Technical Note: Use of a Novel Bacterial Culture Sampling Device for Pooled Whole Blood Platelets

OBJECTIVE

Transfusion associated sepsis is a serious risk after platelet transfusion. Although platelet culture can be performed to avoid such risk, culture results are often available after transfusion due to the 4-hour shelf-life after pooling. To decrease such risk, we implemented a needleless closed system device to culture for contamination before pooling and release for transfusion.

METHODS

We customized a needleless device to permit sterile sampling of whole blood platelets without retrograde or cross-contamination. Then aliquots of platelets were injected into culture media for detection of aerobic organisms and cultured for 24 hours but released for transfusion after 12 hours of negative culture.

RESULTS

In a period of two years, we used this device in 5,741 whole blood derived pooled platelets and only 24 units tested positive (0.4%) but none of initial positive was later confirmed. There were 11 Staphylococcus and 9 Bacillus species identified. All but one of the positive units were discarded; there was no clinical impact in the patient who received the positive unit.

CONCLUSION

This device allows for sampling of whole blood derived platelets before pooling, warranting a transfusion of a culture negative unit after 12 hours of negative culture, consequently reducing transfusion of bacterially contaminated whole blood derived pooled platelets.

Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the Variance of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). According to the results, it is shown that the proposed algorithm achieves the better performance when compared with the previous results of the previous techniques. In addition, the decomposition of the images had been improved in the proposed algorithm.