Impact of Tumor Response After Neoadjuvant Treatment on Overall Survival Among Patients With Pancreatic Ductal Adenocarcinoma: National Cancer Database Analysis.

Background Complete pathologic response following neoadjuvant therapy (NAT) for pancreatic ductal adenocarcinoma (PDAC) is rare; alternative markers associated with survival are needed. The aim of this study was to evaluate the impact of tumor response to NAT on overall survival (OS) in PDAC patients who received NAT and curative-intent surgery. Methods A retrospective study utilizing the 2006-2018 National Cancer Database identified 6,960 adult patients with PDAC who received NAT.

Comparing epidemiological and clinical data from RPS patients documented in a German cancer registry to a cohort from TARPSWG reference centres.

Purpose: Retroperitoneal sarcomas (RPS) are rare, heterogeneous tumours. Treatment recommendations are mainly derived from cohorts treated at reference centres. The applicability of data from cancer registries (CR) is controversial.

Ageing effects of social environments in 'non-social' insects.

It is increasingly clear that social environments have profound impacts on the life histories of 'non-social' animals. However, it is not yet well known how species with varying degrees of sociality respond to different social contexts and whether such effects are sex-specific. To survey the extent to which social environments specifically affect lifespan and ageing in non-social species, we performed a systematic literature review, focusing on invertebrates but excluding eusocial insects.

Benchmarking for healthy food stores: protocol for a randomised controlled trial with remote Aboriginal and Torres Strait Islander communities in Australia to enhance adoption of health-enabling store policy and practice.

Background: Aboriginal and Torres Strait Islander communities in remote Australia have initiated bold policies for health-enabling stores. Benchmarking, a data-driven and facilitated 'audit and feedback' with action planning process, provides a potential strategy to strengthen and scale health-enabling best-practice adoption by remote community store directors/owners. We aim to co-design a benchmarking model with five partner organisations and test its effectiveness with Aboriginal and Torres Strait Islander community stores in remote Australia.

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase.

The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data.