Gambogic acid-loaded magnetic Fe3O4 nanoparticles inhibit Panc-1 pancreatic cancer cell proliferation and migration by inactivating transcription factor ETS1

Cailian Wang1, Haijun Zhang1, Yan Chen1, Fangfang Shi1, Baoan Chen2,31Department of Oncology, 2Department of Hematology, Zhongda Hospital, 3Faculty of Oncology, Medical School, Southeast University, Nanjing, People’s Republic of ChinaBackground: E26 transformation-specific sequence-1 (ETS1) transcription factor plays important roles in both carcinogenesis and the progression of a wide range of malignancies. Aberrant ETS1 expression correlates with aggressive tumor behavior and a poorer prognosis in patients with various malignancies. The aim of the current study was to evaluate the efficacy of a drug delivery system utilizing gambogic acid-loaded magnetic Fe3O4 nanoparticles (GA-MNP- Fe3O4) on the suppression of ETS1-mediated cell proliferation and migration in Panc-1 pancreatic cancer cells.Methods: The effects caused by GA-MNP- Fe3O4 on the proliferation of Panc-1 pancreatic cancer cells were evaluated using a MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay while inhibition of tumor cell migration was investigated in a scratch assay. The expressions of ETS1, cyclin D1, urokinase-type plasminogen activator (u-PA), and VEGF (vascular endothelial growth factor) were examined by Western blot to elucidate the possible mechanisms involved.Results: In Panc-1 pancreatic cancer cells, we observed that application of GA-MNP- Fe3O4 was able to suppress cancer cell proliferation and prevent cells from migrating effectively. After treatment, Panc-1 pancreatic cancer cells showed significantly decreased expression of ETS1, as well as its downstream target genes for cyclin D1, u-PA, and VEGF.Conclusion: Our novel finding reaffirmed the significance of ETS1 in the treatment of pancreatic cancer, and application of GA-MNP- Fe3O4 nanoparticles targeting ETS1 should be considered as a promising contribution for better pancreatic cancer care.Keywords: ETS1 transcription factor, gambogic acid, pancreatic cancer, magnetic nanoparticle

Daunorubicin-TiO2 nanocomposites as a “smart” pH-responsive drug delivery system

Daunorubicin (DNR) has a broad spectrum of anticancer activity, but is limited in clinical application due to its serious side effects. The aim of this study was to explore a novel “smart” pH-responsive drug delivery system (DDS) based on titanium dioxide (TiO2) nanoparticles for its potential in enabling more intelligent controlled release and enhancing chemotherapeutic efficiency of DNR. DNR was loaded onto TiO2 nanoparticles by forming complexes with transition metal titanium to construct DNR-TiO2 nanocomposites as a DDS. DNR was released from the DDS much more rapidly at pH 5.0 and 6.0 than at pH 7.4, which is a desirable characteristic for tumor-targeted drug delivery. DNR-TiO2 nanocomposites induced remarkable improvement in anticancer activity, as demonstrated by flow cytometry, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, and nuclear 4′,6-diamidino- 2-phenylindole staining. Furthermore, the possible signaling pathway was explored by western blot. For instance, in human leukemia K562 cells, it was demonstrated that DNR-TiO2 nanocomposites increase intracellular concentration of DNR and enhance its anticancer efficiency by inducing apoptosis in a caspase-dependent manner, indicating that DNR-TiO2 nanocomposites could act as an efficient DDS importing DNR into target cancer cells. These findings suggest that “smart” DNR delivery strategy is a promising approach to cancer therapy

Scalable Scheduling for Industrial Time-Sensitive Networking: A Hyper-flow Graph Based Scheme

