Valerie Ortiz-Gómez

Background/Objectives: The COVID-19 pandemic has resulted in more than 7.11 million deaths worldwide and has been associated with numerous extrapulmonary complications, including cardiovascular dysfunction. Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily targets the respiratory system, increasing evidence suggests that viral components may directly affect cardiac cells and contribute to long-term cardiovascular sequelae. In this study, we investigated the effects of recombinant spike proteins from the Alpha, Delta, and Omicron SARS-CoV-2 variants on the cytoskeletal organization and proteomic landscape of human AC16 cardiomyocytes. Methods: Confocal microscopy and quantitative image analysis were employed to evaluate cytoskeletal organization, including F-actin filament count and length, phalloidin fluorescence intensity, and cell solidity, following spike-protein exposure at 24, 48, and 72 h, while quantitative tandem mass tag (TMT)-based proteomics at the 72 h exposure time point was used to characterize variant-associated changes in host protein abundance. Results: Quantitative analysis revealed time- and variant-dependent F-actin and morphological remodeling. Mean filament length was significantly reduced by all three spike variants at 48 h, whereas significant reductions in filament count emerged at 72 h across all variant-exposed groups. Phalloidin fluorescence intensity and cell solidity further revealed variant-specific responses, particularly at 72 h, with Delta and Omicron exhibiting distinct quantitative remodeling patterns. Morphologically, Delta spike-exposed cells exhibited the most pronounced disruption of overall cellular architecture at 72 h. Proteomic analyses revealed distinct abundance patterns among proteins involved in cytoskeletal regulation, cell adhesion, membrane dynamics, mechanotransduction, and intracellular trafficking. Notably, Omicron was associated with broader changes in protein abundance, whereas Delta exhibited fewer protein-abundance changes despite showing the most pronounced morphological alterations. Collectively, these findings demonstrate time- and variant-dependent patterns of cytoskeletal and morphological remodeling, together with distinct variant-associated proteomic abundance patterns at the 72 h exposure time point. The divergence between the pronounced morphological alterations associated with Delta and the broader proteomic response induced by Omicron suggests that cytoskeletal, morphological, and molecular remodeling represent distinct but interconnected dimensions of the cardiomyocyte response to SARS-CoV-2 spike proteins. Conclusions: These findings identify cytoskeletal regulation and associated signaling pathways as potential contributors to SARS-CoV-2-associated cardiac cellular dysfunction.

Carbon-based quantum dots and their nitrogen- and sulfur-doped derivatives were used as platforms to improve the aqueous solubility for Paclitaxel, Violacein and Tetrandrine, drugs with known high partition coefficients (Log P). Paclitaxel, Violacein and Tetrandrine showed a significant reduction in their partition coefficient values by coupling with the carbon-based quantum dots. Paclitaxel’s partition coefficient value decreased from 3.96 to 0.2, Violacein’s from 3.34 to 1.17, and Tetrandrine’s from 2.5 to 1.57, thus demonstrating a significant reduction in their hydrophobicity without being encapsulated. The efficiency of each type of carbon-based quantum dot in lowering each drug’s Log P varied and this behavior was correlated with the nature of the surface interaction between each drug and each of the carbon-based quantum dots.

Antimicrobial resistance (AMR) poses an escalating global health crisis driven by multidrug-resistant ESKAPE pathogens and emerging fungal threats such as Candida auris (C. auris). In response to this urgent need for new therapeutic strategies, antimicrobial peptides (AMPs) represent a mechanistically distinct alternative to conventional antibiotics due to their membrane-targeting mechanisms and a reduced propensity for resistance development; however, clinical translation has been hindered by toxicity, instability and manufacturing constraints. Recent advances in artificial intelligence (AI) are reshaping AMP discovery and optimization. Machine learning (ML), deep learning (DL) and transformer-based protein language models now enable improved prediction of antimicrobial activity, selectivity, protease stability and host toxicity. Generative approaches, including variational autoencoders, diffusion models and reinforcement learning, facilitate de novo multi-objective peptide design and pathogen-directed optimization against resistant bacteria and multidrug-resistant fungal pathogens. Integrated design–test–learn pipelines are accelerating iterative peptide engineering by tightly coupling computational prediction with experimental validation. Clinically used peptide-derived antibiotics such as polymyxins and daptomycin demonstrate the therapeutic feasibility of peptide-based antimicrobials, while investigational peptides, including pexiganan, illustrate ongoing translational progress. Although no fully AI-designed AMP has yet achieved regulatory approval, the accelerating convergence of computational modeling and experimental validation suggests a rapidly evolving translational landscape. Advancing scalable, surveillance-informed AI frameworks that integrate resistance data, predictive safety modeling and delivery optimization will be essential to accelerate the clinical translation of next-generation, multi-objective AMPs against high-risk resistant pathogens.

Antimicrobial resistance (AMR) is a growing silent pandemic driven by multidrug-resistant infections, particularly those associated with medical devices such as dental implants, heart valves, and urinary catheters. This review addresses the urgent need for alternative antimicrobial strategies by exploring the integration of artificial intelligence (AI) in the discovery of antimicrobial peptides (AMPs) and the rational design of bioactive surfaces. We describe how AI-based models accelerate the identification and optimization of peptide candidates with potent antibiofilm activity. Moreover, we examine recent advancements in surface engineering, such as biomimetic coatings, quorum sensing inhibitors, and enzyme-based strategies, that disrupt bacterial colonization and biofilm formation. The novelty of this work lies in its unified perspective that bridges computational prediction, materials science, and microbial pathogenesis to inform the next generation of antimicrobial surfaces. By highlighting innovative AI-assisted approaches and emerging hybrid strategies, this review underscores their potential to mitigate device-associated infections and address the broader challenge of AMR in healthcare settings.