A retrospective observational study of traumatic orthopaedic: related infections in Cambodia

Background: The objective of this study was to establish the type of microbiology along with antimicrobial resistance related to orthopedic related trauma infections in this area in order to help guide diagnosis and treatment regimens.Methods:This study evaluated the microbial etiology of orthopedic-related infections (ORI) between September 2015 and September 2016 in three tertiary hospitals in Phnom Penh, Cambodia. Clinical records were for clinical features and demographics. Standard laboratory bacteriology was used to recover, identified and perform antibiotic susceptibility testing (AST) by disk diffusion or broth microdilution.Results:119 patients were categorized as ORI cases. In the cases identified, median interquartile range (IQR) age was 38 (IQR: 26-46) years and 80.0% were male. Of the 119 ORI cases, a total of 156 bacterial strains were recovered, identified and after review, 128 of these pathogenic bacterial strains underwent AST. Among the gram-positive pathogens, the following susceptibilities were as follows: Staphylococcus aureus (n=57) (Methicillin-resistant S. aureus (n=35; 61.4%), (Methicillin‐sensitive S. aureus (n=22; 38.6%)), coagulase-negative staphylococcus (all MS-CoNS; n=6) and four isolates of Enterococcus sp. (non-VRE). A total of 44 gram-negative pathogens were recovered and AST was performed. Among these 44, a total of nine extended-spectrum beta-lactamase (ESBL) producing strains (20.5%) were discovered including Escherichia coli (n=8), Klebsiella pneumoniae (n=1) and carbapenemase-resistant Enterobacteriaceae (CRE) (Morganella morganii). In addition, a single E. coli isolate contained both the ESBL and CRE genotypes was noted.Conclusions:This data suggests that ORI rates in Cambodia appear to be comparable to other studies in the literature. However, further studies need to be done in order to establish definitive data related to orthopedic infections in the region

Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis

Computer-aided diagnosis schemes are being developed to assist radiologists in mammographic interpretation. In this study, the authors investigated whether texture features could be used to distinguish between mass and non-mass regions in clinical mammograms. Forty-five regions of interest (ROIs) containing true masses with various degrees of visibility and 135 ROIs containing normal breast parenchyma were extracted manually from digitized mammograms as case samples. Spatial-grey-level-dependence (SGLD) matrices of each ROI were calculated and eight texture features were calculated from the SGLD matrices. The correlation and class-distance properties of extracted texture features were analysed. Selected texture features were input into a modified decision-tree classification scheme. The performance of the classifier was evaluated for different feature combinations and orders of features on the tree. A classification accuracy of about 89% sensitivity and 76% specificity was obtained for ordered features, sum average, correlation, and energy, during the training procedure. With a leave-one-out method, the test result was about 76% sensitivity and 64% specificity. The results of this preliminary study demonstrate the feasibility of using texture information for classification of mass and normal breast tissue, which will be likely to be useful for classifying true and false detections in computer-aided diagnosis programmes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48958/2/pb941210.pd

Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

The authors studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each ROI. The importance of each feature in distinguishing masses from normal tissue was determined by stepwise linear discriminant analysis. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. The authors investigated the dependence of classification accuracy on the input features, and on the pixel distance and bit depth in the construction of the SGLD matrices. It was found that five of the texture features were important for the classification. The dependence of classification accuracy on distance and bit depth was weak for distances greater than 12 pixels and bit depths greater than seven bits. By randomly and equally dividing the data set into two groups, the classifier was trained and tested on independent data sets. The classifier achieved an average area under the ROC curve, Az, of 0.84 during training and 0.82 during testing. The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48960/2/pb950510.pd

Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network

We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48961/2/m70308.pd

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd

Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms

We have developed a computer-aided detection (CAD) system to detect clustered microcalcifications automatically on full-field digital mammograms (FFDMs) and a CAD system for screen-film mammograms (SFMs). The two systems used the same computer vision algorithms but their false positive (FP) classifiers were trained separately with sample images of each modality. In this study, we compared the performance of the CAD systems for detection of clustered microcalcifications on pairs of FFDM and SFM obtained from the same patient. For case-based performance evaluation, the FFDM CAD system achieved detection sensitivities of 70%, 80% and 90% at an average FP cluster rate of 0.07, 0.16 and 0.63 per image, compared with an average FP cluster rate of 0.15, 0.38 and 2.02 per image for the SFM CAD system. The difference was statistically significant with the alternative free-response receiver operating characteristic (AFROC) analysis. When evaluated on data sets negative for microcalcification clusters, the average FP cluster rates of the FFDM CAD system were 0.04, 0.11 and 0.33 per image at detection sensitivity level of 70%, 80% and 90% compared with an average FP cluster rate of 0.08, 0.14 and 0.50 per image for the SFM CAD system. When evaluated for malignant cases only, the difference of the performance of the two CAD systems was not statistically significant with AFROC analysis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58099/2/pmb7_4_008.pd

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134952/1/mp8389.pd

Genetic Characterization of Zika Virus Strains: Geographic Expansion of the Asian Lineage

Zika virus (ZIKV) is a mosquito-transmitted flavivirus found in both Africa and Asia. Human infection with the virus may result in a febrile illness similar to dengue fever and many other tropical infections found in these regions. Previously, little was known about the genetic relationships between ZIKV strains collected in Africa and those collected in Asia. In addition, the geographic origins of the strains responsible for the recent outbreak of human disease on Yap Island, Federated States of Micronesia, and a human case of ZIKV infection in Cambodia were unknown. Our results indicate that there are two geographically distinct lineages of ZIKV (African and Asian). The virus has circulated in Southeast Asia for at least the past 50 years, whereupon it was introduced to Yap Island resulting in an epidemic of human disease in 2007, and in 2010 was the cause of a pediatric case of ZIKV infection in Cambodia. This study also highlights the danger of ZIKV introduction into new areas and the potential for future epidemics of human disease

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci