Peripheral Glutamate Receptors Are Required for Hyperalgesia Induced by Capsaicin

Transient receptor potential vanilloid1 (TRPV1) and glutamate receptors (GluRs) are located in small diameter primary afferent neurons (nociceptors), and it was speculated that glutamate released in the peripheral tissue in response to activation of TRPV1 might activate nociceptors retrogradely. But, it was not clear which types of GluRs are functioning in the nociceptive sensory transmission. In the present study, we examined the c-Fos expression in spinal cord dorsal horn following injection of drugs associated with glutamate receptors with/without capsaicin into the hindpaw. The subcutaneous injection of capsaicin or glutamate remarkably evoked c-Fos expression in ipsilateral sides of spinal cord dorsal horn. This capsaicin evoked increase of c-Fos expression was significantly prevented by concomitant administration of MK801, CNQX, and CPCCOEt. On the other hand, there were not any significant changes in coinjection of capsaicin and MCCG or MSOP. These results reveal that the activation of iGluRs and group I mGluR in peripheral afferent nerves play an important role in mechanisms whereby capsaicin evokes/maintains nociceptive responses

Tris[2-(2-pyridylimino­meth­yl)phenol­ato(0.67−)]europium(III) nitrate

The title compound, [Eu(C12H9.33N2O)3]NO3, was obtained by the reaction of Eu(NO3)·3H2O and the Schiff base ligand 2-(2-pyridylimino­meth­yl)phenol. The Eu atom is located on a threefold rotation axis and is nine-coordinated by three tridentate Schiff base ligands in a distorted tricapped trigonal-prismatic geometry. The O atom at the phenol hydr­oxy group is partially deprotonated and the H atoms are modelled with one-third occupancy according to the space group R . Offset face-to-face π–π [centroid–centroid distance = 3.886 (3) Å] and edge-to-face C—H⋯π inter­actions are found between adjacent mol­ecules. An intra­molecular O—H⋯N hydrogen bond is also present

LViT: Language meets Vision Transformer in Medical Image Segmentation

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.Comment: Accepted by IEEE Transactions on Medical Imaging (TMI

LViT: Language meets Vision Transformer in Medical Image Segmentation

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT

A role of DNA-dependent protein kinase for the activation of AMP-activated protein kinase in response to glucose deprivation

AbstractThe catalytic subunit of DNA-dependent protein kinase (DNA-PKcs) plays an essential role in double-strand break repair by initially recognizing and binding to DNA breaks. Here, we show that DNA-PKcs interacts with the regulatory γ1 subunit of AMP-activated protein kinase (AMPK), a heterotrimeric enzyme that has been proposed to function as a “fuel gauge” to monitor changes in the energy status of cells and is controlled by the upstream kinases LKB1 and Ca2+/calmodulin-dependent kinase kinase (CaMKK). In co-immunoprecipitation analyses, DNA-PKcs and AMPKγ1 interacted physically in DNA-PKcs-proficient M059K cells but not in DNA-PKcs-deficient M059J cells. Glucose deprivation-stimulated phosphorylation of AMPKα on Thr172 and of acetyl-CoA carboxylase (ACC), a downstream target of AMPK, is substantially reduced in M059J cells compared with M059K cells. The inhibition or down-regulation of DNA-PKcs by the DNA-PKcs inhibitors, wortmannin and Nu7441, or by DNA-PKcs siRNA caused a marked reduction in AMPK phosphorylation, AMPK activity, and ACC phosphorylation in response to glucose depletion in M059K, WI38, and IMR90 cells. In addition, DNA–DNA-PKcs−/− mouse embryonic fibroblasts (MEFs) exhibited decreased AMPK activation in response to glucose-free conditions. Furthermore, the knockdown of DNA-PKcs led to the suppression of AMPK (Thr172) phosphorylation in LKB1-deficient HeLa cells under glucose deprivation. Taken together, these findings support the positive regulation of AMPK activation by DNA-PKcs under glucose-deprived conditions in mammalian cells