One-Dimensional Numerical Study on Ignition of the Helium Envelope in Dynamical Accretion during the Double-Degenerate Merger

In order for a double-detonation model to be viable for normal type Ia supernovae, the adverse impact of helium-burning ash on early-time observables has to be avoided, which requires that the helium envelope mass should be at most 0.02 solar mass. Most of the previous studies introduced detonation by artificial hot spots, and therefore the robustness of the spontaneous helium detonation remains uncertain. In the present work, we conduct a self-consistent hydrodynamic study on the spontaneous ignition of the helium envelope in the context of the double-degenerate channel, by applying an idealized one-dimensional model and a simplified 7 isotope reaction network. We explore a wide range of the progenitor conditions, and demonstrate that the chance of direct initiation of detonation is limited. Especially, the spontaneous detonation requires the primary envelope mass of >~ 0.03 solar mass. Ignition as deflagration is instead far more likely, which is feasible for the lower envelope mass down to ~ 0.01 solar mass, which might lead to subsequent detonation once the deflagration to detonation transition (DDT) would be realized. High-resolution multi-dimensional simulations are required to further investigate the DDT possibility, as well as accurately derive the threshold between the spontaneous detonation and deflagration ignition regimes. Another interesting finding is the effect of the composition; while mixing with the core material enhances detonation as previously suggested, it rather narrows the chance for deflagration due to the slower rate of 12C(alpha,gamma)16O reaction at the lower temperature ~108K, with the caveat that we presently neglect the proton-catalyzed reaction sequence of 12C(p,gamma)13O(alpha,p)16O.Comment: accepted for publication in Ap

ヒストンH3K9メチル化酵素G9aの阻害はメチルグリオキサールによる腹膜線維化を抑制する

広島大学(Hiroshima University)博士(医学)Doctor of Philosophy in Medical Sciencedoctora

大腸癌におけるCDKN2Aのhypermethylationは予後と相関する

取得学位 : 博士（医学）, 学位授与番号 : 医博甲第1597号, 学位授与年月日 : 平成15年6月30日, 学位授与大学 : 金沢大

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes

Successful Re-administration of Osimertinib in Osimertinib-induced Interstitial Lung Disease with an Organizing Pneumonia Pattern: A Case Report and Literature Review

Osimertinib is the standard therapy for epidermal-growth-factor-receptor (EGFR)-mutant lung cancers. We herein report a case of osimertinib-induced interstitial lung disease (OsiILD) with an organizing pneumonia (OP) pattern and provide a literature-based review. Six months after osimertinib administration, a 75-year-old woman with right pleural carcinomatosis developed ILD with an OP pattern. After salvage chemotherapy, osimertinib with corticosteroid was successfully re-administered. A literature review suggested that 1) OsiILD with an OP pattern was rare but should be recognized, and 2) re-administration of osimertinib in OsiILD was successful in select patients. A criterion that determines whether a patient would benefit from re-administration is warranted