Improvement of the quality and efficiency of healthcare in medicine, both at home and in hospital, is becoming more and more important for patients and society at large. As many technologies (micro technologies, telecommunication, low-power design, new textiles, and flexible sensors) are now available, new user-friendly devices can be developed to enhance the comfort and security of the patient. As clothes and textiles are in direct contact with about 90% of the skin surface, smart sensors and smart clothes with noninvasive sensors are an attractive solution for home-based and ambulatory health monitoring. Moreover, wearable devices or smart homes with exosensors are also potential solutions. All these systems can provide a safe and comfortable environment for home healthcare, illness prevention, and citizen medicine.
Current COVID-19 vaccines prevent severe disease, but do not induce mucosal immunity or prevent infection with SARS-CoV-2, especially with recent variants. Furthermore, serum antibody responses wane soon after immunization. We assessed the immunogenicity and protective efficacy of an experimental COVID-19 vaccine based on the SARS-CoV-2 Spike trimer formulated with a novel adjuvant LP-GMP, comprising TLR2 and STING agonists. We demonstrated that immunization of mice twice by the intranasal (i.n.) route or by heterologous intramuscular (i.m.) prime and i.n. boost with the Spike-LP-GMP vaccine generated potent Spike-specific IgG, IgA and tissue-resident memory (TRM) T cells in the lungs and nasal mucosa that persisted for at least 3 months. Furthermore, Spike-LP-GMP vaccine delivered by i.n./i.n., i.m./i.n., or i.m./i.m. routes protected human ACE-2 transgenic mice against respiratory infection and COVID-19-like disease following lethal challenge with ancestral or Delta strains of SARS-CoV-2. Our findings underscore the potential for nasal vaccines in preventing infection with SARS-CoV-2 and other respiratory pathogen.
The mammalian Transient Receptor Potential Vanilloid (TRPV) channels are a family of six tetrameric ion channels localized at the plasma membrane. The group I members of the family, TRPV1 through TRPV4, are heat-activated and exhibit remarkable polymodality. The distal N-termini of group I TRPV channels contain large intrinsically disordered regions (IDRs), ranging from ~ 75 amino acids (TRPV2) to ~ 150 amino acids (TRPV4), the vast majority of which is invisible in the structural models published so far. These IDRs provide important binding sites for cytosolic partners, and their deletion is detrimental to channel activity and regulation. Recently, we reported the NMR backbone assignments of the distal TRPV4 N-terminus and noticed some discrepancies between the extent of disorder predicted solely based on protein sequence and from experimentally determined chemical shifts. Thus, for an analysis of the extent of disorder in the distal N-termini of all group I TRPV channels, we now report the NMR assignments for the human TRPV1, TRPV2 and TRPV3 IDRs.
Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.
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