Industrial Time-Sensitive Networking (TSN) provides deterministic mechanisms for real-time and reliable flow transmission. Increasing attention has been paid to efficient scheduling for time-sensitive flows with stringent requirements such as ultra-low latency and jitter. In TSN, the fine-grained traffic shaping protocol, cyclic queuing and forwarding (CQF), eliminates uncertain delay and frame loss by cyclic traffic forwarding and queuing. However, it inevitably causes high scheduling complexity. Moreover, complexity is quite sensitive to flow attributes and network scale. The problem stems in part from the lack of an attribute mining mechanism in existing frame-based scheduling. For time-critical industrial networks with large-scale complex flows, a so-called hyper-flow graph based scheduling scheme is proposed to improve the scheduling scalability in terms of schedulability, scheduling efficiency and latency & jitter. The hyper-flow graph is built by aggregating similar flow sets as hyper-flow nodes and designing a hierarchical scheduling framework. The flow attribute-sensitive scheduling information is embedded into the condensed maximal cliques, and reverse maps them precisely to congestion flow portions for re-scheduling. Its parallel scheduling reduces network scale induced complexity. Further, this scheme is designed in its entirety as a comprehensive scheduling algorithm GH^2. It improves the three criteria of scalability along a Pareto front. Extensive simulation studies demonstrate its superiority. Notably, GH^2 is verified its scheduling stability with a runtime of less than 100 ms for 1000 flows and near 1/430 of the SOTA FITS method for 2000 flows

Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction

The radiation dose in computed tomography (CT) examinations is harmful for patients but can be significantly reduced by intuitively decreasing the number of projection views. Reducing projection views usually leads to severe aliasing artifacts in reconstructed images. Previous deep learning (DL) techniques with sparse-view data require sparse-view/full-view CT image pairs to train the network with supervised manners. When the number of projection view changes, the DL network should be retrained with updated sparse-view/full-view CT image pairs. To relieve this limitation, we present a fully unsupervised score-based generative model in sinogram domain for sparse-view CT reconstruction. Specifically, we first train a score-based generative model on full-view sinogram data and use multi-channel strategy to form highdimensional tensor as the network input to capture their prior distribution. Then, at the inference stage, the stochastic differential equation (SDE) solver and data-consistency step were performed iteratively to achieve fullview projection. Filtered back-projection (FBP) algorithm was used to achieve the final image reconstruction. Qualitative and quantitative studies were implemented to evaluate the presented method with several CT data. Experimental results demonstrated that our method achieved comparable or better performance than the supervised learning counterparts.Comment: 11 pages, 12 figure

Effect of maternal serum albumin level on birthweight and gestational age: an analysis of 39200 singleton newborns

BackgroundSerum albumin plays a pivotal role in regulating plasma oncotic pressure and modulating fluid distribution among various body compartments. Previous research examining the association between maternal serum albumin levels and fetal growth yielded limited and inconclusive findings. Therefore, the specific influence of serum albumin on fetal growth remains poorly understood and warrants further investigation.MethodsA retrospective study involved 39200 women who had a singleton live birth at a tertiary-care academic medical center during the period from January 2017 to December 2020. Women were categorized into four groups according to the quartile of albumin concentration during early pregnancy: Q1 group, ≤41.0 g/L; Q2 group, 41.1-42.6 g/L; Q3 group, 42.7-44.3 g/L and Q4 group, >44.3 g/L. The main outcome measures were mid-term estimated fetal weight, birthweight and gestational age. Multivariate linear and logistic regression analysis were performed to detect the independent effect of maternal serum albumin level on fetal growth after adjusting for important confounding variables.ResultsIn the crude analysis, a significant inverse correlation was found between early pregnancy maternal serum albumin levels and fetal growth status, including mid-term ultrasound measurements, mid-term estimated fetal weight, birthweight, and gestational age. After adjustment for a number of confounding factors, mid-term estimated fetal weight, birthweight, and birth height decreased significantly with increasing albumin levels. Compared to the Q2 group, the Q4 group had higher rates of preterm birth (aOR, 1.16; 95% CI, 1.01–1.34), small-for-gestational-age (aOR, 1.27; 95% CI, 1.11–1.45) and low birthweight (aOR, 1.41; 95% CI, 1.18–1.69), and lower rate of large-for-gestational-age (aOR, 0.85; 95% CI, 0.78–0.94). Moreover, to achieve the optimal neonatal outcome, women with higher early pregnancy albumin levels required a greater reduction in albumin levels in later pregnancy stages.ConclusionsA higher maternal serum albumin level during early pregnancy was associated with poor fetal growth, with the detrimental effects becoming apparent as early as the mid-gestation period. These findings provided vital information for clinicians to predict fetal growth status and identify cases with a high risk of adverse neonatal outcomes early on

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival