Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM)

2017 ◽  
Vol 29 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Jennifer Przybyla ◽  
Molly Kile ◽  
Ellen Smit
Keyword(s):  
Mixture Modeling ◽  
Environmental Chemicals ◽  
Recursive Partition

Die Identifikation früher Veränderungsmuster in der ambulanten Psychotherapie

2007 ◽  
Vol 36 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Wolfgang Lutz ◽  
Niklaus Stulz ◽  
David W. Smart ◽  
Michael J. Lambert
Keyword(s):  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Mixture

Zusammenfassung. Theoretischer Hintergrund: Im Rahmen einer patientenorientierten Psychotherapieforschung werden Patientenausgangsmerkmale und Veränderungsmuster in einer frühen Therapiephase genutzt, um Behandlungsergebnisse und Behandlungsdauer vorherzusagen. Fragestellung: Lassen sich in frühen Therapiephasen verschiedene Muster der Veränderung (Verlaufscluster) identifizieren und durch Patientencharakteristika vorhersagen? Erlauben diese Verlaufscluster eine Vorhersage bezüglich Therapieergebnis und -dauer? Methode: Anhand des Growth Mixture Modeling Ansatzes wurden in einer Stichprobe von N = 2206 ambulanten Patienten einer US-amerikanischen Psychotherapieambulanz verschiedene latente Klassen des frühen Therapieverlaufs ermittelt und unter Berücksichtigung unterschiedlicher Patientenausgangscharakteristika als Prädiktoren der frühen Veränderungen mit dem Therapieergebnis und der Therapiedauer in Beziehung gesetzt. Ergebnisse: Für leicht, mittelschwer und schwer beeinträchtigte Patienten konnten je vier unterschiedliche Verlaufscluster mit jeweils spezifischen Prädiktoren identifiziert werden. Die Identifikation der frühen Verlaufsmuster ermöglichte weiterhin eine spezifische Vorhersage für die unterschiedlichen Verlaufscluster bezüglich des Therapieergebnisses und der Therapiedauer. Schlussfolgerungen: Frühe Psychotherapieverlaufsmuster können einen Beitrag zu einer frühzeitigen Identifikation günstiger sowie ungünstiger Therapieverläufe leisten.

A Brief Primer on Mixture Modeling

2010 ◽  
Author(s):  
Carol L. Barry ◽  
Pamela Kaliski
Keyword(s):  
Mixture Modeling

Factor mixture modeling of anxiety sensitivity: Support for the three-class solution in a Serbian sample.

2020 ◽  
Vol 32 (10) ◽  
pp. 915-927
Author(s):  
Marija Volarov ◽  
Nicholas P. Allan ◽  
Ljiljana Mihić
Keyword(s):  
Anxiety Sensitivity ◽  
Mixture Modeling ◽  
Factor Mixture Modeling

PET in patients with clear-cut multiple chemical sensitivity (MCS)

2002 ◽  
Vol 41 (06) ◽  
pp. 233-239 ◽  
Author(s):  
C. Hausteiner ◽  
A. Drzezga ◽  
P. Bartenstein ◽  
M. Schwaiger ◽  
H. Förstl ◽  
...  
Keyword(s):  
Single Photon ◽  
Photon Emission ◽  
Multiple Chemical Sensitivity ◽  
Environmental Chemicals ◽  
Chemical Sensitivity ◽  
Clear Cut ◽  
Positron Emission ◽  
Symptom Complex ◽  
Brain Changes ◽  
Multiple Chemical

SummaryAim: Multiple chemical sensitivity (MCS) is a controversially discussed symptom complex. Patients afflicted by MCS react to very low and generally nontoxic concentrations of environmental chemicals. It has been suggested that MCS leads to neurotoxic damage or neuroimmunological alteration in the brain detectable by positron emission tomography (PET) and single photon emission computer tomography (SPECT). These methods are often applied to MCS patients for diagnosis, although they never proved appropriate. Method: We scanned 12 MCS patients with PET, hypothesizing that it would reveal abnormal findings. Results: Mild glucose hypometabolism was present in one patient. In comparison with normal controls, the patient group showed no significant functional brain changes. Conclusion: This first systematic PET study in MCS patients revealed no hint of neurotoxic or neuroimmuno-logical brain changes of functional significance.

HAZARD: An Expert System for Risk Assessment of Environmental Chemicals

1987 ◽  
Vol 26 (01) ◽  
pp. 13-23 ◽  
Author(s):  
H. W. Gottinger
Keyword(s):  
Expert System ◽  
Structural Information ◽  
Chemical Carcinogenesis ◽  
Structural Features ◽  
Environmental Chemicals ◽  
Chemical Carcinogens ◽  
Carcinogenic Activity ◽  
Current State ◽  
Structure Activity ◽  
Carcinogenic Potential

AbstractThe purpose of this paper is to report on an expert system in design that screens for potential hazards from environmental chemicals on the basis of structure-activity relationships in the study of chemical carcinogenesis, particularly with respect to analyzing the current state of known structural information about chemical carcinogens and predicting the possible carcinogenicity of untested chemicals. The structure-activity tree serves as an index of known chemical structure features associated with carcinogenic activity. The basic units of the tree are the principal recognized classes of chemical carcinogens that are subdivided into subclasses known as nodes according to specific structural features that may reflect differences in carcinogenic potential among chemicals in the class. An analysis of a computerized data base of known carcinogens (knowledge base) is proposed using the structure-activity tree in order to test the validity of the tree as a classification scheme (inference engine).

The Molecular Recognition Paradigm of Environmental Chemicals with Biomacromolecules

2016 ◽  
Vol 18 (1) ◽  
pp. 41-47
Author(s):  
Wenjing Zhang ◽  
Liumeng Pan ◽  
Haifei Wang ◽  
Xuan Lv ◽  
Keke Ding
Keyword(s):  
Molecular Recognition ◽  
Environmental Chemicals
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