The Effect of Ranitidine on Olanzapine-Induced Weight Gain

Induced weight gain is a disturbing side effect of Olanzapine that affects the quality of life in psychotic patients. The aim of this study was to assess the efficacy of Ranitidine in attenuating or preventing Olanzapine-induced weight gain. A parallel 2-arm clinical trial was done on 52 patients with schizophrenia, schizoaffective and schizophreniform disorders who received Olanzapine for the first time. All these were first-episode admitted patients. They were randomly allocated to receive either Ranitidine or placebo. The trend of body mass index (BMI) was compared between groups over 16-week course of treatment. Mean weight was 62.3 (SD: 9.6) kg at baseline. Thirty-three subjects (63.5%) had positive family history of obesity. The average BMI increment was 1.1 for Ranitidine group and 2.4 for the placebo group. The multivariate analysis showed this effect to be independent of sex, family history of obesity, and baseline BMI value. The longitudinal modeling after controlling for baseline values failed to show the whole trend slope to be different. Although the slight change in trend’s slope puts forward a hypothesis that combined use of Ranitidine and Olanzapine may attenuate the weight gain long run, this needs to be retested in future larger scale long-term studies. This trial is registered with IRCT.ir 201009112181N5

Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review

Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast Machine Learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001–2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications

Characteristics of a Capable University Teacher from the Students\' Point of View

Introduction: Determining factors affecting teacher's evaluation from the students' point of view (learners) could greatly influence the quality of education. This study aims at identifying those characteristics which qulify a university teacher cabale in students' opinions. Methods: In this descriptive, cross – sectional study, in 2009-2010, 800 students of Ilam University of medical sciences were selected. Multi – stage stratified random sampling was used for sample selection. A standard questionnaire was used for data collection, and data were analyzed afterwards. Results: 68% of the students were female and 52.7% were undergraduates/PhD candidates. The mean for teaching style, interpersonal relationships, the teacher's personal characteristics and the knowledge seeking attitude of the teacher were 83.8±11.3, 83.7±13.9, 81.9±11.2 and 71.6±11.3, respectively. There was a significant relation between students' viewpoints and sex, their field of study, educational level and faculty (p<0.05). Conclusion: Teaching style, interpersonal relationships, personal characteristics and the knowledge – seeking attitude were respective the most import characteristics of a capable University teacher. Students' needs, interests and viewpoints at different educational levels are not necessarily identical therefore, it is suggested that these discrepancies be considered in educational planning

Factors Related to Marital Satisfaction in Women with Major Depressive Disorder

Objective: Major depressive disorder (MDD) is one of the most common psychiatric disorders which affects married couples frequently.The present study aims to explain the role of family processes, social support and demographic factors in marital satisfaction of women with Major Depressive Disorder (MDD).Method: In this cross-sectional study, 188 women with MDD were randomly selected among the patients who visited Bozorgmehr Clinic of Tabriz University of Medical Sciences. The sample selection was carried out through structured psychiatric interviews based on DSM-TV-TR criteria. Data were collected using Index of Marital Satisfaction (IMS), Family Process Scale (FPS) and Norbeck Social Support Questionnaire (NSSQ).The Mann Whitney U, Multivariate and ANOVA tests were used to analyze the data.Results: No relationship was observed between age, educational level, age difference of couples and number of children with family processes and marital satisfaction (p≥0.05). The patients with low educational level reported less social support (p≥0.05).Marital satisfaction and family coherence were lower when the husband had a psychiatric disorder (P≤0.01). The family processes (family coherence, problem-solving skills, communication skills and religious beliefs) and social support positively predicted marital satisfaction, while the husband's psychiatric disorders negatively predicted marital satisfaction.Conclusion: The findings highlight the significance of family processes, social support and husband's psychiatric disorders in marital satisfaction of women with MDD