Abstract-We previously showed that hydrogen peroxide (H 2 O 2 ) contributes to flow-induced dilation in human coronary resistance arteries (HCRAs); however, the source of this H 2 O 2 is not known. We hypothesized that the H 2 O 2 is derived from superoxide (O 2 •Ϫ ) generated by mitochondrial respiration. HCRAs were dissected from right atrial appendages obtained from patients during cardiac surgery and cannulated with micropipettes. H 2 O 2 -derived radicals and O 2•Ϫ were detected by electron spin resonance (ESR) using BMPO as the spin trap and by histofluorescence using hydroethidine (HE, 5 mol/L) and dichlorodihydrofluorescein (DCFH, 5 mol/L). Diameter changes to increases in pressure gradients (20 and 100 cm H 2 O) were examined in the absence and the presence of rotenone (1 mol/L), myxothiazol (100 nmol/L), cyanide (1 mol/L), mitochondrial complex I, III, and IV inhibitors, respectively, and apocynin (3 mmol/L), a NADPH oxidase inhibitor. At a pressure gradient of 100 cm H 2 O, ubisemiquinone and hydroxyl radicals were detected from effluents of vessels. Including superoxide dismutase and catalase in the perfusate reduced the ESR signals. Relative ethidium and DCFH fluorescence intensities in HCRAs exposed to flow were enhanced (1.45Ϯ0.15 and 1.57Ϯ0.12, respectively compared with no-flow) and were inhibited by rotenone (0.87Ϯ0.17 and 0.95Ϯ0.07). Videomicroscopic studies showed that rotenone and myxothiazol blocked flow-induced dilation (% max. dilation at 100 cm H 2 O: rotenone, 74Ϯ3% versus 3Ϯ13%; myxothiazol, 67Ϯ3% versus 28Ϯ4%; PϽ0.05). Neither cyanide nor apocynin altered flow-induced dilation. These results suggest that shear stress induced H 2 O 2 formation, and flow-induced dilation is derived from O 2•Ϫ originating from mitochondrial respiration.

No abstract

Forensic DNA analysis of sexual assault evidence requires unambiguous differentiation of DNA profiles in mixed samples. To investigate the feasibility of magnetic bead-based separation of sperm from cell mixtures using a monoclonal antibody against MOSPD3 (motile sperm domain-containing protein 3), 30 cell samples were prepared by mixing 10(4) female buccal epithelial cells with sperm cells of varying densities (10(3), 10(4), or 10(5) cells/mL). Western blot and immunofluorescence assays showed that MOSPD3 was detectable on the membrane of sperm cells, but not in buccal epithelial cells. After biotinylated MOSPD3 antibody was incubated successively with the prepared cell mixtures and avidin-coated magnetic beads, microscopic observation revealed that each sperm cell was bound by two or more magnetic beads, in the head, neck, mid-piece, or flagellum. A full single-source short tandem repeat profile could be obtained in 80% of mixed samples containing 10(3) sperm cells/mL and in all samples containing ≥10(4) sperm cells/mL. For dried vaginal swab specimens, the rate of successful detection was 100% in both flocked and cotton swabs preserved for 1 day, 87.5% in flocked swabs and 40% in cotton swabs preserved for 3 days, and 40% in flocked swabs and 16.67% in cotton swabs preserved for 10 days. Our findings suggest that immunomagnetic bead-based separation is potentially a promising alternative to conventional methods for isolating sperm cells from mixed forensic samples.

The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC. Previous computer-aided developments have achieved certain accuracy for classification on segmented cystic lesions in CT. However, pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided framework for early differential diagnosis of pancreatic cysts without pre-segmenting the lesions using densely-connected convolutional networks (Dense-Net). The Dense-Net learns high-level features from whole abnormal pancreas and builds mappings between medical imaging appearance to different pathological types of pancreatic cysts. To enhance the clinical applicability, we integrate saliency maps in the framework to assist the physicians to understand the decision of the deep learning method. The test on a cohort of 206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has achieved an overall accuracy of 72.8%, which is significantly higher than the baseline accuracy of 48.1%, which strongly supports the clinical potential of our developed method.

The presentation of benign duodenal tumours is non-specific, with upper abdominal discomfort and upper gastrointestinal bleeding as common symptoms. Surgical resection is the preferable therapeutic choice with satisfactory prognosis.

No abstract

Although nasal extranodal natural killer/T-cell lymphoma (nasal ENKL) shares some prognostic factors with other lymphomas, seldom studies had explored the prognostic value of hemoglobin. The ENKL cases in stage I–IV during 2000 to 2015 were collected from two medical centers (group A, n = 192), and were randomly divided into the group B (n = 155) and C (n = 37). Although the significant factors identified by the univariate analysis differed between the group A and B, the multivariate Cox regression indicated the same factors. C-index of the model was slightly better than Yang’s, but its integrated Brier score (IBS) was obviously lower than Yang’s both in the group A and B. Additionally, minimal depth of random survival forest (RSF) classifier confirmed that the prognostic ability of hemoglobin was better than age both in the group A and B. In the calibration of the nomogram, the predicted 3-year or 5-year OS of our nomogram well agreed with the corresponding actual OS. In conclusion, Hemoglobin is a prognostic factor for nasal ENKL patients in stage I - IV, and integrating it into a validated prognostic nomogram, whose generalization error is the smallest among the evaluated models, can be used to predict the patients’ outcome.

Crowdsourcing services provide an easy means of acquiring labeled training data for supervised learning. However, the labels provided by a single crowd worker are often unreliable. Repeated labeling can be used to solve this problem. After multiple labels have been acquired by repeated labeling for each instance, in general consensus methods are used to obtain the integrated labels of instances. Although consensus methods are effective in practice, it cannot be denied that a level of noise still exists in the set of integrated labels. In this study, an attempt was made to employ noise filters to delete the noise in integrated labels, and consequently, enhance the training data and model quality. In fact, noise handling is a relatively mature field in the machine learning community, and many noise filters for deleting label noise have been presented in the past. However, to the best of our knowledge, in very few studies was noise filtering used to improve crowdsourcing learning. Therefore, in this study we empirically investigated the performance of noise filters in terms of improving crowdsourcing learning. Thus, in this paper some existing noise filters presented in previous papers are reviewed and their experimental application to crowdsourcing learning tasks is described. Experimental results based on 14 benchmark UCI data sets and three real-world data sets show that these noise filters can significantly reduce the noise level in integrated labels and thereby considerably enhance the performance of target classifiers.