The aim of this study was to explore the sensitizing potency of several known skin allergens, focusing on their capacity to induce allergic contact dermatitis (ACD). Our approach involves different bioinformatic tools, including UISS-TOX, a simulation platform designed to predict the immune response following allergen exposure. Using eight well-characterized skin sensitizers, including pyridine and hexyl salicylate, we evaluated docking interactions with keratin and Toll-like receptors (TLRs), and we predicted B-cell epitopes, providing insights into potential antigenic sites. The results were integrated into UISS-TOX simulations to observe T helper cell and cytokine dynamics over time. The simulations revealed distinct Th1-mediated responses consistent with ACD, enabling not only the prediction of skin sensitizer potency but also the differentiation of response intensities among the sensitizers. Pyridine, for instance, demonstrated a higher Th1 activation and associated cytokine release than hexyl salicylate, aligning with its stronger sensitizing profile in literature. This study underscores UISS-TOX’s potential as a reliable in silico method for allergenicity prediction, aligning with New Approach Methodologies and reducing the need for animal testing.

This study explored the possibility to adapt the physiologically based kinetic (PBK) model, originally developed for long-chain and long half-life per- and polyfluoroalkyl substances (PFASs) by the European Food Safety Authority (EFSA), to assess three short-chain and short half-life PFASs namely perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA) and perfluorobutanesulfonic acid (PFBS). The aim was to estimate the plasma concentration of short-chain PFASs following repeated oral exposure and use this data to inform the Universal Immune System Simulator (UISS) model to predict effect on antibody response. In parallel, in vitro to in vivo extrapolation (QIVIVE) was conducted using values obtained from in vitro experiments. Results show the feasibility of applying established long-chain PFASs kinetic models to short-chain PFASs and to investigate their potential health impacts.

Influenza remains a significant public health concern, with annual vaccine formulations traditionally developed based on the most prevalent virus strains from the previous year. This process often results in mismatches between the vaccine and circulating strains, limiting efficacy. In this study, we utilize the UISS-FLU simulator to compare the effects of a novel influenza vaccine formulation, developed in our previous research, against this year's standard vaccine. We aim to evaluate the potential advantages of our formulation in terms of immunogenic response and protective efficacy. By leveraging in silico methodologies, we can enhance vaccine design and optimization, allowing for a more adaptive and responsive approach to influenza immunization. Our findings will provide insights into the future of vaccine development, showcasing how computational tools can improve public health outcomes by potentially yielding a universal vaccine solution.

The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Chemicals such as trimellitic anhydride and toluene diisocyanate can induce allergic reactions, leading to conditions like occupational asthma and rhinitis. Although they manifest differently, these sensitizers possess shared characteristics, notably their low molecular weight and the capacity to trigger immune responses upon protein binding. This study utilizes the Universal Immune System Simulator to simulate immune reactions to these chemicals. Results indicate that the Universal Immune System Simulator can closely replicate trimellitic anhydride induced immune reactions, primarily showing Th2-type responses with cytokine patterns typical of allergies. These findings underscore the Universal Immune System Simulator's potential in predicting immune reactions to certain chemicals, offering insights into the distinctions between skin and respiratory sensitizers. Upcoming research seeks to further elucidate the immunotoxic pathways of these agents, enabling differentiation based on unique immune reactions.

The study used the Universal Immune System Simulator (UISS) to create a virtual model of cutaneous leishmaniasis (CL), a disease caused by Leishmania parasites transmitted through sandfly bites. CL can manifest in various forms, impacting millions of people globally. Current treatments face challenges, including toxicity and a lack of preventive vaccines. Researchers explored the immune response intricacies in CL, focusing on the balance between M1 and M2 macrophages, which significantly affect disease outcomes. The UISS accurately simulated the immune response, representing interactions between digital patients and Leishmania parasites. The simulation represented a scenario where the importance of M2 macrophages and Th2 cells in CL immunopathology is emphasized. These findings demonstrate UISS's potential in understanding complex host-pathogen interactions, paving the way for targeted therapies and innovative treatments in leishmaniasis.

The emergence of Variants of Concern in infectious diseases, particularly in the context of viruses like SARS-CoV-2, has highlighted the critical importance of continuous prediction and monitoring, showcasing the pivotal role of computational biology in addressing the challenges posed by these emerging infectious diseases. This study advocates for implementing a computational approach able to predict the next SARS-CoV-2 variant of concern (VOC). To that end, inspired by natural selection principles, we used the Genetic Algorithm (GA) as it offers a potent framework for optimizing complex problems. We initiated our investigation with the Wuhan spike protein sequence since it is critical target for variant surveillance and used as reference input. Subsequently, we systematically introduced specific mutations into this sequence to make the initial population. Computational modeling generated three-dimensional structures of the mutated spike within the SARS-CoV-2 ACE2 to evaluate the best candidate of each generation. These were later evaluated by predicting their Gibbs free energy (ΔG values) to evaluate the stability and interactions of these mutants, providing insights into their potential effects on viral behavior and the emergence of VOC. Our analysis demonstrates that the ΔG of our predicted variant closely compares to the delta variant, indicating a similar thermodynamic profile in their interactions. Moreover, our finding indicates that the transmission potential of the new variant is nearly on par with that of the delta variants. Additional factors will be taken into account to evaluate the overall importance of our predicted variant, and we will undertake further research and analysis to comprehend its real-world consequences and potential advantages or drawbacks.

Accurate segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) scans is crucial for clinical diagnosis and effective treatment planning. In this work, we investigate the effectiveness of Diffusion Models (DM) in achieving pixel-wise segmentation of MS lesions. DM significantly improves segmentation sensitivity, especially in regions with subtle abnormalities. We conducted extensive experiments using the magnetic resonance volumes from a public dataset, encompassing various imaging modalities. Our analysis demonstrated how DM can achieve performance levels that are on par with state-of-the-art techniques, as evidenced by a mean Dice coefficient comparable to the best existing methods. Furthermore, some variants of standard DM exhibits robustness across various imaging modalities, showcasing its versatility in clinical settings.

When it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence. It concludes with a list of potential proteins with a possible, but not necessarily, list of peptide candidates that need to be experimentally confirmed for vaccine production. During the subsequent years, reverse vaccinology has dramatically changed: now it consists of a large number of bioinformatics tools and processes, namely subtractive proteomics, computational vaccinology, immunoinformatics, and in silico related procedures. However, the state of the art of reverse vaccinology still misses the ability to predict the efficacy of the proposed vaccine formulation. Here, we describe how to fill the gap by introducing an advanced immune system simulator that tests the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we entirely apply this advanced reverse vaccinology approach to design and predict the efficacy of a potential vaccine formulation against influenza H5N1. Climate change and melting glaciers are critical due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in icy lakes that can raise a pandemic. Investigating structural antigen protein is the most profitable therapeutic pipeline to generate an effective vaccine against H5N1. In particular, we designed a multi-epitope vaccine based on predicted epitopes of hemagglutinin and neuraminidase proteins that potentially trigger B-cells, CD4, and CD8 T-cell immune responses. Antigenicity and toxicity of all predicted CTL, Helper T-lymphocytes, and B-cells epitopes were evaluated, and both antigenic and non-allergenic epitopes were selected. From the perspective of advanced reverse vaccinology, the Universal Immune System Simulator, an in silico trial computational framework, was applied to estimate vaccine efficacy using a cohort of 100 digital patients.

The COVID-19 pandemic motivated an intense debate over high transmissibility and unavailability of effective vaccine to cover all existent variants, and also has raised critical questions, such as concerns about new mutations and genetic recombination that could lead to novel variants of concerns. The density of mutation observed in the different residue indices of spike protein sequence, may correlate to the speed of virus distribution. Therefore, predicting an accurate determination of mutation rates is essential to comprehend this virus evolution and assess the risk of emergent infectious disease. The current study predicts the mutations that may be cause of new variants of concerns using a genetic algorithm approach. In this regard, we mutated randomly the wild-type sequence of SARS-CoV-2 spike protein to generate first 100 different sequences (initial population) that were modelled individually and used to evaluate their discrete optimized protein energy score. After applying cross-over and breeding 200 new generations, one of the sequences with the lowest discrete optimized protein energy score was identified and chosen for a further analysis to realize whether this sequence is potential for being the next variant of concern.

Cutaneous leishmaniasis (CL) is the most common form of leishmaniasis, an infectious disease caused by the Leishmania parasite and transmitted by phlebotomine sandflies. CL is manifested as skin lesions, which typically evolve to ulcerative lesions and may last for months or even years, if not treated. Currently available treatments against CL involve the use of particularly toxic and overpriced drugs, which, among other things, require long times of administration and do not always lead to the complete recovery of the patient. The use of in silico technologies, as the Universal Immune System Simulator (UISS), may be helpful in finding new and potentially more effective drugs against CL.