In vitro scratch wound healing assay, a simple and low-cost technique that works along with other image analysis tools, is one of the most widely used 2D methods to determine the cellular migration and proliferation in processes such as regeneration and disease. There are open-source programs such as imageJ to analyze images of in vitro scratch wound healing assays, but these tools require manual tuning of various parameters, which is time-consuming and limits image throughput. For that reason, we developed an optimized plugin for imageJ to automatically recognize the wound healing size, correct the average wound width by considering its inclination, and quantify other important parameters such as: area, wound area fraction, average wound width, and width deviation of the wound images obtained from a scratch/ wound healing assay. Our plugin is easy to install and can be used with different operating systems. It can be adapted to analyze both individual images and stacks. Additionally, it allows the analysis of images obtained from bright field, phase contrast, and fluorescence microscopes. In conclusion, this new imageJ plugin is a robust tool to automatically standardize and facilitate quantification of different in vitro wound parameters with high accuracy compared with other tools and manual identification.

In vitro scratch wound healing assay, a simple and low-cost technique that works along with other image analysis tools, is one of the most widely used 2D methods to determine the cellular migration and proliferation in processes such as regeneration and disease. There are open-source programs such as imageJ to analyze images of in vitro scratch wound healing assays, but these tools require manual tuning of various parameters, which is time-consuming and limits image throughput. For that reason, we developed an optimized plugin for imageJ to automatically recognize the wound healing size, correct the average wound width by considering its inclination, and quantify other important parameters such as: area, wound area fraction, average wound width, and width deviation of the wound images obtained from a scratch/ wound healing assay. Our plugin is easy to install and can be used with different operating systems. It can be adapted to analyze both individual images and stacks. Additionally, it allows the analysis of images obtained from bright field, phase contrast, and fluorescence microscopes. In conclusion, this new imageJ plugin is a robust tool to automatically standardize and facilitate quantification of different in vitro wound parameters with high accuracy compared with other tools and manual identification.

Iron oxide nanoparticles (IONs) have been widely explored for biomedical applications due to their high biocompatibility, surface-coating versatility, and superparamagnetic properties. Upon exposure to an external magnetic field, IONs can be precisely directed to a region of interest and serve as exceptional delivery vehicles and cellular markers. However, the design of nanocarriers that achieve an efficient endocytic uptake, escape lysosomal degradation, and perform precise intracellular functions is still a challenge for their application in translational medicine. This review highlights several aspects that mediate the activation of the endosomal pathways, as well as the different properties that govern endosomal escape and nuclear transfection of magnetic IONs. In particular, we review a variety of ION surface modification alternatives that have emerged for facilitating their endocytic uptake and their timely escape from endosomes, with special emphasis on how these can be manipulated for the rational design of cell-penetrating vehicles. Moreover, additional modifications for enhancing nuclear transfection are also included in the design of therapeutic vehicles that must overcome this barrier. Understanding these mechanisms opens new perspectives in the strategic development of vehicles for cell tracking, cell imaging and the targeted intracellular delivery of drugs and gene therapy sequences and vectors.

Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.

Over the past decade, gene therapies have attracted much attention for the development of treatments for various conditions, including cancer, neurodegenerative diseases, protein deficiencies, and autoimmune disorders. Despite the benefits of this approach, several challenges are yet to be solved to reach clinical implementation. Some of these challenges include low transfection rates, limited stability under physiological conditions, and low specificity towards the target cells. An avenue to overcome such issues is to deliver the therapies with the aid of potent cell-penetrating vectors. Non-viral vectors, such as nanostructured materials, have been successfully tested in drug and gene delivery. Here, we propose the development and in vitro evaluation of a nanostructured cell-penetrating vehicle based on core/shell, magnetite/silver nanoparticles. A subsequent conjugation of a pH-responsive polymer was used to assure that the vehicle can carry and release circular DNA. Additionally, the translocating peptide Buforin II was conjugated with the aid of a polyether amine polymer to facilitate translocation and endosome escape. The obtained nanobioconjugates (magnetite/silver-pDMAEMA-PEA-BUFII) were characterized by UV-Vis spectrophotometry, dynamic light scattering (DLS), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), scanning electron microscope equipped with energy dispersive spectroscopy (SEM+EDS), and transmission electron microscopy (TEM). They were also encapsulated in lecithin liposomes to form magnetoliposomes. The cell viability of Vero cells in the presence of the nanobioconjugates was above 95% and declined to 80% for the magnetoliposomes. The hemolytic tendency of nanobioconjugates and magnetoliposomes was below 10%, while the platelet aggregation approached that of the negative control (i.e., 35%). Cytoplasm coverage values of about 50% for both Vero and neuroblastoma cells confirmed significant cell penetration. Pearson’s correlation coefficients for both cell lines allowed us to estimate 20–40% colocalization of the nanobioconjugates with lysotracker green, which implied high levels of endosomal escape. The developed vehicles were also capable of loading around 16% of the added DNA and releasing such cargo with 8% efficiency. The developed nanoplatform holds a significant promise to enable highly efficient gene therapies as it overcomes some of the major issues associated with their eventual translation to the pre-clinical and clinical scale.

Decellularized extracellular matrices (dECMs) have shown enormous potential for the biofabrication of tissues due to their biomimetic properties that promote enhanced cellular interaction and tissue regeneration. However, biofabrication schemes requiring electrostimulation pose an additional constraint due to the insulating properties of natural materials. Here, we propose a methacryloyl-modified decellularized small intestine submucosa (SISMA) hydrogel, embedded with graphene oxide (GO) nanosheets, for extrusion-based 3D bioprinting applications that require electrostimulation. Methacryloyl biochemical modification is performed to enhance the mechanical stability of dECM constructs by mediating photo-crosslinking reactions, and a multistep fabrication scheme is proposed to harness the bioactive and hydrophilic properties of GO and electroconductive properties of reduced GO. For this, GO was initially dispersed in SISMA hydrogels by exploiting its hydrophilicity and protein adsorption capabilities, and in situ reduction was subsequently performed to confer electroconductive abilities. SISMA-GO composite hydrogels were successfully prepared with enhanced structural characteristics, as shown by the higher crosslinking degree and increased elastic response upon blue-light exposure. Moreover, GO was homogeneously dispersed without affecting photocrosslinking reactions and hydrogel shear-thinning properties. Human adipose-derived mesenchymal stem cells were successfully bioprinted in SISMA-GO with high cell viability after 1 week and in situ reduction of GO during this period enhanced the electrical conductivity of these nanostructures. This work demonstrates the potential of SISMA-GO bioinks as bioactive and electroconductive scaffolds for electrostimulation applications in tissue engineering and regenerative medicine.