PurposeTo evaluate the clinical utility of dual energy spectral CT (DEsCT) in staging and characterizing gastric cancers.Materials and Methods96 patients suspected of gastric cancers underwent dual-phasic scans (arterial phase (AP) and portal venous phase (PP)) with DEsCT mode. Three types of images were reconstructed for analysis: conventional polychromatic images, material-decomposition images, and monochromatic image sets with photon energies from 40 to 140 keV. The polychromatic and monochromatic images were compared in TNM staging. The iodine concentrations in the lesions and lymph nodes were measured on the iodine-based material-decomposition images. These values were further normalized against that in aorta and the normalized iodine concentration (nIC) values were statistically compared. Results were correlated with pathological findings.ResultsThe overall accuracies for T, N and M staging were (81.2%, 80.0%, and 98.9%) and (73.9%, 75.0%, and 98.9%) determined with the monochromatic images and the conventional kVp images, respectively. The improvement of the accuracy in N-staging using the keV images was statistically significant (p<0.05). The nIC values between the differentiated and undifferentiated carcinoma and between metastatic and non-metastatic lymph nodes were significantly different both in AP (p = 0.02, respectively) and PP (p = 0.01, respectively). Among metastatic lymph nodes, nIC of the signet-ring cell carcinoma were significantly different from the adenocarcinoma (p = 0.02) and mucinous adenocarcinoma (p = 0.01) in PP.ConclusionThe monochromatic images obtained with DEsCT may be used to improve the N-staging accuracy. Quantitative iodine concentration measurements may be helpful for differentiating between differentiated and undifferentiated gastric carcinoma, and between metastatic and non-metastatic lymph nodes.
Aims: The aim of this study was to develop an effective method for the identification of Vibrio harveyi based on using the toxR gene as a taxonomic marker. Methods and Results: Primers for the toxR gene were designed for specificity to V. harveyi, and incorporated in a polymerase chain reaction (PCR). The results of the PCR, which took <5 h from DNA extraction to amplification, revealed positive amplification of the toxR gene fragment in 20 V. harveyi isolates including type strains, whereas DNA from 23 other Vibrionaceae type strains and 13 Vibrio parahaemolyticus strains were negative. The detection limit of the PCR was 4.0 × 103 cells ml−1. In addition, the technique enabled the recognition of V. harveyi from diseased fish. Conclusions: The PCR was specific and sensitive, enabling the identification of V. harveyi within 5 h. Significance and Impact of the Study: The PCR allowed the rapid and sensitive detection of V. harveyi.
The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label information. However, in biomedicine, obtaining labeled training data is an expensive and a laborious process. This paper proposes a semi-supervised generative adversarial networks (GANs)-based method to predict binding affinity. Our method comprises two parts, two GANs for feature extraction and a regression network for prediction. The semisupervised mechanism allows our model to learn proteins drugs features of both labeled and unlabeled data. We evaluate the performance of our method using multiple public datasets. Experimental results demonstrate that our method achieves competitive performance while utilizing freely available unlabeled data. Our results suggest that utilizing such unlabeled data can considerably help improve performance in various biomedical relation extraction processes, for example, Drug-Target interaction and protein-protein interaction, particularly when only limited labeled data are available in such tasks. To our best knowledge, this is the first semi-supervised GANs-based method to predict binding affinity.
Accurate tumor, node, and metastasis (TNM) staging, especially N staging in gastric cancer or the metastasis on lymph node diagnosis, is a popular issue in clinical medical image analysis in which gemstone spectral imaging (GSI) can provide more information to doctors than conventional computed tomography (CT) does. In this paper, we apply machine learning methods on the GSI analysis of lymph node metastasis in gastric cancer. First, we use some feature selection or metric learning methods to reduce data dimension and feature space. We then employ the K-nearest neighbor classifier to distinguish lymph node metastasis from nonlymph node metastasis. The experiment involved 38 lymph node samples in gastric cancer, showing an overall accuracy of 96.33%. Compared with that of traditional diagnostic methods, such as helical CT (sensitivity 75.2% and specificity 41.8%) and multidetector computed tomography (82.09%), the diagnostic accuracy of lymph node metastasis is high. GSI-CT can then be the optimal choice for the preoperative diagnosis of patients with gastric cancer in the N staging.
Atherosclerosis (AS), especially the vulnerable AS plaque rupture-induced acute obstructive vascular disease, is a leading cause of death. Accordingly, there is a need for an effective method to draw accurate predictions about AS progression and plaque vulnerability. Herein we report on an approach to constructing a hybrid nanoparticle system using a single-photon-emission computed tomography (SPECT)/magnetic resonance imaging (MRI) multimodal probe, aiming for a comprehensive evaluation of AS progression by achieving high sensitivity along with high resolution. Ultrasmall superparamagnetic iron oxide (USPIO) was covered by aminated poly(ethylene glycol) (PEG) and carboxylated PEG simultaneously and then functionalized with diethylenetriaminepentacetate acid for (99m)Tc coordination and subsequently Annexin V for targeting apoptotic macrophages abundant in vulnerable plaques. The in vivo accumulations of imaging probe reflected by SPECT and MRI were consistent and accurate in highlighting lesions. Intense radioactive signals detected by SPECT facilitated focus recognization and quantification, while USPIO-based T2-weighted MRI improved the focal localization and volumetry of AS plaques. For subsequent ex vivo planar images, targeting effects were further confirmed by immunohistochemistry, including CD-68 and TUNEL staining; meanwhile, the degree of concentration was proven to be statistically correlated with the Oil Red O staining results. In conclusion, these results indicated that the Annexin V-modified hybrid nanoparticle system specifically targeted the vulnerable AS plaques containing apoptotic macrophages and could be of great value in the invasively accurate detection of vulnerable plaques.
In vitro chemosensitivity assays are invaluable for assessing chemotherapeutic agents' effects on cancer cells. Yet the dose-response curves generated by those assays, usually approximated by four-parameter logistic (4PL) models, are oftentimes difficult to interpret, with no clear indication of which metric should be used to compare them. Here, five commonly used metrics, absolute and relative half-maximal inhibitory concentration (IC 50 ), area under the dose-response curve (AUC) based on trapezoidal rule and a parametric approach, and the effect at the maximal concentrations ( E max ), were compared in both simulations and reallife scenarios to evaluate their use with 4PL curves. Despite the fact that IC 50 is the most widely used metric to analyze dose-response curves, this study demonstrated that it was not the most reliable of the metrics tested. Fitted AUC showed the best overall performance in both the simulation and real-life scenarios; trapezoidal AUC showed similar performance to fitted AUC in most cases.
Owing to the moderate and strong relationship between Ktrans and clinicopathological elements, Ktrans might be the prognostic indicator of rectal cancer. Threedimensional DCE high-resolution MR imaging provides a competing opportunity to assess contrast kinetics.
The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.
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