PNPO-PLP axis senses prolonged hypoxia in macrophages by regulating lysosomal activity.

Oxygen is critical for all metazoan organisms on the earth and impacts various biological processes in physiological and pathological conditions. While oxygen-sensing systems inducing acute hypoxic responses, including the hypoxia-inducible factor pathway, have been identified, those operating in prolonged hypoxia remain to be elucidated. Here we show that pyridoxine 5'-phosphate oxidase (PNPO), which catalyses bioactivation of vitamin B6, serves as an oxygen sensor and regulates lysosomal activity in macrophages. Decreased PNPO activity under prolonged hypoxia reduced an active form of vitamin B6, pyridoxal 5'-phosphate (PLP), and inhibited lysosomal acidification, which in macrophages led to iron dysregulation, TET2 protein loss and delayed resolution of the inflammatory response. Among PLP-dependent metabolism, supersulfide synthesis was suppressed in prolonged hypoxia, resulting in the lysosomal inhibition and consequent proinflammatory phenotypes of macrophages. The PNPO-PLP axis creates a distinct layer of oxygen sensing that gradually shuts down PLP-dependent metabolism in response to prolonged oxygen deprivation.

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss to deal with the class-imbalanced dataset, and optimized hyperparameters in order to minimize validation loss. As expected, the resultant PraNet-based deep-learning model outperformed previously published methods. For verification, we used UWF fundus images with NPA and used Bland-Altman plots to compare estimated NPA with the ground truth in FA, which demonstrated that bias between the eNPA and ground truth was smaller than 10% of the confidence limits zone and that the number of outliers was less than 10% of observed paired images. The accuracy of the model was also tested on an external dataset from another institution, which confirmed the generalization of the model. For validation, we employed a contingency table for ROC analysis to judge the sensitivity and specificity of the estimated-NPA (eNPA). The results demonstrated that the sensitivity and specificity ranged from 83.3-87.0% and 79.3-85.7%, respectively. In conclusion, we developed an AI model capable of estimating NPA size from only an UWF image without angiography using PraNet-based deep learning. This is a potentially useful tool in monitoring eyes with ischemic retinal diseases.

Direct Capture and Amplification of Small Fragmented DNAs Using Nitrogen-Mustard-Coated Microbeads.

Circulating cell-free DNA (cfDNA) has been implicated as an important biomarker and has been intensively studied for "liquid biopsy" applications in cancer diagnostics. Owing to its small fragment size and its low concentration in circulation, cfDNA extraction and purification from serum samples are complicated, and the extraction yield affects the precision of subsequent molecular diagnostic tests. Here, we report a novel approach using nitrogen-mustard-coated DNA capture beads (NMD beads) that covalently capture DNA and allow direct subsequent polymerase chain reaction (PCR) amplification from the NMD bead without elusion. The complex DNA extraction and purification processes are not required. To illustrate the diagnostic use of the NMD beads, we detected short DNA fragments (142 bp) that were spiked into fetal bovine serum (as a model serum sample). The spiked DNAs were captured directly from serum samples and detected using real-time PCR at concentrations as low as 10 fg/mL. We anticipate that this DNA capture bead technique has the potential to simplify the preanalytical processes required for cfDNA detection, which could significantly expand the diagnostic applications of liquid biopsy.

Theoretical characterisation of strand cross-correlation in ChIP-seq.

BACKGROUND: Strand cross-correlation profiles are used for both peak calling pre-analysis and quality control (QC) in chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. Despite its potential for robust and accurate assessments of signal-to-noise ratio (S/N) because of its peak calling independence, it remains unclear what aspects of quality such strand cross-correlation profiles actually measure. RESULTS: We introduced a simple model to simulate the mapped read-density of ChIP-seq and then derived the theoretical maximum and minimum of cross-correlation coefficients between strands. The results suggest that the maximum coefficient of typical ChIP-seq samples is directly proportional to the number of total mapped reads and the square of the ratio of signal reads, and inversely proportional to the number of peaks and the length of read-enriched regions. Simulation analysis supported our results and evaluation using 790 ChIP-seq data obtained from the public database demonstrated high consistency between calculated cross-correlation coefficients and estimated coefficients based on the theoretical relations and peak calling results. In addition, we found that the mappability-bias-correction improved sensitivity, enabling differentiation of maximum coefficients from the noise level. Based on these insights, we proposed virtual S/N (VSN), a novel peak call-free metric for S/N assessment. We also developed PyMaSC, a tool to calculate strand cross-correlation and VSN efficiently. VSN achieved most consistent S/N estimation for various ChIP targets and sequencing read depths. Furthermore, we demonstrated that a combination of VSN and pre-existing peak calling results enable the estimation of the numbers of detectable peaks for posterior experiments and assess peak calling results. CONCLUSIONS: We present the first theoretical insights into the strand cross-correlation, and the results reveal the potential and the limitations of strand cross-correlation analysis. Our quality assessment framework using VSN provides peak call-independent QC and will help in the evaluation of peak call analysis in ChIP-seq experiments.

Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study.

PURPOSE: To directly compare the capability of three reconstruction methods using, respectively, forward projected model-based iterative reconstruction (FIRST), adaptive iterative dose reduction using three dimensional processing (AIDR 3D) and filter back projection (FBP) for radiation dose reduction and accuracy of computer-aided volumetry (CADv) measurements on chest CT examination in a phantom study. MATERIALS AND METHODS: An anthropomorphic thoracic phantom with 30 simulated nodules of three density types (100, -630, and -800 HU) and five different diameters was scanned with an area-detector CT at tube currents of 270, 200, 120, 80, 40, 20, and 10mA. Each scanned data set was reconstructed as thin-section CT with three methods, and all simulated nodules were measured with CADv software. For comparison of the capability for CADv at each tube current, Tukey's HSD test was used to compare the percentage of absolute measurement errors for all three reconstruction methods. Absolute percentage measurement errors were then compared by means of Dunett's test for each tube current at 270mA (standard tube current). RESULTS: Mean absolute measurement errors of AIDR 3D and FIRST methods for each nodule type were significantly lower than those of the FBP method at 20mA and 10mA (p<0.05). In addition, absolute measurement errors of the FBP method at 20mA and 10mA was significantly higher than that at 270mA for all nodule types (p<0.05). CONCLUSION: The FIRST and AIDR 3D methods are more effective than the FBP method for radiation dose reduction, while yielding better measurement accuracy of CADv for chest CT examination.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Three-way Comparison of Whole-Body MR, Coregistered Whole-Body FDG PET/MR, and Integrated Whole-Body FDG PET/CT Imaging: TNM and Stage Assessment Capability for Non-Small Cell Lung Cancer Patients.

PURPOSE: To prospectively compare the capabilities for TNM classification and assessment of clinical stage and operability among whole-body magnetic resonance (MR) imaging, coregistered positron emission tomographic (PET)/MR imaging with and without MR signal intensity (SI) assessment, and integrated fluorine 18 fluorodeoxyglucose (FDG) PET/computed tomography (CT) in non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: The institutional review board approved this study, and written informed consent was obtained from each patient. One hundred forty consecutive NSCLC patients (75 men, 65 women; mean age, 72 years) prospectively underwent whole-body MR imaging, FDG PET/CT, conventional radiologic examinations, and surgical, pathologic, and/or follow-up examinations. All factors and clinical stage and operability were then visually assessed. All PET/MR examinations were assessed with and without SI assessment. One examination used anatomic, metabolic, and relaxation-time information, and the other used only anatomic and metabolic information. κ statistics were used for assessment of all factors and clinical stages with final diagnoses. McNemar test was used to compare the capability of all methods to assess operability. RESULTS: Agreements of assessment of every factor (κ = 0.63-0.97) and clinical stage (κ = 0.65-0.90) were substantial or almost perfect. Regarding capability to assess operability, accuracy of whole-body MR imaging and PET/MR imaging with SI assessment (97.1% [136 of 140]) was significantly higher than that of MR/PET without SI assessment and integrated FDG PET/CT (85.0% [119 of 140]; P < .001). CONCLUSION: Accuracies of whole-body MR imaging and PET/MR imaging with SI assessment are superior to PET/MR without SI assessment and PET/CT for identification of TNM factor, clinical stage, and operability evaluation of NSCLC patients.

A comparison of axial versus coronal image viewing in computer-aided detection of lung nodules on CT.

PURPOSE: To compare primarily viewing axial images (Axial mode) versus coronal reconstruction images (Coronal mode) in computer-aided detection (CAD) of lung nodules on multidetector computed tomography (CT) in terms of detection performance and reading time. MATERIALS AND METHODS: Sixty CT data sets from two institutions were collected prospectively. Ten observers (6 radiologists, 4 pulmonologists) with varying degrees of experience interpreted the data sets using CAD as a second reader (performing nodule detection first without then with aid). The data sets were interpreted twice, once each for Axial and Coronal modes, in two sessions held 4 weeks apart. Jackknife free-response receiver-operating characteristic analysis was used to compare detection performances in the two modes. RESULTS: Mean figure-of-merit values with and without aid were 0.717 and 0.684 in Axial mode and 0.702 and 0.671 in Coronal mode; use of CAD significantly increased the performance of observers in both modes (P < 0.01). Mean reading times for radiologists did not significantly differ between Axial (156 ± 74 s) and Coronal mode (164 ± 69 s; P = 0.08). Mean reading times for pulmonologists were significantly lower in Coronal (112 ± 53 s) than in Axial mode (130 ± 80 s; P < 0.01). CONCLUSION: There was no statistically significant difference between Axial and Coronal modes for lung nodule detection with CAD.

Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: a comparative study.

PURPOSE: To compare the reading times and detection performances of radiologists in concurrent-reader and second-reader modes of computer-aided detection (CAD) for lung nodules on multidetector computed tomography (CT). MATERIALS AND METHODS: Fifty clinical multidetector CT datasets containing nodules up to 20mm in diameter were retrospectively collected. For the detection and rating of non-calcified nodules larger than 4mm in diameter, 6 radiologists (3 experienced radiologists and 3 resident radiologists) independently interpreted these datasets twice, once with concurrent-reader CAD and once with second-reader CAD. The reference standard of nodules in the datasets was determined by the consensus of two experienced chest radiologists. The reading times and detection performances in the two modes of CAD were statistically compared, where jackknife free-response receiver operating characteristic (JAFROC) analysis was used for the comparison of detection performances. RESULTS: Two hundreds and seven nodules constituted the reference standard. Reading time was significantly shorter in the concurrent-reader mode than in the second-reader mode, with the mean reading time for the 6 radiologists being 132s with concurrent-reader CAD and 210s with second-reader CAD (p<0.01). JAFROC analysis revealed no significant difference between the detection performances in the two modes, with the average figure-of-merit value for the 6 radiologists being 0.70 with concurrent-reader CAD and 0.72 with second-reader CAD (p=0.35). CONCLUSION: In CAD for lung nodules on multidetector CT, the concurrent-reader mode is more time-efficient than the second-reader mode, and there can be no significant difference between the two modes in terms of detection performance of radiologists.

Potential contribution of multiplanar reconstruction (MPR) to computer-aided detection of lung nodules on MDCT.

PURPOSE: To evaluate potential benefits of using multiplanar reconstruction (MPR) in computer-aided detection (CAD) of lung nodules on multidetector computed tomography (MDCT). MATERIALS AND METHODS: MDCT datasets of 60 patients with suspected lung nodules were retrospectively collected. Using "second-read" CAD, two radiologists (Readers 1 and 2) independently interpreted these datasets for the detection of non-calcified nodules (≥ 4 mm) with concomitant confidence rating. They did this task twice, first without MPR (using only axial images), and then 4 weeks later with MPR (using also coronal and sagittal MPR images), where the total reading time per dataset, including the time taken to assess the detection results of CAD software (CAD assessment time), was recorded. The total reading time and CAD assessment time without MPR and those with MPR were statistically compared for each reader. The radiologists' performance for detecting nodules without MPR and the performance with MPR were compared using jackknife free-response receiver operating characteristic (JAFROC) analysis. RESULTS: Compared to the CAD assessment time without MPR (mean, 69s and 57s for Readers 1 and 2), the CAD assessment time with MPR (mean, 46s and 45s for Readers 1 and 2) was significantly reduced (P<0.001). For Reader 1, the total reading time was also significantly shorter in the case with MPR. There was no significant difference between the detection performances without MPR and with MPR. CONCLUSION: The use of MPR has the potential to improve the workflow in CAD of lung nodules on MDCT.

US-CT 3D dual imaging by mutual display of the same sections for depicting minor changes in hepatocellular carcinoma.

The purpose of this study was to evaluate the usefulness of ultrasound-computed tomography (US-CT) 3D dual imaging for the detection of small extranodular growths of hepatocellular carcinoma (HCC). The clinical and pathological profiles of 10 patients with single nodular type HCC with extranodular growth (extranodular growth) who underwent a hepatectomy were evaluated using two-dimensional (2D) ultrasonography (US), three-dimensional (3D) US, 3D computed tomography (CT) and 3D US-CT dual images. Raw 3D data was converted to DICOM (Digital Imaging and Communication in Medicine) data using Echo to CT (Toshiba Medical Systems Corp., Tokyo, Japan), and the 3D DICOM data was directly transferred to the image analysis system (ZioM900, ZIOSOFT Inc., Tokyo, Japan). By inputting the angle number (x, y, z) of the 3D CT volume data into the ZioM900, multiplanar reconstruction (MPR) images of the 3D CT data were displayed in a manner such that they resembled the conventional US images. Eleven extranodular growths were detected pathologically in 10 cases. 2D US was capable of depicting only 2 of the 11 extranodular growths. 3D CT was capable of depicting 4 of the 11 extranodular growths. On the other hand, 3D US was capable of depicting 10 of the 11 extranodular growths, and 3D US-CT dual images, which enable the dual analysis of the CT and US planes, revealed all 11 extranodular growths. In conclusion, US-CT 3D dual imaging may be useful for the detection of small extranodular growths.

Computer-aided detection of lung nodules on multidetector row computed tomography using three-dimensional analysis of nodule candidates and their surroundings.

PURPOSE: We have been developing a computer-aided detection (CAD) system for lung nodules on multidetector row computed tomography (MDCT). The scheme for nodule detection in this system is featured by three-dimensional analysis of nodule candidates [corrected] and their surroundings, which is designed to discriminate nodules from blood vessels. The purpose of this study was to evaluate the CAD system. MATERIALS AND METHODS: MDCT images from 30 patients with lung nodules were read twice, 3 weeks apart by a chest radiologist to detect noncalcified nodules of > or = 4 mm. The first reading was without CAD, and the second reading was with CAD. Based on the reference standard later determined by another chest radiologist, the sensitivity of the former chest radiologist without or with CAD was obtained; the sensitivity and false-positive rate of the system alone were also obtained. RESULTS: The reference standard consisted of 66 nodules. The sensitivity of the chest radiologist was 77% (51/66) without CAD and 91% (60/66) with CAD, showing a significant improvement. The CAD system alone showed a sensitivity of 79% (52/66) with the false-positive rate of 4.5 per patient. CONCLUSION: Although preliminary, these results indicate the efficacy of the CAD system.

Preoperative RFA simulation for liver cancer using a CT virtual ultrasound system.

We developed a computed tomography (CT) virtual ultrasound system (CVUS) as an imaging system to support treatment under percutaneous ultrasound (US) guidance. This prototype clinical system, produced in collaboration with Tokyo Medical University, uses display software developed by Toshiba Medical Systems. We examined the utility of this system by scheduling treatment plans preoperatively and simulating puncture and radiofrequency ablation (RFA) for liver cancer. The study enrolled 51 liver cancer patients with 66 nodules 0.8-8cm in diameter in which RFA was performed between June 2004 and December 2004. Virtual US and multiplanar reconstruction (MPR) images were constructed on the basis of DICOM CT data and puncture and ablation of liver cancer were simulated. The following were evaluated: (1) how to avoid complications and determine an appropriate puncture route by simulating puncture with C-mode MPR images; (2) determination of the three-dimensional location of the tumor for ablation, as well as the adjacent organs and vessels, by MPR rotation 360 degrees around the center of the tumor (center lock); and (3) how to determine the center and volume of ablation and avoid injuries to nearby organs and vessels by simulating ablation procedures. C -mode MPR images were effective for (1) determining and modifying the puncture route in 35 of 51 cases (69.6%) and (2) determining the spatial location of vessels and nearby organs in 50 of 51 cases (98.0%) by the center lock; and (3) simulating the ablation helped determine the center and volume of ablation by avoiding injuries to vessels and nearby organs in 45 or 51 cases (88.2%). Taken together, the CVUS allowed easy simulation of local treatment of liver cancer under US guidance using CT data alone and the preoperative simulation predicted an improvement in the safety of local therapy of liver cancer.

Vascular flow patterns of hepatic tumors in contrast-enhanced 3-dimensional fusion ultrasonography using plane shift and opacity control modes.

OBJECTIVE: This study was conducted to determine whether contrast-enhanced 3-dimensional (3D) fusion ultrasonography with combined use of the plane shift and opacity control modes can serve as a useful tool for identifying the vascular characteristics of hepatic tumors in 3D perspective. METHODS: Contrast-enhanced 3D fusion ultrasonography was performed in 31 patients with hepatic tumors after the intravenous injection of contrast media. The shapes, courses, and distributions of tumor vessels, displayed in 3D perspective, were examined to identify the characteristic vascular flow patterns of various tumors. Images before and after contrast media administration were compared for evaluation of the effectiveness of contrast enhancement with regard to tumor diameter for various types of tumors. The images were interpreted in a blinded manner by 3 physicians specializing in abdominal ultrasonography, and vascular flow patterns were determined on the basis of consensus. RESULTS: The characteristic vascular flow patterns observed in hepatocellular carcinomas are reticular flow within the tumor and flow showing a ringlike distribution surrounding the tumor. Metastatic tumors show a vascular flow pattern running between adjacent tumor nodules, and hemangiomas show localized nodular flow at the tumor margins. Contrast enhancement was found to be useful for showing the vascular flow patterns in small hepatocellular carcinomas and hemangiomas. CONCLUSIONS: Contrast-enhanced 3D fusion ultrasonography makes it possible to display images combining the plane shift and opacity control modes to show tumor vessels, including minute vascular flow within hepatic tumors, in 3D perspective and to identify tumor-specific vascular flow patterns.