In vitro evaluation of the intensifying photodynamic effect due to the presence of plasmonic hollow gold nanoshells loaded with methylene blue on breast and melanoma cancer cells

BackgroundHypoxia is one of the most important limiting factors in photodynamic therapy that can reduce the effectiveness of this treatment. By designing a nanocomplex of plasmonic nanoparticles and photosensitizers with similar optical properties, the rate of free oxygen radical production can be increased and the efficiency of photodynamic therapy can be improved. in this study, we tried to use the outstanding capacities of hollow gold nanoshells (HGNSs) as a plasmonic nanocarrier of methylene blue (MB) to improve the performance of photodynamic therapy.Methods and materialAfter synthesis and optimization of hollow gold nanoshells loaded with Methylene blue (HGNSs-PEG-MB), the characteristics of MB, HGNSs, HGNSs-PEG, HGNSs-PEG-MB, and their toxicity at different concentrations on the cell lines was determined. After determining of optimum concentration of nano agents, irradiation of cell was performed with non-coherent of light source with 670 nm wavelength and an intensity of 14.9 mW/cm². Twenty-four hours after irradiation, an MTT assay was used to determine cell survival percentage. To compare the results, we defined different indexes such as treatment efficiency (TE), synergism ratio (SYN), and the amount of exposure required for 50% cell death (ED50). All the tests were repeated at least four times on the DFW and MCF-7 cancer cell lines.ResultsFor combination therapies with Lumacare irradiated HGNSs-PEG-MB, the UC index was less than one for all concentrations (P < 0.05). Also, the IC50 index for this nanostructure in non-irradiated conditions and less than 9 min irradiation time was lower than other treatment groups (P < 0.05). ED50 amounts for HGNSs-PEG-MB in all concentrations were greater than the other groups. TE Index was also reported to be greater than 1 in all irradiation conditions and concentrations.ConclusionIn this study, HGNSs-PEG in the role of nanocarriers for methylene Blue was used. The results showed that irradiated HGNSs-PEG-MB by 670 nm light severely induced cell death and greatly improved the efficiency of photodynamic therapy in melanoma and breast cancer cells.     

Multi-scale graph-based grading for Alzheimer’s disease prediction

The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.     

Brain multigraph prediction using topology-aware adversarial graph neural network

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.     

Methylation status of the T-cadherin gene promotor in peripheral blood mononuclear cells is associated with HBV-related hepatocellular carcinoma progression

DNA methylation is one of the epigenetic mechanisms to regulate gene expression and frequently occurs in human cancer cells. T-cadherin (CDH13) is a new member of the cadherin superfamily and possesses multiple functions. Our study included 26 normal controls (NCs), 65 chronic hepatitis B patients (CHB), 14 liver cirrhosis patients (LC) and 157 hepatocellular carcinoma patients (HCC). We mainly focused on the mRNA expression and methylation status of CDH13 in peripheral blood mononuclear cells (PBMCs), which were detected by semi-quantitative real-time polymerase chain reaction (RT-qPCR) and methylation-specific polymerase chain reaction (MSP) respectively. The CDH13 mRNA level was lower in HCC, especially in early-stage of HCC than in NCs and CHB groups (p < 0.05). Methylation frequency of the CDH13 promoter was significantly higher in HCC patients than in the NCs and CHB groups (67.52 % vs 0.00 %, p < 0.001, 67.52 % vs 52.31 %, p < 0.05, respectively). CDH13 mRNA level was significantly and relatively lower in methylated groups than in unmethylated groups among the whole participants. The methylation level of CDH13 promoter in HCC might be influenced or partly influenced by some critical factors such as TBil, ALB and AFP (p < 0.05). As an important factor in signaling pathway regulating by CDH13 to promote carcinogenesis, JNK level was significantly higher in HCC which had a higher methylation frequency than in NCs, CHB and LC (p < 0.05). Furthermore, the combination of the methylated CDH13 level and AFP level showed a better score: AUC = 0.796 (SE = 0.031, 95 %CI 0.735–0.857; p < 0.001) in male and AUC = 0.832 (SE = 0.057, 95 %CI 0.721–0.944; p < 0.001) in female compared to AFP alone for diagnosing HCC from NCs, CHB and LC. The methylation of CDH13 promoter was an independent predictor for assessing the prognosis of HCC patients (r=-1.378 p < 0.05). In conclusion, hypermethylation of CDH13 in PBMCs was associated with the underexpression of mRNA and the high risk of HCC. The methylation status of the CDH13 promoter in PBMCs was a potential noninvasive biomarker to predict the prognosis of HCC patients.     

Theoretical investigations of a new two-dimensional semiconducting boron–carbon–nitrogen structure

A new two-dimensional boron–carbon–nitrogen (BCN) structure is predicted and is theoretically investigated based on density functional theory. The BCN structure belongs to the space group C222, and is composed of twelve B, twelve C and twelve N atoms per orthorhombic cell (named oC-B₁₂C₁₂N₁₂). It consists of small hollow spheres with two hexagons per sphere. The dynamical, thermal and mechanical stabilities of oC-B₁₂C₁₂N₁₂ are respectively evaluated by phonon spectroscopy, ab initio molecular dynamics calculations and elastic constant measurements. The simulated in-plane stiffness and Poisson ratio display anisotropic features. The band structure shows that oC-B₁₂C₁₂N₁₂ is a direct semiconductor with a gap of 2.72 eV (GW). oC-B₁₂C₁₂N₁₂ has an absorption range from the visible light spectrum to the ultraviolet. Therefore, due to its small direct band gap and optical absorption, oC-B₁₂C₁₂N₁₂ may be a good candidate for electronic and optical applications.

A predicted 2D BCN structure has a direct band gap and is a good candidate for electronic and optical applications.     

Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network

We propose a novel shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection (ESD) surgery. This task is of great clinical significance but extremely challenging due to bleeding, lighting reflection, and motion blur in the complicated surgical environment. Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks. We first devise an algorithm to automatically generate relation keypoint heatmaps, which are able to intuitively represent the prior knowledge of spatial relations among landmarks without using any extra manual annotation efforts. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process. While one scheme introduces pixel-level regularization by multi-task learning, the other integrates global-level regularization by harnessing a newly designed grouped consistency evaluator, which adds relation constraints to the proposed network in an adversarial manner. Both schemes are beneficial to the model in training, and can be readily unloaded in inference to achieve real-time detection. We establish a large in-house dataset of ESD surgery for esophageal cancer to validate the effectiveness of our proposed method. Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of accuracy and efficiency, achieving better detection results faster. Promising results on two downstream applications further corroborate the great potential of our method in ESD clinical practice.