Bioelectromagnetics
Maryam Sadri; Parviz Abdolmaleki; Saeed Abroun; Bahare Beiki; Fazel Samani
Volume 6, Issue 2 , June 2012, , Pages 91-98
Abstract
The Mesenchymal Stem cells derived from human newborn cords were cultured and exposed to a 24mT Static magnetic field for 24 hours. The viability percentage and the cell cycle progression was then investigated in exposed samples and the obtained results was compared with the control samples. The results ...
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The Mesenchymal Stem cells derived from human newborn cords were cultured and exposed to a 24mT Static magnetic field for 24 hours. The viability percentage and the cell cycle progression was then investigated in exposed samples and the obtained results was compared with the control samples. The results clearly demonstrated a significant reduction of cell viability due to the exposure of 24 hours of SMF and post-exposure cultures within the time frames of 36,48,60 hours. The cell development through the cell-cycle, also verified this finding, however, 72 hours of post-exposure culture, significantly leveled off the drop in viable stem cell rates.
Bioelectromagnetics
Reza Masoomi Jahandizi; Parviz Abdolmaleki; Seyed Javad Mowla
Volume 5, Issue 2 , June 2011, , Pages 105-115
Abstract
The effect of 15 and 30 mT of static magnetic field on the cell cycle of neural inductive rat BMSC was evaluated. The BMSC was inductived by neural inductive medium (NIM). Duration of inductive and Exposure time were 2, 4 and 6 hours. The cells induction to neural inductive medium associated with SMF ...
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The effect of 15 and 30 mT of static magnetic field on the cell cycle of neural inductive rat BMSC was evaluated. The BMSC was inductived by neural inductive medium (NIM). Duration of inductive and Exposure time were 2, 4 and 6 hours. The cells induction to neural inductive medium associated with SMF as exposed group, besides we have a control group. The apparatus we used to generate the SMF was a locally designed SMF generator in which there was an incubator instrument capable to maintain the humidity, temperature and CO2 concentration in predefined level. After exposing, the cells were fixed, stained and their percent of cell cycle phases; G1, S, G2/M were elucidated using flowcytometer instrument. The WinMdi 2.9 Software was used to process data from flowcytometer and elucidation of phase's percents. The results showed SMF with 15 mT intensity did not significantly alter the cell cycle in three different (2, 4 and 6 hours) exposing times. Exposing 2 hours with 30 mT increased the G2/M phases in neural inductive BMSC comparing to the corresponding control. Static magnetic field decreased the percent of S phase in BMSC, during 4 hours exposure.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Alireza Sheikh Hasani; Abbas Mostafa; Masoume Giti; Parviz Abdolmaleki
Volume -1, Issue 2 , June 2005, , Pages 117-128
Abstract
This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet ...
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This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet transform of the image is computed. Then two wavelet coefficients as well as two statistical features are used with a neural network for a primary classification of the image pixels. In the second stage, some noisy pixels extracted by the first step are eliminated. Then 18 features defined for each microcalcification are used with a nonlinear classifier for accurate detection of microcalcifications. For training of this classifier we used 16 regions from a database containing 379 microcalcifications. Finally, in the third stage five features defined for each microcalcification cluster with a neural network are used to recognize malignant microcalcification clusters. For training of this network, 22 clusters including 8 malignant and 14 benign cases were used. The performance of the algorithm was evaluated using a separate image set composed of 22 clusters including 10 malignant and 12 benign cases. Using these tests images and the threshold value of 0.45, the sensitivity of the algorithm was 100% and its specificity was 91.6%.
