Solvent Mediated Assembly of Nanoparticles Confined in Mesoporous Alumina

The controlled self-assembly of thiol stabilized gold nanocrystals in a mediating solvent and confined within mesoporous alumina was probed in situ with small angle x-ray scattering. The evolution of the self-assembly process was controlled reversibly via regulated changes in the amount of solvent condensed from an undersaturated vapor. Analysis indicated that the nanoparticles self-assembled into cylindrical monolayers within the porous template. Nanoparticle nearest-neighbor separation within the monolayer increased and the ordering decreased with the controlled addition of solvent. The process was reversible with the removal of solvent. Isotropic clusters of nanoparticles were also observed to form temporarily during desorption of the liquid solvent and disappeared upon complete removal of liquid. Measurements of the absorption and desorption of the solvent showed strong hysteresis upon thermal cycling. In addition, the capillary filling transition for the solvent in the nanoparticle-doped pores was shifted to larger chemical potential, relative to the liquid/vapor coexistence, by a factor of 4 as compared to the expected value for the same system without nanoparticles.Comment: 9 pages, 9 figures, appeared in Phys. Rev.

Reduction in cytokine production in colorectal cancer patients: association with stage and reversal by resection

The aim of this study was to assess monocyte/macrophage function, as defined by lipopolysaccharide (LPS)-induced production of tumour necrosis factor (TNF)-α, interleukin (IL)-10 and interferon (IFN)-γ by stimulated whole blood cultures in patients with colorectal carcinoma before and after surgical resection. Forty colorectal cancer patients prior to surgery and 31 healthy controls were studied. Heparinized venous blood was taken from colorectal cancer patients prior to surgery and from healthy controls. Serial samples were obtained at least 3–6 weeks post-operatively. Blood was stimulated with LPS for 24 h and supernatants were assayed for TNF-α, IFN-γ and IL-10 by enzyme-linked immunosorbent assay. LPS-induced production of TNF-α and of IFN-γ was reduced in patients with colorectal carcinoma compared to controls (TNF-α, 11 269 pg ml−1{12 598}; IFN-γ, 0.00 pg ml−1{226}; median {IQR}) (TNF-α, 20 576 pg ml−1{11 637}, P< 0.0001; IFN-γ, 1048 {2428}, P = 0.0051, Mann–Whitney U -test). Production in patients after surgery had increased (TNF-α: 17 620 pg ml−1{7986}; IFN-γ: 410 pg ml−1{2696}; mean {s.d.}) and were no longer significantly reduced when compared to controls (TNF-α, P = 0.28; IFN-γ, P = 0.76). Production of TNF-α and IFN-γ prior to surgery were reduced to a greater extent in patients with Dukes' stage C tumours compared to those with Dukes' stage A and B stage. There was no difference in IL-10 production between any group. Monocytes/macrophages from patients with colorectal carcinoma are refractory to LPS stimulation as reflected by reduction in TNF-α and IFN-γ production and this is more pronounced in patients with advanced stage tumours. This suppression is not mediated by IL-10 and disappears following surgical resection of the tumour. This provides evidence for tumour induced suppression of immune function in patients with colorectal cancer and identifies a potential therapeutic avenue. © 2000 Cancer Research Campaig

Energy dependent saturation width of swift heavy ion shaped embedded Au nanoparticles

The transformation of Aunanoparticles (NPs) embedded in SiO₂ from spherical to rod-like shapes induced by swift heavy ion irradiation has been studied. Irradiation was performed with ¹⁹⁷Au ions at energies between 54 and 185 MeV. Transmission electron microscopy and small angle x-ray scatteringmeasurements reveal an energy dependent saturation width of the NP rods as well as a minimum size required for the NPs to elongate. The NP saturation width is correlated with the ion track diameter in the SiO₂. NP melting and in-plane strain in the irradiatedSiO₂ are discussed as potential mechanisms for the observed deformation.P.K. and M.C.R. thank the Australian Research Council for support. P.K., R.G., D.J.S., and M.C.R. were supported by the Australian Synchrotron Research Program, funded by the Commonwealth of Australia via the Major National Research Facilities Program

MesoGraph: automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

Deterministic and stochastic descriptions of gene expression dynamics

A key goal of systems biology is the predictive mathematical description of gene regulatory circuits. Different approaches are used such as deterministic and stochastic models, models that describe cell growth and division explicitly or implicitly etc. Here we consider simple systems of unregulated (constitutive) gene expression and compare different mathematical descriptions systematically to obtain insight into the errors that are introduced by various common approximations such as describing cell growth and division by an effective protein degradation term. In particular, we show that the population average of protein content of a cell exhibits a subtle dependence on the dynamics of growth and division, the specific model for volume growth and the age structure of the population. Nevertheless, the error made by models with implicit cell growth and division is quite small. Furthermore, we compare various models that are partially stochastic to investigate the impact of different sources of (intrinsic) noise. This comparison indicates that different sources of noise (protein synthesis, partitioning in cell division) contribute comparable amounts of noise if protein synthesis is not or only weakly bursty. If protein synthesis is very bursty, the burstiness is the dominant noise source, independent of other details of the model. Finally, we discuss two sources of extrinsic noise: cell-to-cell variations in protein content due to cells being at different stages in the division cycles, which we show to be small (for the protein concentration and, surprisingly, also for the protein copy number per cell) and fluctuations in the growth rate, which can have a significant impact.Comment: 23 pages, 5 figures; Journal of Statistical physics (2012