemotaxonomic assessment.Pyrolysis-GC-MS analysis of samples of amber collected from the Raritan Formation, New Jersey, clearly indicate the existence of two related-but-distinct forms. Both forms are Class Ib ambers. However, differences in the distributions of other diterpenoids unambiguously differentiate these materials and establish that ambers from this formation are derived from multiple related-but-distinct paleobotanical sources. The presence of both dehydroabietate  and callIn addition to dehydroabietate  and callSupporting interactive supplemental data for Figure Click here for fileSupporting interactive supplemental data for Figure Click here for file


Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification.LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias  or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error.Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.Using  Some key pre-processing steps for profiling data include image quantification and normalization. Several image analysis software  have been designed for image analysis of the spots on microarrays [Molecular profiling technology allows for the simultaneous assaying of the abundance of tens of thousands of transcripts in a biological sample. Once these abundance values have been obtained for many samples, prevalent higher-order data analyses may include clustering, classification, feature selection, and network estimation. A variety of algorithms seeking to address these higher-order tasks have been investigated and applied, to interpret gene expression patterns and to generate biological predictions. However, the accuracy of these predictions may depend on the low-level transformations utilized to produce abundance values from raw measurements, roarrays ,2. Backgroarrays  showed troarrays -6, and tR and G are the spot-specific, quantitated, fluorescent intensities of the target and reference expression signals respectively, relative gene expression is defined as the log ratio M = log2(R / G), and average expression is the log intensity . Based on different biological assumptions and design principles, many normalization methods for cDNA microarray data have been proposed. Global normalization techniques adjust the center  of the distribution of the log ratio M values on each microarray to a constant [In cDNA microarray-based investigations, RNA from two samples are reverse-transcribed and labeled with distinct (red and green) fluorescent dyes, then hybridized to a microarray spotted with DNA sequences ("probes"). An ensuing scanned image of the microarray is processed to yield an intensity measurement for each dye at every spot Figure . If R anconstant ,7-9. Thelowess) [i.e., the numbers of up-regulated and down-regulated genes are similar) [qspline' method uses a target array to adjust R and G values so that their distribution is similar for all arrays [A variety of techniques have been proposed to remove intensity effect. A non-linear approach employs robust locally weighted regression (lowess)  to smootlowess) ,11,12. Tsimilar) ,11,12. Al arrays , but thel arrays . A compol arrays . To relal arrays -18. Semil arrays ,20.M values on physical position can be smoothed using lowess [The removal of intensity effect at the PT level can partially remove spatial effect ,11. To rg lowess , or can g lowess  or mediag lowess , both ofg lowess .M values within and/or between microarrays. The assumption is that since the majority of genes are not differentially expressed, the scale of their M values should be constant. A robust estimate of the scale factor for scale normalization is median absolute deviation [Whereas the above location normalization methods remove spatial- and intensity effect, scale normalization methods adjust differences in the scale of eviation .M) versus average log intensity (A) ("MA plots"). Spatial plots are a color-coded representation of each spot on a microarray that depicts M values, or a quality  measure of some test statistic. These two types of diagnostic plots [M values are often biased estimates of relative expression and that the dye intensities per spot need to be adjusted. Quantitative criteria used to assess the robustness of normalization methods in removing dye effect include (i) rank variations of spot intensity in non-normalized versus normalized data [M values in replicated data.Normalization approaches seek to ensure that dye effect is removed, while biological variations are retained. Spatial- and intensity effect and scale effect arise from printing, hybridization, scanning, or other technical factors, and can mask the signals arising from genuine biological variations in gene expression. Visual aids used to assess the effectiveness of normalization methods ,13,15,21ic plots ,21 suggezed data ,22, and zed data ,21, varized data ,13, or ezed data ,22 of tht-statistics [p-values [To ensure that biological variations are retained after normalization, several functional criteria have been employed. Prevailing approaches determine the ability to predict a fixed number of differentially expressed genes in real or simulated data using quantitative measures based on atistics ,11,13,21p-values . Howeverp-values ,24,25. Hk-NN LOOCV classification error , an alternative quantitative functional measure that is relatively unambiguous, objective and readily computed. We used k-NN classifiers because (i) their sensitivity enables us to discriminate between, and hence evaluate normalization techniques, (ii) they are readily available, (iii) they perform well in practice, and (iv) their non-parametric nature means that assumptions about how the data are distributed have little influence on classification performance. Since the primary aim of our evaluation of normalization methods was to assist practitioners in choosing effective data pre-processing schemes, we did not consider factors that may influence classification performance, such as feature selection and distance metrics. We investigated a wide spectrum of well-known and widely available normalization techniques: ten location normalization methods that adjust spatial effect and/or intensity effect . The non-normalized M values (NONRM) for microarray "5850" display global spatial effect  whereas those for microarray "5938" exhibit local spatial effect . Removal of spatial effect should result in a "random" red and green pattern of M values. SLLOESS and SLFILTERW7 exhibit similar dye bias-removal abilities in that they both remove global spatial effect more effectively than local spatial effect. SLFILTERW3 removes both global and local dye effect effectively, largely because it uses a median filter of a small window size (3 × 3 spots) for normalization. IGSGLOESS removes most, but not all, global and local spatial effect (a strip of red spots on the right side of "5850" and on the bottom of the PT groups in the first row of "5938" remain). IGSGLOESS may not be as effective at removing dye effect as expected because, as the developers indicate, lowess curve construction uses the standardized spatial variables , which may not be appropriate for location variables [We used diagnostic plots to examine the ability of different