Researchers at UCLA have discovered that the levels of interleukin-8 (IL-8) protein in the saliva of healthy individuals and patients with oropharyngeal squamous cell carcinoma (OSCC) are 30 pM and 86 pM, respectively. In this study, we present the development of the first immunoassay for the quantification of picomolar IL-8 concentrations in human saliva using Biacore surface plasmon resonance (SPR) in a microfluidic channel. A sandwich assay using two monoclonal antibodies, which recognize different epitopes on the antigen (IL-8), was used. Only 13 minutes were required to determine the quantity of pure IL-8 added to just 100 microL of either buffer or saliva-based samples. The limit of detection (LOD) of this immunoassay in buffer was 2.5 pM, and the precision of the response for each concentration was <3% of the coefficient of variation. When first analyzing the saliva supernatants, non-specific binding to the surface was observed. By adding carboxymethyl dextran sodium salt (10 mg mL(-1)) to compete with the surface dextran and primary antibody for non-specific interactions, the signal to noise ratio was greatly improved. The LOD of this immunoassay in saliva was 184 pM. A minimum concentration of 250 pM of exogenous IL-8 could then be consistently detected in a salivary environment. The precision of the response for each IL-8 concentration tested was <7% of the coefficient of variation. Diagnostic sensitivity for oral cancer can be achieved by pre-concentrating the saliva samples 10 fold prior to SPR analysis, making the target levels of IL-8 300 pM for healthy individuals and 860 pM for oral cancer patients.
Stroke-prone spontaneously hypertensive rats (SHRSP) induce spontaneous osteoporosis. To elucidate the specific characteristics of bone metabolism, the SHRSP was compared with age matched Wistar-Kyoto (WKY) rats. We investigated the effects of prolonged swimming exercise training on bone mineral density (BMD) and metabolism in the SHRSP. Seven-week-old male SHRSP and WKY were divided into three groups; the sedentary control WKY group (n=6, WKY), the sedentary control SHRSP group (n=6, SP) and the swimming exercise training SHRSP group (n=6, SWIM) (in pool with 60 min./day, 5 days/week for 12 weeks). The femoral BMD, bone mineral content (BMC), strength, Ca and P contents (%) of SHRSP were approximately 17, 27, 25, 20 and 9%, respectively, lower than that of WKY (p<0.001). Serum alkaline phosphatase (AlP) had not changed between both of SP and WKY, but tartrate-resistant acid phosphatase (TrAcP) of SP approximately 3-fold higher than that of WKY (p<0.05). Both serum calcium (Ca) and intact parathyroid hormone (i-PTH) were similar between SP and WKY. However, serum phosphate (P) of SP was approximately 18% lower than that of WKY (N.S.). These results suggested that SHRSP induces osteopenia by the bone turnover of the promoted osteoclast activity with disturbed phosphate homeostasis. On the other hand, the femoral BMD and strength were approximately 7% and 20%, respectively, decreased in the SWIM (p<0.001), and f e mo ra l b on e C a an d P c o n t e n t s ( %) w e re a ls o approximately 11% and 14%, respectively, lower than that of SP (p<0.001). There were no significant difference between SWIM and SP on serum Ca, but serum P of SWIM was significantly lower than that of SP (p<0.05). These results suggested that the prolonged swimming exercise t r a i n i n g i n t h e S H R S P i n d u c e s m o r e c r u e l l y hypophosphatemia, and leading to osteopenia eventually. We conclude that SHRSP induces osteopenia with disturbance of phosphate homeostasis, and the prolonged swimming exercise in the SHRSP might deteriorate hypophosphatemia and osteopenia.
In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: 1) solving a submodular function for selecting object-like segments, and 2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video. We present an iterative update scheme between two tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets show that the proposed method performs favorably against the state-of-the-art algorithms.
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