Evidence of DNA methylation heterogeneity and epipolymorphism in kidney cancer tissue samples.

Clear cell renal cell carcinoma (ccRCC) is characterised by significant genetic heterogeneity, which has diagnostic and prognostic implications. Very limited evidence is available regarding DNA methylation heterogeneity. We therefore generate sequence level DNA methylation data on 136 multi-region tumour and normal kidney tissue from 18 ccRCC patients, along with matched whole exome sequencing (85 samples) and gene expression (47 samples) data on a subset of samples.

An explainable deep learning platform for molecular discovery.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to 'open up' the black box by providing generalizable and human-understandable reasoning for model predictions.

Piezoresistivity and strain-sensing behaviour of poly(butylene adipate--terephthalate)/multiwalled carbon nanotube nanocomposites.

Multiwalled carbon nanotubes (MWCNTs) at different concentrations, ranging from 0.5 to 10 wt%, as a conductive filler, were incorporated into poly(butylene adipate--terephthalate) (PBAT), a flexible biodegradable copolyester, melt-mixing, followed by compression moulding. The electrical conductivity of the prepared nanocomposites was evaluated by considering their volume resistivity value.

Comparative investigation of fillers loading effect on morphological, micromechanical, and thermal properties of polyvinyl alcohol/cellulosicfillers-based composites.

Polyvinyl alcohol (PVA)-based biocomposites were fabricated by the incorporation of chitosan (Ch), cellulose fibers (CS), and their mixture (1:1 ratio). Fillers with various loading (2, 4, 8, and 10 wt.-%) were incorporated into PVA employing the solution casting method.

Prostate cancer detection through unbiased capture of methylated cell-free DNA.

Prostate cancer screening using prostate-specific antigen (PSA) has been shown to reduce mortality but with substantial overdiagnosis, leading to unnecessary biopsies. The identification of a highly specific biomarker using liquid biopsies, represents an unmet need in the diagnostic pathway for prostate cancer. In this study, we employed a method that enriches for methylated cell-free DNA fragments coupled with a machine learning algorithm which enabled the detection of metastatic and localized cancers with AUCs of 0.