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Purpose This study aimed to develop and evaluate a novel strategy for establishing a deep learning‐based gamma passing rate (GPR) prediction model for volumetric modulated arc therapy (VMAT) using dummy target plan data, one measurement process, and a multicriteria prediction method. Methods A total of 147 VMAT plans were used for the training set (two sets of 48 dummy target plans) and test set (51 clinical target plans). The dummy plans were measured using a diode array detector. We developed an original convolutional neural network that accepts coronal and sagittal dose distributions to predict the GPRs of 36 pairs of gamma criteria from 0.5%/0.5 mm to 3%/3 mm. Sixfold cross‐validation and model averaging were performed, and the mean training result and mean test result were derived from six trained models that were produced during cross‐validation. Results Strong or moderate correlations were observed between the measured and predicted GPRs in all criteria. The mean absolute errors and root mean squared errors of the test set (clinical target plan) were 0.63 and 1.11 in 3%/3 mm, 1.16 and 1.73 in 3%/2 mm, 1.96 and 2.66 in 2%/2 mm, 5.00 and 6.35 in 1%/1 mm, and 5.42 and 6.78 in 0.5%/1 mm, respectively. The Pearson correlation coefficients were 0.80 in the training set and 0.68 in the test set at the 0.5%/1 mm criterion. Conclusion Our results suggest that the training of the deep learning‐based quality assurance model can be performed using a dummy target plan.

Growth arrest-specific gene (Gas) 6 is a γ-carboxyglutamic acid domain-containing protein, which shares 43% amino acid identity with protein S. Gas6 has been shown to enhance cancer cell proliferation in vitro. On the other hand, recent studies have demonstrated that Gas6 inhibits toll-like receptor-mediated immune reactions. Immune reactions are known to affect intestinal tumorigenesis. In this study, we investigated how Gas6 contributes to tumorigenesis in the intestine. Administration of recombinant Gas6 weakly, but significantly, enhanced proliferation of intestinal cancer cells (SW480 and HT29), whereas it suppressed the inflammatory responses of Lipopolysaccharide (LPS)-stimulated monocytes (THP-1). Compared with Gas6(+/+) mice, Gas6(-/-) mice exhibited enhanced azoxymethane/dextran sulfate sodium (DSS)-induced tumorigenesis and had a shorter survival. Gas6(-/-) mice also exhibited more severe DSS-induced colitis. DSS-treated Gas6(-/-) mice showed attenuated Socs1/3 messenger RNA expression and enhanced nuclear factor-kappaB activation in the colonic stroma, suggesting that the target of Gas6 is stromal cells. Bone marrow transplantation experiments indicated that both epithelial cells and bone marrow-derived cells are Gas6 sources. Furthermore, the number of intestinal tumors in Apc(Min) Gas6(-/-) mice was higher than that in Apc(Min) Gas6(+/+) mice, resulting in shorter survival. In a group of 62 patients with advanced colorectal cancer, Gas6 immunoreactivity in cancer tissues was positively correlated with prognosis. Thus, we revealed a unique in vivo inhibitory role of Gas6 during the progression of intestinal tumors associated with suppression of stromal immune reactions. These results suggest a novel therapeutic approach for colorectal cancer patients by regulation of stromal immune responses.

A simple soft-core model potential is proposed to discuss the self-diffusion of biomolecules in solution. Extensive Brownian-dynamics simulations are performed to obtain the long-time self-diffusion coefficient. Then the simulation results are compared with the experimental data from a unified point of view recently obtained for suspensions of hard spheres. Thus, it is shown that the proposed potential can qualitatively well describe the experimental data.

Objective The aims of this study were to assess the incidence of pancreatic cancer and the contributing factors for the diagnosis of tumors in patients with acute pancreatitis and to gain insight into how patients with acute pancreatitis should be followed up. Methods Using the electronic medical database of Shizuoka General Hospital, 177 patients admitted for acute pancreatitis in the past 6 years were evaluated retrospectively for pancreatic cancer. Results Twelve patients (6.8%) were newly diagnosed with pancreatic cancer. During the first hospitalization, 5 patients (41.7%) with a detected pancreatic mass underwent surgical treatment: the final tumor stages were IA, IIA, and IIB in 1, 2, and 2 patients, respectively. In 7 patients (58.3%) without a detected pancreatic mass at the first admission, a pancreatic mass was recognized on follow-up computed tomography (CT) in 2 patients with main pancreatic duct (MPD) dilatation, and 1 patient with recurrent acute pancreatitis. The tumor stages were IA, IIA, and IA, respectively. Among the remaining 4 patients without follow-up, the tumor stage was IV. The patient gender, age, MPD dilatation, tumor marker, and serum amylase level were not significantly associated with pancreatic cancer. The detection of a pancreatic mass on CT led to the diagnosis of pancreatic cancer. Conclusion Acute pancreatitis should be considered as a possible diagnostic indicator of pancreatic cancer. Various factors associated with acute pancreatitis and pancreatic cancer were not predictive of a diagnosis of pancreatic cancer. Only the detection of a pancreatic mass led to the early diagnosis of pancreatic cancer. Patients hospitalized for acute pancreatitis should be followed up with a diagnostic imaging modality.

Background The clinical significance of human S100A8/A9 (h-S100A8/A9) in patients with inflammatory bowel disease (IBD) is poorly understood. Objective To clarify whether serum S100A8/A9 is a sensitive biomarker for IBD. Methods Serum specimens from outpatients with IBD (n = 101) and healthy volunteers (HVs) (n = 101) were used in this study. Enzyme-linked immunosorbent assays for h-S100A8/A9 and inflammatory cytokines were performed using these specimens. Further, correlation analysis was performed to investigate the significance of h-S100A8/A9 fluctuation in patients with IBD. Results The average of serum h-S100A8/A9 concentration in outpatients with IBD was significantly higher than that in HVs. The concentration of h-S100A8/A9 in patients with IBD was barely correlated with that of CRP and inflammatory cytokines. Despite that finding, the serum level of h-S100A8/A9 in patients with ulcerative colitis (UC) was correlated with the severity of IBD, compared with other inflammatory proteins. Conclusion Serum h-S100A8/A9 is superior to CRP as a sensitive biomarker for IBD.

Purpose In patient‐specific quality assurance (QA) for static beam intensity‐modulated radiation therapy (IMRT), machine‐learning‐based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two‐dimensional detector, they have not been extended to the analysis of volumetric‐modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient‐specific VMAT QA. Methods A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error‐free plan, we simulated 12 types of errors: two types of multileaf collimator positional errors (systematic or random leaf offset of 2 mm), two types of monitor unit (MU) scaling errors (±3%), two types of gantry rotation errors (±2° in clockwise and counterclockwise direction), and six types of phantom setup errors (±1 mm in lateral, longitudinal, and vertical directions). The error‐introduced predicted dose distributions were created by editing the calculated dose distributions using a TPS with in‐house software. Those 13 types of dose difference maps, consisting of an error‐free map and 12 error maps, were created from the measured and predicted dose distributions and were used to train the convolutional neural network (CNN) model. Our model was a multi‐task model that individually detected each of the 12 types of errors. Two datasets, Test sets 1 and 2, were prepared to evaluate the performance of the model. Test set 1 consisted of 13 types of dose maps used for training, whereas Test set 2 included the dose maps with 25 types of errors in addition to the error‐free dose map. The dose map, which introduced 25 types of errors, was generated by combining two of the 12 types of simulated errors. For comparison with the performance of our model, gamma analysis was performed for Test sets 1 and 2 with the criteria set to 3%/2 mm and 2%/1 mm (dose difference/distance to agreement). Results For Test set 1, the overall accuracy of our CNN model, gamma analysis with the criteria set to 3%/2 mm, and gamma analysis with the criteria set to 2%/1 mm was 0.92, 0.19, and 0.81, respectively. Similarly, for Test set 2, the overall accuracy was 0.44, 0.42, and 0.95, respectively. Our model outperformed gamma analysis in the classification of dose maps containing a single type error, and the performance of our model was inferior in the classification of dose maps containing compound errors. Conclusions A multi‐task CNN model for detecting errors in patient‐specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective ...

Nonalcoholic steatohepatitis (NASH) is an inflammatory form of nonalcoholic fatty liver disease that progresses to liver cirrhosis. It is still unknown how only limited patients with fatty liver develop NASH. Tumor necrosis factor (TNF)-α is one of the key molecules in initiating the vicious circle of inflammations. Nardilysin (N-arginine dibasic convertase; Nrd1), a zinc metalloendopeptidase of the M16 family, enhances ectodomain shedding of TNF-α, resulting in the activation of inflammatory responses. In this study, we aimed to examine the role of Nrd1 in the development of NASH. Nrd1+/+ and Nrd1−/− mice were fed a control choline-supplemented amino acid-defined (CSAA) diet or a choline-deficient amino acid-defined (CDAA) diet. Fatty deposits were accumulated in the livers of both Nrd1+/+ and Nrd1−/− mice by the administration of the CSAA or CDAA diets, although the amount of liver triglyceride in Nrd1−/− mice was lower than that in Nrd1+/+ mice. Serum alanine aminotransferase levels were increased in Nrd1+/+ mice but not in Nrd1−/− mice fed the CDAA diet. mRNA expression of inflammatory cytokines were decreased in Nrd1−/− mice than in Nrd1+/+ mice fed the CDAA diet. While TNF-α protein was detected in both Nrd1+/+ and Nrd1−/− mouse livers fed the CDAA diet, secretion of TNF-α in Nrd1−/− mice was significantly less than that in Nrd1+/+ mice, indicating the decreased TNF-α shedding in Nrd1−/− mouse liver. Notably, fibrotic changes of the liver, accompanied by the increase of fibrogenic markers, were observed in Nrd1+/+ mice but not in Nrd1−/− mice fed the CDAA diet. Similar to the CDAA diet, fibrotic changes were not observed in Nrd1−/− mice fed a high-fat diet. Thus, deletion of nardilysin prevents the development of diet-induced steatohepatitis and liver fibrogenesis. Nardilysin could be an attractive target for anti-inflammatory therapy against NASH.