3 results
Gender, age at onset, and duration of being ill as predictors for the long-term course and outcome of schizophrenia: an international multicenter study
- Konstantinos N. Fountoulakis, Elena Dragioti, Antonis T. Theofilidis, Tobias Wiklund, Xenofon Atmatzidis, Ioannis Nimatoudis, Erik Thys, Martien Wampers, Luchezar Hranov, Trayana Hristova, Daniil Aptalidis, Roumen Milev, Felicia Iftene, Filip Spaniel, Pavel Knytl, Petra Furstova, Tiina From, Henry Karlsson, Maija Walta, Raimo K. R. Salokangas, Jean-Michel Azorin, Justine Bouniard, Julie Montant, Georg Juckel, Ida S. Haussleiter, Athanasios Douzenis, Ioannis Michopoulos, Panagiotis Ferentinos, Nikolaos Smyrnis, Leonidas Mantonakis, Zsófia Nemes, Xenia Gonda, Dora Vajda, Anita Juhasz, Amresh Shrivastava, John Waddington, Maurizio Pompili, Anna Comparelli, Valentina Corigliano, Elmars Rancans, Alvydas Navickas, Jan Hilbig, Laurynas Bukelskis, Lidija I. Stevovic, Sanja Vodopic, Oluyomi Esan, Oluremi Oladele, Christopher Osunbote, Janusz K. Rybakowski, Pawel Wojciak, Klaudia Domowicz, Maria L. Figueira, Ludgero Linhares, Joana Crawford, Anca-Livia Panfil, Daria Smirnova, Olga Izmailova, Dusica Lecic-Tosevski, Henk Temmingh, Fleur Howells, Julio Bobes, Maria P. Garcia-Portilla, Leticia García-Alvarez, Gamze Erzin, Hasan Karadağ, Avinash De Sousa, Anuja Bendre, Cyril Hoschl, Cristina Bredicean, Ion Papava, Olivera Vukovic, Bojana Pejuskovic, Vincent Russell, Loukas Athanasiadis, Anastasia Konsta, Nikolaos K. Fountoulakis, Dan Stein, Michael Berk, Olivia Dean, Rajiv Tandon, Siegfried Kasper, Marc De Hert
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- Journal:
- CNS Spectrums / Volume 27 / Issue 6 / December 2022
- Published online by Cambridge University Press:
- 09 August 2021, pp. 716-723
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Background
The aim of the current study was to explore the effect of gender, age at onset, and duration on the long-term course of schizophrenia.
MethodsTwenty-nine centers from 25 countries representing all continents participated in the study that included 2358 patients aged 37.21 ± 11.87 years with a DSM-IV or DSM-5 diagnosis of schizophrenia; the Positive and Negative Syndrome Scale as well as relevant clinicodemographic data were gathered. Analysis of variance and analysis of covariance were used, and the methodology corrected for the presence of potentially confounding effects.
ResultsThere was a 3-year later age at onset for females (P < .001) and lower rates of negative symptoms (P < .01) and higher depression/anxiety measures (P < .05) at some stages. The age at onset manifested a distribution with a single peak for both genders with a tendency of patients with younger onset having slower advancement through illness stages (P = .001). No significant effects were found concerning duration of illness.
DiscussionOur results confirmed a later onset and a possibly more benign course and outcome in females. Age at onset manifested a single peak in both genders, and surprisingly, earlier onset was related to a slower progression of the illness. No effect of duration has been detected. These results are partially in accord with the literature, but they also differ as a consequence of the different starting point of our methodology (a novel staging model), which in our opinion precluded the impact of confounding effects. Future research should focus on the therapeutic policy and implications of these results in more representative samples.
Storing, combining and analysing turkey experimental data in the Big Data era
- D. Schokker, I. N. Athanasiadis, B. Visser, R. F. Veerkamp, C. Kamphuis
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With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an ‘extract, transform and load’ (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the ‘wall time’ (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.
Modeling psychological function in patients with schizophrenia with the PANSS: an international multi-center study
- Konstantinos N. Fountoulakis, Elena Dragioti, Antonis T. Theofilidis, Tobias Wiklund, Xenofon Atmatzidis, Ioannis Nimatoudis, Erik Thys, Martien Wampers, Luchezar Hranov, Trayana Hristova, Daniil Aptalidis, Roumen Milev, Felicia Iftene, Filip Spaniel, Pavel Knytl, Petra Furstova, Tiina From, Henry Karlsson, Maija Walta, Raimo K.R. Salokangas, Jean-Michel Azorin, Justine Bouniard, Julie Montant, Georg Juckel, Ida S. Haussleiter, Athanasios Douzenis, Ioannis Michopoulos, Panagiotis Ferentinos, Nikolaos Smyrnis, Leonidas Mantonakis, Zsófia Nemes, Xenia Gonda, Dora Vajda, Anita Juhasz, Amresh Shrivastava, John Waddington, Maurizio Pompili, Anna Comparelli, Valentina Corigliano, Elmars Rancans, Alvydas Navickas, Jan Hilbig, Laurynas Bukelskis, Lidija I. Stevovic, Sanja Vodopic, Oluyomi Esan, Oluremi Oladele, Christopher Osunbote, Janusz K. Rybakowski, Pawel Wojciak, Klaudia Domowicz, Maria L. Figueira, Ludgero Linhares, Joana Crawford, Anca-Livia Panfil, Daria Smirnova, Olga Izmailova, Dusica Lecic-Tosevski, Henk Temmingh, Fleur Howells, Julio Bobes, Maria P. Garcia-Portilla, Leticia García-Alvarez, Gamze Erzin, Hasan Karadağ, Avinash De Sousa, Anuja Bendre, Cyril Hoschl, Cristina Bredicean, Ion Papava, Olivera Vukovic, Bojana Pejuskovic, Vincent Russell, Loukas Athanasiadis, Anastasia Konsta, Dan Stein, Michael Berk, Olivia Dean, Rajiv Tandon, Siegfried Kasper, Marc De Hert
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- Journal:
- CNS Spectrums / Volume 26 / Issue 3 / June 2021
- Published online by Cambridge University Press:
- 15 April 2020, pp. 290-298
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Background
The aim of the current study was to explore the changing interrelationships among clinical variables through the stages of schizophrenia in order to assemble a comprehensive and meaningful disease model.
MethodsTwenty-nine centers from 25 countries participated and included 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Multiple linear regression analysis and visual inspection of plots were performed.
ResultsThe results suggest that with progression stages, there are changing correlations among Positive and Negative Syndrome Scale factors at each stage and each factor correlates with all the others in that particular stage, in which this factor is dominant. This internal structure further supports the validity of an already proposed four stages model, with positive symptoms dominating the first stage, excitement/hostility the second, depression the third, and neurocognitive decline the last stage.
ConclusionsThe current study investigated the mental organization and functioning in patients with schizophrenia in relation to different stages of illness progression. It revealed two distinct “cores” of schizophrenia, the “Positive” and the “Negative,” while neurocognitive decline escalates during the later stages. Future research should focus on the therapeutic implications of such a model. Stopping the progress of the illness could demand to stop the succession of stages. This could be achieved not only by both halting the triggering effect of positive and negative symptoms, but also by stopping the sensitization effect on the neural pathways responsible for the development of hostility, excitement, anxiety, and depression as well as the deleterious effect on neural networks responsible for neurocognition.