Is right angular gyrus involved in the metric component of the mental body representation in touch and vision? A tdcs study

Several studies have found in the sense of touch a good sensory modality by which to study body representation. Here, we address the “metric component of body representation”, a specific function developed to process the discrimination of tactile distances on the body. The literature suggests the involvement of the right angular gyrus (rAG) in processing the tactile metricity on the body. The question of this study is the following: is the rAG also responsible for the visual metric component of body representation? We used tDCS (anodal and sham) in 20 subjects who were administered an on-body distance discrimination task with both tactile and visual stimuli. They were also asked to perform the same task in a near-body condition. The results allow us to confirm the role of rAG in the estimation of tactile distances. Further, we also showed that rAG might be involved in the discrimination of distances on the body not only in tactile but also in visual modality. Finally, based on the significant effects of anodal stimulation even in a near-body visual discrimination task, we proposed a higher-order function of the AG in terms of a supramodal com-parator of quantities

SEAL: a distributed short read mapping and duplicate removal tool

Summary: SEAL is a scalable tool for short read pair mapping and duplicate removal. It computes mappings that are consistent with those produced by BWA and removes duplicates according to the same criteria employed by Picard MarkDuplicates. On a 16-node Hadoop cluster, it is capable of processing about 13 GB per hour in map+rmdup mode, while reaching a throughput of 19 GB per hour in mapping-only mode

Transcutol® p containing slns for improving 8-methoxypsoralen skin delivery

Topical psoralens plus ultraviolet A radiation (PUVA) therapy consists in the topical application of 8-methoxypsoralen (8-MOP) followed by the skin irradiation with ultraviolet A radiation. The employment of classical 8-MOP vehicles in topical PUVA therapy is associated with poor skin deposition and weak skin permeability of psoralens, thus requiring frequent drug administration. The aim of the present work was to formulate solid lipid nanoparticles (SLNs) able to increase the skin permeation of 8-MOP. For this purpose, the penetration enhancer Transcutol® P (TRC) was added to the SLN formulation. SLNs were characterized with respect to size, polydispersity index, zeta potential, entrapment efficiency, morphology, stability, and biocompatibility. Finally, 8-MOP skin diffusion and distribution within the skin layers was investigated using Franz cells and newborn pig skin. Freshly prepared nanoparticles showed spherical shape, mean diameters ranging between 120 and 133 nm, a fairly narrow size distribution, highly negative ζ potential values, and high entrapment efficiency. Empty and loaded formulations were almost stable over 30 days. In vitro penetration and permeation studies demonstrated a greater 8-MOP accumulation in each skin layer after SLN TRC 2% and TRC 4% application than that after SLN TRC 0% application. Finally, the results of experiments on 3T3 fibroblasts showed that the incorporation of TRC into SLNs could enhance the cellular uptake of nanoparticles, but it did not increase their cytotoxicity

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall

Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort

Introduction: Prostate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research. Methods: The TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study’s prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks. Results: The cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage. Discussion: This study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa

Molecular-biology-driven treatment for metastatic colorectal cancer

Background: Metastatic CRC (mCRC) is a molecular heterogeneous disease. The aim of this review is to give an overview of molecular-driven treatment of mCRC patients. Methods: A review of clinical trials, retrospective studies and case reports was performed regarding molecular biomarkers with therapeutic implications. Results: RAS wild-type status was confirmed as being crucial for anti-epidermal growth factor receptor (EGFR) monoclonal antibodies and for rechallenge strategy. Antiangiogenic therapies improve survival in first- and second-line settings, irrespective of RAS status, while tyrosine kinase inhibitors (TKIs) remain promising in refractory mCRC. Promising results emerged from anti-HER2 drugs trials in HER2-positive mCRC. Target inhibitors were successful for BRAFV600E mutant mCRC patients, while immunotherapy was successful for microsatellite instability-high/defective mismatch repair (MSI-H/dMMR) or DNA polymerase epsilon catalytic subunit (POLE-1) mutant patients. Data are still lacking on NTRK, RET, MGMT, and TGF-β, which require further research. Conclusion: Several molecular biomarkers have been identified for the tailored treatment of mCRC patients and multiple efforts are currently ongoing to increase the therapeutic options. In the era of precision medicine, molecular-biology-driven treatment is the key to impro patient selection and patient outcomes. Further research and large phase III trials are required to ameliorate the therapeutic management of these patients