Frontal Assessment Battery (FAB) is a simple tool for detecting executive deficits in chronic cannabis users

Background: Cannabis is the most used illicit drug in the world, and its use has been associated with prefrontal cortex (PFC) dysfunction, including deficits in executive functions (EF). Considering that EF may influence treatment outcome, it would be interesting to have a brief neuropsychological battery to assess EF in chronic cannabis users (CCU). In the present study, the Frontal Assessment Battery (FAB), a brief, easy to use neuropsychological instrument aimed to evaluate EF, was used to evaluate cognitive functioning of CCU. Methods: We evaluated 107 abstinent CCU with the FAB and compared with 44 controls matched for age, estimated IQ, and years of education. Results: CCU performed poorly as compared to controls (FAB total score = 16.53 vs. 17.09, p .05). CCU had also a poor performance in the Motor Programming subtest (2.47 vs. 2.73, p .05). Conclusion: This study examined effects of cannabis in executive functioning and showed evidence that the FAB is sensitive to detect EF deficits in early abstinent chronic cannabis users. Clinical significance of these findings remains to be investigated in further longitudinal studies. FAB may be useful as a screening instrument to evaluate the necessity for a complete neuropsychological assessment in this population.

Introduction to Discounted Cash Flow Analysis and Financial Functions in Excel

The financial and economic analysis of investment projects is typically carried out using the technique of discounted cash flow (DCF) analysis. This module introduces concepts of discounting and DCF analysis for the derivation of project performance criteria such as net present value (NPV), internal rate of return (IRR) and benefit to cost (B/C) ratios. These concepts and criteria are introduced with respect to a simple example, for which calculations using MicroSoft Excel are demonstrated.

Suicide behavior and neuropsychological assessment of type I bipolar patients

Background: Neuropsychological deficits are often described in patients with bipolar disorder (BID). Some symptoms and/or associated characteristics of BD can be more closely associated to those cognitive impairments. We aimed to explore cognitive neuropsychological characteristics of type I bipolar patients (BPI) in terms Of lifetime Suicide attempt history. Method: We studied 39 BPI Outpatients compared with 53 healthy controls (HC) matched by age, educational and intellectual level. All Subjects were submitted to a neuropsychological assessment of executive functions, decision-making and declarative episodic memory. Results: When comparing BD1 patients, regardless of suicide attempt history or HC, we observed that bipolar patients performed worse than controls oil measures of memory, attention, executive functions and decision-making, Patients with a history of suicide attempt performed worse than non-attempters on measures of decision-making and there were a significant negative correlation between the number of suicide attempts and decision-making results (block 3 and net score). We also found significant Positive correlation between the number Of Suicide attempts and amount Of errors in Stroop Color Word Test (part 3). Limitations: The sample Studied call be considered small and a potentially confounding variable - medication status - were not controlled. Conclusion: Our results show the presence of neuropsychological deficits in memory, executive functions, attention and decision-making in BPI patients. Suicide attempts BPI scored worse than non-suicide attempt Bill oil measures of decision-making. More suicide attempts were associated with a worse decision-making process. Future research should explore the relationship between the association between this specific cognitive deficits in BPIs, serotonergic function and suicide behavior in bipolar patients as well other diagnostic groups. (C) 2009 Elsevier B.V. All rights reserved.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Some results on holomorphic generalized functions

In this note, we present three independent results within generalized complex analysis (in the Colombeau sense). The first of them deals with non-removable singularities; we construct a generalized function u on an open subset Omega of C(n), which is not a holomorphic generalized function on Omega but it is a holomorphic generalized function on Omega\S, where S is a hypersurface contained in Omega. The second result shows the existence of a holomorphic generalized function with prescribed values in the zero-set of a classical holomorphic function. The last result states the existence of a compactly supported solution to the (partial derivative) over bar operator.