Adjuvants are a diverse family of substances whose main objective is to increase the strength, quality, and duration of the immune response caused by vaccines. The most commonly used adjuvants are aluminum-based, oil-water emulsion, and bacterial-origin adjuvants. In this paper, we will discuss how the election of adjuvants is important for the adjuvant-mediated induction of immunity for different types of vaccines. Aluminum-based adjuvants are the most commonly used, the safest, and have the best efficacy, due to the triggering of a strong humoral response, albeit generating a weak induction of cell-mediated immune response. Freund’s adjuvant is the most widely used oil-water emulsion adjuvant in animal trials; it stimulates inflammation and causes aggregation and precipitation of soluble protein antigens that facilitate the uptake by antigen-presenting cells (APCs). Adjuvants of bacterial origin, such as flagellin,E. colimembranes, and monophosphoryl lipid A (MLA), are known to potentiate immune responses, but their safety and risks are the main concern of their clinical use. This minireview summarizes the mechanisms that classic and novel adjuvants produce to stimulate immune responses.

Hyaluronan (HA) is a ubiquitous macromolecule in the human body with remarkable structure and function. HA presents a key role in several biological processes in mammals. The synthesis/catabolism of HA is critical in several pathologies and has been used as a marker for the prognosis of cancers. Among its physiological roles, HA is used for wound healing applications. This review reports many of the latest developments of hyaluronan and its derivatives in research, preclinical, and published clinical trials for wound healing. An adequate physico-chemical characterization and identification of selected physico-chemical properties of the prepared material are mandatory. Moreover, cytotoxicity and evaluation of biological effects in vitro using standardized protocols are required as preclinical. Finally, to choose adequate in vivo models for testing efficacy is requested. Unfortunately, the biological role of HA is still not well understood. Therefore, an overview of several HA-based products is provided and discussed. Several ways of HA chemical modification were evaluated. Finally, this review focuses on products containing HA, novel developments, gaps, and limitations of the current state of the art.

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the ‘curse of dimensionality’, leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Motivation: Cyclodextrins (CDs) are amongst the most versatile/multi-functional molecules used in molecular research and chemical applications. They are natural cyclic oligosaccharides typically employed to encapsulate hydrophobic groups in their central cavity. This allows solubilizing, protecting or reducing the toxicity of a large variety of different molecules including drugs, dyes and surfactant agents. In spite of their great potential, atomic level information of these molecules, which is key for their function, is really scarce. Computational Molecular Dynamics (MD) simulations have the potential to efficiently fill this gap, providing structural-dynamic information at atomic level in time scales ranging from ps to μs.