Acupuncture Anesthesia and Analgesia for Clinical Acute Pain in Japan

Acupuncture anesthesia has been practiced in China since about 1960. In Japan, Hyodo reported 30 cases of acupuncture anesthesia in 1972. However, from around 1980, the direction of acupuncture investigations turned from anesthesia to analgesia. Acupuncture analgesia is presently considered a way to activate the body's endogenous analgesic system. Recently, with the rise of acupuncture as one of the most well known CAM therapies, acupuncture or moxibustion treatment has been reported for both acute and chronic pain. Even so, few clinical reports and original articles have been reported in Japan. This review illustrates how acupuncture is being used in Japan for acute pain such as surgical operations, post- operative pain (POP), neuropathic pain, pain associated with teeth extractions and after the extraction of impacted wisdom teeth

The Origin of Highly Crystallized Face-Centered Cubic YH3 High-Pressure Phase when quenched to ambient condition

高圧相であるfcc構造のYH3が序温情圧下に回収される起源を透過型電子顕微鏡、X線回折、放射光その場観察技術により調べた。試料の急冷によって導入される欠陥が常圧相であるhcp構造への相転移を抑制していることが分かった

Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging

Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues