We explore clinical and institutional parameters potentially associated with benefit with this strategy in a decision-analytic model based on available evidence from randomised trials. A decision analysis model was constructed comparing three P2Y12 inhibitors in addition to aspirin in patients with NSTE-ACS. Based on clinical trial data, the cumulative probability of 30 day mortality, myocardial infarction (MI) and major bleeding were determined, and used to calculate the net clinical benefit (NCB) with and without pre-treatment. Sensitivity analysis was performed to assess the relationship between NCB and baseline ischemic risk, bleeding risk, time to angiography and local surgical revascularization rates. Pre-treatment with ticagrelor and clopidogrel was associated with a greater than 50% likelihood of providing a >1% increase in 30 day NCB when baseline estimated ischemic risk exceeds 11% and 14%, respectively. Prasugrel pre-treatment did not achieve a greater than 50% probability of an increase in NCB regardless of baseline ischemic risk. Institutional surgical revascularization rates and time to coronary angiography did not correlate with the likelihood of benefit from P2Y12 pre-treatment. In conclusion, pre-treatment with P2Y12 inhibition is unlikely to be beneficial to the majority of patients presenting with NSTE-ACS. A tailored assessment of each patient’s individual ischemic and bleeding risk may identify those likely to benefit.Current guidelines recommend initiation of a P2Y OutcomeWithin this model, the baseline of death or MI within 30 days of diagnosis was set at 7% ,14. HalfThe median time to angiography was set at 24 h, with the proportion of patients selected for surgical revascularization (CABG) initially set at 10%, and the medical management without PCI set at 30%, with the remainder of the patients undergoing PCI, reflecting rates observed in local registries ,14. RevaThe relative effect of clopidogrel pre-treatment versus placebo, ticagrelor versus clopidogrel, and prasugrel versus clopidogrel were drawn from the results of the key large-scale randomized trials or subsequent published subgroup analyses where available ,4,11,15.To provide estimates of potential risks and benefits, 10,000 runs of the model were undertaken with bleeding events, ischemic events, and the NCB for each agent was calculated for each run. The proportion of scenarios where the net clinical benefit or net mortality benefit was positive relative to the comparator for each of the literature-based therapy comparisons  was summed and reported as a percentage. These estimates were then used to calculate the relative effect of ticagrelor and prasugrel over placebo and reported similarly. In order to quantify the magnitude of potential benefit, the absolute differences in the overall 30 day rate of ischemic events and bleeding events associated with pre-treatment compared with no pre-treatment were used to calculate the number needed to treat (NNT) for each run of the model.12 inhibitors was allowed to vary across the range of the reported 95% confidence bounds for both death or MI and major bleeding. However, due to the differences in reported bleeding events associated with both ticagrelor and prasugrel, the bleeding risk associated with the proportion of patients undergoing CABG were modeled separately using relative hazards that differed from the overall population. For all of these analyses, P2Y12 inhibition was considered not to have any effect on peri-operative ischemic events.The treatment effects associated with each of the P2YTo explore the relationship between varying degrees of baseline ischemic and bleeding risk, time to angiography, institutional rates of CABG and medical management, and treatment effects associated with the specific therapies, multi-way sensitivity analysis was employed. Within each iteration of the model, baseline ischemic risk was allowed to randomly vary between 0% and 15%, while the risk of major bleeding was varied between 0% and 6% with 70% of this variation linked to the ischemic risk given the strong correlation between ischemic and bleeding risks observed among individual patients clinically. Again, with half the 30 day risk experienced within the first seven days, both the bleeding and ischemic risks were converted to an hourly risk.The time to angiography was allowed to vary randomly between 0 and 96 h, while the proportion of patients deemed appropriate for CABG and medical management were varied between 7% and 15%, and 35%–45%, respectively. However, recognizing that patients with an increased ischemic risk were more likely to undergo CABG rather than PCI, the likelihood of CABG was adjusted by a factor of 1.25 in those with an increased ischemic risk and 0.5 for those models with an ischemic risk of <5%. Therefore, a CABG rate of 10% was increased to 12.5% if ischemic risk was >7%.The relationship between the likely NCB rates and the estimates of the NNT with varying degrees of: ischemic risk; bleeding risk; time to angiography; and the local surgical revascularization rate were explored. The number needed to harm (NNH) was calculated as 100/absolute event increase %. All models were constructed in STATA 14.0 .12 inhibitor with NNH varying from 22 to 500 with prasugrel and ticagrelor respectively.A routine pre-treatment strategy was modelled, and the outcomes can be seen in A one- and two-way sensitivity analyses was performed for each of the following variables assuming a range of plausible values: (1) estimated ischemic risk; (2) estimated major bleeding risk; (3) institutional CABG rate and (4) time to angiography.12 inhibition had a variable likelihood of achieving a positive net clinical benefits (NCB) depending on the agent used and the patient’s level ischemic and bleeding risk. Pre-treatment with P2YImportantly, a greater than 50% likelihood of achieving a positive NCB was only reached with clopidogrel and ticagrelor. Prasugrel was associated with low likelihoods of achieving a positive NCB, at all levels of ischemic and bleeding risk. Even using estimates of efficacy and safety from the TRITON TIMI 38 study for prasugrel suggests low likelihoods of a positive NCB due to the higher rates of bleeding with this agent (data not shown).12 regardless of a patient’s ischemic and bleeding risk.There was no correlation between the time to angiography and benefits with a strategy of pre-treatment with any of the three P2YOverall, variability in the institutional CABG rate had a minimal effect on providing an overall benefit with pre-treatment with each of the three agents.As patients’ ischemic risk increased, there was a higher likelihood that pre-treatment with either ticagrelor and clopidogrel would achieve a significant clinical benefit defined as a 1% increase in NCB at 30 day . Hence, 12 inhibitors is confined only to patients at relatively high predicted ischemic risk. Consequently, decisions to initiate pre-treatment should be dependent on careful and potentially objective evaluation of ischemic and bleeding risk. This can be done easily at the patient bedside with well-validated risk scores such as the Global Registry of Acute Coronary Events (GRACE) score.Major society guidelines either support the use of pre-treatment in patients with acute coronary syndromes or refrain from advising on pre-treatment due to the lack of support from clinical trial data ,16. ACCO12 inhibition in NSTE-ACS remains controversial because much of the evidence supporting its practice is derived from trials which included patients with stable angina, STE-ACS, and those having received fibrinolysis for STE-ACS [Large-scale randomized studies of intravenous glycoprotein IIb/IIIa inhibition given “upstream” before coronary angiography in NSTE-ACS have failed to demonstrate a significant reduction in ischemic events when compared with these agents used in the peri-procedural setting ,18. Pre- STE-ACS ,10,11. D STE-ACS ,20,21,22 STE-ACS  included STE-ACS  reported STE-ACS .p < 0.001) in those pre-treated with clopidogrel [Several meta-analyses attempting to address this question have included these studies, and have reported a benefit of pre-treatment with clopidogrel in PCI patients ,23,24. Tpidogrel . This stpidogrel . Howeverpidogrel . In a mepidogrel .12 inhibition in the management of NSTE-ACS [ACCOAST is the only other randomized control trial of pre-treatment in ACS patients without ST elevation. This study compared a half load of prasugrel as pre-treatment with a further half dose after the coronary anatomy had been identified by angiography versus 60 mg of prasugrel post-angiography among those undergoing PCI, with mean time from pre-treatment to coronary angiogram times of 4 h . PCI wasNSTE-ACS .Based on our analysis, pre-treatment with ticagrelor or clopidogrel is anticipated to be of benefit in specific populations of patients presenting with NSTE-ACS, particularly those with a high risk of ischemic events. Pre-treatment with ticagrelor and clopidogrel could result in a greater than 50% probability of achieving a positive 30 day NCB at lower levels of individual bleeding risk combined with higher baseline ischemic risk. This emphasises the need for the clinician to be able to accurately identify a patient’s ischemic risk using well-validated tools such as the GRACE risk scoring system. For example, a GRACE score of approximately 150 translates to an ischemic risk of >11%, representing the threshold for ticagrelor pretreatment . Hence, Given the greater levels of platelet inhibition, ticagrelor was associated with an increase in NCB at lower thresholds of ischemic risk than observed with clopidogrel. However, among patients with the highest bleeding rates (5%), ticagrelor pre-treatment was only beneficial when the ischemic risk exceeded 18%. Conversely, in the same high risk bleeding population, clopidogrel pre-treatment required an ischemic risk of only 16% in order to obtain a greater than 50% probability of achieving a positive 30 day NCB.12 agents. For the purposes of the decision-making analysis, the model assumes therapeutic efficacy at the time of administration of the P2Y12 inhibitor. In the clinical setting, there would be a delay prior to peak therapeutic efficacy with the time dependent on the P2Y12 inhibitor employed.Given the paucity of clinical trial data specifically addressing the question of pre-treatment in patients with NSTE-ACS, this decision-making analysis relies upon assumptions from numerous clinical trials. Firstly, the initial assumptions for clopidogrel are based on the CURE trial, which reflected clinical practice of over a decade ago, before the introduction of radial arterial access, longer delays to angiography, less refined coronary stent technology and less adjunctive anti-ischemic therapies available . The netSecondary, the pre-treatment effects of ticagrelor were modelled on the PLATO trial, which did not formally examine pre-treatment of this agent . NeverthThis analysis does not take into account patients with presumed NSTE-ACS who go on to have normal coronaries with an alternative diagnosis such as myocarditis. Given these patients do not proceed to PCI or CABG, the absolute effect on their ischemic benefit or bleeding complication rate is likely to be negligible. Again, as a consequence, actual benefits of pre-treatment in real world practice may be less striking than what is presented in this decision-analytic model.12 inhibitors, ticagrelor and clopidogrel, with randomization stratified by objective measures of ischaemic and bleeding risk.Decision analysis using treatment effects observed in randomized clinical trials, across plausible ranges of ischemic and bleeding risk suggests that pre-treatment may only benefit patients with NSTE-ACS with a very high ischemic risk, and the practice of routine use may be harmful. The magnitude of this benefit is likely to benefit those in the subgroups of patients at a low risk of bleeding and is likely to be small. Further analysis with an appropriately powered randomised control trial would help clarify this issue but will be challenging given the very small effect expected from pre-treatment. This decision- analysis model is not sufficient to warrant a change in current guidelines for the management of NSTE-ACS; however, it highlights the need for controlled trials assessing the benefits of pre-treatment with P2Y


Circulating miRNA-34a is increased in blood of patients with different liver diseases when compared to healthy controls. However, the origin of miRNA-34a and its possible relationship with hemodynamics and outcome in cirrhotic patients with portal hypertension is unknown. We analyzed the levels of miRNA-34a in cirrhotic patients with severe portal hypertension.We included 60 cirrhotic patients receiving TIPS for prevention of rebleeding and/or therapy-refractory ascites. miRNA-34a levels were measured using qPCR and normalized by SV-40 in the portal and hepatic venous blood of these patients taken at TIPS procedure. Hemodynamic and clinical parameters were assessed before TIPS and during follow-up.Levels of miRNA-34a were higher in the hepatic vein than in the portal vein. Circulating miRNA-34a in the hepatic vein correlated with ALT, CHE and sodium excretion after TIPS. miRNA-34a showed no correlation with portal pressure, but its levels in the portal vein correlated inversely with the congestion index. Interestingly, the levels of miRNA-34a in the portal and hepatic vein showed inverse correlation with arterial pressure. Furthermore, levels of miRNA-34a in the hepatic vein had a predictive value for survival, but MELD, creatinine at short-time follow-up 14 days after TIPS-insertion and portal pressure after TIPS performed better.This study demonstrates for the first time, that miRNA-34a may originate to a large extent from the liver. Even though higher levels of miRNA-34a are possibly associated with better survival at long-term follow-up in cirrhotic patients with severe portal hypertension receiving TIPS, classical prognostic parameters predict the survival better. Chronic liver injury of different etiologies leads to hepatic fibrosis, which might progress to end-stage liver disease. Portal hypertension often develops in these patients and is responsible for severe complications, such as variceal bleeding, refractory ascites and hepato-renal syndrome Circulating micro ribonucleic acid (miRNA) have been increasingly evaluated as potential biomarkers in different disorders, including liver diseases In the present study, circulating levels of miRNA-34a were measured in the portal and hepatic vein of cirrhotic patients with severe portal hypertension receiving TIPS. Our aim was to investigate the portentiol role of miRNA-34a as a predictor of complications and survival for cirrhotic patients receiving TIPS.We retrospectively included 60 patients with liver cirrhosis underwent TIPS implantation. General clinical characteristics are shown in The patients received TIPS for therapy-refractory ascites (n = 27), recurrent bleeding (n = 26), or both indications (n = 7). TIPS  insertion was performed as previously described 260-measurement using the ND-1000 NanoDrop spectrophotometer  and quality was assessed by microcapillary electrophoresis .During the TIPS procedure, blood from the portal and hepatic veins to determine levels of miRNA-34a was collected from all patients as soon as the right branch of the portal vein was cannulated as previously described miRNA was analyzed by a two-step real-time PCR using the miScript-Reverse Transcription Kit and the miRNA-SYBR Green PCR Kit . miRNA-34a and SV-40 primers used for cDNA synthesis and real-time PCR were selected and purchased from the GeneGlobe Search Center . All steps were performed in triplicate and in agreement with the supplier's guidelines. For normalization of extracellular miRNA-34a levels, spike-in SV40-miRNA  was used.In our study, we used the non-parametric Wilcoxon test to compare paired data and the Mann-Whitney test for unpaired comparisons. Correlations were analyzed with the Spearman correlation coefficient. Univariate time-to-event analysis was performed to identify parameters, which significantly predict survival. Cox-regression analysis (forward step-wise likelihood-quotient) using the significant predictors in the univariate analysis was performed to identify independent predictors of survival. Kaplan-Meier curves were used to analyze the survival rates of patients using the Log-rank test. Statistical analysis was performed by means of SPSS 22 for Windows SPSS Inc. Chicago,IL, USA).The clinical characteristics of the included patients are listed in The short-term effects of TIPS on the biochemical parameters are shown in Furthermore, after TIPS portal vein flow velocity (p>0.001) and portal vein flow (p<0.001) increased, while levels of portal vein pressure gradient (p<0.001), as well as the congestion index (p<0.001) decreased as marker of successfully TIPS intervention treatment of portal hypertension .Interestingly, the percentage of portal pressure gradient reduction after TIPS showed a median value of 56.7% (21–91.7). Patients with a greater reduction in portal pressure gradient than the median showed a better survival after 5 years (p = 0.035).Interestingly, the levels of miRNA-34a were significantly higher in the hepatic vein compared to the portal vein (p = 0.019) .s = 0.409; p = 0.001; s = 0.372; p = 0.025) and sodium excretion in 24 h urine in the short-term follow-up after TIPS .We observed a relationship between miRNA-34a and hepatic and renal parameters. Levels of circulating miRNA-34a in the hepatic vein correlated significantly with ALT before TIPS  and diastolic  pressure measured non-invasively. There was no correlation of miRNA-34a with HPVG (data not shown). Additionally, the levels of miRNA-34a in the portal vein correlated inversely with the congestion index  before TIPS. However, miRNA-34a measured in the portal vein showed inverse correlations with the cross sectional area in the portal vein before TIPS .Furthermore, the levels of miRNA-34a in the portal and hepatic vein showed a significant inverse correlation with systolic , creatinine in short-term follow-up  and portal pressure after TIPS  as independent predictor of survival .This study demonstrates for the first time that miRNA-34a originates to a large extent from the liver and that levels of circulating miRNA-34a in hepatic vein correlated with parameters of hepatic dysfunction. Interestingly, miRNA-34a levels in the portal vein correlated inversely with portal venous congestion. Also, higher levels of circulating miRNA-34a in the hepatic vein might have a predictive value for survival in patients receiving TIPS.Liver cirrhosis with portal hypertension is a major cause of morbidity and mortality world-wide Circulating miRNAs have been discussed as biomarkers for different disorders. This study demonstrates for the first time that miRNA-34a originates to a large extent from the liver. In the setting of TIPS-insertion, blood from the portal vein and from the hepatic vein was withdrawn simultaneously and the gradient showed a significant increase of miRNA-34a across the liver. This finding is supported by the positive correlation of the levels in the hepatic vein with aminotransferases and cholinesterase.Interestingly, miRNA-34a in the portal venous compartment showed an inverse correlation with the cross-sectional area of portal vein measured by ultrasound and the portal congestion index. The congestion index, first described by Moriyasu, is calculated from parameters measured by ultrasound. This calculated index mirrors best the congestion due to portal hypertension Although, levels of miRNA-34a in the portal vein possibly explain the portal vein congestion in the situation of severe portal hypertension, these levels showed no predictive value after portal venous decompression using TIPS. In contrast, the levels of circulating miRNA-34a measured in the hepatic vein tended to predict survival in these patients in univariate analysis. This finding might be explained by the correlation of these levels in the hepatic vein with ALT and CHE, which possibly mirror conserved liver function. Indeed, when stratifying the patients using the median of the miRNA-34a levels in the hepatic vein, patients with higher circulating levels of miRNA-34a had a better survival. After TIPS, patients with better conserved liver function showed a better survival, since MELD was an independent predictor of mortality in multivariate analysis in our collective besides renal function and portal pressure after TIPS. Interestingly, circulating miRNA-34a correlated with sodium excretion after TIPS as a marker of restored renal function. Thus, both the preserved hepatic function before TIPS and the restored renal function after TIPS may play a role in survival and are associated with the hepatic venous levels of miRNA-34a. Therefore, levels of miRNA-34a in the hepatic vein might represent a valuable biomarker for the outcome of patients receiving TIPS, but not in the same degree as MELD; creatinine in short-term follow-up and portal pressure after TIPS. In this collective of patients MELD, creatinine and portal pressure are more appropriate to predict outcome than the levels of miRNA-34a.Although our patient collective was very well characterized before and after TIPS with a long-term follow up, our study has several limitations. First, the extraction of miRNA was performed phenol-based and therefore, we did not distinguish between vesicle bound and free miRNA. The analysis of the patient data was retrospective and could not elucidate the cause of death in these patients. Despite these limitations, the studied cohort was consistent with other studies, since MELD, creatinine in follow up and portal pressure after TIPS were independent predictors of mortality.As already shown by Malinchoc et al, MELD-Score is the best predictor of survival in patients receiving TIPS In conclusion, this study demonstrates for the first time that miRNA-34a might originate to a large extent from the diseased liver and hepatic venous level of miRNA-34a might be a predictor of outcome in cirrhotic patients receiving TIPS. Future studies are needed to confirm these findings.


