Jiadifenolide induces the expression of cellular communication network factor (CCN) genes, and CCN2 exhibits neurotrophic activity in neuronal precursor cells derived from human induced pluripotent stem cells

Jiadifenolide has been reported to have neurotrophin-like activity in primary rat cortical neurons, and also possesses neurotrophic effects in neuronal precursor cells derived from human induced pluripotent stem cells (hiPSCs), as we have previously reported. However, the molecular mechanisms by which jiadifenolide exerts its neurotrophic effects in rat and human neurons are unknown. Thus, we aimed to investigate the molecular mechanisms and pathways by which jiadifenolide promotes neurotrophic effects. Here, we found that jiadifenolide activated cellular communication network factor (CCN) signaling pathways by up-regulating mRNA level expression of CCN genes in human neuronal cells. We also found that CCN2 (also known as connective tissue growth factor, CTGF) protein promotes neurotrophic effects through activation of the p44/42 mitogen-activated protein kinase signaling pathway. This is the first discovery which links neurotrophic activity with CCN signaling

ヘルシンキ メトロポリア オウヨウ カガク ダイガク エノ タンキ リュウガク ニオケル カツドウ ホウコク

We visited Helsinki Metropolia University of Applied Sciences from August 14th to 22nd as a short-term study abroad supported by Japan Student Services Organization (JASSO). We participated in the basic and clinical training of the oral hygiene course. Furthermore, we also visited the institution for elderly in Espoo and the orthopedic hospital in Helsinki. We reported the outline of the study abroad here

Androgen’s effects in female

The metabolic effects of androgens and their underlying mechanisms in females have been revealed by recent studies. An excess of androgens can have adverse effects on feeding behavior and metabolic functions and induce metabolic disorders / diseases, such as obesity, insulin resistance, and diabetes, in women and experimental animals of reproductive age. Interestingly, these effects of androgens are not observed in ovariectomized animals, indicating that their effects might be dependent on the estrogen milieu. Central and peripheral mechanisms, such as alterations in the activity of hypothalamic factors, reductions in energy expenditure, skeletal muscle insulin resistance, and β-cell dysfunction, might be related to these androgens’ effects

Biotin levels in blood and follicular fluid

It has been shown that biotin, a water-soluble vitamin (B7), plays roles in reproductive functions, such as oocyte maturation and embryo development, in experimental animals. On the other hand, little is known about the clinical effects of biotin on human reproduction. In this study, serum and follicular fluid biotin levels were measured in patients who underwent in vitro fertilization / intracytoplasmic sperm injection (IVF / ICSI), and their associations with reproductive outcomes were evaluated. As a result, biotin was detected in follicular fluid, as well as serum, and the biotin levels of follicular fluid were found to be positively correlated with those of serum. The biotin levels of serum were higher than those of follicular fluid, suggesting that biotin may be taken up into the follicular fluid from the blood. Although serum and follicular fluid biotin levels tended to be higher in pregnant patients than in non-pregnant patients, these data did not show the significant statistical difference. These findings indicate that biotin does not contribute to the maintenance of oocyte quality, and hence, it does not increase fertilization and pregnancy rates

Cutoff Values of Serum IgG4 and Histopathological IgG4+ Plasma Cells for Diagnosis of Patients with IgG4-Related Disease

IgG4-related disease is a new disease classification established in Japan in the 21st century. Patients with IgG4-related disease display hyper-IgG4-gammaglobulinemia, massive infiltration of IgG4+ plasma cells into tissue, and good response to glucocorticoids. Since IgG4 overexpression is also observed in other disorders, it is necessary to diagnose IgG4-related disease carefully and correctly. We therefore sought to determine cutoff values for serum IgG4 and IgG4/IgG and for IgG4+/IgG+ plasma cells in tissue diagnostic of IgG4-related disease. Patients and Methods. We retrospectively analyzed serum IgG4 concentrations and IgG4/IgG ratio and IgG4+/IgG+ plasma cell ratio in tissues of 132 patients with IgG4-related disease and 48 patients with other disorders. Result. Serum IgG4 >135  mg/dl demonstrated a sensitivity of 97.0% and a specificity of 79.6% in diagnosing IgG4-related disease, and serum IgG4/IgG ratios >8% had a sensitivity and specificity of 95.5% and 87.5%, respectively. IgG4+cell/IgG+ cell ratio in tissues >40% had a sensitivity and specificity of 94.4% and 85.7%, respectively. However, the number of IgG4+ cells was reduced in severely fibrotic parts of tissues. Conclusion. Although a recent unanimous consensus of all relevant researchers in Japan recently established the diagnostic criteria for IgG4-related disease, findings such as ours indicate that further discussion is needed

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space

Frequent Closed Item Set Mining Based on Zero-suppressed BDDs

Frequent item set mining is one of the fundamental techniques for knowledge discovery and data mining. In the last decade, a number of efficient algorithms for frequent item set mining have been presented, but most of them focused on just enumerating the item set patterns which satisfy the given conditions, and it was a different matter how to store and index the result of patterns for efficient data analysis. Recently, we proposed a fast algorithm of extracting all frequent item set patterns from transaction databases and simultaneously indexing the result of huge patterns using Zero-suppressed BDDs (ZBDDs). That method, ZBDD-growth, is not only enumerating/listing the patterns efficiently, but also indexing the output data compactly on the memory to be analyzed with various algebraic operations. In this paper, we present a variation of ZBDD-growth algorithm to generate frequent closed item sets. This is a quite simple modification of ZBDD-growth, and additional computation cost is relatively small compared with the original algorithm for generating all patterns. Our method can conveniently be utilized in the environment of ZBDD-based pattern indexing.論文特集:データマイニングと統計数

Increased c-Myc activity and DNA damage in hematopoietic progenitors precede myeloproliferative disease in Spa-1-deficiency.

Mice deficient for Spa-1 encoding Rap GTPase-activating protein develop myeloproliferative disorder (MPD) of late onset with frequent blast crises. The mechanisms for MPD development as well as the reasons for long latency, however, remain elusive. We demonstrate here that preleukemic, disease-free Spa-1(-/-) mice show reduced steady-state hematopoiesis and attenuated resistance to whole body γ-ray irradiation, which are attributable to the sustained p53 response in hematopoietic progenitor cells (HPCs). Preleukemic Spa-1(-/-) HPCs show c-Myc overexpression with increased p19Arf as well as enhanced γH2AX expression with activation of Atm/Chk pathway. We also show that deregulated Rap signaling in the absence of Spa-1 enhances post-transcriptional c-Myc stability and induces DNA damage in a p38MAPK-dependent manner, leading to p53 activation. Genetic studies indicate that the introduction of p53(+/-) and p53(-/-) mutations in Spa-1(-/-) mice results in the acceleration of typical MPD and rapid development of blastic leukemia, respectively. These results suggest that increased c-Myc expression and DNA damage in HPCs precede MPD development in Spa-1(-/-) mice, and the resulting p53 response functions as a barrier for the onset of MPD and blast crises progression