Socio-economic inequalities in second primary cancer incidence: A competing risks analysis of women with breast cancer in England between 2000 and 2018.

We aimed to investigate socio-economic inequalities in second primary cancer (SPC) incidence among breast cancer survivors. Using Data from cancer registries in England, we included all women diagnosed with a first primary breast cancer (PBC) between 2000 and 2018 and aged between 18 and 99 years and followed them up from 6 months after the PBC diagnosis until a SPC event, death, or right censoring, whichever came first. We used flexible parametric survival models adjusting for age and year of PBC diagnosis, ethnicity, PBC tumour stage, comorbidity, and PBC treatments to model the cause-specific hazards of SPC incidence and death according to income deprivation, and then estimated standardised cumulative incidences of SPC by deprivation, taking death as the competing event.

DET induces apoptosis and suppresses tumor invasion in glioma cells via PI3K/AKT pathway.

Introduction: Gliomas, particularly glioblastomas (GBM), are highly aggressive with a poor prognosis and low survival rate. Currently, deoxyelephantopin (DET) has shown promising anti-inflammatory and anti-tumor effects. Using clinical prognostic analysis, molecular docking, and network pharmacology, this study aims to explore the primary targets and signaling pathways to identify novel GBM treatment approaches.

A living library concept to capture the dynamics and reactivity of mixed-metal clusters for catalysis.

The exploration of ligated metal clusters' chemical space is challenging, partly owing to an insufficiently targeted access to reactive clusters. Now, dynamic mixtures of clusters, defined as living libraries, are obtained through organometallic precursor chemistry. The libraries are populated with interrelated clusters, including transient and highly reactive ones, as well as more accessible but less reactive species.

Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning.

3D disordered fibrous network structures (3D-DFNS), such as cytoskeletons, collagen matrices, and spider webs, exhibit remarkable material efficiency, lightweight properties, and mechanical adaptability. Despite their widespread in nature, the integration into engineered materials is limited by the lack of study on their complex architectures. This study addresses the challenge by investigating the structure-property relationships and stability of biomimetic 3D-DFNS using large datasets generated through procedural modeling, coarse-grained molecular dynamics simulations, and machine learning.

Development of sensitive and rapid immunoassays for Moniliformin (MON) detection based on nanomaterials labeled monoclonal antibodies.

Moniliformin (MON) is a toxic secondary metabolite from Fusarium species. The natural contamination of MON in cereals and cereal by-products, poses a risk of exposure to MON. However, so far, no immunoassay method has been reported to detect MON in field samples.