Currently available studies have suggested that a number of exosome-encapsulated microRNAs (miRNAs) are recognized as stable biomarkers for cancers. However, little is known about the effect of exosomal miRNAs on colorectal cancer (CRC). The aim of study is to identify specific miRNAs in serum exosomes, which may serve as potential diagnostic and prognostic biomarkers and therapeutic targets for CRC. Microarray analyses of miRNAs in serum exosomes from 3 primary CRC patients and 3 healthy controls were performed. Those differentially expressed exosome-encapsulated miRNAs were verified in exosome-enriched serum samples from 77 CRC patients and 20 healthy controls by quantitative real-time PCR (qRT-PCR). A total of 39 aberrantly expressed miRNAs in serum exosomes were identified by microarray analysis. After confirmation by qRT-PCR, we found that 5 exosome-encapsulated miRNAs (miR-638, miR-5787, miR-8075, miR-6869-5p and miR-548c-5p) were significantly down-regulated, while 2 exosome-encapsulated miRNAs (miR-486-5p and miR-3180-5p) were significantly up-regulated in serum. Decreased levels of miR-638 in serum exosomes were associated with increased risk of liver metastasis and later TNM stage of CRC. Networks analyses revealed that 5 aberrantly expressed miRNAs (miR-638, miR-5787, miR-8075, miR-6869-5p, and miR-548c-5p) might be involved in the process of glucose metabolism in CRC. The present study shows the specific serum profile of exosome-encapsulated miRNAs in CRC. Those specific miRNAs in serum exosomes may serve as disease biomarkers and novel therapeutic targets for CRC.
Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = −0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.
Coalbed gas content is the most important parameter for forecasting and preventing the occurrence of coal and gas outburst. However, existing methods have difficulty obtaining the coalbed gas content accurately. In this study, a numerical calculation model for the rapid estimation of coal seam gas content was established based on the characteristic values of gas desorption at specific exposure times. Combined with technical verification, a new method which avoids the calculation of gas loss for the rapid estimation of gas content in the coal seam was investigated. Study results show that the balanced adsorption gas pressure and coal gas desorption characteristic coefficient (Kt) satisfy the exponential equation, and the gas content and Kt are linear equations. The correlation coefficient of the fitting equation gradually decreases as the exposure time of the coal sample increases. Using the new method to measure and calculate the gas content of coal samples at two different working faces of the Lubanshan North mine (LBS), the deviation of the calculated coal sample gas content ranged from 0.32% to 8.84%, with an average of only 4.49%. Therefore, the new method meets the needs of field engineering technology.
Bone mineral density (BMD) is a clinically critical indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Due to the limited accessibility of DEXA machines and examinations, osteoporosis is often under-diagnosed and under-treated, leading to increased fragility fracture risks. Thus it is highly desirable to obtain BMDs with alternative cost-effective and more accessible medical imaging examinations such as X-ray plain films. In this work, we formulate the BMD estimation from plain hip X-ray images as a regression problem. Specifically, we propose a new semi-supervised self-training algorithm to train the BMD regression model using images coupled with DEXA measured BMDs and unlabeled images with pseudo BMDs. Pseudo BMDs are generated and refined iteratively for unlabeled images during selftraining. We also present a novel adaptive triplet loss to improve the model's regression accuracy. On an in-house dataset of 1,090 images (819 unique patients), our BMD estimation method achieves a high Pearson correlation coefficient of 0.8805 to ground-truth BMDs. It offers good feasibility to use the more accessible and cheaper X-ray imaging for opportunistic osteoporosis screening.
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