High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points.A sample of fifty-seven youth  was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson’s correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels.Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas.The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points. DiscoveNeuroimaging and pattern recognition techniques can also be used to identify relationships between patterns of brain structure or activity and continuous measures of behaviour or symptoms. Such information can then be used to decode individual-level scores on behavioral measures in independent series of individuals ,17. ThesThe Longitudinal Assessment of Manic Symptoms (LAMS) study is a multi-site study of youth with a variety of behavioral and emotional dysregulation diagnoses , and provides a unique opportunity to examine whether individual PGBI-10M scores can be decoded from individual-level measures of neural activity in LAMS2 youth.The aim of the present study was thus to use functional neuroimaging and pattern regression analysis to determine, as a proof of concept, whether PGBI-10M scores at different time points  could be decoded from patterns of brain activity during a reward-processing task in individual LAMS2 youth. This approach has the potential to identify important biomarkers not only to help determine severity of behavioral and emotional dysregulation at the individual level, but also to pave the way forward for future studies using a combination of pattern recognition and neuroimaging to identify individual-level patterns of neural function that predict future clinical outcomes in behaviorally and emotionally dysregulated youth. Given the association between PGBI-10M scores and reward sensitivity, we employed a reward-processing task previously employed in neuroimaging studies in youth ,22 to foTo investigate whether individual symptom severity could be decoded from whole-brain pattern of activity, we revisited a LAMS2 dataset that was originally analyzed using conventional univariate statistical analysis . A samplParticipants were excluded from the LAMS2 neuroimaging study if they had severe systemic medical illness, neurological disorders, history of head trauma with loss of consciousness, IQ<70 , positivBecause of data loss and excessive head movement (>4 mm) during scanning, data from 22 LAMS youth were excluded, leaving data on 85 LAMS youth. In addition, in order to define a more homogenous sample, which is important for the success of pattern recognition analysis (a common assumption of pattern recognition models is that the training examples are generated from the same distribution), we defined additional conservative exclusion criteria for the sample. This conservative exclusion criterion was employed to reduce, as much as possible, noise in the data which can particularly affect pattern recognition analyses. Firstly, we excluded subjects that had any evidence of acquisition artifact (10 subjects). Secondly, we limited the age range from 12–17 years, as this is a developmental period at which puberty is well underway in American youth ,26. HereThe pediatric DSM-IV  primary PGBI-10M at screen, mean = 15.5, SD = 6.3, Range = 1–30). The PGBI-10M score nearest the scanning session (within six months) was included in analyses as a measure of most recent PGBI-10M score .The PGBI-10M is a 10-item empirically-derived adaptation of the General Behavior Inventory ,28. PareA block-design reward fMRI task was used comprising three experimental conditions “win”, “loss" and control blocks. At the beginning of each guessing trial, participants pressed a button to guess whether they thought the value of a visually presented card  would be higher or lower than five (3000 msec). The actual numerical value of the card (500 msec), outcome feedback  and a fixation cross (3000 ms) was then visually presented. Control trials required participants to press a button to the letter “X” (3000 msec) and view an asterisk (500 msec), yellow circle (500 msec), and fixation cross (3000 msec).The fMRI paradigm comprised a total of nine blocks: three “win” blocks , three “loss" blocks , and three control blocks (no change in earnings). Five trials with an oddball format were presented in a predetermined outcome order in the guessing block (Win and Loss) . Participants completed the entire experimental paradigm in approximately six minutes. Participants were naive to the fixed outcomes, and believed outcomes to be randomly presented. The control blocks consisted of six trials. Before scanning, participants practiced the task and underwent scanner simulation to become familiar with the scanner environment and learn to avoid head movement before scanning. Participants were encouraged to perform to the best of their abilities and to minimize movement in the scanner. Participants were not debriefed about predetermined outcomes because they were to perform the task 18 months later.Neuroimaging data were collected using three different scanners: 1) 3T Siemens Verio MRI scanner at CWRU, 2) 3T Philips Achieva X-series MRI scanner at CCH, and 3) 3T Siemens Trio MRI scanner at UPMC . Structuhttp://www.fil.ion.ucl.ac.uk/spm/) and involved realignment using the first slice as a reference, coregistration with the participant's MP-RAGE image, segmentation, normalization into a standard stereotactic space , and spatially smoothing using a Gaussian kernel (FWHM: 8mm). For each participant, a General Linear Model (GLM) was implemented in SPM8 in which the effect of each condition (i.e. "win" and "loss") was modeled by the convolution of the blocks with the hemodynamic response function. Movement parameters from the realignment stage were entered in the GLM as nuisance covariates. Images corresponding to the GLM coefficients for each condition ("win" and "loss") defined the spatial patterns of brain activation used as input to the pattern regression analysis. To reduce inter-scan site variability, as recommended by the Biomedical Informatics Research Network , the signal:noise was monitored monthly using a BIRN phantom at all three sites to ensure scanner signal stability over time [http://www.mlnl.cs.ucl.ac.uk/pronto/) as the first level mask.Neuroimaging data were pre-processed using standard procedures in SPM8  and a label . Once the model has “learned” the association between patterns and labels from the training data, it can be used to decode or predict the label of a new test example . We usedPGBI-10M at screen and PGBI-10M closest to scan) from patterns of brain activity during win and loss blocks of the reward task (i.e. we trained one independent RVR model for each time point). These two clinical scores are referred to as “labels”.RVR was applied to decode PGBI-10M scores recorded at two time points . The four-fold cross-validation involves dividing the data into 4 disjoint folds. Data from each fold is left out once for test and data from the remaining 3 folds are used to train the model. This procedure is then repeated four times, so that each fold is left out once. Because our sample size was not divisible by four, three folds contained 14 participants each, while the remaining fold contained 15 participants. In the four-fold cross-validation procedure, we applied a t-test to insure that the distribution of the to-be-predicted variables (PGBI-10M at screen and closest to scan) did not differ among the folds. In both cross-validation strategies the clinical score of the participant(s) left out for test was/were decoded from the corresponding GLM coefficient image(s) using the RVR model trained on the remaining participants. Note that the leave-one-out procedure can also be considered a N-fold cross-validation where N is the number of subjects (in this case each fold contains a single subject).To evaluate the RVR performance, we used two different p-value was then calculated by dividing this number by the number of permutations (1000). Results were considered significant if the p-value< 0.05.We measured agreement between decoded and actual clinical scores using two different metrics: Pearson’s correlation coefficient (r) and mean squared error (MSE). The correlation coefficient describes the strength of a linear relationship between two variables. The higher this coefficient, the more accurate were the predictions. We also computed the MSE, which is the mean of the squared differences between the decoded and true scores. MSE represents the mean error between the decoded and actual clinical scores and is commonly used to evaluate the performance of predictive models. For these metrics, statistical significance was determined by permutation tests, i.e., we performed the same cross-validation procedures described above but with the labels permuted across participants. We repeated this procedure 1000 times and counted how many times the absolute value of the metric (r or MSE) with the permuted labels was equal to or higher than (or lower than in the case of the MSE) than the one obtained with the correct labels. The Medication was considered as a potential confounder, and was regressed out of the pattern regression analysis using the residual forming matrix framework –35. MediFor pattern regression models showing significant values of correlation and MSE, weight maps were built. For linear models, the weight map is a voxel space representation of the model’s parameters or decision function. Because each cross-validation fold yields a different weight vector, the final weight map is the average map across folds divided by its Euclidean norm. Linear pattern recognition models generate weights for each voxel, while model predictions are based on the whole pattern. Thus, the weight map cannot be thresholded to make regionally-specific inferences as in classical (univariate) techniques. Here we applied a novel methodology, referred to as pattern localization, based on a labelled anatomical template to summarize the weight map in terms of anatomical regions . Brieflyp-value = 0.70, medicated: M = 15.9, SD = 5.8, unmedicated: M = 15, SD = 7.0) and on PGBI-10M closest to scan .In the present study, we used two sample t-tests to determine whether medication was systematically related to the clinical scores that we intended to decode. Youth taking medication did not score significantly differently than youth not taking medication on PGBI-10M at screen  and 32.35 , respectively  and MSE between the decoded and actual PGBI-10M ectively . Similarat screen were 0.41  and 34.38 , respectively. Similar significant results were obtained without controlling for potential medication effects  and MSE between the decoded and actual PGBI-10M  effects .at screen were 0.46  and 31.12 , respectively. Similar significant results were obtained without controlling for potential medication effects  and MSE between the decoded and actual PGBI-10M  effects .at screen were 0.34  and 38.95 , respectively. Similar significant results were obtained without controlling for potential medication effects  and MSE between the decoded and actual PGBI-10M  effects .For both cross-validation frameworks , the correlation coefficient and the MSE between actual and decoded symptoms were significant only for the models not controlling for medication effects . After cDue to space limitation, and to avoid large number of tables in the main manuscript, we choose to display only the models using the leave-one-out cross-validation based on the win blocks which were significant after controlling for medication effects. Please see on Table C in The main goal of the present study was to apply pattern regression analysis to functional neuroimaging to determine whether a dimensional scale measuring behavioural and emotional dysregulation at different time points  could be decoded from patterns of whole-brain activity in a large multi-center of youth with a variety of different diagnoses, including BPSD, other mood disorders, ADHD, anxiety, and disruptive disorders. Our results show that pattern recognition models have the potential to decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation from individual-level patterns of brain activity. These results represent early evidence that neuroimaging techniques may inform the clinical assessment of psychiatric youth by allowing accurate and objective quantitative estimation of psychopathology.et al 2014[et al 2014 as, instead of investigating association between symptoms and brain activity in individual brain regions at the group level using conventional neuroimaging data analytic techniques , we applied machine learning techniques, with the aim of determining whether measures of behavioural and emotional dysregulation at different time points  could be decoded from patterns of whole-brain activity. To the best of our knowledge, there is only one