Our results show that the approximation works well under the modeled assumptions and the serial application of this model to a two-generation airway geometry provides reasonable approximations.Biomarker inference from biomedical images is one of the main tasks of medical image analysis. Standard techniques follow a segmentation-and-measure strategy, where the structure is first segmented and then the measurement is performed. Recent work has shown that such strategy could be replaced by a direct regression of the biomarker value in using regression networks. While achieving high correlation coefficients, such techniques operate as a 'black-box', not offering quality-control images. We present a methodology to regress the biomarker from the image while simultaneously computing the quality control image. Our proposed methodology does not require segmentation masks for training, but infers the segmentations directly from the pixels that used to compute the biomarker value. The network proposed consists of two steps a segmentation method to an unknown reference and a summation method for the biomarker estimation. The network is optimized using a dual loss function, L2 for the biomarkers and an L1 to enforce sparsity. We showcase our methodology in the problem of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) inference in a single slice from chest-CT images. We use a database of 7000 cases to which only the value of the biomarker is known for training and a test set of 3000 cases with both, biomarkers and segmentations. We achieve a correlation coefficient of 0.97 for PMA and 0.98 for SFA with respect to the reference standard. The average DICE coefficient is of 0.88 (PMA) and 0.89 (SFA). Comparing with standard segment-and-measure techniques, we achieve the same correlation for the biomarkers but smaller DICE coefficients in segmentation. Such is of little surprise, since segmentation networks are the upper limit of performance achievable, and we are not using segmentation masks for training. We can conclude that it is possible to infer segmentation masks from biomarker regression networks.Cold atmospheric plasma (CAP) has been recognized as a potential alternative or supplementary cancer treatment tool, which is attributed by its selective antiproliferation effect on cancer cells over normal cells. Standardization of the CAP treatment in terms of biological outputs such as cell growth inhibition and gene expression change is essential for its clinical application. This study aims at identifying genes that show consistent expression profiles at a specific CAP condition, which could be used to monitor whether CAP is an appropriate treatment to biological targets. To do this, genes showing differential expression by two different CAP treatment conditions were screened in the MCF-7 breast cancer cells. As a result, ZNRD1 was identified as a potential marker with being consistently upregulated by 600 s but downregulated by the 10 × 30 s CAP treatment scheme. Expression of ZNRD1 was increased in breast cancer tissues compared to normal tissues, judged by cancer tissue database analysis, and supported by the antiproliferation after siRNA-induced downregulation in MCF-7. Interestingly, the antisense long noncoding RNA (lncRNA) of ZNRD1, ZNRD1-AS1, was regulated to the opposite direction of ZNRD1 by CAP. The siRNA-based qPCR analysis indicates that ZNRD1 downregulates ZNRD1-AS1, but not vice versa. ZNRD1-AS1 was shown to increase a few cis-genes such as HLA-A, HCG9, and PPP1R11 that were also regulated by CAP. Altogether, this study identified a pair of gene and its antisense lncRNA of which expression is precisely controlled by CAP in a dose-dependent manner. These genes could help elucidate the molecular mechanism how CAP regulates lncRNAs in cancer cells.Pain is the most important clinical feature of acute pancreatitis (AP); however, its specific mechanism is currently unclear. In this study, we showed that AP caused an increase in nitric oxide (NO) secretion, activated the NF-κB pathway in the dorsal root ganglia (DRGs), and caused pain. We established an AP model in vivo and tested the expression of NO, the kappa opioid receptor (KOR), and pain factors. We showed that NO in AP was significantly elevated and increased the expression of pain factors. Next, by treating DRGs in vitro, it was found that NO activated the NF-κB pathway; conversely, NF-κB had no effect on NO. Moreover, inhibition of NF-κB promoted the KOR, whereas NF-κB did not change after KOR activation. Finally, behavioral experiments showed that a NO donor increased the pain behavior of mice, while a NO scavenger, NF-κB inhibitor, or KOR agonist attenuated the pain response in mice. These results suggest that iNOS/NO/NF-κB/KOR may be a key mechanism of pain in AP, providing a theoretical basis for the use of peripheral-restricted KOR agonists for pain treatment in AP.This study is aimed at evaluating the relationship between leukocyte telomere length (LTL) and mitochondrial DNA copy number (mtDNAcn) in a noninterventional rural community of China with different glucose tolerance statuses. In addition, we investigate whether the indicators of oxidative stress and inflammation were involved and identify mediators among them. A total of 450 subjects in rural China were included and divided into two groups according to a 75 g oral glucose tolerance test (OGTT) the abnormal glucose metabolism (AGM, n = 257, 57.1%) group and the normal glucose tolerance (NGT, n = 193, 42.9%) group. Indicators of oxidative stress (superoxide dismutase (SOD) and glutathione reductase (GR)) and inflammatory indices (tumor necrosis factor α (TNFα) and interleukin-6 (IL-6)) were all determined by ELISA.  https://www.selleckchem.com/products/U0126.html  LTL and mtDNAcn were measured using a real-time PCR assay. Linear regressions were used to adjust for covariates that might affect the relationship between LTL and mtDNAcn. Mediation analyses were utilized to evaluate the mediators. In the AGM, LTL was correlated with mtDNAcn (r = 0.214, p = 0.001), but no correlation was found in the NGT. The association between LTL and mtDNAcn was weakened after adjusting for inflammatory factors in the AGM (p = 0.087). LTL and mtDNAcn were both inversely related to HbA1c, IL-6, TNFα, and SOD activity. Mediation analysis demonstrated that TNFα was a significant mediator in the telomere-mitochondrial interactome in the AGM. This result suggests that inflammation and oxidative stress may play a vital role in telomere shortening as well as mitochondrial dysfunction. In the subjects with hyperglycemia, a significant positive correlation is observed between LTL and mtDNAcn, which is probably mediated by TNFα. TNFα may be considered a potential therapeutic target against aging-related disease in hyperglycemia.