Determining the diagnostic value of mycobacterium tuberculosis DNA in the differentiation of blood samples of patients with active pulmonary tuberculosis and healthy controls using polymerase chain reaction

Tuberculosis [TB] is now a major cause of mortality and morbidity in the world. Nowadays, different methods are used to diagnose tuberculosis. Although classical microbiological methods [such as sputum smear] are specific, they have little sensitivity and the culture is also time-consuming. Using Polymerase Chain Reaction [PCR] in blood samples in terms of Mycobacterium tuberculosis DNA, this study examines diagnostic power of this test in the diagnosis of pulmonary tuberculosis compared with other standard methods. In a cross-sectional descriptive-analytic study, blood samples were taken from 40 TB patients and 40 non-TB cases. Following DNA extraction by the commercial kit QIAGEN, the PCR assay was performed using IS6110 primer. In this study, there were 80 people in two groups of TB and non-TB cases. Each group composed of 14 men [35%] and 26 women [65%]. Sensitivity, specificity as well as positive and negative predictive values obtained 37.5, 100, 100 and 61.5%, respectively. Despite high costs of using PCR for TB diagnosis, sensitivity of this method is low due to various factors and cannot replace current standard methods for TB diagnosis such as smear and culture. It can only be used as a complementary method to confirm diagnosis in strongly suspected cases of tuberculosis

Causes of delay in diagnosis of smear-positive pulmonary tuberculosis patients referred to the tuberculosis center of Zahedan

Tuberculosis is now the major cause of mortality in the world. This study has tried to identify the factors affecting the diagnosis of this disease by determining the relationship between delay in diagnosis and factors associated with patient and health system. This research was a cross-sectional study conducted on smear positive pulmonary tuberculosis patients referred to the tuberculosis center in the first half of 2008. Required information was completed through patient records and patient interviews. A total 98 patients were studied including 42 males and 56 females. Average age of patients was 51.6 +/- 19.57. Average delay in diagnosis was 2.8 +/- 1.78 months. The average delay of patients and health system was respectively 2.6 +/- 1.76 months and 6 +/- 4.27days. Data analysis showed that there is no relationship between the delay in diagnosis and individual variables such as age, gender, occupation, etc., and examination of sputum smears at the first visit. However, there is a significant relationship between patient delay with different factors such as education [p=0.03], marital status [p=0.03], existence of hospital or medical centers in the residence [p=0.02], distance to the medical center [p=0.02] and between health system delay and residence in the city [p=0.01], distance to this medical center [p=0.03] and obtaining chest X-ray [CXR] in the first visit [p=0.003]. The results showed that with the increase of literacy, the establishment of new hospitals and health centers in remote areas and suburbs as well as chest X-ray in the first visit, the amount of delay in diagnosis can be reduced.

Comparing the performance of image enhancement methods to detect microcalcification clusters in digital mammography

Mammography is the primary imaging technique for detection and diagnosis of breast cancer; however, the contrast of a mammogram image is often poor, especially for dense and glandular tissues. In these cases the radiologist may miss some diagnostically important microcalcifications. In order to improve diagnosis of cancer correctly, image enhancement technology is often used to enhance the image and help radiologists. This paper presents a comparative study in digital mammography image enhancement based on four different algorithms: wavelet-based enhancement [Asymmetric Daubechies of order 8], Contrast-Limited Adaptive Histogram Equalization [CLAHE], morphological operators and unsharp masking. These algorithms have been tested on 114 clinical digital mammography images. The comparison for all the proposed image enhancement techniques was carried out to find out the best technique in enhancement of the mammogram images to detect microcalcifications. For evaluation of performance of image enhancement algorithms, the Contrast Improvement Index [CII] and profile intensity surface area distribution curve quality assessment have been used after any enhancement. The results of this study have shown that the average of CII is about 2.61 for wavelet and for CLAHE, unsharp masking and morphology operation are about 2.047, 1.63 and 1.315 respectively. Experimental results strongly suggest that the wavelet transformation can be more effective and improve significantly overall detection of the Computer-Aided Diagnosis [CAD] system especially for dense breast. Compare to other studies, our method achieved a higher CII