location normalization methods to remove spatial- and/or intensity effect Tables  and 2. Fariables .lowess curve (left) or one lowess curve per print tip group (right) using six methods designed to correct intensity effect . For non-normalized M values (NONRM), the curvature in the MA plot indicates the presence of intensity effect at the array (left) and PT (right) level. All six methods remove global intensity effect completely , but only ILLOESS and IGSGLOESS remove local intensity effect thoroughly (right).Figure LFILTERW3 is an effective method for removing both global and local spatial effect, whereas ILLOESS is good at removing intensity effect.Visual inspection of the diagnostic plots in Figures k-NN LOOCV classification error rates for data normalized using these methods individually and/or in combination. Then for each data set, we ranked the normalization methods based on their LOOCV classification error rates. The smaller the LOOCV classification error rate, the lower the rank of the normalization strategy. In order to assess whether normalization is beneficial (or not), we also computed the following quantity for a normalization method in each data set:For a functional, quantitative evaluation of location normalization methods, we first computed NONRM) - ErrorRate(Method)) / ErrorRate(NONRM) × 100%IMPROVEMENT = (ErrorRate(NONRM) is the error rate of NONRM, and ErrorRate(Method) is the error rate of the method. Tables where ErrorRate reduce k-NN LOOCV classification error rates to a similar extent  reduce k-NN LOOCV classification error rates to different degrees. The k-NN LOOCV classification error rate and rank of IGLOESS are similar to those of the three spatial-dependent methods   Figure , whereass Figure .LLOESS), the single-bias-removal normalization methods consistently yield smaller LOOCV classification error rates than no-bias-removal methods, NONRM and GMEDIAN . The greatest benefit, an IMPROVEMENT of 56%, is seen with GASTRIC CARCINOMA  .Ik-NN LOOCV classification error rates and bigger IMPROVEMENT than single-bias-removal methods, and all perform better than NONRM and GMEDIAN  appear to be the best methods overall. These three two-step strategies not only have the lowest ranks amongst all normalization methods and across all data sets  range from an IMPROVEMENT value of 40% in LUNG CANCER to 58% in LYMPHOMA  are higher than their SLFILTERW7 counterparts . This indicates that the one-step IGSGLOESS has no apparent advantage over the two-step bias-removal strategies.Compared to the two-step approaches, the rank of the one-step method, INONRM, and the benefits (IMPROVEMENT) of doing so range from 21% in the case of LUNG CANCER (IGSGLOESS) to 100% in GASTRIC CARCINOMA (IGSGLOESS)  Table .qspline-related approaches require a target array. QSPLINEG and QSPLINER are single-bias-removal techniques and use G and R respectively as the target array. The reduction in k-NN LOOCV classification error rates for these methods is quite significant compared to the other single-bias-removal methods. However, it is noticeable that although QSPLINEG and QSPLINER produce similar results in almost all data sets, their results are different in LYMPHOMA  and of 100% in GASTRIC CARCINOMA . None of these qSpline-related methods, however, outperforms the IGLOESS-SLFILTERW7  rather than within (left) microarrays in a study. The LYMPHOMA data set shows considerable variations in box size and whisker length both within and between microarrays.Figure LOOCV classification error rates, ranks and IMPROVEMENT for the k-NN classifiers estimated using 3 scale normalization methods combined with other spatial- and/or intensity-dependent normalization methods . For data normalized first with spatial- and/or intensity-dependent methods, little or no reduction in LOOCV classification error rates was observed when within-microarray scale normalization (WSCALE) was applied later. However, when between-microarray scale normalization (BSCALE) was used alone, or when both scale normalization techniques were used sequentially (WBSCALE), there was an increase in both median and upper quantile ranks , all of which removes intensity effect at the global level and spatial effect at the local level, appear to be the most effective at reducing LOOCV classification error. However, removing spatial- or intensity effect alone is not sufficient for reducing LOOCV classification error (see below).Our results show that the LLOESS and the related methods) may add additional noise to normalized data, and suggested that a safe alternative was removing only intensity effect at the local level (ILLOESS) [LLOESS alone can be worse than non-normalized data as in the case of the LYMPHOMA data set, when SLLOESS is combined with another intensity-dependent approach , there is considerable improvement over NONRM, with IMPROVEMENT ranging from 23% in LIVER CANCER (QSPLINER-SLLOESS) to 78% in GASTRIC CARCINOMA . Thus, removing both spatial- and intensity effect is beneficial for the downstream analytical task of classification. Another study compared various lowess-based single-bias-removal intensity normalization approaches, and found that ILLOESS may not significantly improve the results compared to IGLOESS [GLOESS over NONRM range from 5% in LUNG CANCER to 44% in RENAL CELL CARCINOMA; while that the benefits (IMPROVEMENT) of ILLOESS over NONRM range from 23% in RENAL CELL CARCINOMA to 46% in LIVER CANCER. Therefore, ILLOESS performs better than IGLOESS in our study. However, as a single-bias-removal approach, ILLOESS still fail to outperform IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, which are the best overall methods and whose IMPROVEMENT values over NONRM range from 40% in LUNG CANCER to 58% in LYMPHOMA for IGLOESS-SLFILTERW7, from 33% in GASTRIC CARCINOMA to 62% in LYMPHOMA for ISTSPLINE-SLLOESS and from 33% in LUNG CANCER to 56% in GASTRIC CARCINOMA for IGLOESS-SLLOESS  . Our res IGLOESS . Our resk-NN classification of diluted samples to assess a small number of global linear methods for normalization [k-NN LOOCV classification error of real biological samples provides an informative functional quantitative measure that can be used to evaluate normalization approaches.A previous study employed lization . The stuLOOCV classification error. Indeed, two between-microarray normalization methods  result in an overall increase in LOOCV classification error , as well as from genuine biological variations. The scale normalization techniques applied here aim to remove unwanted technical factors, and assume the existence of little biological variations between samples. For the five studied data sets, scale normalization of non- or location-normalized data do not result in an overall reduction in LOOCV classification error as a functional measure to assess normalization methods. In principle, LOOCV provides an almost unbiased estimate of the generalization ability of a classifier [LOOCV error estimator may have high variance in some situations [LOOCV errors we obtained from various round of classification are quite stable, therefore we believe that our estimation is in practice reliable and suitable for ranking. Nevertheless, error estimators that have shown to have low variance  are k-NN classification without the aid of auxiliary techniques, such as feature selection. The reasons we did not employ feature selection, but rather used all the probes that are present in the majority of the microarrays for classification are as follow: i) We believe that the influence of the dye effect  on the downstream data analysis can be better and more consistently reflected when a large number of the probes are examined. As such, using all valid probes for training a classifier can best reflect the effectiveness of the normalization methods, whereas using subsets of the probes may generate inconsistent results due to the heterogeneous nature of the dye effect across microarrays. ii) We also included low intensity probes in the analyses. Although this may add noise and therefore could compromise the absolute classification performance of the examined normalization methods, we nevertheless think that these probes should not be excluded because reducing variability in low intensity probes is by itself an important objective of normalization methods. That is, a good normalization approach should be able to reduce variability in both low intensity- and high intensity probes effectively. And iii) we are aware that k-NNs without feature selection may add variability to the classification results, however, k-NN classification is also appealing in that it is simple and requires no data pre-processing or assumption on data distribution. In addition, k-NN classifiers have been widely used in many classification tasks including high-dimensional problems arising from image and text data [The second caveat of this work is that normalization methods were evaluated using ext data .GLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS reduce LOOCV classification errors most consistently and effectively across all five data sets. It is difficult, however, to determine further which of these three strategies is the best, because small differences in classification results can either arise from inherent differences in these approaches and/or from the variability introduced by the LOOCV error estimator and less optimal k-NN classifiers. Moreover, our results should not be taken as a warrant of directly using baseline methods, such as k-NNs without feature selection, for high-dimensional classification tasks. More investigations are needed to understand the interplay between normalization  and feature selection (which improves the classifier by throwing away non-informative data) to ascertain normalization strategies to produce an optimal classifier.Due to the above two caveats, the relative rankings of the investigated normalization strategies can hardly be obtained accurately in this work. For example, our results show that ILOOCV error of k-NNs as the evaluation criterion, we assessed a variety of normalization methods that remove spatial effect, intensity effect and scale differences from cDNA microarray data. Overall, the single-bias-removal location approaches  perform better than GMEDIAN and NONRM, while the double-bias-removal location strategies  perform better than the single-bias-removal location approaches. Of the 41 different strategies examined, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLBSCALE, all of which are two-step approaches and remove both intensity effect at the global level and spatial effect at the local level, appear to be the most effective at reducing LOOCV classification error. The investigated scale normalization methods do not have beneficial effect on classification performance. These results also indicate that spatial- and intensity effect do have profound impact on downstream data analyses, such as classification, and that removing these effects can improve the quality of such analyses.Using Table [, MAANOVA , tRMA , and braju  packages. Standard R and Bioconductor packages and functions were used apart from one normalization method found in MAANOVA  and two normalization methods found in tRMA .A variety of computational tools for manipulating, analyzing, and visualizing microarray data are available. These include open source implementations based on the R language for statistical computing  such as Rf) and green (Gf) quantitated fluorescent intensities  of the arrayed DNA sequences were used to compute the non-normalized log ratio, M = log2(Rf / Gf), and average log intensity, . Because of the concern that local background values estimated by GenePix may add additional noise to the data [For each spot, the foreground red  GMEDIAN does not remove any effect but acts as a baseline normalization method because it sets the mean or median of M to zero. There are eight methods that remove either spatial- or intensity effect: (i) SLBSCALE removes spatial effect at the PT level using lowess; (ii) SLFILTERW3 and SLFILTERW7 remove spatial effect using median filters of the block sizes 3 × 3 and 7 × 7, respectively [GBSCALE removes intensity effect at the global level; (iv) ILBSCALE removes intensity effect at the local level and as a byproduct removes spatial effect partially; (v) ISTSPLINE removes intensity effect at the global level using rank invariant set and a spline smoothing technique [SPLINER (QSPLINEG) removes intensity effect at the global level using spline smoothing applied to quantiles obtained from R (G) and using the geometric mean of the R (G) channels of all arrays as the target array [GSGLOESS is a one-step process that removes global intensity effect and global spatial effect, while the remaining thirteen strategies are two-step processes that remove both dye effect by combining methods in Table Tables lsewhere ,21,38,39ectively ; (iii) Iet array . In TablM values can be found elsewhere [WSCALE adjusts the scale of M values at the PT level. BSCALE adjusts the scale of M values globally across all microarrays in a data set. WBSCALE adjusts the scale locally followed by globally, in two steps. These scale normalization methods were applied to non-normalized data (NONRM) and to data that had been normalized using the five location methods SLLOESS, IGLOESS, ILLOESS, ILLOESS-SLLOESS, or IGLOESS-SLFILTERW7. These methods were selected to represent methods that remove spatial and/or intensity effect at different levels.Table lsewhere ). WSCALEN microarrays associated with a given study  in 95% of the N microarrays. If a probe met these criteria, missing M values were imputed using the k-NNimpute algorithm [pamr/pamr.knnimpute(k = 10) [M values for a probe in all N microarrays were centered and rescaled to a unit norm. For LYMPHOMA, the final dimensionality  of each of the N = 81 data points was 6,850 ("Probes"). The 41 post-normalized data sets for the five examined studies are available at .For the five cancer-biology studies, examination of the published data indicated that probes printed on different microarrays (even those with the same configuration) were not necessarily identical. For the lgorithm  as impleD data points, each of which is assigned to one of K categories , a LOOCV procedure for this K-class data set is as follows. The data set is partitioned into a test set of one data point and a learning set of D-1 