Browsing by Author "Xu, Jun"

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    COMPARISON OF BRAIN METABOLITE CHANGES IN MANGANESE-EXPOSED WELDERS AND SMELTERS
    (Office of the Vice Chancellor for Research, 2012-04-13) Long, Zaiyang; Jiang, Yueming; Li, Xiangrong; Xu, Jun; Long, Liling; Zheng, Wei; Murdoch, James; Dydak, Ulrike
    Excessive manganese (Mn) exposure is known to cause cognitive, psychiatric and motor deficits. Mn overexposure occurs in different occupational settings, where the type and level of exposure may vary. Magnetic resonance imaging (MRI) and spectroscopy (MRS) can be used to evaluate brain Mn accumulation and to measure Mn-induced metabolite changes non-invasively. The aim of this study was to compare metabolite changes among different brain regions of welders and smelters following occupational Mn exposure. Nine Mn-exposed smelters, 14 Mn-exposed welders and 23 male matched controls were recruited from a cohort of workers from two factories in China (mean airborne Mn level: 0.227 and 0.025 mg/m3 for smelters and welders, respectively). Short-echo-time 1H MRS spectra were acquired in each subject from four volumes of interest: the frontal cortex, posterior cingulate cortex, hippocampus, and thalamus. We found that 1) in the frontal cortex, significantly decreased creatine (Cr), glutamate (Glu) and glutathione (GSH) were found in welders, whereas decreased Glu was found in smelters as compared to controls. 2) In the thalamus, reduced myo-inositol was found in both smelters and welders, while Glu and GSH were decreased in welders. These results suggest that Mn-induced brain metabolite changes may be regional in nature and more extensive in welders than in smelters. The frontal cortex seems to show a more profound change than the other brain areas tested among Mn exposed subjects. Further studies are needed to investigate the effects of exposure type and length on the mechanism of Mn neurotoxicity. (Supported by NIH/NIEHS R21 ES-017498, National Science Foundation of China Grant #81072320 and 30760210).
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    Editorial: Computational pathology for precision diagnosis, treatment, and prognosis of cancer
    (Frontiers Media, 2023-06-06) Cheng, Jun; Huang, Kun; Xu, Jun; Biostatistics and Health Data Science, School of Medicine
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    GABA and Glutamate Levels in Occlusal Splint-Wearing Males with Possible Bruxism
    (Elsevier B.V., 2015-07) Dharmadhikari, Shalmali; Romito, Laura M.; Dzemidzic, Mario; Dydak, Ulrike; Xu, Jun; Bodkin, Cynthia L.; Manchanda, Shalini; Byrd, Kenneth E.; Department of Oral Biology, IU School of Dentistry
    Objective The inhibitory neurotransmitter γ-aminobutyric acid (GABA) plays an important role in the pathophysiology of anxiety behavioural disorders such as panic disorder and post-traumatic stress disorder and is also implicated in the manifestation of tooth-grinding and clenching behaviours generally known as bruxism. In order to test whether the stress-related behaviours of tooth-grinding and clenching share similar underlying mechanisms involving GABA and other metabolites as do anxiety-related behavioural disorders, we performed a Magnetic Resonance Spectroscopy (MRS) study for accurate, in vivo metabolite quantification in anxiety-related brain regions. Design MRS was performed in the right hippocampus and right thalamus involved in the hypothalamic−pituitary−adrenal axis system, together with a motor planning region (dorsal anterior cingulate cortex/pre-supplementary motor area) and right dorsolateral prefrontal cortex (DLPFC). Eight occlusal splint-wearing men (OCS) with possible tooth-grinding and clenching behaviours and nine male controls (CON) with no such behaviour were studied. Results Repeated-measures ANOVA showed significant Group × Region interaction for GABA+ (p = 0.001) and glutamate (Glu) (p = 0.031). Between-group post hoc ANOVA showed significantly lower levels of GABA+ (p = 0.003) and higher levels of Glu (p = 0.002) in DLPFC of OCS subjects. These GABA+ and Glu group differences remained significant (GABA+, p = 0.049; Glu, p = 0.039) after the inclusion of anxiety as a covariate. Additionally, GABA and Glu levels in the DLPFC of all subjects were negatively related (Pearson's r = −0.75, p = 0.003). Conclusions These findings indicate that the oral behaviours of tooth-grinding and clenching, generally known as bruxism, may be associated with disturbances in brain GABAergic and glutamatergic systems.
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    Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
    (BioMed Central, 2016-08-26) Zhang, Yaoyun; Wu, Heng-Yi; Xu, Jun; Wang, Jingqi; Soysal, Ergin; Li, Lang; Xu, Hua; Department of Medicine, IU School of Medicine
    BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.