Intravaginal and subcutaneous immunization induced vaccine specific CD8 T cells and tumor regression in the bladder.

PURPOSE: Vaccines targeting tumor associated antigens are in development for bladder cancer. Most of these cancers are nonmuscle invasive at diagnosis and confined in the mucosa and submucosa. However, to our knowledge how vaccination may induce the regression of tumors at such mucosal sites has not been examined previously. We compared different immunization routes for the ability to induce vaccine specific antitumor CD8 T cells in the bladder and bladder tumor regression in mice. MATERIALS AND METHODS: In the absence of a murine bladder tumor model expressing a tumor antigen relevant for human use we established an orthotopic model expressing the HPV-16 tumor antigen E7 as a model. We used an adjuvant E7 polypeptide to induce CD8 T cell mediated tumor regression. RESULTS: Subcutaneous and intravaginal but not intranasal vaccination induced a high number of TetE7(+)CD8(+) T cells in the bladder as well as bladder tumor regression. The entry of vaccine specific T cells in the bladder was not the only key since persistent regression of established bladder tumors by intravaginal or subcutaneous immunization was associated with tumor infiltration of total CD4 and CD8 T cells. This resulted in an increase in TetE7(+)CD8(+) T cells and a decrease in T regulatory cells, leading to an increased number of effector interferon-γ secreting vaccine specific CD8 T cells in the regressing bladder tumor. CONCLUSIONS: These data show that immunization routes should be tailored to each mucosal tumor site. Subcutaneous or intravaginal vaccination may be of additional value to treat patients with bladder cancer.

Probabilistic evidential assessment of gunshot residue particle evidence (Part II) : Bayesian parameter estimation for experimental count data.

Part I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models - in the form of Bayesian networks - address the issues of background presence of GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphical models. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR).

Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature

Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.

Substantial advantage of a combined Bayesian and genotyping approach in testosterone doping tests.

Testosterone abuse is conventionally assessed by the urinary testosterone/epitestosterone (T/E) ratio, levels above 4.0 being considered suspicious. A deletion polymorphism in the gene coding for UGT2B17 is strongly associated with reduced testosterone glucuronide (TG) levels in urine. Many of the individuals devoid of the gene would not reach a T/E ratio of 4.0 after testosterone intake. Future test programs will most likely shift from population based- to individual-based T/E cut-off ratios using Bayesian inference. A longitudinal analysis is dependent on an individual's true negative baseline T/E ratio. The aim was to investigate whether it is possible to increase the sensitivity and specificity of the T/E test by addition of UGT2B17 genotype information in a Bayesian framework. A single intramuscular dose of 500mg testosterone enanthate was given to 55 healthy male volunteers with either two, one or no allele (ins/ins, ins/del or del/del) of the UGT2B17 gene. Urinary excretion of TG and the T/E ratio was measured during 15 days. The Bayesian analysis was conducted to calculate the individual T/E cut-off ratio. When adding the genotype information, the program returned lower individual cut-off ratios in all del/del subjects increasing the sensitivity of the test considerably. It will be difficult, if not impossible, to discriminate between a true negative baseline T/E value and a false negative one without knowledge of the UGT2B17 genotype. UGT2B17 genotype information is crucial, both to decide which initial cut-off ratio to use for an individual, and for increasing the sensitivity of the Bayesian analysis.

Intravaginal TLR agonists increase local vaccine-specific CD8 T cells and human papillomavirus-associated genital-tumor regression in mice.

Human papillomaviruses (HPV)-related cervical cancer is the second leading cause of cancer death in women worldwide. Despite active development, HPV E6/E7 oncogene-specific therapeutic vaccines have had limited clinical efficacy to date. Here, we report that intravaginal (IVAG) instillation of CpG-ODN (TLR9 agonist) or poly-(I:C) (TLR3 agonist) after subcutaneous E7 vaccination increased ∼fivefold the number of vaccine-specific interferon-γ-secreting CD8 T cells in the genital mucosa (GM) of mice, without affecting the E7-specific systemic response. The IVAG treatment locally increased both E7-specific and total CD8 T cells, but not CD4 T cells. This previously unreported selective recruitment of CD8 T cells from the periphery by IVAG CpG-ODN or poly-(I:C) was mediated by TLR9 and TLR3/melanoma differentiation-associated gene 5 signaling pathways, respectively. For CpG, this recruitment was associated with a higher proportion of GM-localized CD8 T cells expressing both CCR5 and CXCR3 chemokine receptors and E-selectin ligands. Most interestingly, IVAG CpG-ODN following vaccination led to complete regression of large genital HPV tumors in 75% of mice, instead of 20% with vaccination alone. These findings suggest that mucosal application of immunostimulatory molecules might substantially increase the effectiveness of parenterally administered vaccines.Mucosal Immunology advance online publication 12 September 2012; doi:10.1038/mi.2012.83.

Language Regression Associated With Autistic Regression and Electroencephalographic (EEG) Abnormalities: A Prospective Study.

We report a boy, referred at 25 months following a dramatic isolated language regression antedating autistic-like symptomatology. His sleep electroencephalogram (EEG) showed persistent focal epileptiform activity over the left parietal and vertex areas never associated with clinical seizures. He was started on adrenocorticotropic hormone (ACTH) with a significant improvement in language, behavior, and in EEG discharges in rapid eye movement (REM) sleep. Later course was characterized by fluctuations/regressions in language and behavior abilities, in phase with recrudescence of EEG abnormalities prompting additional ACTH courses that led to remarkable decrease in EEG abnormalities, improvement in language, and to a lesser degree, in autistic behavior. The timely documentation of regression episodes suggesting an "atypical" autistic regression, striking therapy-induced improvement, fluctuation of symptomatology over time could be ascribed to recurrent and persisting EEG abnormalities.

U.S. Universities’ Net Returns from Patenting and Licensing: A Quantile Regression Analysis, September 2006

In line with the rights and incentives provided by the Bayh-Dole Act of 1980, U.S. universities have increased their involvement in patenting and licensing activities through their own technology transfer offices. Only a few U.S. universities are obtaining large returns, however, whereas others are continuing with these activities despite negligible or negative returns. We assess the U.S. universities’ potential to generate returns from licensing activities by modeling and estimating quantiles of the distribution of net licensing returns conditional on some of their structural characteristics. We find limited prospects for public universities without a medical school everywhere in their distribution. Other groups of universities (private, and public with a medical school) can expect significant but still fairly modest returns only beyond the 0.9th quantile. These findings call into question the appropriateness of the revenue-generating motive for the aggressive rate of patenting and licensing by U.S. universities.

Clinical consequences of imatinib plasma concentrations variability in hemato-oncologic patients

Background: Imatinib has revolutionized the treatment of chronic myeloid leukemia (CML) and gastrointestinal stromal tumors (GIST). Considering the large inter-individual differences in the function of the systems involved in its disposition, exposure to imatinib can be expected to vary widely among patients. This observational study aimed at describing imatinib pharmacokinetic variability and its relationship with various biological covariates, especially plasma alpha1-acid glycoprotein (AGP), and at exploring the concentration-response relationship in patients. Methods: A population pharmacokinetic model (NONMEM) including 321 plasma samples from 59 patients was built up and used to derive individual post-hoc Bayesian estimates of drug exposure (AUC; area under curve). Associations between AUC and therapeutic response or tolerability were explored by ordered logistic regression. Influence of the target genotype (i.e. KIT mutation profile) on response was also assessed in GIST patients. Results: A one-compartment model with first-order absorption appropriately described the data, with an average oral clearance of 14.3 L/h (CL) and volume of distribution of 347 L (Vd). A large inter-individual variability remained unexplained, both on CL (36%) and Vd (63%), but AGP levels proved to have a marked impact on total imatinib disposition. Moreover, both total and free AUC correlated with the occurrence and number of side effects (e.g. OR 2.9±0.6 for a 2-fold free AUC increase; p<0.001). Furthermore, in GIST patients, higher free AUC predicted a higher probability of therapeutic response (OR 1.9±0.5; p<0.05), notably in patients with tumor harboring an exon 9 mutation or wild-type KIT, known to decrease tumor sensitivity towards imatinib. Conclusion: The large pharmacokinetic variability, associated to the pharmacokinetic-pharmacodynamic relationship uncovered are arguments to further investigate the usefulness of individualizing imatinib prescription based on TDM. For this type of drug, it should ideally take into consideration either circulating AGP concentrations or free drug levels, as well as KIT genotype for GIST.

Long-term outcome after cognitive and behavioral regression in non-lesional epilepsy with CSWS

Purpose: To present the long-term outcome (LTO) of 10 adolescents and young adults with documented cognitive and behavioral regression as children due to non-lesional focal, mainly frontal epilepsy with continuous spike-waves during slow wave sleep (CSWS). Method: Past medical and EEG data of all patients were reviewed and neuropsychological tests exploring main cognitive functions were administered. Result: After a mean duration of follow-up of 15.6 years (range 8-23 years), none of the 10 patients had recovered fully, but four regained borderline to normal intelligence and were almost independent. Patients with prolonged global intellectual regression had the worst outcome, whereas those with more specific and short-lived deficits recovered best. The marked behavioral disorders that were so disturbing during the active period (AP) resolved in all but one patient. Executive functions were neither severely nor homogenously affected. Three patients with a frontal syndrome during the AP disclosed only mild residual executive and social cognition deficits. The main cognitive gains occurred shortly after the AP, but qualitative improvements continued to occur. LTO correlated best with duration of CSWS. Conclusion: Our findings emphasize that cognitive recovery after cessation of CSWS depends on the severity and duration of the initial regression. None of our patients had major executive and social cognition deficits with preserved intelligence as reported in adults with destructive lesions of the frontal lobes during childhood. Early recognition of epilepsy with CSWS and rapid introduction of effective therapy are crucial for a best possible outcome.

The Effect of Four-Lane to Three-Lane Conversion on the Number of Crashes and Crash Rates in Iowa Roads, June 2005

We analyze crash data collected by the Iowa Department of Transportation using Bayesian methods. The data set includes monthly crash numbers, estimated monthly traffic volumes, site length and other information collected at 30 paired sites in Iowa over more than 20 years during which an intervention experiment was set up. The intervention consisted in transforming 15 undivided road segments from four-lane to three lanes, while an additional 15 segments, thought to be comparable in terms of traffic safety-related characteristics were not converted. The main objective of this work is to find out whether the intervention reduces the number of crashes and the crash rates at the treated sites. We fitted a hierarchical Poisson regression model with a change-point to the number of monthly crashes per mile at each of the sites. Explanatory variables in the model included estimated monthly traffic volume, time, an indicator for intervention reflecting whether the site was a “treatment” or a “control” site, and various interactions. We accounted for seasonal effects in the number of crashes at a site by including smooth trigonometric functions with three different periods to reflect the four seasons of the year. A change-point at the month and year in which the intervention was completed for treated sites was also included. The number of crashes at a site can be thought to follow a Poisson distribution. To estimate the association between crashes and the explanatory variables, we used a log link function and added a random effect to account for overdispersion and for autocorrelation among observations obtained at the same site. We used proper but non-informative priors for all parameters in the model, and carried out all calculations using Markov chain Monte Carlo methods implemented in WinBUGS. We evaluated the effect of the four to three-lane conversion by comparing the expected number of crashes per year per mile during the years preceding the conversion and following the conversion for treatment and control sites. We estimated this difference using the observed traffic volumes at each site and also on a per 100,000,000 vehicles. We also conducted a prospective analysis to forecast the expected number of crashes per mile at each site in the study one year, three years and five years following the four to three-lane conversion. Posterior predictive distributions of the number of crashes, the crash rate and the percent reduction in crashes per mile were obtained for each site for the months of January and June one, three and five years after completion of the intervention. The model appears to fit the data well. We found that in most sites, the intervention was effective and reduced the number of crashes. Overall, and for the observed traffic volumes, the reduction in the expected number of crashes per year and mile at converted sites was 32.3% (31.4% to 33.5% with 95% probability) while at the control sites, the reduction was estimated to be 7.1% (5.7% to 8.2% with 95% probability). When the reduction in the expected number of crashes per year, mile and 100,000,000 AADT was computed, the estimates were 44.3% (43.9% to 44.6%) and 25.5% (24.6% to 26.0%) for converted and control sites, respectively. In both cases, the difference in the percent reduction in the expected number of crashes during the years following the conversion was significantly larger at converted sites than at control sites, even though the number of crashes appears to decline over time at all sites. Results indicate that the reduction in the expected number of sites per mile has a steeper negative slope at converted than at control sites. Consistent with this, the forecasted reduction in the number of crashes per year and mile during the years after completion of the conversion at converted sites is more pronounced than at control sites. Seasonal effects on the number of crashes have been well-documented. In this dataset, we found that, as expected, the expected number of monthly crashes per mile tends to be higher during winter months than during the rest of the year. Perhaps more interestingly, we found that there is an interaction between the four to three-lane conversion and season; the reduction in the number of crashes appears to be more pronounced during months, when the weather is nice than during other times of the year, even though a reduction was estimated for the entire year. Thus, it appears that the four to three-lane conversion, while effective year-round, is particularly effective in reducing the expected number of crashes in nice weather.

Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Modeling the forensic two-trace problem with Bayesian networks

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375-381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727-732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this problem.

Estimating Bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise andpreferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decisionmaking. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisionsover heterogeneous priors. Relative to existing estimation approaches, our \Prior-Based Identification" extends the possible environments which can be estimated,and also substantially improves the accuracy and precision of estimates in thoseenvironments which can be estimated using existing methods.