Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method.

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.

Deficits in executive and memory processes in delusional disorder: a case-control study

OBJECTIVE Delusional disorder has been traditionally considered a psychotic syndrome that does not evolve to cognitive deterioration. However, to date, very little empirical research has been done to explore cognitive executive components and memory processes in Delusional Disorder patients. This study will investigate whether patients with delusional disorder are intact in both executive function components (such as flexibility, impulsivity and updating components) and memory processes (such as immediate, short term and long term recall, learning and recognition). METHODS A large sample of patients with delusional disorder (n = 86) and a group of healthy controls (n = 343) were compared with regard to their performance in a broad battery of neuropsychological tests including Trail Making Test, Wisconsin Card Sorting Test, Colour-Word Stroop Test, and Complutense Verbal Learning Test (TAVEC). RESULTS When compared to controls, cases of delusional disorder showed a significantly poorer performance in most cognitive tests. Thus, we demonstrate deficits in flexibility, impulsivity and updating components of executive functions as well as in memory processes. These findings held significant after taking into account sex, age, educational level and premorbid IQ. CONCLUSIONS Our results do not support the traditional notion of patients with delusional disorder being cognitively intact.

Non-medical prescription drug and illicit street drug use among young Swiss men and associated mental health issues.

Non-medical use of prescription drugs (NMUPD) is increasing among the general population, particularly among teenagers and young adults. Although prescription drugs are considered safer than illicit street drugs, NMUPD can lead to detrimental consequences. The aim of the present study was to investigate the relationship between drug use (NMUPD on the one side, illicit street drugs on the other side) with mental health issues and then compare these associations. A representative sample of 5719 young Swiss men aged around 20 years filled in a questionnaire as part of the ongoing baseline Cohort Study on Substance Use Risk Factors (C-SURF). Drug use (16 illicit street drugs and 5 NMUPDs, including sleeping pills, sedatives, pain killers, antidepressants, stimulants) and mental health issues (depression, SF12) were assessed. Simple and multiple linear regressions were employed. In simple regressions, all illicit and prescription drugs were associated with poorer mental health. In multiple regressions, most of the NMUPDs, except for stimulants, were significantly associated with poorer mental health and with depression. On the contrary, the only associations that remained significant between illicit street drugs and mental health involved cannabis. NMUPD is of growing concern not only because of its increasing occurrence, but also because of its association with depression and mental health problems, which is stronger than the association observed between these problems and illicit street drug use, excepted for cannabis. Therefore, NMUPD must be considered in screening for substance use prevention purposes.

Improved earthquake response via simulation and integrated space- and ground-based technologies: the TREMOR proposal

Earthquakes occurring around the world each year cause thousands ofdeaths, millions of dollars in damage to infrastructure, and incalculablehuman suffering. In recent years, satellite technology has been asignificant boon to response efforts following an earthquake and itsafter-effects by providing mobile communications between response teamsand remote sensing of damaged areas to disaster management organizations.In 2007, an international team of students and professionals assembledduring theInternational Space University’s Summer Session Program in Beijing, Chinato examine how satellite and ground-based technology could be betterintegrated to provide an optimised response in the event of an earthquake.The resulting Technology Resources for Earthquake MOnitoring and Response(TREMOR) proposal describes an integrative prototype response system thatwill implement mobile satellite communication hubs providing telephone anddata links between response teams, onsite telemedicine consultation foremergency first-responders, and satellite navigation systems that willlocate and track emergency vehicles and guide search-and-rescue crews. Aprototype earthquake simulation system is also proposed, integratinghistorical data, earthquake precursor data, and local geomatics andinfrastructure information to predict the damage that could occur in theevent of an earthquake. The backbone of these proposals is a comprehensiveeducation and training program to help individuals, communities andgovernments prepare in advance. The TREMOR team recommends thecoordination of these efforts through a centralised, non-governmentalorganization.

Gender inequality in health among elderly people in a combined framework of socioeconomic position, family characteristics and social support

This study analyses gender inequalities in health among elderly people in Catalonia (Spain) by adopting a conceptual framework that globally considers three dimensions of health determinants : socio-economic position, family characteristics and social support. Data came from the 2006 Catalonian Health Survey. For the purposes of this study a sub-sample of people aged 65–85 years with no paid job was selected (1,113 men and 1,484 women). The health outcomes analysed were self-perceived health status, poor mental health status and long-standing limiting illness. Multiple logistic regression models separated by sex were fitted and a hierarchical model was fitted in three steps. Health status among elderly women was poorer than among the men for the three outcomes analysed. Whereas living with disabled people was positively related to the three health outcomes and confidant social support was negatively associated with all of them in both sexes, there were gender differences in other social determinants of health. Our results emphasise the importance of using an integrated approach for the analysis of health inequalities among elderly people, simultaneously considering socio-economic position, family characteristics and social support, as well as different health indicators, in order fully to understand the social determinants of the health status of older men and women.

New validated prognostic models and prognostic calculators in patients with low-grade gliomas diagnosed by central pathology review: a pooled analysis of EORTC/RTOG/NCCTG phase III clinical trials.

Background In a previous study, the European Organisation for Research and Treatment of Cancer (EORTC) reported a scoring system to predict survival of patients with low-grade gliomas (LGGs). A major issue in the diagnosis of brain tumors is the lack of agreement among pathologists. New models in patients with LGGs diagnosed by central pathology review are needed. Methods Data from 339 EORTC patients with LGGs diagnosed by central pathology review were used to develop new prognostic models for progression-free survival (PFS) and overall survival (OS). Data from 450 patients with centrally diagnosed LGGs recruited into 2 large studies conducted by North American cooperative groups were used to validate the models. Results Both PFS and OS were negatively influenced by the presence of baseline neurological deficits, a shorter time since first symptoms (<30 wk), an astrocytic tumor type, and tumors larger than 5 cm in diameter. Early irradiation improved PFS but not OS. Three risk groups have been identified (low, intermediate, and high) and validated. Conclusions We have developed new prognostic models in a more homogeneous LGG population diagnosed by central pathology review. This population better fits with modern practice, where patients are enrolled in clinical trials based on central or panel pathology review. We could validate the models in a large, external, and independent dataset. The models can divide LGG patients into 3 risk groups and provide reliable individual survival predictions. Inclusion of other clinical and molecular factors might still improve models' predictions.

A new hybrid summarizer based on vector Space model, statistical physics and linguistics

In this article we present a hybrid approach for automatic summarization of Spanish medical texts. There are a lot of systems for automatic summarization using statistics or linguistics, but only a few of them combining both techniques. Our idea is that to reach a good summary we need to use linguistic aspects of texts, but as well we should benefit of the advantages of statistical techniques. We have integrated the Cortex (Vector Space Model) and Enertex (statistical physics) systems coupled with the Yate term extractor, and the Disicosum system (linguistics). We have compared these systems and afterwards we have integrated them in a hybrid approach. Finally, we have applied this hybrid system over a corpora of medical articles and we have evaluated their performances obtaining good results.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

Effects of web-based electrocardiography simulation on strategies and learning styles

OBJECTIVETo identify the association between the use of web simulation electrocardiography and the learning approaches, strategies and styles of nursing degree students.METHODA descriptive and correlational design with a one-group pretest-posttest measurement was used. The study sample included 246 students in a Basic and Advanced Cardiac Life Support nursing class of nursing degree.RESULTSNo significant differences between genders were found in any dimension of learning styles and approaches to learning. After the introduction of web simulation electrocardiography, significant differences were found in some item scores of learning styles: theorist (p < 0.040), pragmatic (p < 0.010) and approaches to learning.CONCLUSIONThe use of a web electrocardiogram (ECG) simulation is associated with the development of active and reflexive learning styles, improving motivation and a deep approach in nursing students.

