Functional evaluation and clinical classification of BRCA2 variants.

Germline BRCA2 loss-of function variants, which can be identified through clinical genetic testing, predispose to several cancers. However, variants of uncertain significance limit the clinical utility of test results. Thus, there is a need for functional characterization and clinical classification of all BRCA2 variants to facilitate the clinical management of individuals with these variants.

Tuning Nitrogen Configurations in Nitrogen-Doped Graphene Encapsulating Fe3C Nanoparticles for Enhanced Nitrate Electroreduction.

Electrochemical nitrate reduction reaction (NO3RR) offers a promising technology for the synthesis of ammonia (NH3) and removal of nitrate in wastewater. Herin, we fabricate a series of Fe3C nanoparticles in controllable pyridinic-N doped graphene (Fe3C@NG-X) by a self-sacrificing template method for the NO3RR. Fe3C@NG-10 exhibits high catalytic performance with a Faradaic efficiency (FE) of 94.

Polydimethylsiloxane loaded capsaicin afflicts membrane integrity, metabolic activity and biofilm formation of nosocomial pathogens.

Biofilms constitute 80 % of all nosocomial infections associated with invasive medical devices. Polydimethylsiloxane, a highly elastic, inert, non-reactive, biocompatible silicone polymer is widely used as implant biomaterial due to its non-toxic and low-immunogenic nature. Owing to its hydrophobicity, PDMS suffers from microbial adhesion.

Trabecular meshwork ultrastructural changes in primary and secondary glaucoma.

To examine ultrastructural changes in the trabecular meshwork (TM) in patients with primary and secondary glaucoma using scanning electron microscopy (SEM). This was a qualitative descriptive hospital-based study on the ultrastructure of the TM. Pure TM samples were collected after microincisional trabeculectomy from 26 patients with primary or secondary glaucoma and 10 control samples from eye bank donor corneas.

A comparative study of wavelet families for schizophrenia detection.

Schizophrenia (SZ) is a chronic mental disorder, affecting approximately 1% of the global population, it is believed to result from various environmental factors, with psychological factors potentially influencing its onset and progression. Discrete wavelet transform (DWT)-based approaches are effective in SZ detection. In this report, we aim to investigate the effect of wavelet and decomposition levels in SZ detection.