Late-Stage Minimal Labeling of Peptides and Proteins for Real-Time Imaging of Cellular Trafficking.

The cellular uptake routes of peptides and proteins are complex and diverse, often handicapping therapeutic success. Understanding their mechanisms of internalization requires chemical derivatization with approaches that are compatible with wash-free and real-time imaging. In this work, we developed a new late-stage labeling strategy for unprotected peptides and proteins, which retains their biological activity while enabling live-cell imaging of uptake and intracellular trafficking.

Biomarkers of auditory cortical plasticity and development of binaural pathways in children with unilateral hearing loss using a hearing aid.

Congenital or early-onset unilateral hearing loss (UHL) can disrupt the normal development of the auditory system. In extreme cases of UHL (i.e.

Site-Specific Acetylation of the Transcription Factor Protein Max Modulates Its DNA Binding Activity.

Chemical protein synthesis provides a powerful means to prepare novel modified proteins with precision down to the atomic level, enabling an unprecedented opportunity to understand fundamental biological processes. Of particular interest is the process of gene expression, orchestrated through the interactions between transcription factors (TFs) and DNA. Here, we combined chemical protein synthesis and high-throughput screening technology to decipher the role of post-translational modifications (PTMs), e.

Siblings With Thrombocytopenia Found To Have a Pathogenic Variant in the NFkB1 Gene.

Immune thrombocytopenic purpura is one of the most common causes of low platelet count in the pediatric population. Secondary thrombocytopenia has a wide differential diagnosis in children, including rheumatological, hematological, and immunological etiologies. Underlying etiologies must be excluded if suspected before labeling the patient as primary thrombocytopenia.

A deep-learning approach for identifying prospective chemical hazards.

With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity.