Showing 4 results for Kalvandi
Volume 3, Issue 5 (Supplementary Issue - 2014)
Abstract
Asparaginase and amylase are widely used enzymes in various industries, which can be produced by endophytic fungi. In this study, the ability of producing these two enzymes in endophytic fungi isolated from six species of Thymus has been reported for the first time in the world. Among 89 isolates of the test, 34 isolates produced asparaginase among which M24 (Fusarium subglutinans) displayed the greatest enzyme activity. Thirty three isolates showed the ability to produce amylase while the greatest enzyme activity belonged to M53 (Curvularia akaii). This study can be regarded as a preliminary work and endophytic fungi of high activity are proposed as possible resources for control of cancer in humans and for industrial applications.
Volume 9, Issue 3 (Summer 2021)
Abstract
Aims: The world hospital systems are presently facing many unprecedented challenges from COVID‐19 disease. Prediction the deteriorating or critical cases can help triage patients and assist in effective medical resource allocation. This study aimed to develop and validate a prediction model based on Machine Learning algorithms to predict hospitalized COVID-19 patients for transfer to ICU based on clinical parameters.
Materials & Methods: This retrospective, single-center study was conducted based on cumulative data of COVID-19 patients (N=1225) who were admitted from March 9, 2020, to December 20, 2020, to Mostafa Khomeini Hospital, affiliated to Ilam University of Medical Sciences (ILUMS), focal point center for COVID-19 care and treatment in Ilam, West of Iran. 13 ML techniques from six different groups applied to predict ICU admission. To evaluate the performances of models, the metrics derived from the confusion matrix were calculated. The algorithms were implemented using WEKA 3.8 software.
Findings: This retrospective study's median age was 50.9 years, and 664 (54.2%) were male. The experimental results indicate that Meta algorithms have the best performance in ICU admission risk prediction with an accuracy of 90.37%, a sensitivity of 90.35%, precision of 88.25%, F-measure of 88.35%, and ROC of 91%.
Conclusion: Machine Learning algorithms are helpful predictive tools for real-time and accurate ICU risk prediction in patients with COVID-19 at hospital admission. This model enables and potentially facilitates more responsive health systems that are beneficial to high-risk COVID-19 patients.
H. Homayounfar, R. Amiri Chayjan, H. Sarikhani, R. Kalvandi,
Volume 22, Issue 3 (4-2020)
Abstract
Lavender leaves, widely used as flavors for foods and beverages, are a rich source of phenol components and antioxidant. Drying method is of vital importance for keeping these compounds. In this study, lavender leaves were dried by means of Atmospheric Freeze (AF), Multi-Stage Semi-Industrial Continuous (MSSIC), and Near Infrared-Vacuum (NIR-Vacuum) dryers and optimized by Response Surface Methodology (RSM) for the highest drying rate, total phenol content, antioxidant capacity, and the lowest color indicators change. Lavender leaves were also dried under natural conditions as the traditional method. Multi-stage drying caused tempering phenomenon and, consequently, drying rate increased obviously. Near infrared-vacuum dryer had suitable performance on keeping the active ingredients of lavender leaves. Optimum point to dry lavender leaves in atmospheric freeze dryer was found to be -5℃. The optimum temperature points in the multi-stage semi-industrial continuous dryer were achieved to be 60, 40, and 60℃ for the first, second, and third stages, respectively. The optimum point in near infrared-vacuum dryer was 60℃ and 20 kPa for air temperature and pressure, respectively. Based on the results, among several drying methods, near infrared-vacuum dryer was the more suitable for drying lavender leaves.
Volume 28, Issue 1 (Winter 2025)
Abstract
Introduction: Diazinon is an organophosphate pesticide that has widespread applications in both agriculture and household settings. Diazinon poisoning can have detrimental effects on the cardiovascular, gastrointestinal, and central nervous systems. The aim of this study was to investigate the effects of chronic exposure to diazinon on histological features of the liver and kidney of rats.
Materials and Methods: Twenty adult male Wistar rats, aged 10 - 12 weeks and weighing 150-200 grams, were purchased and divided into two groups of 10. In the treatment group, diazinon was administered at a dose of 20 mg/kg/day to each animal for 3 months, while the control group was maintained on a normal diet and drinking water. After 12 weeks of exposure, the animals were prepared for H & E staining, and the samples were examined under a light microscope.
Findings: The structural integrity of the liver and kidney was preserved in the diazinon-treated group compared to the control group, but mild effects of diazinon were observed in the liver tissue, such as foci of inflammation and hyperplasia of Kupffer cells, and in the kidney, such as the presence of epithelial cells in the distal tubule and cell detachment from the basement membrane.
Conclusion: Based on the results of this study, the rats exposed to diazinon did not show significant changes in histological features in the liver and kidney, and these changes were subtle, possibly indicating a need for higher doses or longer exposure durations to diazinon.
