Indoleamine 2,3-dioxygenase (IDO-1) is emerging as an important new therapeutic target for the treatment of cancer, neurological disorders, and other diseases that are characterized by pathological tryptophan metabolism. However, only a few structural classes are known to be IDO-1 inhibitors. In this study, a natural compound tryptanthrin was discovered to be a novel potent IDO-1 inhibitor by screening of indole-based structures. Three series of 13 tryptanthrin derivatives were synthesized, and the structure-activity analysis was undertaken. The optimization led to the identification of 5c, which exhibited the inhibitory activity at a nanomolar level. In vitro 5c dramatically augmented the proliferation of T cells. When administered to Lewis lung cancer (LLC) tumor-bearing mice, 5c significantly inhibited IDO-1 activity and suppressed tumor growth. In addition, 5c reduced the numbers of Foxp3(+) regulatory T cells (Tregs), which are known to prevent the development of efficient antitumor immune responses.

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Risk factors of mydelodysplastic syndromes (MDS) remain largely unknown. We conducted a hospital-based case-control study consisting of 403 newly diagnosed MDS patients according to World Health Organization classification and 806 individually gender and age-matched patient controls from 27 major hospitals in Shanghai, China, to examine relation of lifestyle, environmental, and occupational factors to risk of MDS. The study showed that all MDS (all subtypes combined) risk factors included anti tuberculosis drugs [odds ratio (OR) IntroductionMyelodysplastic syndromes (MDS) represent a heterogeneous group of neoplastic clonal stem cell disorders characterized by clinical presentations of anemia, thrombocytopenia, and leucopenia. MDS may be categorized into subtypes according to histological, immunological, and genetic characteristics. MDS was usually diagnosed by French-American-British (FAB) classification with subtypes including refractory anemia (RA), RA with ringed sideroblasts (RARS), RA with excess of blasts (RAEB), RAEB in transformation (RAEB-T), and chronic myelomonocytic leukemia (CMML) [1]. Since its publication in 2001, World Health Organization (WHO) classification for MDS has become widely adopted [2]. In the WHO MDS system, blast cutoff is less than 20% compared to 30% in FAB system. Additional WHO MDS subtypes include refractory cytopenia with multiple dysplasia (RCMD), MDS with isolated del(5q), and MDS unclassifiable (MDS-u) and the WHO MDS system does not include CMML [3].Secondary MDS is usually resulted from radiation and chemotherapy. Little is known about the etiology of primary or de novo MDS. Most previous studies on MDS risk factors focused on FAB MDS [4][5][6]. Here, we report a large hospital-based case-control study of 403 WHO MDS cases and 806 age and sex-matched controls in a Chinese population to assess effects of lifestyle, environmental, and occupational factors on MDS development.

Indoleamine 2,3-dioxygenase (IDO), the first and rate-limiting enzyme in the kynurenine pathway (KP) of tryptophan catabolism, was recently established as one of the potential players involved in the pathogenesis of Alzheimer's disease (AD). Coptisine is a main pharmacological active constituent of the traditional Chinese medicinal prescription Oren-gedoku-to (OGT) which has therapeutic potential for the treatment of AD. Our recent studies have demonstrated that OGT significantly inhibited recombinant human IDO activity, which shed light on the possible mechanism of OGT's action on AD. Here, we characterized the effects of coptisine in an AD mouse model on the basis of its IDO inhibitory ability. Coptisine was found to be an efficient uncompetitive IDO inhibitor with a Ki value of 5.8 μM and an IC50 value of 6.3 μM. In AβPP/PS1 transgenic mice, oral administration of coptisine inhibited IDO in the blood and decreased the activation of microglia and astrocytes, consequently prevented neuron loss, reduced amyloid plaque formation, and ameliorated impaired cognition. Neuronal pheochromocytoma (PC12) cells induced with amyloid-β peptide 1-42 and interferon-γ showed reduction of cell viability and enhancement of IDO activity, while coptisine treatment increased cell viability based on its reversal effect on the enhanced activity of IDO. In conclusion, our present findings provide further evidence supporting the critical links between IDO, KP, and AD, and demonstrate coptisine, a novel IDO inhibitor, as a potential new class of drugs for AD treatment.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.