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NEUROIMAGE (2006)
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JS Bauer et al.
OSTEOPOROSIS INTERNATIONAL (2006)
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CYJ Hsieh et al.
JOURNAL OF MANIPULATIVE AND PHYSIOLOGICAL THERAPEUTICS (2000)