data points. The learning set is used to train a classifier and the ensuing model is employed to predict the class label of the test data point. This process is repeated so that the class of each data point is predicted using a classifier estimated from all other data points in the data set. Classification error is the number of the instances in which the predicted class of a data point differs from its known class. The error rate is this value divided by the number of data points, D.Given K-class data set, the k-NN algorithm predicts the class label of a test data point by first finding which of the data points in the data set are its k closest neighbors. The classes of these k nearest neighbors are examined and the class of the test data point decided by a majority vote, with ties being broken at random. If there are ties for the kth nearest data point, all candidates are included in the vote. Classification using k-NNs does not require any special handling of multi-class data sets. A widely employed measure of the proximity of two data points and the one utilized here is the standardized Euclidean distance [Given a distance ,43. Sincdistance .k*, was determined via leave-one-out cross-validation. An original data set of D data points was partitioned into a test set of one data point and a learning set of D - 1 data points. Given a specific k, the k-NN algorithm was used to predict the class of each data point in the learning set using the D - 2 remaining data points. The classification error, εk, of the learning set was determined. This procedure was performed using k = {3,...,10} and k* taken to be the k producing the smallest classification error, i.e., mink(εk). The class of the test data point for the original data set was predicted using k* and the k-NN algorithm. This entire process was repeated such that each of the data points in the original data set was used as a test set. The classification error of the original data was calculated. The k-NN step was performed using the R class/package [class/knn.cv(k = "number of neighbors") where "number of neighbors" was set to 3,...,10. The prediction step was performed using class/knn where "optimal k" was k*.The optimal number of nearest neighbors, /package class/knnAC: adenocarcinoma;CCC: clear cell carcinoma;DLBCL: diffuse large B-cell lymphoma;FL: follicular lymphoma;GCC: granular cell carcinoma;k-NN: k-nearest neighbor;LCLC: large cell lung cancer;LOOCV: Leave-one-out cross-validation;lowess: local regression estimation;PC: papillary carcinoma;PT: print-tip group;SCC: squamous cell carcinoma;SCLC: small cell lung cancer;SMD: Stanford Microarray Database.WW designed and performed computational experiments, and drafted the manuscript. EPX participated in experimental design and in drafting the manuscript. ISM participated in experimental design and edited the manuscript. CM and MJB read and edited the manuscript. All authors contributed to, read and approved the final manuscript.


Lemierre's syndrome presents a classic clinical picture, the pathophysiology of which remains obscure. Attempts have been made to trace genetic predispositions that modify the host detection of pathogen or the resultant systemic reaction.A 17-year old female, with no previous medical history, was admitted to the intensive care unit for septic shock, acute respiratory distress syndrome and Lemierre's syndrome. Her DNA was assayed for single nucleotide polymorphisms previously incriminated in the detection of the pathogen, the inflammatory response and the coagulation cascade. We observed functional variations in her Toll like 5 receptor (TLR 5) gene and two coagulation variations (Tissue Factor (TF) 603 and Plasminogen-Activator-Inhibitor-1 (PAI-1) 4G-4G homozygosity) associated with thrombotic events.The innate immune response and the prothrombogenic mutations could explain, at least in part, the symptoms of Lemierre's syndrome. Genomic study of several patients with Lemierre's syndrome may reveal its pathophysiology. Lemierre's syndrome presents a classic  but exce-3, including 90% neutrophils) and a CRP at 450 mg/L, combined with an X-ray shadow on the bottom left lung. This led to a diagnosis of community acquired pneumonia. Blood samples were collected for culture and the patient benefited from treatment with amoxicillin/clavulanic acid and erythromycin.A 17-year old female with no previous medical history was admitted to the emergency room of a general hospital due to a deterioration of her general condition, with fever, dyspnea and a paroxysmal severe cough. Two weeks previously, she had presented with an acute pharyngitis, which was treated with amoxicillin for 8 days. At admission, she presented with persistent lateral cervical pain, hyperleukocytosis  and/or abdominal symptomatology  suggesting septic metastases, points to Lemierre's syndrome and justifies the implementation of a suitable antibiotic regimen. Other septic phenomena, such as arthritis, mediastinitis, meningitis and/or endocarditis, are found more rarely.Lemierre's syndrome is frequently due to infection with a strictly anaerobic Gram negative bacillus, Fusobacterium necrophorum [Fusobacterium necrophorum becomes pathogenic in certain individuals, although a lowering of local pharyngeal defenses following a viral or bacterial infection may encourage this invasion by creating an anaerobic micro environment [The physiopathology of Lemierre's syndrome remains controversial. rophorum  is a comironment . AnotherThe recent discovery of anti-infection defense mechanisms, in particular Toll like receptors, and the finding that these receptors have functional variations open up new avenues in the pathophysiology of the Lemierre's syndrome, as illustrated in this clinical case.Fusobacterium necrophorum is complex and involves both tissue immunity (cathelicidins and defensins) and cellular immunity (TLR receptors). Although Fusobacterium necrophorum is not a flagellate bacterium, it is capable of synthesizing Pilin [Legionella pneumophila [Fusobacterium necrophorum infections. To date, there is no scientific proof with this assertion.The innate immune response to ng Pilin . Pilin aumophila , may alsA second facet of the symptomatology of Lemierre's syndrome is the presence of hypercoagulability, resulting in thrombophilia. A procoagulating mutation of prothrombin was recently associated with this syndrome . InteresTo our knowledge, this article is the first to identify SNPs in a patient suffering from Lemierre's syndrome. The innate immune response and the prothrombogenic mutations observed in this patient could explain, at least in part, the symptoms of Lemierre's syndrome. Genomic study of several patients with Lemierre's syndrome may help to reveal its complex pathophysiology.SNP: Single nucleotide polymorphismTLR 5: Toll like 5 receptor geneTF: Tissue FactorPAI-1: Plasminogen-Activator-Inhibitor-1PEEP: Positive end expiratory pressureFc-GRII: Fc receptor for IgGMIF: macrophage migration inhibitory factorEPCR: Endothelial protein C receptorTNF: Tumor Necrosis FactorSPD: Surfdactant Protein DThe author(s) declare that they have no competing interests.JMC, JPM and RG drafted the manuscript, and FG, OL, JPR, HL oversaw the sections on infectious disease. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Our data support the possibility that rhupus is an overlap between RA and SLE, because highly specific autoantibodies for RA (anti-CCP) and for SLE (anti-dsDNA and anti-Sm) are detected in coexistence.'Rhupus' is a rare condition sharing features of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). If rhupus is a distinctive entity, an overlap between RA and SLE or a subset of SLE is currently debated. This study was performed to explore the prevalence of antibodies against cyclic citrullinated peptides (anti-CCP antibodies) in rhupus. Patients meeting American College of Rheumatology criteria for RA, SLE, or both were included. Clinical and radiographic features were recorded and sera were searched for anti-CCP antibodies, rheumatoid factor, antinuclear antibodies, anti-extractable nuclear antigens, and antibodies against double-stranded DNA (anti-dsDNA antibodies). Seven patients for each group were included. Clinical and serological features for RA or SLE were similar between rhupus and RA patients, and between rhupus and SLE patients, respectively. Values for anti-CCP antibodies obtained were significantly ( The clinical coexistence of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) was first described in 1969 by Kantor and was termed 'rhupus syndrome' by Schur (both cited in . In an ePrevious reports have shown that the patients with rhupus display an array of autoantibodies including anti-double-stranded DNA (anti-dsDNA), anti-Sm (both highly specific for SLE), anti-SSA, anti-SSB, anti-ribonucleoprotein, antinuclear antibodies (ANA), anti-cardiolipins, and rheumatoid factor (RF) ,2. HowevThis descriptive, cross-sectional study was performed to investigate the frequency of anti-CCP antibodies in a cohort of patients with rhupus.Crithidia luciliae substrate) antibodies were also determined. Except for healthy individuals, standard radiographs of hands were available. For statistical analysis, ANOVA and the Mann–Whitney U test were performed as appropriate with GraphPad Prism 4.0 software .We included all patients fulfilling American College of Rheumatology (ACR) classification criteria for both RA  and SLE Seven female patients with a median age of 44 years (range 25 to 64) met our inclusion criteria. The major clinical and laboratory findings are presented in Table Mean ACR criteria for SLE were 5.57 (range 4 to 8) in the SLE group, and 5.57 (4 to 8) in the rhupus group. In the same way, mean ACR criteria for RA were 6 (4 to 7) in the RA group, and 5.14 (4 to 6) for the patients with rhupus. In all patients with rhupus, RA was presented as the initial disease, as has been described previously . In accop < 0.05). The mean concentration of anti-CCP antibodies was 584 U/ml  in patients with rhupus , and 0 U/ml (0 to 2) for healthy controls (p < 0.05 compared with rhupus). Two of three patients with rhupus who were negative for anti-CCP antibodies were also negative for anti-dsDNA antibodies, RF and anti-extractable nuclear antigen antibodies, although both patients met RA and SLE classification criteria, including ANA and erosive arthritis.Anti-CCP antibodies were found in four of seven (57%) patients with rhupus, and in six of seven (86%) patients with RA, whereas neither patients with SLE nor healthy individuals showed reactivity. When the concentrations in each group were compared, a statistical significant difference between groups was found  molecules and the risk of RA is well established. These major histocompatibility complex (MHC) class II molecules share the same amino acid sequence (QKRAA or QRRAA) in positions 69 to 74 of the β-chain, namely the 'shared epitope'. Recent works have demonstrated that this 'shared epitope' preferentially binds peptides containing the non-standard amino acid citrulline (deiminated arginine) . In addiPatients with SLE are often part of the control group when determining the specificity of anti-CCP antibodies for RA , althougIn the present study we demonstrate that the patients with rhupus show a very similar arthritis pattern (including erosive disease) and similar autoantibody production (RF and anti-CCP antibodies) to those in patients with RA. In addition, patients with rhupus display a clinical and serological profile indistinguishable from patients with SLE. Moreover, the presence of other coexistent autoimmune diseases was similar in all groups of patients .+) were included. In contrast, the mean ACR criterion for SLE was similar between patients with rhupus and those with SLE, including the 'robust' features of SLE such as renal and neurological involvement, and anti-dsDNA and anti-Sm antibodies. These clinical and serological features shared between patients with rhupus and those with SLE also place rhupus in the SLE spectrum.We found high titers of anti-CCP antibodies in four of seven (57%) patients with rhupus, a frequency similar to that reported for RA . This fiTitration of anti-CCP antibodies in the rhupus group clearly shows a bimodal distribution, suggesting the existence of two different subpopulations. Because of the small number of patients, we are unable to define the differential features underlying each subset. However, two of three patients negative for anti-CCP antibodies were also negative for both RF and anti-dsDNA antibodies.On the basis of the presence of shared clinical features of RA (mainly erosive arthritis) and SLE  along with the presence of anti-dsDNA and anti-CCP autoantibodies in our patients with rhupus, our findings strongly support the contention that rhupus is a true overlap between RA and SLE, not merely a part of the clinical spectrum of the articular involvement seen in SLE. Moreover, on the basis of the mean ACR criteria for both diseases, we have confirmed that patients with rhupus have more RA-associated and less SLE-associated damage, an issue that has been suggested previously .To our knowledge, this is the first report exploring the prevalence of anti-CCP antibodies specifically in patients with rhupus. More studies are needed to expand the pathogenetic knowledge of this overlap syndrome.ANA = antinuclear antibodies; anti-CCP antibodies = antibodies against cyclic citrullinated peptides; anti-dsDNA antibodies = antibodies against double-stranded DNA; ELISA = enzyme-linked immunosorbent assay; RA = rheumatoid arthritis; RF = rheumatoid factor; SLE = systemic lupus erythematosus; SS = Sjögren's syndrome.The authors declare that they have no competing interests.LA-G participated in the conception and design of the experiments, in the acquisition, analysis and interpretation of data, and was involved in drafting the manuscript. RS performed the immunoassays. RM-V participated in the analysis and interpretation of data and performed the statistical analysis. LG-G participated in the analysis and interpretation of data. AV participated in the recruitment of patients and the acquisition of data. RB participated in the interpretation of data, revising the manuscript for intellectual content and giving the final approval of the version to be published. All authors read and approved the final manuscript.


It is well known that cytokines are highly involved in the disease process of rheumatoid arthritis (RA). Recently, targeting of neuropeptides has been suggested to have potential therapeutic effects in RA. The aim of this study was to investigate possible interrelations between five neuropeptides (bombesin/gastrin-releasing peptide (BN/GRP), substance P (SP), vasoactive intestinal peptide, calcitonin-gene-related peptide, and neuropeptide Y) and the three cytokines tumour necrosis factor (TNF)-α, IL-6, and monocyte chemoattractant protein-1 in synovial fluid of patients with RA. We also investigated possible interrelations between these neuropeptides and soluble TNF receptor 1 in serum from RA patients. Synovial fluid and sera were collected and assayed with ELISA or RIA. The most interesting findings were correlations between BN/GRP and SP and the cytokines. Thus, in synovial fluid, the concentrations of BN/GRP and SP grouped together with IL-6, and SP also grouped together with TNF-α and monocyte chemoattractant protein-1. BN/GRP and SP concentrations in synovial fluid also grouped together with the erythrocyte sedimentation rate. In the sera, BN/GRP concentrations and soluble TNF receptor 1 concentrations were correlated. These results are of interest because blocking of SP effects has long been discussed in relation to RA treatment and because BN/GRP is known to have trophic and growth-promoting effects and to play a role in inflammation and wound healing. Furthermore, the observations strengthen a suggestion that combination treatment with agents interfering with neuropeptides and cytokines would be efficacious in the treatment of RA. In conclusion, BN/GRP and SP are involved together with cytokines in the neuroimmunomodulation that occurs in the arthritic joint. To the in vitro on cells of human origin have suggested that modulation of the effects of neuropeptides may be a new strategy for the treatment of arthritis )lagenase . In morelagenase . The ovelagenase .SP is present in the nerve fibres of the synovium . HoweverIt is not clear whether BN/GRP-like peptides are present in joint innervation, or how the release of the peptides into the synovial fluid occurs. An interesting observation is that BN/GRP-like peptides have been detected in human chondrocytes . In any in vivo for collagen-induced arthritic mice [in vitro showed that VIP can down-regulate the expression and synthesis of chemokines in synovial tissue cells and fibroblast-like synoviocytes from RA patients and that VIP inhibits the production of TNF-α after TNF-α stimulation [The present study is relevant because neuropeptides have been suggested to be efficacious antiarthritic drugs ,50,51. Ttic mice ,86. Furtmulation  and othemulation . Howevermulation . Alternamulation . In a remulation .As discussed above, there is indeed evidence showing that neuropeptides can modify the secretion of proinflammatory cytokines from immunocompetent cells. That includes the situation for peripheral blood cells from patients with RA . As seenThe neuropeptides that showed correlations with the proinflammatory cytokines in the patient material examined were BN/GRP and SP, particularly BN/GRP. The observations give new insight into further studies of neuropeptide/cytokine interrelations in RA.BN/GRP = bombesin/gastrin-releasing peptide; CGRP = calcitonin-gene-related peptide; ELISA = enzyme-linked immunosorbent assay; ESR = erythrocyte sedimentation rate; GRP = gastrin-releasing peptide; IL = interleukin; MCP-1 = monocyte chemoattractant protein-1; NPY = neuropeptide Y; RA = rheumatoid arthritis; RIA = radio-immunoassay; SP = substance P; sTNFR1 = soluble TNF receptor 1; Th = T helper (cells); TNF = tumour necrosis factor; VIP = vasoactive intestinal peptide.The author(s) declare that they have no competing interests.OG participated in the design of the study, carried out the immunoassays, and performed the statistical analysis. SF participated in the design, coordinated the study, and took part in the statistical analysis. OG and SF drafted the manuscript. SRD was responsible for collection of the samples, participated in the statistical analysis, contributed to the coordination of the study, and took part in discussions concerning the manuscript. All authors read and approved the final manuscript.


Amino-terminal pro-brain natriuretic peptide (NT-proBNP) is useful in evaluating heart failure, but its role in evaluating patients with shock in the intensive care unit (ICU) is not clear.Forty-nine consecutive patients in four different ICUs with shock of various types and with an indication for pulmonary artery catheter placement were evaluated. Analyses for NT-proBNP were performed on blood obtained at the time of catheter placement and results were correlated with pulmonary artery catheter findings. Logistic regression identified independent predictors of mortality.P = 0.80), but an NT-proBNP value below 1,200 pg/ml had a negative predictive value of 92% for cardiogenic shock. NT-proBNP levels did not correlate with filling pressures or hemodynamics (findings not significant). NT-proBNP concentrations were higher in patients who died in the ICU , and the mortality rate of patients in the highest log-quartile of NT-proBNP (66.7%) was significantly higher than those in other log-quartiles (P < 0.001); NT-proBNP independently predicted ICU mortality , and was superior to Acute Physiology and Chronic Health Evaluation II score and brain natriuretic peptide in this regard.A wide range of NT-proBNP levels were observed . There was no difference in median NT-proBNP levels between patients with a cardiac and those with a noncardiac origin to their shock (3,046 pg/ml versus 2,959 pg/ml; Elevated levels of NT-proBNP do not necessarily correlate with high filling pressures among patients with ICU shock, but marked elevation in NT-proBNP is strongly associated with ICU death. Low NT-proBNP values in patients with ICU shock identifed those at lower risk for death, and may be useful in excluding the need for pulmonary artery catheter placement in such patients. Patients with shock in the intensive care unit (ICU) setting are at high risk for morbidity and mortality, and optimal strategies to evaluate and manage ICU patients with shock are needed. One method to evaluate patients with ICU shock includes invasive hemodynamic monitoring with pulmonary artery catheter (PAC) placement. Such invasive monitoring, although widely employed, has been shown to be associated only equivocally with improved outcomes in the ICU setting  and, in Biomarker measurement has been used in the optimal evaluation and management of multiple disease states, ranging from endocrinologic states such as diabetes or thyroid diseases to cardiovascular diseases such as acute coronary syndromes and heart failure. However, the role of biomarkers in the evaluation of patients in the ICU setting remains largely unexplored. Because hemodynamic monitoring is a frequent theme in the evaluation of patients in the ICU setting, there may be logic behind evaluating ICU patients by testing for B-type (brain) natriuretic peptide (BNP) or its amino-terminal pro-fragment NT-proBNP. These markers are useful in diagnosing or excluding heart failure ,7 as welGiven their value in evaluating and managing patients with heart failure, the natriuretic peptide class of biomarkers might be useful as a surrogate for PAC placement in patients with undefined hemodynamics, in whom PAC placement might otherwise be contemplated. In ICU patients with undifferentiated shock, we recently demonstrated that BNP concentrations are frequently elevated but unrelated to cardiac filling pressures or hemodynamics . DespiteThis study was approved by the institutional review board and was conducted in the surgical, cardiac, and medical ICUs at the Massachusetts General Hospital. The design of the predecessor BNP study was previously reported . The preFor each patient, 5 ml blood was collected into a tube containing EDTA at the time of PAC placement. Samples were processed and analyzed for BNP concentrations . Following the original study, frozen samples from each patient were thawed and NT-proBNP concentrations were measured using a commercially available immunoassay . The patients, researchers and ICU physicians were blinded to natriuretic peptide findings.Clinical characteristics, including demographics, past medical history, presenting syndrome, PAC variables (including filling pressures and hemodynamics), and subsequent hospital course, were recorded.Cardiac hemodynamics (including cardiac index) were calculated using the thermodilution technique and pulmonary capillary wedge pressures (PCWPs) were measured at end expiration. Acute Physiology and Chronic Health Evaluation (APACHE) II scores were calculated using data available from the 24-hour period at the time of enrollment.2 and PCWP above 18 mmHg. The 'noncardiac' group included those with distributive, hypovolemic, and mixed shock etiologies.Patients were classified on a binary system as having shock of cardiac or noncardiac origin. The former group ('cardiac origin') included patients with symptoms/signs of shock following an acute myocardial infarction with or without acute destabilized heart failure. In addition, we further considered a subgroup of patients within the category of 'cardiac origin' of shock, namely those with cardiogenic shock, defined as having suffered a cardiac event associated with the combination of both cardiac index below 2.2 l/min per mThe primary end-points for this study were similar to those in the original report , with suNT-proBNP levels were log-transformed to achieve normality. Correlations between NT-proBNP values and PCWP and between NT-proBNP values and cardiac index were performed by bivariate analyses with Spearman correlation. Comparisons of median NT-proBNP levels between survivors and those who died were made by nonparametric testing.P values are two-sided, and P < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS software .In order to analyze the prognostic influence of NT-proBNP in the ICU, receiver operating characteristic analyses for death as a function of NT-proBNP concentration were performed, using Analyze-it software . Patients were categorized into quartiles using log-transformed NT-proBNP values. Multivariable analysis utilizing stepwise logistic regression was used to identify independent predictors of death. Factors included in the multivariable model included age, sex, diagnosis of cardiogenic shock, use of mechanical ventilation, cardiac troponin T concentration, renal function as indicated by serum creatinine, blood pH, BNP concentration, and APACHE II score. Logistic regression analysis used stepping with verification of goodness-of-fit with the Hosmer-Lemeshow test. Odds ratios were generated and expressed with 95% confidence intervals. All The baseline characteristics of the study group are shown in Table P = 0.02 for the difference), there was no statistically significant difference in median NT-proBNP levels between patients with cardiogenic (as defined above) and the rest of the patients in the study . Notably, an NT-proBNP of below 1,200 pg/ml had a negative predictive value of 92% for ruling out the hemodynamic indices and filling pressures consistent with cardiogenic shock.The median (interquartile range) NT-proBNP for all patients was 3,046  pg/ml; a wide range of NT-proBNP values was documented . Seven patients (14%) had NT-proBNP values below the threshold for diagnosis of heart failure, as defined previously [r = 0.05, P = 0.75) or cardiac index  at the time of PAC placement ; notably, an NT-proBNP level below 1000 pg/ml had a negative predictive value of 98% for ICU death.Sixteen patients (33%) died during the course of the study. The median NT-proBNP levels in patients who died were significantly higher than in those who survived .Figure P = 0.013). Although in univariate analyses BNP concentrations in the highest log-quartile  and APACHE II scores ≥ 25  were both significant predictors of death, in multivariate models that included NT-proBNP neither BNP nor APACHE II scores were significantly associated with ICU death.Several factors were found by multivariate analyses to be significant predictors of death in the ICU Table , includiIn patients with ICU shock NT-proBNP concentrations did not correlate with cardiac hemodynamics or with filling pressures, but NT-proBNP levels were considerably higher among those patients who died in the ICU and represented the strongest predictor of death in the ICU. Conversely, low NT-proBNP levels identified shock patients at lower risk for death. Notably, in a multivariate model identifying independent predictors of death in the ICU, NT-proBNP was superior to BNP and APACHE II scoring, both of which were previously identified as being useful for prognostication in patients with ICU shock.Regarding the dissociation between natriuretic peptide concentrations and filling pressures, we previously demonstrated a similar lack of association between BNP and cardiac parameters in ICU shock , and a r2 and PCWP above 18 mmHg – the classical definition of cardiogenic shock. In this setting of an NT-proBNP below 1,200 pg/ml (a situation noted in approximately 29% of patients), a clinician concerned about the presence of elevated cardiac filling pressures or abnormal hemodynamic parameters, and otherwise inclined to place a PAC, might consider conservative management without the potential risk associated with PAC placement. That such patients with lower NT-proBNP concentrations were also generally at lower risk further underscores the potential utility for natriuretic peptide 'screening' in the hope of potentially avoiding PAC placement, because such lower risk patients are those in whom an unacceptable risk/benefit ratio from PAC placement has been described [In the present study, although an elevated NT-proBNP had poor predictive value for identifying elevated filling pressures, an NT-proBNP concentration below 1,200 pg/ml had a negative predictive value of 92% for the combination of cardiac index below 2.2 l/min per mThe severity of the processes that underlie release of natriuretic peptide appear to parallel that of risk for mortality; this risk is contributed to, but independent of, renal function, because NT-proBNP was equally important for risk estimation in critically ill ICU patients even in the presence of abnormal serum creatinine levels. Notably, although renal function, which is known to be a potent measure of prognosis in the ICU setting ,20, was Estimating prognosis in the ICU may be challenging, and in a patient population with shock in the ICU – although high mortality rates are expected – a simple, reproducible, and especially reliable method for stratifying risk for death is welcome. Established methods for estimating risk include the use of risk scoring systems, such as the APACHE II score . BecauseIn the present study, we compared the utility of both natriuretic peptide markers for their ability to predict death. We demonstrated – in the first head-to-head analysis of NT-proBNP and BNP testing in the ICU – that, in the presence of NT-proBNP results, BNP results added no further prognostic information and was no longer a significant predictor of death. These findings to not necessarily contradict those from prior reports suggesting the value of BNP in prognosticating ICU shock , but theFinally, it is necessary to emphasize that those patients below the fourth log-quartile for NT-proBNP did not have a benign outcome, with a mortality rate at or around 20% in each log-quartile below the fourth. Such patients could be further substratified using the results of serum creatinine and blood pH, such that those with an NT-proBNP below the fourth log quartile, serum creatinine <1.8 mg/ml, and a blood pH ≥ 7.15 have the lowest risk for death (<10%) whereas those with all three risk factors are at greatest risk for ICU death (80%). Although data exist supporting the concept that individual biomarkers may predict ICU mortality, including natriuretic peptides ,21, seruLimitations of the present study include the fact that PAC placement was done at the discretion of the managing physician rather than as part of a protocol-based decision-making process. However, PAC placement in shock is not widely recommended unless it is clinically indicated using the same decision-making process as was employed by our clinicians , and so High concentrations of NT-proBNP should not be used to estimate high cardiac filling pressures or impaired hemodynamics in ICU patients with undefined shock; however, low concentrations of NT-proBNP in patients with shock confidently excludes elevated filling pressures/low cardiac index, and identifies a low-risk patient in whom placement of a PAC might not be expected to be useful. In light of the recent call for a more thoughtful approach when considering which patients are eligible for PAC placement ,26, the • 	In random patients with shock of various types in the ICU setting, elevated NT-proBNP concentrations were common and not necessarily related to high filling pressures or low cardiac output.• 	Elevated levels of NT-proBNP were strongly associated with risk for death in the ICU, and were a stronger predictor of death than APACHE II scoring.• 	A low level of NT-proBNP identified a low risk patient in whom lower filling pressures were expected. Because these lower risk patients are less likely to benefit from PAC placement, a low NT-proBNP value in ICU shock would allow clinicians to reconsider placement of a PAC.APACHE = Acute Physiology and Chronic Health Evaluation; BNP = brain natriuretic peptide; ICU = intensive care unit; NT-proBNP = amino-terminal pro-brain natriuretic peptide; PAC = pulmonary artery catheter; PCWP = pulmonary capillary wedge pressure.Dr Januzzi reports having received grant support, consulting fees, and speaking fees from Roche Diagnostics, Inc.JLJ conceived the study, procured study funds, performed data analysis, and drafted the manuscript. AM collected data, performed data analysis, and drafted the manuscript. RT conceived the study, collected data, and drafted the manuscript. RP, MAF, and BTT assisted in patient recruitment, and contributed to the drafting of the manuscript. ELL ran the NT-proBNP assays and interpreted data. All authors read and approved the final manuscript.