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Yet, art remains underutilized in anti-racism education, training and organizing efforts within public health. This commentary includes several arts-based examples to illustrate how art can facilitate insights, observations and strategies to address racism and achieve health equity.

Art can be an important tool to facilitate moving past intellectual arguments that seek to explain, justify and excuse racism. Art may be particularly important in efforts to illuminate how racism operates in organizational or institutional contexts and to communicate hope, resilience, and strength amid what seems impossible.

Art can be an important tool to facilitate moving past intellectual arguments that seek to explain, justify and excuse racism. Art may be particularly important in efforts to illuminate how racism operates in organizational or institutional contexts and to communicate hope, resilience, and strength amid what seems impossible.In the era of precision medicine, survival outcome data with high-throughput predictors are routinely collected. Models with an exceedingly large number of covariates are either infeasible to fit or likely to incur low predictability because of overfitting. Variable screening is key in identifying and removing irrelevant attributes. Recent years have seen a surge in screening methods, but most of them rely on some particular modeling assumptions. Motivated by a study on detecting gene signatures for multiple myeloma patients' survival, we propose a model-free L q -norm learning procedure, which includes the well-known Cramér-von Mises and Kolmogorov criteria as two special cases. The work provides an integrative framework for detecting predictors with various levels of impact, such as short- or long-term impact, on censored outcome data. The framework naturally leads to a scheme which combines results from different q to reduce false negatives, an aspect often overlooked by the current literature. We show that our method possesses sure screening properties. The utility of the proposal is confirmed with simulation studies and an analysis of the multiple myeloma study.

Gastroenteropancreatic neuroendocrine tumours (GEP-NETs) are slow-growing cancers that arise from diffuse endocrine cells in the gastrointestinal tract (GI-NETs) or the pancreas (P-NETs). They are relatively uncommon, accounting for 2% of all gastrointestinal malignancies. The usual treatment options in advanced GEP-NET patients with metastatic disease include chemotherapy, biological therapies, and peptide receptor radionuclide therapy. Understanding the impact of treatment on GEP-NET patients is paramount given the nature of the disease. Health-related quality of life (HRQoL) is increasingly important as a concept reflecting the patients' perspective in conjunction with the disease presentation, severity and treatment.

To conduct a systematic literature review to identify literature reporting HRQoL data in patients with GEP-NETs between January 1985 and November 2019.

The PRISMA guiding principles were applied. MEDLINE, Embase and the Cochrane library were searched. Data extracted from the publicationcifically for GI-NET and P-NET patients.

Type I

(

) infection causes severe gastric inflammation and is a predisposing factor for gastric carcinogenesis. However, its infection status in stepwise gastric disease progression in this gastric cancer prevalent area has not been evaluated; it is also not known its impact on commonly used epidemiological gastric cancer risk markers such as gastrin-17 (G-17) and pepsinogens (PGs) during clinical practice.

To explore the prevalence of type I and type II

infection status and their impact on G-17 and PG levels in clinical practice.

Thirty-five hundred and seventy-two hospital admitted patients with upper gastrointestinal symptoms were examined, and 523 patients were enrolled in this study.

infection was confirmed by both

C-urea breath test and serological assay. Patients were divided into non-atrophic gastritis (NAG), non-atrophic gastritis with erosion (NAGE), chronic atrophic gastritis (CAG), peptic ulcers (PU) and gastric cancer (GC) groups. Selleck MLT-748 Their serological G-17, PG I and PG II values aajor form of infection in this geographic region, and a very low percentage (11.6%) of GC patients are not infected by

. Both types of

induce an increase in G-17 level, while type I

is the major strain that affects PG I and PG IIs level and PG I/PG II ratio in stepwise chronic gastric disease. The data provide insights into

infection status and indicate the necessity and urgency for bacteria eradication and disease prevention in clinical practice.

Type I H. pylori infection is the major form of infection in this geographic region, and a very low percentage (11.6%) of GC patients are not infected by H. pylori. Both types of H. pylori induce an increase in G-17 level, while type I H. pylori is the major strain that affects PG I and PG IIs level and PG I/PG II ratio in stepwise chronic gastric disease. The data provide insights into H. pylori infection status and indicate the necessity and urgency for bacteria eradication and disease prevention in clinical practice.

The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks.

To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT.

A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses.