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Nucleic acid mimics (NAMs) have demonstrated high potential as antibacterial drugs. However, very few studies have assessed their possible diffusion across the bacterial envelope. In this work, we studied NAMs' diffusion in lipid bilayer systems that mimic the bacterial outer membrane using molecular dynamics (MD) simulations. Additionally, we examined the interactions of a NAM sequence with lipid membranes and ascertained the partition constants ( K p ) through MD and spectroscopic investigations. The NAM sequences were composed of locked nucleic acid (LNA) and 2'-O-methyl (2'-OMe) residues, whereas the membrane models were composed of 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) or 1-palmitoyl-2-oleoyl- sn -glycero-3-phospho-(1'-rac-glycerol) (POPG) phospholipids. The parametrization protocol followed was validated against literature data and demonstrated the reliability of our approach for simulating NAM sequences. Investigation into the interaction of the sequences with zwitterionic and anionic membranes revealed a preference for hydrogen bond formation with the anionic model over the zwitterionic one. Additionally, potential of mean force (PMF) calculations unveiled a lower free energy barrier for translocation across the zwitterionic bilayer model. Contrarily, the partition constants derived suggested a slightly higher partitioning within the anionic membrane, emphasizing a nuanced interplay of factors. Finally, spectroscopic partition measurements with liposomes presented challenges in quantifying the partition of NAMs due to minimal signal variations. However, a tendency for quenching in anionic vesicles suggested a potential, albeit small, partitioning effect that warrants further investigation. In summary, our study revealed that NAMs will not, in principle, be able to cross an intact bacterial outer membrane by passive diffusion.

The importance of radiology in modern medicine is acknowledged for its non-invasive diagnostic capabilities, yet the manual formulation of unstructured medical reports poses time constraints and error risks. This study addresses the common limitation of Artificial Intelligence applications in medical image captioning, which typically focus on classification problems, lacking detailed information about the patient's condition. Despite advancements in AI-generated medical reports that incorporate descriptive details from X-ray images, which are essential for comprehensive reports, the challenge persists. The proposed solution involves a multimodal model utilizing Computer Vision for image representation and Natural Language Processing for textual report generation. A notable contribution is the innovative use of the Swin Transformer as the image encoder, enabling hierarchical mapping and enhanced model perception without a surge in parameters or computational costs. The model incorporates GPT-2 as the textual decoder, integrating cross-attention layers and bilingual training with datasets in Portuguese PT-BR and English. Promising results are noted in the proposed database with ROUGE-L 0.748, METEOR 0.741, and NIH CHEST X-ray with ROUGE-L 0.404 and METEOR 0.393., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)