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Background: The link between aggression and mental disorders has been the focus of diverse studies in persons with and without intellectual disabilities (ID). Because of discrepancies in the finding of studies in persons with ID to date, and because of differences in research design, instruments used and the population studied, more research is needed. The purpose of this study was to delineate any significant association between certain psychiatric disorders and specific domains of aggressive behaviours in a large sample of persons with ID controlling for sex, age, autism and degree of ID. Method: Data from the present study were obtained from 47% of all persons with ID receiving services from New York State agencies, using the Institute for Basic Research-Modified Overt Aggression Scale (IBR-MOAS between 2006 and 2007). The IBR-MOAS was completed by the chief psychologists of 14 agencies based on information from the participants' files. Demographic information obtained included the psychiatric diagnosis made by the treating psychiatrist as well as information on age, sex and degree of ID. Data from 4069 participants were analysed. Results: Impulse control disorder and bipolar disorder were strongly associated with all five domains of aggressive behaviour in the IBR-MOAS. Psychotic disorder was highly associated with four domains except for physical aggression against self (PASLF), which was of borderline significance. Anxiety was most associated with PASLF and verbal aggression against self (VASLF); depression with VASLF; obsessive compulsive disorder with physical aggression against objects (PAOBJ); personality disorders with verbal aggression against others (VAOTH), VASLF and PASLF; and autism with physical aggression against others (PAOTH), PAOBJ and PASLF. Mild to moderate ID was associated with VAOTH and VASLF and severe to profound ID with PAOBJ and PASLF. Female sex was most associated with VASLF. Conclusions: Impulse control, mood dysregulation and perceived threat appear to underlie most of the aggressive behaviours reported. Psychosis and depression appeared to have been over-diagnosed in persons with mild to moderate ID and under-diagnosed in persons with severe and profound ID. These findings replicate and extend findings from previous studies. The pattern of associations reported can be used as helpful indicators by professionals involved in the treatment of aggressive behaviours in persons with ID.

Porous metallic materials have been widely used in many fields including aerospace, atomic energy, electro chemistry and environmental protection. Their unique structures make them very useful as lightweight structural materials, fluid filters, porous electrodes and catalyst supports. In this study, we fabricated Ni-based porous metallic glasses having uniformly dispersed micro meter pores by the sequential processes of ball-milling and chemical dissolution method. We investigated the application of our porous metal supported for Pt catalyst. The oxidation test was performed in an atmosphere of 1% CO and 3% O2. Microstructure observation was performed by using a scanning electron microscope. Oxidation properties and BET (Brunauer, Emmett, and Teller) were analyzed to understand porous structure developments. The results indicated that CO Oxidation reaction was dependent on the specific surface area.

This study was undertaken to identify psychiatric morbidity among Korean second and third elementary school children using an objective diagnostic interview tool. Data from 5,118 (2,723 boys, 2,395 girls; mean age = 8.41 years (SD = 0.66) second and third grade schoolchildren were drawn from 28 elementary schools. After initial screening assessments using the 28-item Child Problem-Behavior Screening Test for parents, 672 primary caregivers were subjected to secondary screening using the Korean version of the CBCL. Two hundred and seventy-one high-risk children selected according to the K-CBCL results, 138 (50.9%) children and their primary caregivers were interviewed using the Korean version of K-SADS-PL for objective DSM-IV psychiatric diagnoses. Sixty-four (46.4%) of the hundred and thirty eight children were determined to have at least one psychiatric disorder. More boys were diagnosed as psychiatric patients than girls (46 males, 18 females; OR = 2.21). The most frequently diagnosed disorder was ADHD (46/64; 71.9%), followed by tic disorder, anxiety disorders, mental retardation, oppositional defiant disorder, and separation anxiety disorder. Two or more comorbid psychiatric disorders were found in 22 (34.4%) of the 64 patients. The computed crude percentage of psychiatric patients in this study was 4.12%, which is similar to previously published data.

OBJECTIVE: This study aimed to define a method of classifying patients with mild cognitive impairment caused by Alzheimer’s disease by the retrieval of functional near-infrared spectroscopy (fNIRS) signal characteristics obtained during olfactory stimulation and the validation of deep learning findings. PATIENTS AND METHODS: Participants were recruited for the study from March 02 and August 30, 2021. A total of 78 participants met the criteria for categorization. The Mini-Mental State Examination and the Seoul Neuropsychological Scale were used to distinguish between patients with mild Alzheimer’s disease-related cognitive impairment and healthy controls. fNIRS data received during olfactory stimulation were used to create 1,680 time-series sample values. A total of 150 indices with a p-value ≤ 0.1 were used as deep learning features to construct the result values for 120 models accounting for all conceivable combinations of data ratios. RESULTS: For this trial, 78 participants were recruited for the original intervention trial. The average accuracy of the 120 deep-learning models for classifying patients with Alzheimer’s-related mild cognitive impairment ranged from 0.78 to 0.90. Sensitivity ranged from 0.88 to 0.96 for the 120 models, while specificity ranged from 0.86 to 0.94. The F1 scores ranged from 0.74 to 0.88. At 0.78 to 0.90, the precision and recall were equivalent. CONCLUSIONS: This trial using a deep-learning model found that the representative value extracted from the time series data of each channel could distinguish between healthy people and patients with mild cognitive impairment caused by Alzheimer’s disease. [ABSTRACT FROM AUTHOR]