کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9271265 | 1220000 | 2005 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Feasibility of flow cytometry for the detection of bacteria from body fluid samples
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موضوعات مرتبط
علوم زیستی و بیوفناوری
ایمنی شناسی و میکروب شناسی
میکروبیولوژی و بیوتکنولوژی کاربردی
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چکیده انگلیسی
The detection of microorganisms in body fluids is indispensable for identifying the source of infection and is one of the important examinations that influence subsequent treatment. In order to quickly detect bacteria in body fluid samples, a flow cytometry-based experimental automated bacteria counter (BF-FCM), was tested to determine its clinical value. The results for detectability obtained with the BF-FCM were compared with those obtained by conventional culture and Gram-staining techniques. We evaluated a total of 318 body fluid samples, excluding bile samples from which fungus alone was isolated. The samples consisted of 176 bile, 64 ascites, 42 pleural fluid, and 36 cerebrospinal fluid samples. Among the 318 samples, 154 (48.4%) were culture-positive. Of these 154, the BF-FCM identified 130 as positive (sensitivity, 84.4%). Of the 164 samples that were culture-negative, 141 were negative by the BF-FCM (specificity, 86.0%). Based on the culture results, the BF-FCM detected bacteria with a positive predictive value of 85.0% (130 of 153 samples), a negative predictive value of 85.5% (141 of 165 samples), and percent agreement of 85.2%. Although there were 23/164 (14.0%) false-positive samples, we consider that the BF-FCM, in combination with Gram staining and conventional cultures, would be helpful in the diagnosis and management of patients with diseases such as bacterial meningitis that present emergently.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Infection and Chemotherapy - Volume 11, Issue 5, 2005, Pages 220-225
Journal: Journal of Infection and Chemotherapy - Volume 11, Issue 5, 2005, Pages 220-225
نویسندگان
Takashi Saito, Yoshitsugu Iinuma, Shunji Takakura, Naoko Fujihara, Satoshi Ichiyama, Junya Inoue, Yukio Hamaguchi,