Profile of male adolescents with conduct disorder on intellectual efficacy, cognitive flexibility, cognitive coping, impulsivity and alexithymia: a comparison with high-risk controls

Background and Objectives: To specify which of the documented cognitive and emotional deficits characterize adolescents with conduct disorder (CD) compared with high-risk controls. Methods: High-risk adolescent males with and without CD were compared on intellectual efficiency, cognitive flexibility, impulsivity, alexithymia, and cognitive coping strategies. Substance use was controlled for in analyses. Results: Both groups showed normal intellectual efficiency and cognitive flexibility, as weil as heightened alexithymia and bebavioral impulsivity. Youths with CD evidenced more self-defeating and black-and-white tbinking under stress, and more acting-out under negative affect, than those without CD. Conclusions: Deficits specifie to CD resided in facets of emotional functioning and cognitive coping that might be targeted by a coping skills intervention.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Cognition, mood and fatigue in patients in the early stage of multiple sclerosis.

QUESTION UNDER STUDY: Cognitive impairment occurs during multiple sclerosis (MS) and contributes to the burden of the disease, but its effect in the initial phase of MS still needs to be better understood. METHODS: We prospectively studied 127 early MS patients presenting with a clinically isolated syndrome (CIS) or definite MS, a mean disease duration of 2.6 years, and with minor disability (mean Expanded Disability Status Scale score 1.8). Patients were tested for long-term memory, executive functions, attention, fatigue, mood disorders, functional handicap and quality of life (QoL). Twenty-one CIS patients were excluded from study as the diagnosis of MS could not be confirmed. RESULTS: Over the 106 MS patients analysed, 31 (29.3%) were cognitively impaired (23.6% for memory, 10.4% for attention and 5.7% for executive functions). Cognitive deficits were already present in CIS patients in whom the diagnosis was not yet confirmed (20%). Impaired cognition was associated with anxiety (p = 0.05), depression(p = 0.004), fatigue (p = 0.03), handicap (p <0.001) and a lower QoL (p <0.001). After adjustment for QoL, handicap, depression, anxiety and fatigue were no longer associated with the presence of cognitive deficits. CONCLUSIONS: In this well-defined early MS group one third of the patients already exhibited cognitive deficits, which were usually apparent in an effortful learning situation and were generally mild. Mood disorders, fatigue, handicap and decreased QoL were all associated with the occurrence of cognitive deficits. QoL itself appeared to take all the other factors into account. Our results confirm the existence of an interplay between cognitive, affective and functional changes and fatigue in early MS.

Genetic vulnerability factor for schizophrenia: association with glutamate cysteine ligase modifier gene and abnormal enzyme activity

Background: We previously reported in schizophrenia patients a decreased level of glutathione ([GSH]), the principal non-protein antioxidant and redox regulator, both in cerebrospinal-fluid and prefrontal cortex. To identify possible genetic causation, we studied genes involved in GSH metabolism. Methods: Genotyping: mass spectrometry analysis of polymerase chain reaction (PCR) amplified DNA fragments purified from peripheral blood. Gene expression: real-time PCR of total RNA isolated from fibroblast cultures derived from skin of patients (DSM-IV) and healthy controls (DIGS). Results: Case-control association study of single nucleotide polymorphisms (SNP) from the GSH key synthesizing enzyme glutamate-cysteine-ligase (GCL) modifier subunit (GCLM) was performed in two populations: Swiss (patients/controls: 40/31) and Danish (349/348). We found a strong association of SNP rs2301022 in GCLM gene (Danish: c2=3.2; P=0.001 after correction for multiple testing). Evidence for GCLM as a risk factor was confirmed in linkage study of NIMH families. Moreover, we observed a decrease in GCLM mRNA levels in patient fibroblasts, consistently with the association study. Interestingly, Dalton and collaborators reported in GCLM knock-out mice an increased feedback inhibition of GCL activity, resulting in 60% decrease of brain [GSH], a situation analogous to patients. These mice also exhibited an increased sensitivity to oxidative stress. Similarly, under oxidative stress conditions, GCL enzymatic activity was also decreased in patient fibroblasts. Conclusions: These results at the genetic and functional levels, combined with observations that GSH deficient models reveal morphological, electrophysiological, and behavioral anomalies analogous to those observed in patients, suggest that GCLM allelic variant is a vulnerability factor for schizophrenia.

Integrating species distribution models (SDMs) and phylogeography for two species of Alpine Primula.

The major intention of the present study was to investigate whether an approach combining the use of niche-based palaeodistribution modeling and phylo-geography would support or modify hypotheses about the Quaternary distributional history derived from phylogeographic methods alone. Our study system comprised two closely related species of Alpine Primula. We used species distribution models based on the extant distribution of the species and last glacial maximum (LGM) climate models to predict the distribution of the two species during the LGM. Phylogeographic data were generated using amplified fragment length polymorphisms (AFLPs). In Primula hirsuta, models of past distribution and phylogeographic data are partly congruent and support the hypothesis of widespread nunatak survival in the Central Alps. Species distribution models (SDMs) allowed us to differentiate between alpine regions that harbor potential nunatak areas and regions that have been colonized from other areas. SDMs revealed that diversity is a good indicator for nunataks, while rarity is a good indicator for peripheral relict populations that were not source for the recolonization of the inner Alps. In P. daonensis, palaeo-distribution models and phylogeographic data are incongruent. Besides the uncertainty inherent to this type of modeling approach (e.g., relatively coarse 1-km grain size), disagreement of models and data may partly be caused by shifts of ecological niche in both species. Nevertheless, we demonstrate that the combination of palaeo-distribution modeling with phylogeographical approaches provides a more differentiated picture of the distributional history of species and partly supports (P. hirsuta) and partly modifies (P. daonensis and P. hirsuta) hypotheses of Quaternary distributional history. Some of the refugial area indicated by palaeodistribution models could not have been identified with phylogeographic data.

Thymic stromal lymphopoietin plays divergent roles in murine models of atopic and nonatopic airway inflammation.

BACKGROUND: Thymic stromal lymphopoietin (TSLP) is a cytokine primarily produced by epithelial cells, which has been shown to be a potent inducer of T-helper 2 (Th2)-type responses. However, TSLP has pleiotropic effects upon immune cells, and although extensively studied in the context of atopic asthma, its relevance as a therapeutic target and its role in the pathogenesis of nonatopic asthma remains unknown. We sought to investigate the role of TSLP in atopic, nonatopic and viral-induced exacerbations of pulmonary inflammation. METHODS: Using stringently defined murine models of atopic, nonatopic and virally exacerbated forms of pulmonary inflammation, we compared inflammatory responses of C57BL/6 wild-type (WT) and TSLP receptor-deficient (TSLPR KO) mice. RESULTS: Thymic stromal lymphopoietin receptor (TSLPR) signaling was crucial for the development of atopic asthma. Specifically, TSLPR signaling to lung recruited CD4+ T cells enhanced eosinophilia, goblet cell hyperplasia, and overall inflammation within the airways. In contrast, the absence of TSLPR signaling was associated with strikingly exaggerated pulmonary neutrophilic inflammation in a nonatopic model of airway inflammation. The inflammation was associated with excessive levels of interleukin (IL)-17A in the lungs, indicating that TSLP negatively regulates IL-17A. In addition, in a model of influenza-induced exacerbation of atopic airway inflammation, the absence of TSLPR signaling also led to exaggerated neutrophilic inflammation. CONCLUSION: Thymic stromal lymphopoietin plays divergent roles in the pathogenesis of atopic and nonatopic asthma phenotypes by either enhancing Th2 responses or curtailing T-helper 17 responses. These findings raise important caveats for the design of therapeutic interventions targeting TSLP in asthma.