Streptococcus agalactiae(also known Group BStreptococcusor GBS) represents the main pathogen responsible for early- and late-onset infections in newborns. The present study aimed to determine the antimicrobial susceptibility pattern and the capsular serotypes of GBS isolated in Eastern Sicily over 5 years, from January 2015 to December 2019. A total of 3494 GBS were isolated from vaginal swabs of pregnant women (37–39 weeks), as recommended by the Centers for Disease Control and Prevention. Capsular polysaccharide’s typing of GBS was determined by a commercial latex agglutination test containing reagents to serotypes I–IX. The antimicrobial resistance pattern of GBS was determined through the disk diffusion method (Kirby-Bauer) and the double-disk diffusion test on Mueller-Hinton agar plates supplemented with 5% defibrinated sheep blood, according to the guidelines of the Clinical and Laboratory Standards Institute. Serotypes III (1218, 34.9%) and V (1069, 30.6%) were the prevalent colonizers, followed by not typable (570, 16.3%) and serotypes Ia (548, 15.7%), Ib (47, 1.3%), II (40, 1.1%), and IV (2, 0.1%). All 3494 clinical isolates were susceptible to cefditoren and vancomycin. Resistance to penicillin, ampicillin, levofloxacin, clindamycin, and erythromycin was observed in 6 (0.2%), 5 (0.1%), 161 (4.6%), 1090 (31.2%), and 1402 (40.1%) of the strains, respectively. Most of erythromycin-resistant GBS (1090/1402) showed the cMLS_Bphenotype, 276 the M phenotype, and 36 the iMLS_Bphenotype. Our findings revealed a higher prevalence of serotype III and a relevant resistance rate, among GBS strains, to the most frequently used antibiotics in antenatal screening.

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose–response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40‐2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.

Recent concerns about off-target immune activation following non-targeted mRNA vaccine delivery have prompted the need for rational design strategies that optimize nanoparticle formulations. Building upon our previous in silico work using the Universal Immune System Simulator to characterize immune responses to mRNA vaccines, we present a computational framework that integrates synthetic transcriptomics with artificial intelligence-driven optimization to guide the development of safer and more targeted lipid nanoparticles. We generated biologically informed, synthetic RNA-seq datasets to emulate gene expression profiles in immune-related tissues post-vaccination. Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). The proposed framework enables early-stage, fully in silico screening of mRNA vaccine delivery strategies. Our results highlight the potential of combining mechanistic immune modeling, synthetic transcriptomic validation, and Artificial Intelligence-based design to accelerate the development of safer and more effective mRNA-based therapies. By enabling rapid, data-driven optimization of delivery systems prior to experimental validation, this approach can significantly shorten vaccine development timelines, reduce costs, and support the creation of more personalized and adaptable immunization strategies. In the long term, this paradigm shift toward computationally guided vaccine development could redefine the future of immunization, paving the way for next-generation vaccines that are safer, more targeted, and rapidly adaptable to emerging infectious threats and individual patient needs.

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.

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

When it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence and concludes with a list of potential protein and/or 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 introducing an advanced immune system simulator able to test the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we present a fully application of 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 issues due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in frozen 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. In 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.