A simplified but equally accurate C-13 urea breath test for Helicobacter pylori

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A combination of functional magnetic resonance imaging and diffusion tensor image to explore structure-function relationship in healthy and myelopathic spinal cord

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Exploration of functional reorganization in cervical spondylosis myelopathy: a DTI and fMRI study

Poster: abstract no. 30792INTRODUCTION: The morphological and signal change in anatomical magnetic resonance images (MRI) did not necessarily parallel with clinical symptoms in cervical spondylosis myelopathy (CSM), which poses a big challenge to clinician for early diagnosis and precise prognosis. Functional reorganization may play an important role in the pathophysiological mechanism of this chronic degenerative disease. The present study sought to explore the relationship between functional reorganization and structural damage in …postprin

Probing Dark Matter

Recent novel observations have probed the baryonic fraction of the galactic dark matter that has eluded astronomers for decades. Late in 1993, the MACHO and EROS collaborations announced in this journal the detection of transient and achromatic brightenings of a handful of stars in the Large Magellanic Cloud that are best interpreted as gravitational microlensing by low-mass foreground objects (MACHOS). This tantalized astronomers, for it implied that the population of cool, compact objects these lenses represent could be the elusive dark matter of our galactic halo. A year later in 1994, Sackett et al. reported the discovery of a red halo in the galaxy NGC 5907 that seems to follow the inferred radial distribution of its dark matter. This suggested that dwarf stars could constitute its missing component. Since NGC 5907 is similar to the Milky Way in type and radius, some surmised that the solution of the galactic dark matter problem was an abundance of ordinary low-mass stars. Now Bahcall et al., using the Wide-Field Camera of the recently repaired Hubble Space Telescope, have dashed this hope.Comment: 3 pages, Plain TeX, no figures, published as a News and Views in Nature 373, 191 (1995

An evaluation of PyloriTek test for the diagnosis of Helicobacter pylori infection in Chinese - before and after eradication therapy

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A study on the effect of COX-2 inhibitors on gastric mucosal prostaglandin synthesis

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Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches