A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms.

Purpose: To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose iterative reconstruction for brain computed tomography (CT) phantom images.

Methods: Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDI): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother).

Comprehensive quantum calculation of the first dielectric virial coefficient of water.

We present a complete calculation, fully accounting for quantum effects and for molecular flexibility, of the first dielectric virial coefficient of water and its isotopologues. The contribution of the electronic polarizability is computed from a state-of-the-art intramolecular potential and polarizability surface from the literature, and its small temperature dependence is quantified. The dipolar polarizability is calculated in a similar manner with an accurate literature dipole-moment surface; it differs from the classical result both due to the different molecular geometries sampled at different temperatures and due to the quantization of rotation.

Paving the Path for Immune Enhancing Nutrition in Colon Cancer: Modulation of Tumor Microenvironment and Optimization of Outcomes and Costs.

Introduction: Published evidence suggests that immunonutrition has the potential to decrease postoperative complications and reduce length of stay in patients undergoing surgery for colorectal cancer. However, only a few studies have analyzed the effects of immunonutrition on tumor microenvironment and evaluated its prognostic impact.

Material And Methods: This is a single center retrospective study enrolling 50 patients undergoing elective surgery for colorectal cancer managed with immunonutrition and 50 patients managed with standard nutrition for comparison.

"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose: a phantom study at different dose levels".

Purpose: To characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging.

Methods: CT images of the Catphan-600 phantom (equipped with an external annulus) were acquired using an abdominal protocol at four dose levels and reconstructed using FBP, iDose (levels 2,5) and PI ('Soft Tissue' definition, levels 'Sharper','Sharp','Standard','Smooth','Smoother'). Image noise, image non-uniformity, noise power spectrum (NPS), target transfer function (TTF), detectability index (d'), CT numbers accuracy and image histograms were analyzed.

Immunohistochemical Markers of the Epithelial-to-Mesenchymal Transition (EMT) Are Related to Extensive Lymph Nodal Spread, Peritoneal Dissemination, and Poor Prognosis in the Microsatellite-Stable Diffuse Histotype of Gastric Cancer.

Background: Although the prognostic value of the epithelial-to-mesenchymal transition (EMT) in gastric cancer has been reported in several studies, the strong association with the diffuse type may represent a confounding factor. Our aim is to investigate potential correlations among EMT status, tumor advancement, and prognosis in diffuse gastric cancer. Methods: Between 1997 and 2012, 84 patients with microsatellite-stable (MSS) diffuse-type tumors underwent surgery.