Antitumor studies. Part 1: Design, synthesis, antitumor activity, and AutoDock study of 2-deoxo-2-phenyl-5-deazaflavins and 2-deoxo-2-phenylflavin-5-oxides as a new class of antitumor agents

Novel 2-deoxo-2-phenyl-5-deazaflavins and 2-deoxo-2-phenylflavin-5-oxides were prepared as a new class of antitumor agents and showed significant antitumor activities against NCI-H 460, HCT 116, A 431, CCRF-HSB-2, and KB cell lines. In vivo investigation, 2-deoxo-10-methyl-2-phenyl-5-deazaflavin exhibited the effective antitumor activity against A 431 human adenocarcinoma cells transplanted subcutaneously into nude mouse. Furthermore, AutoDock study has been done by binding of the flavin analogs into PTK pp60(c-src), where a good correlation between their IC50 and AutoDock binding free energy was exhibited. In particular, 2-deoxo-2-phenylflavin-5-oxides exhibited the highest potential binding affinity within the binding pocket of PTK

抗PD-1抗体への化学療法の併用はmyeloid-derived suppressor cellsを減少させることにより中皮腫の増殖を抑制する

Background: The combination of anti-PD-1/PD-L1 antibody with chemotherapy has been approved for the first-line therapy of lung cancer. However, the effects against malignant mesothelioma (MPM) and the immunological mechanisms by which chemotherapy enhances the effect of targeting PD-1/PD-L1 in MPM are poorly understood. Materials and Methods: We utilized syngeneic mouse models of MPM and lung cancer and assessed the therapeutic effects of anti-PD-1 antibody and its combination with cisplatin (CDDP) and pemetrexed (PEM). An immunological analysis of tumor-infiltrating cells was performed with immunohistochemistry. Results: We observed significant therapeutic effects of anti-PD-1 antibody against MPM. Although the effect was associated with CD8+ and CD4+ T cells in tumors, the number of Foxp3+ cells was not reduced but rather increased. Consequently, combination with CDDP/PEM significantly enhanced the antitumor effects of anti-PD-1 antibody by decreasing numbers of intratumoral myeloid-derived suppressor cells (MDSCs) and vessels probably through suppression of VEGF expression by CDDP+PEM. Conclusions: The combination of anti-PD-1 antibody with CDDP+PEM may be a promising therapy for MPM via inhibiting the accumulation of MDSCs and vessels in tumors

Predicting reliable H2_2 column density maps from molecular line data using machine learning

The total mass estimate of molecular clouds suffers from the uncertainty in the H$_2$-CO conversion factor, the so-called $X_{\rm CO}$ factor, which is used to convert the $^{12}$CO (1--0) integrated intensity to the H$_2$ column density. We demonstrate the machine learning's ability to predict the H$_2$ column density from the $^{12}$CO, $^{13}$CO, and C$^{18}$O (1--0) data set of four star-forming molecular clouds; Orion A, Orion B, Aquila, and M17. When the training is performed on a subset of each cloud, the overall distribution of the predicted column density is consistent with that of the Herschel column density. The total column density predicted and observed is consistent within 10\%, suggesting that the machine learning prediction provides a reasonable total mass estimate of each cloud. However, the distribution of the column density for values $> \sim 2 \times 10^{22}$ cm$^{-2}$, which corresponds to the dense gas, could not be predicted well. This indicates that molecular line observations tracing the dense gas are required for the training. We also found a significant difference between the predicted and observed column density when we created the model after training the data on different clouds. This highlights the presence of different $X_{\rm CO}$ factors between the clouds, and further training in various clouds is required to correct for these variations. We also demonstrated that this method could predict the column density toward the area not observed by Herschel if the molecular line and column density maps are available for the small portion, and the molecular line data are available for the larger areas.Comment: Accepted for publication in MNRA

Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results

Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the ``Near'' solution, and the ``Far'' solution, can be derived simultaneously. We attempted to construct a two-class (``Near'' or ``Far'') inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| < 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with ``Near'' or ``Far'' annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of < 8.15 kpc identified in the 12CO data follows a power-law distribution with an index of about -2.3 in the mass range of M >10^3 Msun. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined.Comment: 29 pages, 12 figure