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.

In 2019, humanity witnessed one of the most violent and dangerous pandemics ever seen: SARS-CoV-2 emerged suddenly, spreading rapidly across the world. The speed of its spread and the severity of its infections—with clinical symptoms ranging from flu-like manifestations to various forms of pneumonia, and even death due to acute respiratory distress syndrome (ARDS)—made SARS-CoV-2 one of the deadliest members of the coronavirus family. This triggered an urgent, global search for new technologies capable of curbing its spread and mitigating its effects as quickly as possible. In this context, mRNA vaccines established themselves as the most promising solution for a rapid response, even though the long-term effects of using this technology in vaccines remained unclear. Here, we shed light on the mechanism of action and potential side effects of mRNA vaccines, using the Universal Immune System Simulator (UISS) for in silico simulation of the human immune response.

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.

Breast cancer is among the leading causes of mortality in women of all ages worldwide. Prevention and timely diagnosis can make the difference between life and death for most patients, and it is for this reason that today there is a great deal of focus on raising public awareness about the possibility of using the various techniques of preventive screening available today. In cases where surgery is necessary, beyond the purely medical aspects, it is important to take into account also the psychological aspects, which are sometimes underestimated with negative consequences for the patient. However, it is very difficult to determine a priori what the final outcome of the intervention will be, especially with regard to the level of personal satisfaction of patients. The aim of this work is to identify a possible correlation between the commonly used measurements of breast morphology, necessary for the assessment of any surgical procedure, and the personal satisfaction level of the patients, based on the results of anonymous questionnaires, through the use of machine learning techniques.

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.

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing.

Technological advances in fields such as computer modelling and simulation are playing a fundamental role, during the COVID-19 pandemic, that has been afflicting us for 2 years now. These methodologies may help in the development, assessment and monitoring for better prevention, diagnosis, treatment and generation of specific therapeutic strategies. In this perspective, in silico platforms are emerging thanks to their ability to predict the efficacy and safety of new therapies and vaccines. Here, our integrated bioinformatics pipeline for evaluating the elicited response of human immune system against every pathogen is applied to predict the cross-reactive immunity induced by pre-existing vaccinations against SARS-CoV-2 UK variants.

One of the main problems in the fight against cancer is represented by the immunosuppression it causes. Several research lines have been undertaken to address this issue; one of the most promising is represented by oncolytic viruses. Oncolytic viruses are designed from live attenuated recombinant viruses and are designed to preferentially infect cancer cells, within which they replicate causing their lysis and enhance, through various mechanisms, innate and adaptive immunity. In collaboration with the Etna Biotech company, which was involved in designing five protein constructs composed of five different variants of a monoclonal antibody linked to a viral surface protein, we worked on the modeling and evaluation of these proteins associated with the Measles Virus. The software we used was Swiss-model (https://swissmodel.expasy.org/), a bioinformatics webserver dedicated to homology modeling of 3D protein structures, and Pro-Q (https://proq.bioinfo.se/ProQ/ProQ.html) to perform quality control. We shared our results with the company, which completed laboratory tests for two of the five constructs, confirming our predictions. Transfection experiments are currently underway with two other constructs, while the last one has not yet been subjected to such investigations.

Thyroid cancer is the commonest endocrine malignancy. Mutation in the BRAF serine/threonine kinase is the most frequent genetic alteration in thyroid cancer. Target therapy for advanced and poorly differentiated thyroid carcinomas include BRAF pathway inhibitors. Here, we evaluated the role of MAP3K8 expression as a potential driver of resistance to BRAF inhibition in thyroid cancer. By analyzing Gene Expression Omnibus data repository, across all thyroid cancer histotypes, we found that MAP3K8 is up-regulated in poorly differentiated thyroid carcinomas and its expression is related to a stem cell like phenotype and a poorer prognosis and survival. Taken together these data unravel a novel mechanism for thyroid cancer progression and chemo-resistance and confirm previous results obtained in cultured thyroid cancer stem cells.

BACKGROUND: DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets. RESULTS: We present EpiMethEx (Epigenetic Methylation and Expression), a R package to perform a large-scale integrated analysis by cyclic correlation analyses between methylation and gene expression data. For each gene, samples are segmented according to the expression levels to select genes that are differentially expressed. This stratification allows to identify CG methylation probesets modulated among gene-stratified samples. Subsequently, the methylation probesets are grouped by their relative position in gene sequence to identify wide genomic methylation events statically related to genetic modulation. CONCLUSIONS: The beta-test study showed that the global methylation analysis was in agreement with scientific literature. In particular, this analysis revealed a negative association between promoter hypomethylation and overexpression in a wide number of genes. Less frequently, this overexpression was sustained by intragenic hypermethylation events.