Tehran University Medical Journal

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Background: Chronic Obstructive Pulmonary Disease (COPD) is one of the most prevalent pulmonary diseases. Use of an automatic system for the detection and diagnosis of the disease will be beneficial to the patients' treatment decision-making process. In this paper, we propose a new approach for the Computer Aided Diagnosis (CAD) of the disease and determination of its severity axial CT scan images.
Methods: In this study, 24 lung CT scans in full inspiratory and expiratory states were performed. Variations in the normalized pattern of the lungs' external parenchyma were exploited as a feature for COPD diagnosis.Subsequently, a Bayesian classifier was used to classify variations into two normal and abnormal patterns for the discrimination of patients and healthy individuals. Finally, the accuracy of the classification was assessed statistically.
Results: With the proposed method, the lungs parenchymal elasticity and air-trapping were determined quantitatively. The more this feature tended to zero, the more severe air-trapping and obstructive pulmonary disease is. By analyzing CT images in the healthy and patient groups, we calculated the hard threshold for the diagnosis of the disease. Clinical results tested by the mentioned method, suggested the effectiveness of this approach.
Conclusion: In regard to the challenges of COPD diagnosis, we propose a new computer-aided design which may be helpful to physicians for a more accurate diagnosis of the disease. Moreover, this severity scoring algorithm may be useful for targeted disease management and risk-adjustment.

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Background: Lung diseases and lung cancer are among the most dangerous diseases with high mortality in both men and women. Lung nodules are abnormal pulmonary masses and are among major lung symptoms. A Computer Aided Diagnosis (CAD) system may play an important role in accurate and early detection of lung nodules. This article presents a new CAD system for lung nodule detection from chest computed tomography (CT) images.

Methods: Twenty-five adult patients with lung nodules in their CT scan images presented to the National Research Institute of Tuberculosis and Lung Disease, Masih Daneshvari Hospital, Tehran, Iran in 2011-2012 were enrolled in the study. The patients were randomly assigned into two experimental (9 female, 6 male, mean age 43±5.63 yrs) and control (6 female, 4 male, mean age 39±4.91 yrs) groups. A fully-automatic method was developed for detecting lung nodules by employing medical image processing and analysis and statistical pattern recognition algorithms.

Results: Using segmentation methods, the lung parenchyma was extracted from 2-D CT images. Then, candidate regions were labeled in pseudo-color images. In the next step, some features of lung nodules were extracted. Finally, an artificial feed forward neural network was used for classification of nodules.

Conclusion: Considering the complexity and different shapes of lung nodules and large number of CT images to evaluate, finding lung nodules are difficult and time consuming for physicians and include human error. Experimental results showed the accuracy of the proposed method to be appropriate (P<0.05) for lung nodule detection.

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Background: Mycoplasma contamination in cell cultures is considered as a major economic, research and production problem. In this study, mycoplasma-infected Vero cell lines were treated by various dilutions of ciprofloxacin and enrofloxacin in a timely manner. Removal of mycoplasma contamination from infected cell cultures was evaluated and demonstrated by polymerase chain reaction (PCR) method. Methods: This study was done from October 2013 to May 2014, in Human Rabies Vaccine Laboratory, Pasteur Institute Production and Research Complex, Tehran, Iran. Different dilutions of ciprofloxacin and enrofloxacin were used in sequential passages for treatment of infected Vero cell line. Based on lowest passages of the cell line, antibiotic treatment with ciprofloxacin and enrofloxacin was done. Amelioration of the infection and removal of mycoplasma contamination was confirmed in each step by PCR method. The technique for order of preference by similarity to ideal solution, TOPSIS method, was used to suggest the most efficient concentration of ciprofloxacin and enrofloxacin. Results: Proposed concentration of ciprofloxacin is 20 μg/ml, and in the second order is 200 μg/ml. For enrofloxacin the best proposed concentrations are 30, 300 and 3 μg/ml respectively. Ciprofloxacin and enrofloxacin and ability of them for removal of mycoplasma and also the time of treatment were verified by evaluation of the recurrence of infection through consecutive subcultures of the treated cell line. Conclusion: Our results showed that 20 μg/ml of ciprofloxacin was the dilution of choice for mycoplasma elimination followed by 200 μg/ml of ciprofloxacin. Concentrations of 3, 30 and 300 of enrofloxacin, respectively, are appropriate for mycoplasma removal. More detailed works would be needed to verify the authenticity of the proposed simple and affordable way of mycoplasma elimination.