Editorial for the special issue on carbon based electronic devices

For more than 50 years, silicon has dominated the electronics industry [...]

Self-Assembling Peptides and Carbon Nanomaterials Join Forces for Innovative Biomedical Applications

Self-assembling peptides and carbon nanomaterials have attracted great interest for their respective potential to bring innovation in the biomedical field. Combination of these two types of building blocks is not trivial in light of their very different physico-chemical properties, yet great progress has been made over the years at the interface between these two research areas. This concise review will analyze the latest developments at the forefront of research that combines self-assembling peptides with carbon nanostructures for biological use. Applications span from tissue regeneration, to biosensing and imaging, and bioelectronics

Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science

Recent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multi-disciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing com-mon ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characteriza-tion protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artifi-cial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambi-tious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)

Deploying single-cell transcriptomics for assessing CAR T cell generation: alleviating antiviral restriction factors enhances gene transfer

Lentiviral vectors (LV) have become the dominant tool for stable gene transfer into lymphocytes including chimeric antigen receptor (CAR) gene delivery to T cells, a major breakthrough in cancer therapy. Yet, room for improvement remains, especially for the latest LV generations delivering genes selectively into T cell subtypes, a key requirement for in vivo CAR T cell generation. Towards improving gene transfer rates with these vectors, transcriptome analyses on human T lymphocytes after exposure to CAR-encoding conventional vector VSV-LV, and vectors targeted to CD8+ (CD8-LV) or CD4+ T cells (CD4-LV) was conducted. Genes related to quiescence and antiviral restriction were found to be upregulated in CAR-negative cells exposed to all types of LVs. Down-modulation of various antiviral restriction factors including the interferon-induced transmembrane proteins (IFITMs) was achieved with rapamycin as verified by mass spectrometry (LC-MS). Strikingly, rapamycin enhanced transduction by up to 7-fold for CD8-LV and CD4-LV without compromising CAR T cell activities, but did not improve VSV-LV. When administered to humanized mice, CD8-LV resulted in higher rates of GFP gene delivery as well as faster in vivo CAR T cell generation and tumor control. The data favor multi-omics approaches for improvements in gene delivery