previous study applying pattern regression to predict individual proximal clinical assessment in adults psychiatric patients from patterns of brain activity using fMRI [The functional neuroimaging data used in the current study have been analyzed and reported previously by Bebko t al 2014. There, t al 2014, the vast al 2014,13,39. Oing fMRI .win blocks and to the loss blocks and also when using different cross-validation strategies . The initial screen measures of behavioral and emotional dysregulation were obtained when youth entered LAMS1 several years prior to the neuroimaging scan in LAMS2 (at least 5 years earlier). These results suggest that the relationship between PGBI-10M score and the brain activity is stable over the years.Our findings show that pattern regression analysis has the potential to decode behavioral and emotional dysregulation symptoms from patterns of brain activation during a reward task in individual LAMS2 youth not only close to the scanning session, but also at the initial study screen. The correlation and mean squared error between actual and decoded measures were significant when the model was based on patterns of brain activation to the at screen than closest to scan. In addition, when controlling for medication the models were not able to predict PGBI-10M closest to scan. One possible explanation for these counterintuitive findings is the fact that the PGBI-10M score decreased for both medicated and non-medicated youth relative to PGBI-10M scores obtained at the study screen, such that the distribution of the PGBI-10M scores closest to the scanning session was skewed towards low values. This could have made it more difficult for the learning algorithm to learn the relationship between the full range of PGBI-10M scores and brain activity. Furthermore, the procedure applied to control for potential medication effects removed the variability in the data (patterns of brain activity) associated with medication. Although this approach is commonly used when controlling for nuisance variables, it can potentially remove information from the data which is useful for prediction [closest to scan, as suggested by the lower p-value (0.09). It is possible that by removing the variability from the data associated with medication we may have also removed important predictive information for PGBI-10M closest to scan.An unexpected aspect of our findings is that the correlation value (r) between the predicted and actual PGBI-10M was higher when the model predicted PGBI-10M ediction . In the Interestingly, the brain regions with the highest contribution to decoding the PGBI-10M score at initial study screen were fronto-parietal regions, sensory-motor areas, insula and cerebellum. These regions include many neural regions important for emotional regulation ,43, atteN-1 participants , demonstration of reproducibility between different cross-validation strategies is important to show the stability of the results and for future clinical use of machine learning methods. In addition, one of the biggest challenges for the translation of neuroimaging-based predictive models into clinical practice is the validation of models using large multi-center datasets. This validation is important to demonstrate the robustness of models with regard to variability caused by scanner type and acquisition protocols. Given that participants were scanned at multiple sites, our findings demonstrate that the pattern regression model was able to treat the site effect as noise and learn a common predictive pattern across different sites, which highlights the robustness of the model when applied to data from different scanners.A strength of the current study was the inclusion of two different cross-validation strategies. Although it is common to leave one participant out and train the model with There were some limitations in the present study. One important issue is the potential confounding effect of medication on neuroimaging data. Most participants of this study were medicated (n = 30), and of those, 12 were taking more than one class of psychotropic medications. As explained in the methods and results sections, to address this issue medication was treated as a binary variable (medicated versus non medicated) and regressed out of the pattern regression analysis using the residual forming matrix framework ,35 when The present study was a proof of concept study designed to examine whether pattern regression analysis could be applied to neuroimaging to decode individual-level severity along dimensions of behavioural measures. Our results suggest that it is possible to identify patterns of neural activity associated with symptoms of behavioral and emotional dysregulation from whole-brain patterns of activity during a reward-processing task in youth. Future studies, using a combination of pattern regression and neuroimaging, can build on the present findings to determine the extent to which individual-level patterns of neural function either instead of or in combination with clinical, familial and demographic measures can predict individual-level future clinical outcomes. Furthermore multivariate predictive models such as the one used in the current study could prove useful for predicting the effect of a given treatment.S1 Texthttp://www.mlnl.cs.ucl.ac.uk/pronto/). Table E: The complete list of brain regions according to the normalized weights (NW) to decode PGBI-10M at screen based on pattern of brain activation to win blocks using a leave-one-out cross-validation framework. Table F: The complete list of brain regions according to the normalized weights (NW) to decode PGBI-10M at screen based on pattern of brain activation to loss blocks using a leave-one-out cross-validation framework. Fig A: Maps for decoding PGBI-10M at screen based on patterns of activation to loss blocks using a leave-one-out cross-validation framework. Aa: Voxel-based predictive pattern. The colour bar indicates the weight of the voxels for decoding the clinical score. Ab: Region-based pattern localization map computed from the voxel based predictive pattern displayed in Fig Aa. The colour bar indicates the percentage of the total normalized weights that each anatomically labelled region explains.Table A: Demographic information, clinical variables, and current medication usage (Mean ± Standard Deviation or Proportion) describing the LAMS participants , Includ(DOC)Click here for additional data file.


Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues . On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first , third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on:  Acinetobacter, vancomycin-resistant Enterococcus, methicillin and vancomycin-resistant Staphylococcus aureus, and multi-drug-resistant Mycobacterium tuberculosis  are believed to have similarly timed evolutionary origins. Metagenomic analyses  have revealed an extensive dissemination of resistance elements across ecosystemsBiofilms are highly structured, sessile communities characterised by cells adhering to a substratum and embedded in a self-produced matrix of extracellular polymeric substances In the context of infectious human diseases, the general understanding is that acute infections are caused by planktonic bacterial growth, whereas chronic infections are sustained principally by biofilm formation. It is estimated that 60–70% of all nosocomial infections are due to biofilms present on implanted medical devices. 95% of urinary tract infections are associated with a urinary catheter, 80% of pneumonias with mechanical ventilation, and 87% of bloodstream infections with intravascular devicesin vitro18192021Biofilms promote mutability and drastically enhance horizontal gene transfer of plasmid-borne antibiotic resistancedesign Peptides Against Bacterial Biofilms) is the first web server for predicting biofilm-active peptides and creating mutants thereof with improved activity and optimised physicochemical properties.Presently, there are more than 60 FDA-approved peptide drugs in the market, with at least another 640 in different stages of preclinical development and clinical trials25282932Our knowledge-based approach to dataset-selection for predicting anti-biofilm peptides is, admittedly, unconventional. Since the exclusively biofilm-inactive peptides retrieved from the BaAMPs database could not constitute a substantive negative dataset, other alternatives had to be considered. As surmised above, peptides found to be inactive against planktonic bacteria could not have been used for the purpose of building computational models capable of reliably predicting anti-biofilm activity. We therefore reasoned that quorum sensing peptides (QSPs) that exist and function within the biofilm, irrespective of their direct or indirect involvement in enabling the biofilm phenotype, would possess a contrasting set of properties as compared to those peptides which disrupt biofilms. QS peptides with reported anti-biofilm/antimicrobial activity were diligently identified and removed (see Methods for dataset preparation) from the final set used for training the models. Despite their anti-biofilm/antimicrobial activity, these 26 peptides were not included in the positive dataset either. This is because the inclusion would have meant that their analogs too could be predicted as ‘biofilm-active’, some of which may exhibit receptor antagonism (a mechanism of quorum quenching)Quorum sensing (QS) is a sophisticated system of intercellular communication in bacteria that involves the production, detection and response to signalling molecules called autoinducers (AI). At low cell densities, AIs diffuse away, but as the bacterial populations grow, the AIs begin to accumulate in the extracellular milieu and reach the threshold concentration required for detection by their cognate cytoplasmic or membrane-bound receptors, following which the collective gene expression is altered. There are specific autoinducing molecules for intra-species signaling  and a “universal language” (autoinducer-2) for inter-species communication. The QS machinery induces genes that control processes which are beneficial when performed in unison, like bioluminescence, sporulation, competence, antibiotic production, biofilm formation, and virulence factor secretion35Pseudomonas aeruginosaP. aeruginosa are functionally involved in the biofilm lifecycleVibrio choleraIn 1998, Greenberg and his colleagues first confirmed the role of the las QS in the formation of biofilms by in vitro and in vivo in S. epidermidis, whose virulence is often associated with its ability to form biofilmsS. aureus (both TRAP and agr negative strains are incapable of biofilm formation) and emphatically prevented graft infections of methicillin resistant S. aureus in ratsS. mutans and S. intermedius, for instance, CSP appears to operate in the initial stages of the biofilm mode of growth and the inhabitants of biofilms can incorporate antibiotic resistance and virulence-causing elements with at least 10 times the efficiency seen in their planktonic counterparts4445More importantly, QS inhibitor  was reported to have prevented adhesion and biofilm formation both in vitro or in vivo. For the negative dataset, from a total of 220 unique QSPs, one peptide sequence contained pyrrolysine (O), corresponding entries in Quorumpeps were missing for 30 peptides, 68 peptides had cyclic structures and the 7 amidated linear peptides were RNAIII inhibiting (RIPs), all of which were removed. Out of the remaining 114 non-amidated linear peptides, 26 were identified to have antimicrobial or biofilm-inhibitory activity  and were excluded to yield the final 88 peptide sequences used. When run on the independent validation dataset, the best model among these performed with an overall accuracy of 95.24%. FDA-approved peptide drugs as well as antimicrobial peptides in clinical development were also analysed to identify potential repurposing candidates with anti-biofilm properties.In this study, machine learning tools were used to build six Support Vector Machine (SVM) and Weka-based models trained on 80 biofilm-active AMPs  and 88 QSPs  and negative  datasets were meticulously curated . For a dThe 80 ABPs used in this study were reported to be active against 27 Gram positive and 20 Gram negative bacteria .The length of the ABPs used in this study varied from 6 to 36 amino acid residues. Out of main set of 90 sequences, 57 were ≤18 residues in length, with the maximum number of peptides (18) being 12 residues long. The length of the QSPs was found to vary within a similar range of 5 to 30 residues. Out of the main set of 98 sequences, 52 were ≤15 residues in length, with the maximum number of peptides (18) being 5 residues long  in the ABPs. Furthermore, positively charged residues  were more numerous in the ABPs while the QSPs had more negatively charged residues . Additionally, the number of polar amino acids was also considerably higher in ABPs whereas QSPs were richer in neutral amino acids. It was also observed that the number of tiny amino acids  was relatively higher in QSPs whereas there were more large amino acids  in the ABPs 49. To quBased on the observation that the ABPs and QSPs could be distinguished on the basis of their amino acid composition, models were generated with the whole amino acid (%) composition as the input feature . The SVMThe most important amino acid residues to be used as input features were selected for building the SVM and Weka-based models . The besIn an attempt to utilize the information about the frequency and the preferred order of the residues at the N- and C- termini, predictive models using the binary profile patterns of the ABPs and QSPs were developed . The accIn order to visualize the positional preference for specific amino acid residues in ABPs and QSPs, sequence logos were generated using the web application WebLogoFor a threshold-independent evaluation of the models, receiver operating characteristic (ROC) curves were plotted using SigmaPlot Version 11.0. The AUC provided a single value to gauge the performance of a classifier. The whole amino acid (%) composition based SVM  and WekadPABBs proposes six models for predicting peptides with potential anti-biofilm activity. Positive and negative independent datasets of 10 peptides each were used to judge the predictive capacity of the four models based on amino acid composition, while datasets consisting of five peptides each were used to validate the two BPP models. The SVM model based on the whole amino acid composition of the peptides performed with the highest accuracy  followed by the Weka model based on selected eight input features . The SVM and Weka models trained on the NT5 dataset perform with an accuracy, sensitivity, specificity and MCC of 100%, 100%, 100% and 1 respectively, with the independent dataset of only five peptides . Performhttp://ab-openlab.csir.res.in/abp/antibiofilm/feature.php) on the dPABBs web server. Out of the 31 peptides submitted, positive predictions were obtained for 29 FDA-approved peptide drugs. Only one peptide (DB04897) was predicted to be anti-biofilm by five out of the six models available on dPABBs  and thus, helps in the identification of putative anti-biofilm peptides within a protein sequence. The Batch module is specially designed to screen putative ABPs from a library of peptides in a single run. The MultiModel module permits the selection of multiple models simultaneously for prediction. The user can then identify peptides which have been predicted to be biofilm-active by the maximum number of models and could be prioritized for testing. The results displayed in all the four modules  can be sorted to view the values in ascending or descending order.http://ab-openlab.csir.res.in/abp/antibiofilm/.The dPABBs web server is accessible from the URL: A majority of the ABPs (57 out of the 90 peptides used in the study) were ≤18 residues in length, possibly because the shorter derivatives of known AMPs or minimally long synthetic peptides are generally desired and evaluated for retained or improved anti-biofilm activity2021P. aeruginosa biofilmsAbundant in cationic residues like K, H, R and polar residues like D, E, R, K, Q and N, antimicrobial peptides found to be active against biofilms (ABPs) possess a greater net charge than QSPs . An average GRAVY value of −0.53 indicates at their hydrophilic nature. These physicochemical properties could enable the ABPs to navigate the primarily aqueous environment of the EPS matrix. The EPS matrix (net negative charge) has been shown to sequester the positively charged antibiotic tobramycin (via ionic interaction with unknown matrix component), inhibit the penetration of negatively charged aminoglycosides, but allow ciprofloxacin, a neutral antibiotic to diffuse readily through the biofilm55Once formed, biofilms are notoriously tenacious. However, evidence for the adaptive nature of this recalcitrance comes from the demonstration that upon dispersal from a biofilm, the susceptibility to antibiotics is restored in the bacteria4), a 21 residue-long peptide designed to mimic the C-terminus of the human lung surfactant protein-B (SP-B), was the only FDA-approved peptide sequence to be predicted ‘biofilm-active’ by 5 out of the 6 models. Possessing particularly strong surface tension reducing properties, it forms a variable amphipathic helix in a lipid environmentIn an attempt to identify possible drug repurposing candidates for anti-biofilm therapy, predictions were obtained for FDA-approved peptide drugs and AMPs in clinical development. Out of the 31 FDA-approved peptide drugs analysed, 29 were predicted as ‘biofilm-active’ by at least one and at most five models. An interesting observation was that the SVM and Weka NT5 BPP models gave positive predictions to the 10 peptides which were ‘biofilm-inactive’ according to the other four models that are based solely on the amino acid composition and do not factor in the positional preference of residues. Sinapultide , OP-145 and Pexiganan acetate (MSI 78), which have been shown to be effective against catheter infections, chronic bacterial middle ear infection, and diabetic foot ulcers, respectively; biofilm involvement has been reported in all of these conditionsTo understand the limitations of these models, predictions were also obtained for the 26 QSPs with anti-biofilm/antimicrobial activity which had been excluded during the negative dataset preparation. The SVM Whole AAC model predicted the anti-biofilm activity of only six out of the 26 peptides, while the SVM model based on 14 selected residue features gave positive predictions for just two peptides. In comparison, the Weka-based models fared better, with the Weka Whole AAC model correctly predicting 12 out of 26 and the Weka model based on eight selected features predicting 14 out of the 26 (53.84%) peptides as being biofilm-active. The two BPP models, however, outperformed the remaining four in this regard. The SVM NT5 model could correctly predict the anti-biofilm activity of 20 out of the 26 peptides, but the Weka NT5 model could correctly classify 23 out of the 26 peptides (88.46%), achieving the best performance in terms of the percentage of peptides correctly predicted .Research into the biofilm phenotype, spanning over almost three decades, has presented us with overwhelming evidence of its clinical relevance. Still, no approved drugs specifically targeting bacterial biofilms exist, and susceptibility testing against biofilms is conspicuously missing from antibiotic discovery pipelines. AMPs with activity against biofilms offer a promising avenue for the development of new standalone therapeutics or adjuvants acting synergistically with pre-existing antibiotics. However, since biofilms are perplexingly heterogeneous and dynamic, any predictions regarding the activity of these peptides should ideally be made in a species-specific manner, taking into account the stage of the biofilm lifecycle to be targeted. Going forward, as more peptides are experimentally evaluated for their activity against various bacterial biofilms, sufficient volumes of data would become available for building models capable of predictions with such specificity.The dPABBs web server is an attempt to develop a prediction strategy for the identification and optimisation of such anti-biofilm peptides, offering a comprehensive platform that allows the user to check both peptides and protein fragments for potential anti-biofilm activity and provides features like simultaneous multi-model predictions and mutant generation.Initially, a total of 767 entries were retrieved from the BaAMPs database on 30 April 2015, out of which 652 were active and the remaining 115 were inactive peptides. After removing the duplicate peptide sequences , 140 unique active and 51 unique inactive peptides were obtained. VENNYThe 18 inactive peptides obtained from the BaAMPs database could not constitute a substantive negative dataset and therefore quorum-sensing peptides (QSPs) were selected for this purpose. QSPs are oligopeptides used as autoinducing molecules by Gram positive bacteria in intra-species QS known to enable the biofilm phenotype. A total of 220 unique QSPs were retrieved from QSPpred web server comprising of positive (training + independent) datasets, from which one pyrrolysine (O) containing peptide sequence was removed. The remaining 219 peptides were then manually screened and simultaneously cross-referenced with the Quorumpeps database for corresponding entries. Entries were missing for 30 peptides in Quorumpeps, 68 peptides had cyclic structures and 7 amidated linear peptides were RNAIII inhibiting (RIPs) . Out of light version 6.02 software32light software. In addition to SVMlight, we have also used Weka version 3.6.12, a Java based software package with various machine learning and feature selection algorithmsSVM-based classification was carried out by the means of the SVMlight and Weka, 2500 SVM and 433 Weka-based models were build. The models performing with an accuracy of at least 85% on the independent dataset were selected. Of these, the best results were obtained using the amino acid composition as the input feature. Hence, the amino acid composition percentage was taken as the input feature for building the SVM and Weka models that were used on the web server. Amino acid composition percentage was calculated using the following formula:In the past, studies have proven the importance of the amino acid composition, dipeptide and tripeptide composition percentage of peptide sequences in the development of classification models that predict their therapeutic potential3233where i is any naturally occurring amino acid.It has often been demonstrated that not all properties equally contribute to the biological activity of therapeutic peptidesNT5 contained first five residues  of 78 peptides; (2) CT5 contained last five residues  of 53 peptides.In order to understand the role of N- and C-terminal residues in the ABPs and QSPs, terminus datasets were created using the N- and C-terminal residues of those peptides from main dataset which were ≥10 residues long. The following were derived from main dataset: (1) In order to determine the frequency with which various amino acids appeared at different positions in both the ABPs and QSPs, sequence logos were created using WebLogo software. The size of a residue’s logo represents its frequency at a given position. The height of the residue’s logo is also a measure of the residue variability at that position, in that the taller the logo, the lesser the variability at that position.e.g. Ala is represented by 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0). A pattern of window length W was represented by a vector of dimensions 20× W. Binary profile patterns for the first 5 N-terminal residues and for the last 5 C-terminal residues were created for those peptides with ≥10 amino acid residues from the main datasets. In the N-terminal approach, five residues of the N-terminus were extracted from each peptide (NT5 subset), whereas in the C-terminal approach, five residues of the C-terminus were extracted from each peptide (CT5 subset). A binary profile of 5 × 20 dimensions was generated. These profiles were then used to develop SVM and Weka-based models. The BPP analysis has previously been used effectivelyBPPs were generated for each peptide, where a vector of 20 dimensions represents each amino acid . The following formulae were used for their calculation:where TP and TN are correctly predicted positive and negative examples, respectively. Similarly, FP and FN are wrongly predicted positive and negative examples, respectively.The models were also evaluated in a threshold independent manner by their receiver operating characteristic (ROC) curves and the areas under these curves (AUC). SigmaPlot 11.0 was used for generating the ROC curves and the calculating the corresponding AUC values.To prepare the positive independent dataset, 10 anti-biofilm peptides were randomly selected from the final 90 anti-biofilm peptides. An equal number of QSPs were manually curated from published literature and QSPpred (peptides with missing Quorumpeps entries) for the negative independent dataset.In-house PERL scripts were written to generate mutants of the query peptides based on the user’s requirement. This feature was incorporated in dPABBs because substitutions of single amino acid residues have been shown to improve the anti-biofilm activity of biofilm-active peptides18How to cite this article: Gupta, P. et al. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides. Sci. Rep.6, 21839; doi: 10.1038/srep21839 (2016).


