Integration of Genomic and Transcriptional Features in Pancreatic Cancer Reveals Increased Cell Cycle Progression in Metastases

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Intima media thickness of carotid arteries in familial Mediterranean fever: a systematic review and meta-analysis

Clinical Rheumatology - To perform a systematic review and meta-analysis of studies reporting data on atherosclerosis and inflammatory markers in familial Mediterranean fever (FMF). EMBASE and...     

Serum alpha-fetoprotein and clinical outcomes in patients with advanced hepatocellular carcinoma treated with ramucirumab

Background Post hoc analyses assessed the prognostic and predictive value of baseline alpha-fetoprotein (AFP), as well as clinical outcomes by AFP response or progression, during treatment in two placebo-controlled trials (REACH, REACH-2).Methods Serum AFP was measured at baseline and every three cycles. The prognostic and predictive value of baseline AFP was assessed by Cox regression models and Subpopulation Treatment Effect Pattern Plot method. Associations between AFP (≥ 20% increase) and radiographic progression and efficacy were assessed.Results Baseline AFP was confirmed as a continuous (REACH, REACH-2; p < 0.0001) and dichotomous (≥400 vs. <400 ng/ml; REACH, p < 0.01) prognostic factor, and was predictive for ramucirumab survival benefit in REACH (p = 0.0042 continuous; p < 0.0001 dichotomous). Time to AFP (hazard ratio [HR] 0.513; p < 0.0001) and radiographic (HR 0.549; p < 0.0001) progression favoured ramucirumab. Association between AFP and radiographic progression was shown for up to 6 (odds ratio [OR] 5.1; p < 0.0001) and 6–12 weeks (OR 1.8; p = 0.0065). AFP response was higher with ramucirumab vs. placebo (p < 0.0001). Survival was longer in patients with an AFP response than patients without (13.6 vs. 5.6 months, HR 0.451; 95% confidence interval, 0.354–0.574; p < 0.0001).Conclusions AFP is an important prognostic factor and a predictive biomarker for ramucirumab survival benefit. AFP ≥ 400 ng/ml is an appropriate selection criterion for ramucirumab.Clinical Trial Registration ClinicalTrials.gov, REACH ({"type":"clinical-trial","attrs":{"text":"NCT01140347","term_id":"NCT01140347"}}NCT01140347) and REACH-2 ({"type":"clinical-trial","attrs":{"text":"NCT02435433","term_id":"NCT02435433"}}NCT02435433).Subject terms: Oncology, Biomarkers     

A Novel Nomogram to Identify Candidates for Extended Pelvic Lymph Node Dissection Among Patients with Clinically Localized Prostate Cancer Diagnosed with Magnetic Resonance Imaging-targeted and Systematic Biopsies

Background

Available models for predicting lymph node invasion (LNI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP) might not be applicable to men diagnosed via magnetic resonance imaging (MRI)-targeted biopsies.

Analyzing Social Robotics Research with Natural Language Processing Techniques

Cognitive Computation - The fast growth of social robotics (SR) has not been unidirectional, but rather towards a multidisciplinary scenario, creating a need for collaboration between different...     

Gut vascular barrier impairment leads to intestinal bacteria dissemination and colorectal cancer metastasis to liver

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Validity and reliability of the Unified Classification System applied to periprosthetic femur fractures: a comparison with the Vancouver system

Abstract

Objective: The Unified Classification System (UCS) presents itself as an evolution of the Vancouver Classification (VCS) for the evaluation of periprosthetic fractures of the proximal femur (PPF). The aim of our study was to highlight any loss of reproducibility or validity of the new classification system, compared to the previous one.

Material and methods: We tested the interobserver and intraobserver agreement using 40 PPF clinical cases. Each classifying subtype of the UCS and VCS was present in at least two cases. Six experienced hip surgeons (Senior Surgeon, SS) and 5 surgeons in training (Junior Surgeon, JS) classified the clinical cases, using VCS and UCS. The validity of both classifications was then tested with intraoperative surveys.

Results: The mean κ value for interobserver agreement for the VCS in the JS group was 0.65 and 0.81 for the SS group. The mean κ value for interobserver agreement for the UCS in the JS group was 0.63 and 0.65 for the SS group. The mean κ value for intraobserver agreement for the VCS in the JS group was 0.71 and 0.73 for the SS group. The mean κ value for intraobserver agreement for the UCS in the JS group was 0.72 and 0.7 for the SS group. Validity analysis showed a moderate agreement for the VCS and a good agreement for the UCS.

Conclusion: The UCS completes the Vancouver classification, expanding it. It is reliable, despite the increase in classification categories and number of parameters to evaluate, with a slightly higher validity.     

Information diffusion,Facebook clusters,and the simplicial model of social aggregation: a computational simulation of simplicial diffusers for community health interventions

By integrating the simplicial model of social aggregation with existing research on opinion leadership and diffusion networks, this article introduces the constructs of simplicial diffusers (mathematically defined as nodes embedded in simplexes; a simplex is a socially bonded cluster) and simplicial diffusing sets (mathematically defined as minimal covers of a simplicial complex; a simplicial complex is a social aggregation in which socially bonded clusters are embedded) to propose a strategic approach for information diffusion of cancer screenings as a health intervention on Facebook for community cancer prevention and control. This approach is novel in its incorporation of interpersonally bonded clusters, culturally distinct subgroups, and different united social entities that coexist within a larger community into a computational simulation to select sets of simplicial diffusers with the highest degree of information diffusion for health intervention dissemination. The unique contributions of the article also include seven propositions and five algorithmic steps for computationally modeling the simplicial model with Facebook data.