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Somatic multicomorbidity and disability in patients with psychiatric disorders in comparison to the general population: a quasi-epidemiological investigation in 54,826 subjects from 40 countries (COMET-G study)
- Konstantinos N. Fountoulakis, Grigorios N. Karakatsoulis, Seri Abraham, Kristina Adorjan, Helal Uddin Ahmed, Renato D. Alarcón, Kiyomi Arai, Sani Salihu Auwal, Michael Berk, Sarah Bjedov, Julio Bobes, Teresa Bobes-Bascaran, Julie Bourgin-Duchesnay, Cristina Ana Bredicean, Laurynas Bukelskis, Akaki Burkadze, Indira Indiana Cabrera Abud, Ruby Castilla-Puentes, Marcelo Cetkovich, Hector Colon-Rivera, Ricardo Corral, Carla Cortez-Vergara, Piirika Crepin, Domenico De Berardis, Sergio Zamora Delgado, David De Lucena, Avinash De Sousa, Ramona Di Stefano, Seetal Dodd, Livia Priyanka Elek, Anna Elissa, Berta Erdelyi-Hamza, Gamze Erzin, Martin J. Etchevers, Peter Falkai, Adriana Farcas, Ilya Fedotov, Viktoriia Filatova, Nikolaos K. Fountoulakis, Iryna Frankova, Francesco Franza, Pedro Frias, Tatiana Galako, Cristian J. Garay, Leticia Garcia-Álvarez, Maria Paz García-Portilla, Xenia Gonda, Tomasz M. Gondek, Daniela Morera González, Hilary Gould, Paolo Grandinetti, Arturo Grau, Violeta Groudeva, Michal Hagin, Takayuki Harada, Tasdik M. Hasan, Nurul Azreen Hashim, Jan Hilbig, Sahadat Hossain, Rossitza Iakimova, Mona Ibrahim, Felicia Iftene, Yulia Ignatenko, Matias Irarrazaval, Zaliha Ismail, Jamila Ismayilova, Asaf Jakobs, Miro Jakovljević, Nenad Jakšić, Afzal Javed, Helin Yilmaz Kafali, Sagar Karia, Olga Kazakova, Doaa Khalifa, Olena Khaustova, Steve Koh, Svetlana Kopishinskaia, Korneliia Kosenko, Sotirios A. Koupidis, Illes Kovacs, Barbara Kulig, Alisha Lalljee, Justine Liewig, Abdul Majid, Evgeniia Malashonkova, Khamelia Malik, Najma Iqbal Malik, Gulay Mammadzada, Bilvesh Mandalia, Donatella Marazziti, Darko Marčinko, Stephanie Martinez, Eimantas Matiekus, Gabriela Mejia, Roha Saeed Memon, Xarah Elenne Meza Martínez, Dalia Mickevičiūtė, Roumen Milev, Muftau Mohammed, Alejandro Molina-López, Petr Morozov, Nuru Suleiman Muhammad, Filip Mustač, Mika S. Naor, Amira Nassieb, Alvydas Navickas, Tarek Okasha, Milena Pandova, Anca-Livia Panfil, Liliya Panteleeva, Ion Papava, Mikaella E. Patsali, Alexey Pavlichenko, Bojana Pejuskovic, Mariana Pinto Da Costa, Mikhail Popkov, Dina Popovic, Nor Jannah Nasution Raduan, Francisca Vargas Ramírez, Elmars Rancans, Salmi Razali, Federico Rebok, Anna Rewekant, Elena Ninoska Reyes Flores, María Teresa Rivera-Encinas, Pilar Saiz, Manuel Sánchez de Carmona, David Saucedo Martínez, Jo Anne Saw, Görkem Saygili, Patricia Schneidereit, Bhumika Shah, Tomohiro Shirasaka, Ketevan Silagadze, Satti Sitanggang, Oleg Skugarevsky, Anna Spikina, Sridevi Sira Mahalingappa, Maria Stoyanova, Anna Szczegielniak, Simona Claudia Tamasan, Giuseppe Tavormina, Maurilio Giuseppe Maria Tavormina, Pavlos N. Theodorakis, Mauricio Tohen, Eva Maria Tsapakis, Dina Tukhvatullina, Irfan Ullah, Ratnaraj Vaidya, Johann M. Vega-Dienstmaier, Jelena Vrublevska, Olivera Vukovic, Olga Vysotska, Natalia Widiasih, Anna Yashikhina, Panagiotis E. Prezerakos, Daria Smirnova
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- Journal:
- CNS Spectrums / Volume 29 / Issue 2 / April 2024
- Published online by Cambridge University Press:
- 25 January 2024, pp. 126-149
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Background
The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.