Both schizophrenia and antipsychotic treatment are known to modulate brain morphology. However, it is difficult to establish whether observed structural brain abnormalities are due to disease or the effects of treatment. The aim of this study was to investigate the effects of illness and antipsychotic treatment on brain structures in antipsychotic-naïve first-episode schizophrenia based on a longitudinal short-term design. Twenty antipsychotic-naïve subjects with first-episode schizophrenia and twenty-four age- and sex-matched healthy controls underwent 3T MRI scans. Voxel-based morphometry (VBM) was used to examine the brain structural abnormality in patients compared to healthy controls. Nine patients were included in the follow-up examination after 8 weeks of treatment. Tensor-based morphometry (TBM) was used to identify longitudinal brain structural changes. We observed significantly reduced grey matter volume in the right superior temporal gyrus in antipsychotic-naïve patients with schizophrenia compared with healthy controls. After 8 weeks of treatment, patients showed significantly increased grey matter volume primarily in the bilateral prefrontal cortex, insula, right thalamus, left superior occipital cortex and the bilateral cerebellum. In addition, a greater enlargement of the prefrontal cortex is associated with the improvement in negative symptoms, and a more enlarged thalamus is associated with greater improvement in positive symptoms. Our results suggest the following: (1) the abnormality in the right superior temporal gyrus is present in the early stages of schizophrenia, possibly representing the core region related to schizophrenia; and (2) atypical antipsychotics could modulate brain morphology involving the thalamus, cortical grey matter and cerebellum. In addition, examination of the prefrontal cortex and thalamus might facilitate an efficient response to atypical antipsychotics in terms of symptom improvement. Schizophrenia is a complex psychotic disorder characterized by significant brain abnormalities. Numerous imaging studies have revealed reduced grey matter volume in patients with schizophrenia involving multiple brain regions, such as the frontal cortex, temporal lobe, and insula . Of thesAccumulating evidence has indicated that antipsychotic medication might modulate brain morphology. For example, in a cross-sectional study comparing drug-free schizophrenia patients to schizophrenia patients treated with antipsychotics, Dazzan et al. (2005) demonstrated that increased thalamus volume was associated with atypical antipsychotics by comparing drug-free patients and patients with the use of antipsychotics . SimilarIn the present study, we first investigated whole brain structural changes by comparing antipsychotic-naïve patients with first-episode schizophrenia to healthy controls to identify the core regions related to schizophrenia. We further examined the effects of antipsychotics on brain structures after 8 weeks of atypical antipsychotic treatment. Few investigations to our knowledge have performed such a comprehensive study using antipsychotic-naïve patients to investigate disease-related changes in brain structure and then further performed a longitudinal study to examine the effects of atypical antipsychotics. This longitudinal study will be helpful in identifying small structural changes in the brain due to antipsychotic treatment in the early stage of disease.Twenty-five subjects diagnosed with first-episode schizophrenia were recruited for the present study. Two subjects did not complete scanning, and 3 subjects were excluded because of poor image quality. Therefore, 20 patients  were ultimately included in the patient group, with a mean age of 24.45 years (SD = 5.51) and an average of 11.95 years of education (SD = 2.61). Patients received atypical antipsychotics at an average daily dose of 285 mg/day (SD = 108.94) of chlorpromazine equivalents  with T1-Weighted 3D magnetization with the following parameters: time to repetition (TR) = 7.8 ms; time to echo (TE) = 3.0 ms; flip angle = 7°; matrix = 256×256; voxel size = 1×1×1 mmhttp://www.fil.ion.ucl.ac.uk/spm) with the default parameters on the Matlab 7.1 platform . Detailed processing steps of VBM have been presented previously [All of the T1-weighed MRI images were processed using the voxel-based morphometry (VBM) toolbox in SPM8  , which wStudent’s t-test and chi-square tests using SPSS 17  were utilized to examine group differences in basic demographic characteristics and clinical data. Between-group differences in grey matter volume based on VBM analysis were analyzed using a t-test in SPM8, with age as a covariate. The results were obtained on a voxel-level height of threshold p<0.001, uncorrected, and a cluster-level extent threshold of 50 continuous voxels. The associations of grey matter volume in the distinct regions with PANSS and its subscores as well as untreated duration of illness were further performed using SPSS. The effect of untreated duration of illness on grey matter volume in the whole brain was also tested in the patients group by general linear models (GLM) in SPM.The demographic characteristics of the follow-up and non follow-up groups were performed using Student’s t-test and chi-square tests using SPSS. The normality of the grey matter volume of the sample was conducted with descriptive statistics in SPSS. A paired t-test in SPM was used to identify regional grey matter changes after 8 weeks of treatment. Multi-regression analysis was used to examine the associations between the changes in regional grey matter volume and PANSS scores. The results were observed with a voxel-level height of threshold p<0.05, family-wise error (FWE) corrected, and cluster size of 100. The change in PANSS from baseline to follow-up was examined using a t-test in SPSS 17. The effect of dosage of medication on grey matter volume was tested in the patients group by GLM in SPM.There were no significant differences between patients and healthy subjects in age, sex or education. The detailed information was described in There were no significant difference between the follow-up group and non follow-up group in age, sex and education. The detailed information of the two sub-groups was described in the After 8 weeks of treatment, pronounced grey matter increases were noted mainly in the prefrontal cortex , bilateral insula, left superior occipital cortex, left/right cerebellum and right thalamus . SignifiIn this study, we first used VBM to investigate regional grey matter abnormalities in antipsychotic-naïve schizophrenia patients compared to healthy controls. We then examined the effect of antipsychotics on brain structures after 8 weeks of treatment in patients with schizophrenia. Our principal findings were that (1) decreased grey matter volume in the right superior temporal gyrus was observed in the early stage of the disease; and (2) after 8 weeks of antipsychotic treatment, patients showed significantly increased grey matter volume mainly in the bilateral prefrontal cortex, insula, right thalamus, left superior occipital cortex and the bilateral cerebellum. In addition, the grey matter volume in the prefrontal cortex and thalamus was associated with negative and positive symptoms, respectively.Decreased grey matter volume in the right superior temporal gyrus was observed in the antipsychotic-naïve patients with schizophrenia relative to the healthy controls, which suggests an alteration in this region due to the disease. Our present results are consistent with those from a previous study investigating brain structural abnormality in antipsychotic-naïve patients with deficits in the superior temporal cortex . AbnormaResearch has documented the ability of antipsychotic medication to produce brain volumetric changes even over a short period of time . In the Increased prefrontal volume was also found after treatment, which is consistent with the previous findings of increased cortical thickness in the prefrontal cortex over 8 weeks of atypical antipsychotic treatment in first-episode schizophrenia  and an eThe insula plays an important role in the regulating of emotion and cognition , and itsThe cerebellum is known to be involved with motor coordination as well as with aspects of cognitive functioning such as attention, working memory, and sensory discrimination . As suchFinally, we also observed an increase in grey matter volume of the superior occipital cortex associated with atypical antipsychotics. Prior research has also shown volume enlargement of the superior occipital cortex to be associated with antipsychotics , so thisThe present study also has some limitations. The modest sample size is the main limitation, although the study design was longitudinal. Additionally, this was a naturalistic study, meaning that the choice of medicine is clinician-led. In addition, the untreated duration of illness in the present study is a little long (mean duration of illness were 1.91 years). Although whether untreated duration of illness was associated with brain morphology in patients with schizophrenia was still equivalent  and our Therefore, based on our findings, we suggest that (1) the right superior temporal gyrus may represent the core region of pathological change in schizophrenia; and (2) the effects of atypical antipsychotics could involve multiple regions, and examination of increased grey matter volume in the prefrontal cortex and thalamus may be particularly effective in evaluating the efficiency of response to exposure to atypical antipsychotics in the improvement of schizophrenia symptoms.


