The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

Bacterial nanocellulose (BNC) is a novel nanomaterial known for its large surface area, biocompatibility, and non-toxicity. BNC contributes to regenerative processes in the skin but lacks antimicrobial and anti-inflammatory properties. Herein, the development of bioactive wound dressings by loading antibacterial povidone-iodine (PVI) or anti-inflammatory acetylsalicylic acid (ASA) into bacterial cellulose is presented. BNC is produced using Hestrin–Schramm culture media and loaded via immersion in PVI and ASA. Through scanning electron microscopy, BNC reveals open porosity where the bioactive compounds are loaded; the mechanical tests show that the dressing prevents mechanical wear. The loading kinetic and release assays (using the Franz cell method) under simulated fluids present a maximum loading of 589.36 mg PVI/g BNC and 38.61 mg ASA/g BNC, and both systems present a slow release profile at 24 h. Through histology, the complete diffusion of the bioactive compounds is observed across the layers of porcine skin. Finally, in the antimicrobial experiment, BNC/PVI produced an inhibition halo for Gram-positive and Gram-negative bacteria, confirming the antibacterial activity. Meanwhile, the protein denaturation test shows effective anti-inflammatory activity in BNC/ASA dressings. Accordingly, BNC is a suitable platform for the development of bioactive wound dressings, particularly those with antibacterial and anti-inflammatory properties.

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling of CDR3 sill represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

In Colombia, an average of 75 tons of mercury is released annually due to the illegal mining activity of gold, causing natural water sources to be contaminated in regions that collect water through rivers and streams, generating mercury poisoning. Nanochitosan, a biopolymer synthesized at the nanoscale, has shown its ability to retain different heavy metals in wastewater due to its greater availability of amino groups at its surface. In this work, a set of preliminary parameters were identified and established for the development of cellulose acetate membranes with chitosan nanoparticles for mercury retention. Nanoparticles were synthesized by ionic gelation, varying the addition rate of sodium tripolyphosphate. The membranes were prepared by solvent exchange, varying the concentration of polyethylene glycol, the pore-forming agent. Finally, the samples were characterized by scanning electron microscopy and dynamic light scattering. The results showed that there is a proportional relationship between the rate of addition and the size of the particles obtained, while, for the membranes, 11% polyethylene glycol produced a porous network with cellulose acetate fibers inside the pores.