Combining automatic speech recognition with semantic natural language processing in schizophrenia

Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly speed up the NLP research process. In this study, we assessed the performance of a state-of-the-art ASR tool and its impact on diagnostic classification accuracy based on a NLP model. We compared ASR to human transcripts quantitatively (Word Error Rate (WER)) and qualitatively by analyzing error type and position. Subsequently, we evaluated the impact of ASR on classification accuracy using semantic similarity measures. Two random forest classifiers were trained with similarity measures derived from automatic and manual transcriptions, and their performance was compared. The ASR tool had a mean WER of 30.4%. Pronouns and words in sentence-final position had the highest WERs. The classification accuracy was 76.7% (sensitivity 70%; specificity 86%) using automated transcriptions and 79.8% (sensitivity 75%; specificity 86%) for manual transcriptions. The difference in performance between the models was not significant. These findings demonstrate that using ASR for semantic analysis is associated with only a small decrease in accuracy in classifying schizophrenia, compared to manual transcripts. Thus, combining ASR technology with semantic NLP models qualifies as a robust and efficient method for diagnosing schizophrenia.</p

Molecular Characterization of Plasma HDL, LDL, and VLDL Lipids Cargos from Atherosclerotic Patients with Advanced Carotid Lesions: A Preliminary Report

Carotid atherosclerosis represents a relevant healthcare problem, since unstable plaques are responsible for approximately 15% of neurologic events, namely transient ischemic attack and stroke. Although statins treatment has proven effective in reducing LDL-cholesterol and the onset of acute clinical events, a residual risk may persist suggesting the need for the detection of reliable molecular markers useful for the identification of patients at higher risk regardless of optimal medical therapy. In this regard, several lines of evidence show a relationship among specific biologically active plasma lipids, atherosclerosis, and acute clinical events. We performed a Selected Reaction Monitoring-based High Performance Liquid Chromatography-tandem Mass Spectrometry (SRM-based HPLC-MS/MS) analysis on plasma HDL, LDL, and VLDL fractions purified, by isopycnic salt gradient ultracentrifugation, from twenty-eight patients undergoing carotid endarterectomy, having either a "hard" or a "soft" plaque, with the aim of characterizing the specific lipidomic patterns associated with features of carotid plaque instability. One hundred and thirty lipid species encompassing different lipid (sub)classes were monitored. Supervised multivariate analysis showed that lipids belonging to phosphatidylethanolamine (PE), sphingomyelin (SM), and diacylglycerol (DG) classes mostly contribute to discrimination within each lipoprotein fraction according to the plaque typology. Differential analysis evidenced a significant dysregulation of LDL PE (38:6), SM (32:1), and SM (32:2) between the two groups of patients (adj. p-value threshold = 0.05 and log(2)FC &gt;= |0.58|). Using this approach, some LDL-associated markers of plaque vulnerability have been identified, in line with the current knowledge of the key roles of these phospholipids in lipoprotein metabolism and cardiovascular disease. This proof-of-concept study reports promising results, showing that lipoprotein lipidomics may present a valuable approach for identifying new biomarkers of potential clinical relevance

Inhibition of the Exocyst Complex Attenuates the LRRK2 Pathological Effects

Pathological mutations in leucine-rich repeat kinase 2 (LRRK2) gene are the major genetic cause of Parkinson's disease (PD). Multiple lines of evidence link LRRK2 to the control of vesicle dynamics through phosphorylation of a subset of RAB proteins. However, the molecular mechanisms underlying these processes are not fully elucidated. We have previously demonstrated that LRRK2 increases the exocyst complex assembly by Sec8 interaction, one of the eight members of the exocyst complex, and that Sec8 over-expression mitigates the LRRK2 pathological effect in PC12 cells. Here, we extend this analysis using LRRK2 drosophila models and show that the LRRK2-dependent exocyst complex assembly increase is downstream of RAB phosphorylation. Moreover, exocyst complex inhibition rescues mutant LRRK2 pathogenic phenotype in cellular and drosophila models. Finally, prolonged exocyst inhibition leads to a significant reduction in the LRRK2 protein level, overall supporting the role of the exocyst complex in the LRRK2 pathway. Taken together, our study suggests that modulation of the exocyst complex may represent a novel therapeutic target for PD