Nearly half of the world’s population is at risk for dengue, yet no licensed vaccine or anti-viral drug is currently available. Dengue is caused by any of four dengue virus serotypes (DENV-1 through DENV-4), and infection by a DENV serotype is assumed to provide life-long protection against re-infection by that serotype. We investigated the validity of this fundamental assumption during a large dengue epidemic caused by DENV-2 in Iquitos, Peru, in 2010–2011, 15 years after the first outbreak of DENV-2 in the region.We estimated the age-dependent prevalence of serotype-specific DENV antibodies from longitudinal cohort studies conducted between 1993 and 2010. During the 2010–2011 epidemic, active dengue cases were identified through active community- and clinic-based febrile surveillance studies, and acute inapparent DENV infections were identified through contact tracing studies. Based on the age-specific prevalence of DENV-2 neutralizing antibodies, the age distribution of DENV-2 cases was markedly older than expected. Homologous protection was estimated at 35.1% . At the individual level, pre-existing DENV-2 antibodies were associated with an incomplete reduction in the frequency of symptoms. Among dengue cases, 43% (26/66) exhibited elevated DENV-2 neutralizing antibody titers for years prior to infection, compared with 76% (13/17) of inapparent infections .Our data indicate that protection from homologous DENV re-infection may be incomplete in some circumstances, which provides context for the limited vaccine efficacy against DENV-2 in recent trials. Further studies are warranted to confirm this phenomenon and to evaluate the potential role of incomplete homologous protection in DENV transmission dynamics. Dengue is a mosquito-borne viral illness that imposes a tremendous public health burden on tropical and sub-tropical regions. An estimated 390 million infections occur globally each year, and up to 4 billion people are at risk. Dengue is caused by four dengue virus (DENV) serotypes (DENV-1 to DENV-4). Infection with any DENV can lead to a range of disease outcomes, from mild febrile illness to severe, hemorrhagic manifestations and death. Infection by one serotype has been assume to provide complete and lifelong protection against re-infection by the same serotype, and to our knowledge, instances of re-infection by the same serotype have not been rigorously documented. However, few long-term studies have been conducted in such a way that re-infection by the same serotype could be observed, if it did in fact occur. Our study provides evidence that re-infection may occur in certain circumstances. We draw from data collected during a 2010–2011 DENV-2 epidemic in northeastern Peru, 15 years after the initial DENV-2 outbreak in the region. This finding has significant implications for our understanding of dengue epidemiology and for dengue vaccine formulation, which may need to consider multiple genotypes of each serotype. Data from other long-term dengue epidemiology studies should be analyzed to determine if homologous re-infection is a more widespread phenomenon. Dengue is a mosquito-borne viral illness that imposes a tremendous public health burden on tropical and sub-tropical regions. An estimated 390 million infections occur globally each year, and up to 4 billion people are at risk . Dengue DENVs display substantial inter-serotypic genetic heterogeneity. Serotypes share less than 70% identity at the nucleotide level and less than 80% identity at the amino acid level , similarAlthough DENVs are genetically and phenotypically diverse within serotypes, infection with one strain of a serotype is thought to induce lifelong protection against infection by all other strains of the homologous serotype . This asBetween late 2010 and early 2011, Iquitos, Peru, experienced an unprecedented outbreak of severe dengue caused predominantly by an American/Asian genotype of DENV-2 (AA-DENV-2) ,14. The All human subjects protocols , DENV-2 16681 , DENV-3 IQD1728 , and DENV-4 1036 . DENV-2 16681 (Asian genotype) was selected for the seroprevalence assays because previous experiments in our laboratory showed that this strain minimized a cross-reactive response from DENV-1 infection [Clinic-based surveillance was conducted as described previously . BrieflyContact tracing studies were ongoing during the 2010–2011 epidemic as part of a project aiming to capture inapparent infections and to measure the importance of human movement patterns in dengue virus transmission . Upon idThe age distribution of clinical cases is determined by the age structure of the population and at least three key epidemiological processes: immunity, the force of infection (risk of exposure) and the development of clinical symptoms. Given age-specific profiles for each of these and the age structure of the population, the expected age distribution of cases can be inferred. If we consider the scenario of a novel pathogen invading a completely susceptible population with no age dependence in risk of exposure or manifestation of disease, then the age distribution of cases would mirror the age distribution of the population. By contrast, the re-introduction of an immunizing pathogen should result in a shift of cases to lower age groups who have no prior exposure and, therefore, no protection. We would expect this shift would be less marked, however, if immunity were imperfect.N), (2) the age distribution of individuals who appeared at the clinics and hospitals with a febrile illness[i, pi) and from (3) we found what percent of those who appeared at a clinic or hospital were from that age group . Letting qi be the probability that the next DENV-2 case that appears at a clinic or hospital is of age group i, we have:To compare the observed age distribution of DENV-2 cases in clinics and hospitals to the age distribution we computed an expected age distribution of cases based on: (1) the total number of observed cases . While we included individuals in the 0–5 age range in the calculations, cases in this age range have limited impact on the model overall since few were enrolled in the febrile surveillance study prior to or during the DENV-2 epidemic.This equation is based on the assumption that an individual of any age is equally likely to become infected. By multiplying these probabilities by the total number of DENV-2 cases identified at the clinics and hospitals, we arrive at the expected number of cases by age that would have been expected if the patterns of age at time of infection were identical during the epidemic as compared with before the epidemic  to 1 (corresponding to complete protection from disease resulting from infection).We further investigated the possibility of incomplete homologous protection against symptomatic infection by adjusting the age-based distribution of DENV-2 neutralizing antibodies (3). Above we assumed that the presence of neutralizing antibodies conferred immunity, and that only those without these antibodies could become symptomatically infected in the future. To investigate imperfect immunity, we assumed that the percentage of the individuals with neutralizing antibodies did not directly translate into the percentage of individuals who were immune. Letting γ, we calculate a distribution of cases by age. We can then compare this “expected” distribution with the “observed” distribution of cases at the clinics. By calculating the probability of observing a deviation from the expected distribution as large as or larger than the one observed, we can identify which values of γ would not result us in concluding that there is a statistically significant difference between the expected and observed distributions. Exploiting the duality between hypothesis tests and confidence intervals, and using a cutoff of 5% for the above probability for each value of γ, we create a 95% confidence interval. The value of γ that maximizes this probability we use as our point estimate of the value of γ that is most supported by the data and the model.For each value of Based on two cohort studies that together sampled people (n = 3127) in neighborhoods located throughout the city ,20,21, iGiven that the observed DENV-2 antibody prevalence in the Iquitos population was primarily the result of prior exposure to Am-DENV-2 ten years earlier and the sheer magnitude of the 2010–2011 dengue outbreak, we postulated that the pre-existing antibodies failed to protect against reinfection and disease. Although we did not detect any individuals with virologically-confirmed acute DENV-2 infections in both 1995 and 2010–2011, likely because of limitations of febrile surveillance activities in the mid-1990s and challenges in linking participant results across studies, we were able to test this hypothesis by analyzing population and individual level infection patterns.Age-dependent patterns of infection and disease in a population are in part a reflection of the extent of pre-existing immunity . Given tThe observed distribution of AA-DENV-2 cases could have been influenced by age-specific differences in infection rates. We addressed this concern using data from a case-control contact-tracing study  that capTo address the possibility that the observed DENV-2 neutralizing antibodies were the result of cross-reaction from heterologous DENV infection, we utilized multiple cross-sectional samples from cohort studies conducted in Iquitos since the early 1990s ,18,19,23In 1993, following the DENV-1 epidemic and prior to the known emergence of DENV-2, most of the population had DENV-1 antibodies , panel AThe results from the population-level analysis indicate that some individuals with pre-existing DENV-2 neutralizing antibodies prior to the 2010 epidemic were infected by AA-DENV-2 and presented with clinically apparent disease. To address this possibility, we examined the serological histories see  of indivOur results suggest the possibility that Am-DENV-2 infection failed to generate antibodies that robustly neutralize AA-DENV-2. To address this, we utilized pre-epidemic serum collected between 2006 and 2010 from individuals who were later detected with AA-DENV-2 infections (n = 21) and compared end point titers against two strains of Am-DENV-2 and two strains of AA-DENV-2 . Robust We provide indirect evidence that challenges a fundamental assumption of dengue immunology: that infection with one serotype conveys lifelong protection to reinfection by all strains belonging to the same serotype. A newly introduced genotype of DENV-2 exhibited characteristics similar to a novel serotype in a population with a high prevalence of DENV-2 neutralizing antibodies. Moreover, we isolated DENV-2 virus from numerous individuals with pre-existing, robust DENV-2 antibody titers. Epidemiological data from long-term longitudinal cohort studies show thaOne key piece of evidence in our study arises from the observation of more infections than expected in older age groups. Our calculations for the expected age distribution of DENV-2 cases take into account the age-dependent participation in our febrile illness studies. Our previous analysis of DENV-3 and DENV-4 seroconversions did not indicate a substantial age-dependent risk for infection . FurtherOur results depend in part on PRNTs, which are widely considered the most specific serological tests available for DENV. Yet even PRNTs can be prone to cross-reaction in some circumstances . To reduMore importantly, we directly address the issue of cross-reaction by utilizing samples collected prior to the first documented DENV-2 outbreak in 1995. Detectable DENV-2 antibody prevalence was low prior to DENV-2 emergence in Iquitos, spiked immediately following DENV-2 emergence in 1995, and remained stable among adults up to the 2010–2011 epidemic . FurtherIt is not clear if the homologous serotype re-infection we identified is unique to the DENV-2 genotypes we studied  or if it is a more widespread phenomenon among other dengue viruses. The 1995 Am-DENV-2 outbreak occurred 5 years after the start of a DENV-1 outbreak, which apparently modulated disease outcomes . No caseThere is some support for re-infection with a homologous DENV serotype in animal and in vitro studies, although most studies do not utilize naturally occurring virus variants and do not challenge hosts with a different genotype of a homologous serotype. In vaccine studies in non-human primates, viremia may be attenuated but detectable despite the presence of a robust immune response . Even whTo our knowledge, this is the first study to present epidemiological data as evidence for homologous reinfection by DENV. Numerous other studies have noted a poor correlation between pre-existing virus neutralization antibody titers and clinical protection, both in natural infection ,28 and vin vitro measures of protection, because positive PRNT results do not accurately predict protection from symptomatic infection, particularly at the liberal cutoffs used in vaccine immunogenicity studies .Consistent with other reports ,27,28, oS1 Checklist(PDF)Click here for additional data file.


Client adherence is vital for effective methadone maintenance treatment (MMT). This study explores the pattern and associated factors of client adherence, drop-out and re-enrolment in the Chinese MMT programme over the period of 2006–2013.This retrospective study was conducted in 14 MMT clinics in Guangdong Province, China. We employed Kaplan-Meier survival analysis to estimate the rates of drop-out and re-enrolment of MMT clients and multivariate Cox regression to identify associated factors.Among 1,512 study participants, 79% have experienced ‘drop-out’ during the 7-year study period. However, 82% ‘dropped-out’ clients resumed treatment at a later time. Low education level , low methadone dosage in the first treatment episode  and higher proportion of positive urine test  during the first treatment episode were strong predictors of subsequent drop-outs of the participants. Among the ‘dropped-out’ clients, being female , being married , and having a higher proportion of positive urine tests in the first treatment episode  had greater likelihood of subsequent re-enrolment in MMT. Clients receiving lower methadone dosage  were also more likely to re-enrol.Persistent cycling in-and-out of clients in MMT programmes is common. Insufficient dosage and higher proportion of positive urine samples in the first treatment episode are the key determinants for subsequent client drop-out and re-enrolment. Interventions should target clients in their early stage of treatment to improve retention in the long term. Methadone maintenance treatment (MMT) is a widely used opioid substitution treatment for drug users. By using methadone as a synthetic agent to block brain receptor affected by heroine and other opiates, the therapy is proven to effectively reduce the craving for drugs and clinical symptoms associated with withdrawal from opiates –3. NumerMMT has been implemented in 63 countries around the world . In ChinBased on an ongoing follow-up study in fourteen MMT clinics in the Chinese province of Guangdong over the period of 2005–2013, this study investigates the pattern of client adherence, drop-out and re-enrolment in the Chinese MMT programme and associated factors. Understanding the cycling pattern in treatment may foster future implementation of the programme and tailor targeted interventions in those most needed to minimise drop-outs.Guangdong is one of the most economically developed provinces in Southern China and has more than 370,000 drug users, accounting for about one-sixth of all registered drug use in China . The latChina has developed a national integrated web-based data system for MMT to collect client information. The database is administered by the National Centre for AIDS/STD Control and Prevention under the supervision of the Chinese Centre for Disease Control and Prevention. All participants received standardised MMT treatment including daily administration of methadone and regular urine testing.Participants were required to pay ten RMB ($1.57 dollars) before receiving methadone in liquid solution under the supervision of clinic staff. The patients were subjected to urine testing on a random day per fortnight. Urine samples were tested for opioids (except methadone) using the immune-gold labelling technique . Blood samples were collected at baseline and in every 6 months to test for HIV, HCV, and syphilis infections. The staff in clinics monitored the daily dosage of methadone for the participants, the status of urine tests and infections. Referral to other MMT clinics can be obtained from the hosting clinics if participants were to transfer or relocate. Participants were referred to hospitals if they were diagnosed with TB infection or other clinical problems at enrolment.Participants who met the eligibility criteria for MMT and received the treatment in the selected clinics were recruited over the period of December 1st, 2011 and January 31th, 2012. The recruited participants must 1) have diagnosed opioid dependence according to the International Classification of Diseases-10 (ICD-10); 2) be aged 20 years or above; 3) be a local resident who settled in the catchment areas of the clinics; 4) have provided written informed consent for the use of their clinical data in MMT databases. We excluded clients only visit the clinics casually and those with incomplete treatment records.Treatment records of recruited clients were collected both retrospectively (up to January 5th 2006) and prospectively (till March 12th 2014). We collected the following information from participants: (1) demographic information including age, gender, occupation, marital status, and education level; (2) baseline HIV and HCV status; (3) risk behaviours at the first enrolment; and (4) daily uptake dosage of methadone and (5) biweekly urine test results. Collected data was double-entered into the national MMT database and de-identified before analysis. We also collected the information related to their accessibility to MMT clinics, including travelling time and out-of-pocket payment.Primary indicators for treatment adherence were drop-out and re-enrolment rates of MMT clients. An individual was considered as ‘drop-out’ if he/she missed methadone intakes for 14 consecutive days or more. Participants were considered as “ever drop-outs” if only he/she drop out in their initial treatments or in the subsequent period of time during the study duration. Participants who did not drop-out were considered ‘retained’. Dropped- out individuals may be ‘loss-to-follow-up’ (LTFU) or return to treatment. LTFU clients were those left the programme for more than 14 days after the initial enrolment and did not return by the end of the study. For those who returned, they were classified as ‘re-enrolled’.We defined the duration of treatment episode as the time from the enrolment (or re-enrolment) in MMT to the first day of following drop-out. We also explored the treatment gap days which defined as the durations from drop-outs to re-enrolments. The average methadone dosage was calculated as the total dosages divided by the duration of the treatment episodes. The positive urine testing rate was defined as the proportion of submitted urine samples that tested positive.This study was approved by Ethics Review Committee at Sun Yat-sen University . All participants gave their written consent for their information to be stored in the national integrated web-based system for the MMT programme and the use of this study.p value less than 0.25 in univariate Cox regressions were included in multivariate analyses. We used multivariate Cox proportional hazard model to identify the predictors of drop-out [HRs) were calculated with 95% confidence intervals (CIs). All analysis was conducted using SAS9.3 .We calculated the ever drop-out rate as the proportion of drop-outs among participants who have ever experience drop-out from MMT during the study duration. We calculate the times of drop-outs after the initial enrollment and each enrollment. Baseline information was compared across the retained, withdrawn, and re-enrolled groups using chi- square test for dichotomous variables. Kruskal Wallis H test was used to compare characteristics between retained, LTFU and re-enrolled groups. One-way ANOVA was used for the comparisons of normally distributed continuous variables. We employed Kaplan-Meier method to estimate the duration of the first treatment and calculate the gap days between the first drop-out and the subsequent re-enrolment, and the drop-out rates after one, two, and seven years of initial treatment. We explored independent correlates to the occurrence of drop-outs and re-enrolments using univariate Cox regression. Factors with drop-out , whereasdrop-out . Hazard A total of 1,512 MMT clients were included in this study. Of which, 1,194 (79.0%) had ever dropped out with 225 (18.8%) participants lost-to-follow-up and 969 (81.2%) re-enrolled later in the study period.Participants had an average age 38.6 years (range 21–63), and was predominantly male (90.0%) and unemployed at first enrolment (63.2%). Just over half of the participants (53.4%) were divorced, widowed or single. Education level was low . Among clients who experienced drop-out, LTFU clients had significantly lower dosage than returned clients in the last administration before drop-out .The average methadone dosage in the first treatment episode was 55.5  millilitres among all participants, among whom, the dosage in the retained group was the highest  participants were urine tested positive in more than 50% of testing occasions. This rate was lowest in the retained group (11.6%), followed by the LTFU group (30.2%) but highest among the re-enrolled group (46.7%)  days among re- enrolled individuals. The duration of the following episodes then declined substantially , low methadone dosage during the first treatment episode  and higher proportion of positive urine test  in first treatment episode were strong predictors of subsequent drop-outs of the participants  with every 10-year increase in age. Low education level , being married , and having higher proportion of positive urine samples in the first treatment episode  had greater likelihood of re-enrolment in MMT. Clients received lower methadone dosage  were more likely to re-enrol  among MMT participants, most clients (~80%) choose to return to MMT after a short period of ‘drop-out’. The underlying reason for this ongoing drop-out and re-enrollment pattern could be multi-fold. MMT participants in China had common misconceptions towards the treatment programme. They regard MMT as a transient programme for drug detoxification. Most participants did not want to retain on treatment once their addictive reactions are alleviated . HoweverConsistent with the regression trend for drop-outs, clients received substantially higher dosage and being urine tested positive more frequently during the first treatment episode predict a higher re-entry probability among drop-outs. That is, performance during the first treatment episode has decisive effects on the latter adherence of clients. It also becomes harder to sustain effective treatment if individual clients have developed the habit of cycling in-and-out the treatment programme. The finding also suggests a greater chance of treatment resume among relapsed drug users or those continuing to use drugs while on treatment. Their treatment is likely deterred by ongoing drug use/relapse due to insufficient methadone dosage. However, they choose to return to treatment often when they can no longer afford drugs and with MMT their addictive reactions can at least be temporarily released .A number of study limitations should be noted. First, we adapt the definition of drop-out as 14-day absence based on guidelines from China CDC and past literature. However, there has been ongoing debate about whether a 30-day threshold should be used . Second,We investigated a very important behavioural pattern of MMT clients cycling in-and-out the service in China. Conclusively, drop-out rate after the first treatment episode may not reflect the actual adherence of MMT clients, as the majority of clients retain or return to treatment after a brief gap of 3–4 weeks. This calls for effective interventions to reduce the frequency of repeated enrolment of the programme.Improvement on methadone dosage with a more customised adjustment scheme, monitoring of additive reactions and health education to reduce misconception on MMT are necessary for the provision of sustaining treatment to drug users. Further exploratory follow-up study of LTFU clients is required to understand the underlying causes of treatment termination. Staff plays a critical role in retaining the participants in the treatment. Further studies on the impacts of the MMT staff on the drop-out are necessary to explore this.Persistent cycling in-and-out of clients in MMT programme is common. Insufficient dosage and higher proportion of positive urine samples in the first treatment episode are the key determinant for subsequent client drop-out and re-enrolment. Interventions should target clients in their early stage of treatment to improve retention in the long term.S1 Table(DOCX)Click here for additional data file.