An automated computerized scheme has been developed for the detection and characterization of diffuse lung diseases on high-resolution computed tomography (HRCT) images. Our database consisted of 315 HRCT images selected from 105 patients, which included normal and abnormal slices related to six different patterns, i.e., ground-glass opacities, reticular and linear opacities, nodular opacities, honeycombing, emphysematous change, and consolidation. The areas that included specific diffuse patterns in 315 HRCT images were marked by three radiologists independently on the CRT monitor in the same manner as they commonly describe in their radiologic reports. The areas with a specific pattern, which three radiologists marked independently and consistently as the same patterns, were used as "gold standard" for specific abnormal opacities in this study. The lungs were first segmented from the background in each slice by use of a morphological filter and a thresholding technique, and then divided into many contiguous regions of interest (ROIs) with a 32x32 matrix. Six physical measures which were determined in each ROI included the mean and the standard deviation of the CT value, air density components, nodular components, line components, and multilocular components. Artificial neural networks (ANNs) were employed for distinguishing between seven different patterns which included normals and six patterns associated with diffuse lung disease. The sensitivity of this computerized method for a detection of the six abnormal patterns in each ROI was 99.2% (122/123) for ground-glass opacities, 100% (15/15) for reticular and linear opacities, 88.0% (132/150) for nodular opacities, 100% (98/98) for honeycombing, 95.8% (369/385) for emphysematous change, and 100% (43/43) for consolidation. The specificity in detecting a normal ROI was 88.1% (940/1067). This computerized method may be useful in assisting radiologists in their assessment of diffuse lung disease in HRCT images.

Graphical Abstract Highlights d A method for comprehensive analysis of HTLV-1 proviruses in infected individuals d The method provides viral sequence, integration site, and degree of cell expansion d Defective proviruses are present in asymptomatic carriers and HAM/TSP, as well as ATL d Infected cells with defective proviruses proliferate more than those with intact ones Correspondence y-satou@kumamoto-u.ac.jp In Brief Katsuya et al. demonstrate that HTLV-1 DNA-capture-seq provides comprehensive information, including the entire viral sequence, integration site, and clonal abundance of infected cells.Infected clones with defective-type proviruses are present in disease states and in asymptomatic carriers, and they proliferate more than full-length proviruses. SUMMARYThe retrovirus human T-cell leukemia virus type 1 (HTLV-1) integrates into the host DNA, achieves persistent infection, and induces human diseases.Here, we demonstrate that viral DNA-capture sequencing (DNA-capture-seq) is useful to characterize HTLV-1 proviruses in naturally virus-infected individuals, providing comprehensive information about the proviral structure and the viral integration site. We analyzed peripheral blood from 98 naturally HTLV-1-infected individuals and found that defective proviruses were present not only in patients with leukemia, but also in those with other clinical entities. We further demonstrated that clones with defective-type proviruses exhibited a higher degree of clonal abundance than those with full-length proviruses. The frequency of defective-type proviruses in HTLV-1-infected humanized mice was lower than that in infected individuals, indicating that defective proviruses were rare at the initial phase of infection but preferentially selected during persistent infection. These results demonstrate the robustness of viral DNA-captureseq for HTLV-1 infection and suggest potential applications for other virus-associated cancers in humans.

Combination anti-retroviral therapy (cART) has drastically improved the clinical outcome of HIV-1 infection. Nonetheless, despite effective cART, HIV-1 persists indefinitely in infected individuals. Clonal expansion of HIV-1-infected cells in peripheral blood has been reported recently. cART is effective in stopping the retroviral replication cycle, but not in inhibiting clonal expansion of the infected host cells. Thus, the proliferation of HIV-1-infected cells may play a role in viral persistence, but little is known about the kinetics of the generation, the tissue distribution or the underlying mechanism of clonal expansion in vivo. Here we analyzed the clonality of HIV-1-infected cells using high-throughput integration site analysis in a hematopoietic stem cell-transplanted humanized mouse model. Clonally expanded, HIV-1-infected cells were detectable at two weeks post infection, their abundance increased with time, and certain clones were present in multiple organs. Expansion of HIV-1-infected clones was significantly more frequent when the provirus was integrated near host genes in specific gene ontological classes, including cell activation and chromatin regulation. These results identify potential drivers of clonal expansion of HIV-1-infected cells in vivo.

Mammography is considered the most effective method for early detection of breast cancers. However, it is difficult for radiologists to detect microcalcification clusters. Therefore, we have developed a computerized scheme for detecting early-stage microcalcification clusters in mammograms. We first developed a novel filter bank based on the concept of the Hessian matrix for classifying nodular structures and linear structures. The mammogram images were decomposed into several subimages for second difference at scales from 1 to 4 by this filter bank. The subimages for the nodular component (NC) and the subimages for the nodular and linear component (NLC) were then obtained from analysis of the Hessian matrix. Many regions of interest (ROIs) were selected from the mammogram image. In each ROI, eight features were determined from the subimages for NC at scales from 1 to 4 and the subimages for NLC at scales from 1 to 4. The Bayes discriminant function was employed for distinguishing among abnormal ROIs with a microcalcification cluster and two different types of normal ROIs without a microcalcification cluster. We evaluated the detection performance by using 600 mammograms. Our computerized scheme was shown to have the potential to detect microcalcification clusters with a clinically acceptable sensitivity and low false positives.

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