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Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation. We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories. Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right. The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation. DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods. • Developing a novel deep-learning algorithm to differentiate different mental disorders • Using robust exact low-resolution electromagnetic tomography for source localization • Differentiating mental disorders with more than 90 % accuracy [ABSTRACT FROM AUTHOR]

Discusses the outcome of a factor analysis conducted for the Psychosocial Adjustment to Illness Scale--Self Report (PAIS-SR) on data obtained from a sample of patients who had a variety of cancer diagnoses. Seven conceptual scales of the PAIS-SR; Internal consistency; Validity; Correlations with other measures.

We studied perceptions of the importance and difficulty of coping behaviors from the perspectives of cancer patients (n = 33) and health care providers (i.e. nursing and technical personnel, n = 24). They rated the importance and difficulty of 43 behaviors that correspond to six domains of coping with cancer. Generally, ratings of importance were similar for both patients and providers. However, patients rated the domains of Coping with Treatment Related Side Effects and Seeking Support as less important than staff. Ratings of difficulty were quite divergent. Patients perceived coping as less difficult than the medical staff in the following domains: Maintenance of Activity and Independence, Coping With Treatment-Related Side-Effects, Accepting Cancer/Maintaining Positive Attitude, and Seeking Support. These differences in difficulty ratings between patients and health care providers may reflect distinct perspectives or schemas about coping. That is, patients may hold ‘positive illusions’ about their coping capacity and, therefore, rate coping as less difficult. Health care providers may overestimate patients' distress and, therefore, underestimate patients' coping capacity. An understanding of these different perspectives may assist medical staff in fostering self-efficacy expectations for coping behaviors. © 1997 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background The neurosurgical boot camp has been fully incorporated into U.S. postgraduate education. This is the first implementation of the neurosurgical boot in a developing country. To advance neurosurgical education, we developed a similar boot camp program, in collaboration with Bolivian neurosurgeons, to determine its feasibility and effectiveness in an international setting. Methods In a collective effort, the Bolivian Society for Neurosurgery, Foundation for International Education in Neurological Surgery, Solidarity Bridge, and University of Massachusetts organized and executed the first South American neurosurgical boot camp in Bolivia in 2015. Both U.S. and Bolivian faculty led didactic lectures followed by a practicum day using mannequins and simulators. South American residents and faculty were surveyed after the course to determine levels of enthusiasm and their perceived improvement in fund of knowledge and course effectiveness. Results Twenty-four neurosurgery residents from 5 South American countries participated. Average survey scores ranged between 4.2 and 4.9 out of 5. Five Bolivian neurosurgeons completed the survey with average scores of 4.5–5. This event allowed for Bolivian leaders in the field to unify around education, resulting in the formation of an institute to continue similar initiatives. Total cost was estimated at $40 000 USD; however, significant faculty, industry, and donor support helped offset this amount. Conclusion The first South American neurosurgical boot camp had significant value and was well received in Bolivia. This humanitarian model provides a sustainable solution to education needs and should be expanded to other regions as a means for standardizing the core competencies in neurosurgery. [ABSTRACT FROM AUTHOR]

The article reports on results of a study of the early intensive care unit (ICU) mobility therapy in the treatment of acute respiratory failure. A description of the experimental set-up and measurement methods is provided. The study concluded that an ICU Mobility Team using a mobility protocol initiated earlier physical therapy that was feasible, safe did not increase costs.

The Cancer Behavior Inventory (CBI), a measure of self-efficacy for coping with cancer, was revised by adding a new stress management scale and reducing its length from 43 to 33 items. The 33-item CBI was administered to 280 cancer patients. A principal factors analysis with varimax rotation yielded the hypothesized seven factors (αs in parentheses): (1) maintenance of activity and independence (α=0.86), (2) seeking and understanding medical information (α=0.88), (3) stress management (α=0.86), (4) coping with treatment-related side-effects (α=0.82), (5) accepting cancer/maintaining positive attitude (α=0.86), (6) affective regulation (α=0.81), and (7) seeking support (α=0.80). The α for the entire CBI was 0.94, the test-retest (1 week) reliability coefficient was 0.74, and correlations with measures of quality of life and coping supported its validity. The CBI may be useful to researchers and clinicians and can be integrated into a self-regulation model of coping. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Gestational diabetes mellitus (GDM) is a highly prevalent clinical entity that prospectively allows to identify a population of women with a high risk of developing type 2 diabetes mellitus (T2DM). The rate of persistent metabolic abnormalities after delivery and the evidence regarding risk factors related are limited and variable. Objectives: 1) To describe the prevalence of carbohydrate metabolism (CHM) abnormalities after childbirth in Argentine women with GDM. 2) To categorize risk factors involved in such abnormalities. Methods: During the last 5 years, 165 women with diagnosis of GDM (according to ADA criteria) were evaluated. Patients with diagnosed or suspected pregestational diabetes, clinical relevance comorbidities and those in treatment with hyperglycemic drugs were excluded. FPG and 2-hours plasma glucose during a 75-g OGTT were performed from 6 weeks postpartum. Results: In the reassessment, 106 women (64.2%) had normal parameters (NP) and 59 (35.8%) showed impared glucemic metabolism (IGM): IGT in 18, IFG in 25, both abnormalities in 13 and 3 developed T2DM. In the IGM group, 74.6% were 30 yr or older while in the NP group only 52.8% (p=0.006). In women with pathologic reevaluation, 18.5% had a history of previous GDM compared to 4.8% in the other group (p=0.005). In patients with IGM, 62.5% showed pre-pregnancy BMI ≥ 27 vs. 44.3% in those with NP (p=0,039). A logistic regression analysis was performed to identify the pregnancy factors that independently predict IGM. As expected, age ≥ 30 yr (OR 4.05, 95%CI 1.01-15.08) and previous GDM (OR 11.35, 95%CI 1.22-104.86) were associated significantly (p=0.037). Conclusions: In our population, the analysis showed a high prevalence of CHM abnormalities following GDM. We found a higher risk for IGM in patients who had a history of previous GDM, age ≥ 30 yr and BMI ≥ 27. We therefore believe it is important to categorize risk factors in order to establish preventive therapeutic actions, particularly in cases with greater vulnerability. Disclosure: J.D. Baran: None. M.I. Aranguren: None. M.X. Tapper: None. M.S. Paredes: None. M. Gentile: None. V. Grud: None. M.R. Fernandez Sanchez: None. J. Nogueira: None. H.R. Boquete: None. [ABSTRACT FROM AUTHOR]