Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0038454 (stroke)
 147,016 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The distribution analysis of (essential, beneficial, or toxic) metals (e.g., Cu, Fe, Zn, Pb, and others), metalloids, and non-metals in biological tissues is of key interest in life science. Over the past few years, the development and application of several imaging mass spectrometric techniques has been rapidly growing in biology and medicine. Especially, in brain research metalloproteins are in the focus of targeted therapy approaches of neurodegenerative diseases such as Alzheimer's and Parkinson's disease, or stroke, or tumor growth. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) using double-focusing sector field (LA-ICP-SFMS) or quadrupole-based mass spectrometers (LA-ICP-QMS) has been successfully applied as a powerful imaging (mapping) technique to produce quantitative images of detailed regionally specific element distributions in thin tissue sections of human or rodent brain. Imaging LA-ICP-QMS was also applied to investigate metal distributions in plant and animal sections to study, for example, the uptake and transport of nutrient and toxic elements or environmental contamination. The combination of imaging LA-ICP-MS of metals with proteomic studies using biomolecular mass spectrometry identifies metal-containing proteins and also phosphoproteins. Metal-containing proteins were imaged in a two-dimensional gel after electrophoretic separation of proteins (SDS or Blue Native PAGE). Recent progress in LA-ICP-MS imaging as a stand-alone technique and in combination with MALDI/ESI-MS for selected life science applications is summarized.
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PMID:Bioimaging of metals by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). 1955 38

The determination of the localization and distribution of essential and beneficial metals (e.g., Cu, Fe, Zn, Mn, Co, Ti, Al, Ca, K, Na, Cr and others), toxic metals (like Cd, Pb, Hg, U), metalloids (e.g., As, Se, Sb), and non-metals (such as C, S, P, Cl, I) in biological tissues is a challenging task for life science studies. Over the past few years, the development and application of mass spectrometric imaging (MSI) techniques for elements has been rapidly growing in the life sciences in order to investigate the uptake and the transport of both essential and toxic metals in plant and animal sections. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a very sensitive and efficient trace, surface, and isotopic analytical technique for biological samples. LA-ICP-MS is increasingly utilized as an elemental mass spectrometric technique using double-focusing sector field (LA-ICP-SFMS) or quadrupole mass spectrometers (LA-ICP-QMS) to produce images of detailed regionally specific element distributions in thin biological tissue sections. Nowadays, MSI studies focus on brain research for studying neurodegenerative diseases such as Alzheimer's or Parkinson's, stroke, or tumor growth, or for the imaging of cancer biomarkers in tissue sections.The combination of the mass spectrometry imaging of metals by LA-ICP-MS with proteomics using biomolecular mass spectrometry (such as MALDI-MS or ESI-MS) to identify metal-containing proteins has become an important strategy in the life sciences. Besides the quantitative imaging of metals, non-metals and metalloids in biological tissues, LA-ICP-MS has been utilized for imaging metal-containing proteins in a 2D gel after electrophoretic separation of proteins. Recent progress in applying LA-ICP-MS in life science studies will be reviewed including the imaging of thin slices of biological tissue and applications in proteome analysis in combination with MALDI/ESI-MS to analyze metal-containing proteins.
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PMID:Imaging of metals, metalloids, and non-metals by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) in biological tissues. 2068 May 84

The rapid growth in data sharing presents new opportunities across the spectrum of biomedical research. Global efforts are underway to develop practical guidance for implementation of data sharing and open data resources. These include the recent recommendation of 'FAIR Data Principles', which assert that if data is to have broad scientific value, then digital representations of that data should be Findable, Accessible, Interoperable and Reusable (FAIR). The spinal cord injury (SCI) research field has a long history of collaborative initiatives that include sharing of preclinical research models and outcome measures. In addition, new tools and resources are being developed by the SCI research community to enhance opportunities for data sharing and access. With this in mind, the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH) hosted a workshop on October 5-6, 2016 in Bethesda, MD, in collaboration with the Open Data Commons for Spinal Cord Injury (ODC-SCI) titled "Preclinical SCI Data: Creating a FAIR Share Community". Workshop invitees were nominated by the workshop steering committee (co-chairs: ARF and VPL; members: AC, KDA, MSB, KF, LBJ, PGP, JMS), to bring together junior and senior level experts including preclinical and basic SCI researchers from academia and industry, data science and bioinformatics experts, investigators with expertise in other neurological disease fields, clinical researchers, members of the SCI community, and program staff representing federal and private funding agencies. The workshop and ODC-SCI efforts were sponsored by the International Spinal Research Trust (ISRT), the Rick Hansen Institute, Wings for Life, the Craig H. Neilsen Foundation and NINDS. The number of attendees was limited to ensure active participation and feedback in small groups. The goals were to examine the current landscape for data sharing in SCI research and provide a path to its future. Below are highlights from the workshop, including perspectives on the value of data sharing in SCI research, workshop participant perspectives and concerns, descriptions of existing resources and actionable directions for further engaging the SCI research community in a model that may be applicable to many other areas of neuroscience. This manuscript is intended to share these initial findings with the broader research community, and to provide talking points for continued feedback from the SCI field, as it continues to move forward in the age of data sharing.
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PMID:Developing a data sharing community for spinal cord injury research. 2857 67