Syntactic Network Analysis in Schizophrenia-Spectrum Disorders

BACKGROUND: Language anomalies are a hallmark feature of schizophrenia-spectrum disorders (SSD). Here, we used network analysis to examine possible differences in syntactic relations between patients with SSD and healthy controls. Moreover, we assessed their relationship with sociodemographic factors, psychotic symptoms, and cognitive functioning, and we evaluated whether the quantification of syntactic network measures has diagnostic value. STUDY DESIGN: Using a semi-structured interview, we collected speech samples from 63 patients with SSD and 63 controls. Per sentence, a syntactic representation (ie, parse tree) was obtained and used as input for network analysis. The resulting syntactic networks were analyzed for 11 local and global network measures, which were compared between groups using multivariate analysis of covariance, considering the effects of age, sex, and education. RESULTS: Patients with SSD and controls significantly differed on most syntactic network measures. Sex had a significant effect on syntactic measures, and there was a significant interaction between sex and group, as the anomalies in syntactic relations were most pronounced in women with SSD. Syntactic measures were correlated with negative symptoms (Positive and Negative Syndrome Scale) and cognition (Brief Assessment of Cognition in Schizophrenia). A random forest classifier based on the best set of network features distinguished patients from controls with 74% cross-validated accuracy. CONCLUSIONS: Examining syntactic relations from a network perspective revealed robust differences between patients with SSD and healthy controls, especially in women. Our results support the validity of linguistic network analysis in SSD and have the potential to be used in combination with other automated language measures as a marker for SSD.</p

Combining automatic speech recognition with semantic natural language processing in schizophrenia

Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly speed up the NLP research process. In this study, we assessed the performance of a state-of-the-art ASR tool and its impact on diagnostic classification accuracy based on a NLP model. We compared ASR to human transcripts quantitatively (Word Error Rate (WER)) and qualitatively by analyzing error type and position. Subsequently, we evaluated the impact of ASR on classification accuracy using semantic similarity measures. Two random forest classifiers were trained with similarity measures derived from automatic and manual transcriptions, and their performance was compared. The ASR tool had a mean WER of 30.4%. Pronouns and words in sentence-final position had the highest WERs. The classification accuracy was 76.7% (sensitivity 70%; specificity 86%) using automated transcriptions and 79.8% (sensitivity 75%; specificity 86%) for manual transcriptions. The difference in performance between the models was not significant. These findings demonstrate that using ASR for semantic analysis is associated with only a small decrease in accuracy in classifying schizophrenia, compared to manual transcripts. Thus, combining ASR technology with semantic NLP models qualifies as a robust and efficient method for diagnosing schizophrenia

Seroprevalence and risk factors for Toxoplasma gondii in sheep in Grosseto district, Tuscany, Italy

Abstract Background Serum samples from 630 milk sheep, in 33 dairy flocks representative of the southern area of the Tuscany region, were tested for the presence of antibodies to Toxoplasma gondii using an indirect immunofluorescence antibody test (IFAT). Questionnaires exploring the management system were completed by the veterinarian in charge of the flocks. Results At least one seropositive animal was found in 32 of the 33 flocks tested (97.0%; 95% CI: 84.2%, 99.9%). In the positive flocks, median seroprevalence was 29.4% (interquartile range: 15.9%-46.1%). Overall animal-level seroprevalence, adjusted for sampling weights and test sensitivity and specificity, was 33.3% (95% CI: 24.8%, 42.7%). In a multivariable negative binomial regression model the number of seropositive animals in a flock decreased with increasing flock size (for >400 vs. CR) = 0.62; 95% CI: 0.41, 0.95; P = 0.028) and was greater on farms where stray cats had access to animals’ water (CR = 1.54; 95% CI: 1.05, 2.26; P = 0.027). Conclusions Small flock size and access of cats to water are potential risk factors for Toxoplasma infection in sheep in the Grosseto district in Tuscany, Italy. Sheep could be an important source of T. gondii infection in humans, since we estimate that between 25% and 43% of sheep in the district were seropositive. Toxoplasmosis is also likely to be an important cause of abortion in sheep in the district. Control and prophylactic measures must be adopted to improve the rearing system and the implementation of health promoting programmes in a joint effort between sheep farmers, farmers’ associations and veterinarians to inform about the means of transmission of the infection and for a better understanding of the disease.</p

Seroprevalence and risk factors for Toxoplasma gondii in sheep in Grosseto district, Tuscany, Italy

BACKGROUND: Serum samples from 630 milk sheep, in 33 dairy flocks representative of the southern area of the Tuscany region, were tested for the presence of antibodies to Toxoplasma gondii using an indirect immunofluorescence antibody test (IFAT). Questionnaires exploring the management system were completed by the veterinarian in charge of the flocks. RESULTS: At least one seropositive animal was found in 32 of the 33 flocks tested (97.0%; 95% CI: 84.2%, 99.9%). In the positive flocks, median seroprevalence was 29.4% (interquartile range: 15.9%-46.1%). Overall animal-level seroprevalence, adjusted for sampling weights and test sensitivity and specificity, was 33.3% (95% CI: 24.8%, 42.7%). In a multivariable negative binomial regression model the number of seropositive animals in a flock decreased with increasing flock size (for >400 vs. <300 animals: count ratio (CR) = 0.62; 95% CI: 0.41, 0.95; P = 0.028) and was greater on farms where stray cats had access to animals’ water (CR = 1.54; 95% CI: 1.05, 2.26; P = 0.027). CONCLUSIONS: Small flock size and access of cats to water are potential risk factors for Toxoplasma infection in sheep in the Grosseto district in Tuscany, Italy. Sheep could be an important source of T. gondii infection in humans, since we estimate that between 25% and 43% of sheep in the district were seropositive. Toxoplasmosis is also likely to be an important cause of abortion in sheep in the district. Control and prophylactic measures must be adopted to improve the rearing system and the implementation of health promoting programmes in a joint effort between sheep farmers, farmers’ associations and veterinarians to inform about the means of transmission of the infection and for a better understanding of the disease.Additional file 1: Audit form on rearing practices from 33 farms in Grosseto district, Tuscany, Italy.The study idea was conceived by BCG. BCG, PS and AC participated in the design of the study. FV and IM participated in the acquisition of the laboratory data. AC collected serum samples with the attending veterinarians and helped administering the questionnaire. PT and VC provided previously acquired reference data. BCG and PT carried out the statistical analysis. Data interpretation was done by all authors. BCG and PT drafted the manuscript. All authors contributed to the critical revision of the manuscript for important intellectual content and have seen and approved the final draft.The authors thank all those colleagues who have helped in our on-going research projects, in particular Prof. Barend Louis Penzhorn and Dr Nada Abu Samra of the University of Pretoria, Faculty of Veterinary Science, Onderstepoort, South Africa; Dr Carlo Crotti and Dr Ludovico Renda, veterinary practitioners in Perugia, Italy, and Prof. Piergili Fioretti, University of Perugia, Italy. The authors also express sincere appreciation to members of Polyglot, Perugia, for a careful reading and comments on the article. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the University of Perugia. Within the framework of the dottorato di ricerca «Sanità animale, produzioni zootecniche e sicurezza degli alimenti – XXVIII Ciclo» this research was supported by a grant from Fondazione Cassa di Risparmio di Perugia, Italy.http://www.biomedcentral.com/1746-6148/9/25am2013ab201

Outbreak of a new measles B3 variant in the Roma/Sinti population, with transmission in the nosocomial setting, Italy, November 2015-April 2016

A measles outbreak occurred from November 2015 to April 2016 in two northern Italian regions, affecting the Roma/Sinti ethnic population and nosocomial setting. Overall, 67 cases were reported. Median age of 43 cases in three Roma/Sinti camps was four years, nosocomial cases were mainly adults. The outbreak was caused by a new measles virus B-3.1 variant. Immunisation resources and strategies should be directed at groups with gaps in vaccine coverage, e.g. Roma/Sinti and healthcare workers. Despite a national goal to eliminate measles by 2015, Italy is one of 18 European Region Member States where endemic transmission of measles has not been interrupted [1]. We report here a recent measles outbreak in northern Italy (Figure 1) affecting the Roma/Sinti ethnic population and the nosocomial setting