MethodsThe sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.
ResultsAbout 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15–20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.
ConclusionsThe finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.
Diffusion Wavelet Decomposition for Coarse-Graining of Polymer Chains
- B. Christopher Rinderspacher, Jaydeep P. Bardhan, Ahmed E. Ismail
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- Journal:
- MRS Online Proceedings Library Archive / Volume 1753 / 2015
- Published online by Cambridge University Press:
- 11 February 2015, mrsf14-1753-nn01-09
- Print publication:
- 2015
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Here we present an alternative approach to coarse graining, based on the multiresolution diffusion-wavelet approach to operator compression, which does not require explicit atomistic-to-coarse-grained mappings. Our diffusion-wavelet method takes as input the topology and sparsity of the molecular bonding structure of a system, and returns as output a hierarchical set of degrees of freedom (DoFs) of system-specific coarse-grained variables. Importantly, the hierarchical compression provides a clear framework for modeling at many model scales (levels), beyond the common two-level CG representation. Our results show that the resulting hierarchy separates localized modes, such as a single C-C vibrational mode, from larger-scale motions, e.g., long-range concerted backbone vibrational modes. Our approach correctly captures small-scale chemical features, such as cellulose ring structures, and alkane side chains or CH2 units, as well as large-scale features of the backbone. In particular, the new method’s finest-scale modes describe DoFs similar to united atom models and other chemically-defined CG models. Modes at coarser levels describe increasingly large connected portions of the target polymers. For polyethylene and polystyrene, spatial coordinates and their associated forces were compressed by up to two orders of magnitude. The compression in forces is of particular interest as this allows larger timesteps as well as reducing the number of DoFs.
Establishment of Uncertainty Ranges and Probability Distributions of Actinide Solubilities for Performance Assessment in the Waste Isolation Pilot Plant
- Yongliang Xiong, Jim Nowak, Laurence H. Brush, Ahmed E. Ismail, Jennifer Long
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- Journal:
- MRS Online Proceedings Library Archive / Volume 1265 / 2010
- Published online by Cambridge University Press:
- 01 February 2011, 1265-AA01-03
- Print publication:
- 2010
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The Fracture-Matrix Transport (FMT) code developed at Sandia National Laboratories solves chemical equilibrium problems using the Pitzer activity coefficient model with a database containing actinide species. The code is capable of predicting actinide solubilities at 25 °C in various ionic-strength solutions from dilute groundwaters to high-ionic-strength brines. The code uses oxidation state analogies, i.e., Am(III) is used to predict solubilities of actinides in the +III oxidation state; Th(IV) is used to predict solubilities of actinides in the +IV state; Np(V) is utilized to predict solubilities of actinides in the +V state. This code has been qualified for predicting actinide solubilities for the Waste Isolation Pilot Plant (WIPP) Compliance Certification Application in 1996, and Compliance Re-Certification Applications in 2004 and 2009.
We have established revised actinide-solubility uncertainty ranges and probability distributions for Performance Assessment (PA) by comparing actinide solubilities predicted by the FMT code with solubility data in various solutions from the open literature. The literature data used in this study include solubilities in simple solutions (NaCl, NaHCO3, Na2CO3, NaClO4, KCl, K2CO3, etc.), binary mixing solutions (NaCl+NaHCO3, NaCl+Na2CO3, KCl+K2CO3, etc.), ternary mixing solutions (NaCl+Na2CO3+KCl, NaHCO3+Na2CO3+NaClO4, etc.), and multi-component synthetic brines relevant to the WIPP.