Search Results (428)

One concern in the yearly re-formulation of influenza vaccines is the time-consuming manufacturing of vaccine potency reagents, particularly for emergency responses. The continuous evaluation of modern techniques such as reversed-phase (RP) chromatography is an asset for streamlining this process. One challenge with RP methods, however, is the need to re-optimize methods for antigens that show poor separation, which can be highly dependent on analyst experience and available data. In this study, we leveraged a large RP dataset of influenza antigens to explore machine learning (ML) approaches of classifying challenging separations for computer-assisted method re-optimization across years, products, and analysts. Methods: To address recurring chromatographic issues—such as poor resolution, strain co-elution, and signal absence—we applied data augmentation techniques to correct class imbalance and trained multiple supervised ML classifiers to distinguish between these peak profiles. Results: With data augmentation, several ML models demonstrated promising accuracy in classifying chromatographic profiles according to the provided labels. These models effectively distinguished patterns indicative of separation issues in real-world data. Conclusions Our findings highlight the potential of ML as a computer assisted tool in the evaluation of vaccine quality, offering a scalable and objective approach to chromatogram classification. By reducing reliance on manual interpretation, ML can expedite the optimization of analytical methods, which is particularly needed for rapid responses. Future research involving larger, inter-laboratory datasets will further elucidate the utility of ML in vaccine analysis. Full article

African, Caribbean, and Black (ACB) communities face increased COVID-19 morbidity and mortality, coupled with significant barriers to vaccine acceptance and uptake. Addressing these challenges requires innovative, multifaceted strategies. Peer-led interventions, grounded in critical health literacy (CHL) and critical racial literacy (CRL), and integrating collaborative equity learning processes, can enhance community capacity, empowerment, and health outcomes, contributing to long-term health equity. This paper describes and presents the evaluative outcomes of a peer-led intervention aimed at enhancing COVID-19 vaccine confidence and acceptance. The Peer-Equity Navigator (PEN) intervention consisted of a specialized training curriculum grounded in CHL and CRL. Following training, PENs undertook a 5-month practicum in community or health settings, engaging in diverse outreach and educational activities to promote vaccine literacy in ACB communities. The evaluation utilized a modified Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) Framework, using quantitative and qualitative methods to collect data. Sources of data included tracking records with community feedback, and a PEN focus group, to assess program feasibility, outreach, and effectiveness. From 16 September 2022, to 28 January 2023, eight trained PENs conducted 56+ community events, reaching over 1500 community members. Both PENs and community members reported high engagement, endorsing peer-led, community-based approaches and increased vaccine literacy. The PEN approach proves feasible, acceptable, and effective in promoting positive health behaviors among ACB communities. This intervention has clear implications for health promotion practice, policy, and research in equity-deserving communities, including immigrants and refugees, who also face multiple and intersecting barriers to health information and care. Full article

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. Full article

Background: Vaccine hesitancy (VH) represents a growing concern among healthcare professionals and students, potentially undermining public health efforts. Nursing, pediatric nursing, and midwifery students are future vaccinators and educators, making it essential to understand their attitudes, knowledge, and confidence toward vaccination. This study aims to assess vaccine-related perceptions and behaviors among these student populations in an Italian university. Methods: A cross-sectional survey was conducted between November 2022 and February 2024 at the University of Rome “Tor Vergata”. A structured, anonymous questionnaire, including the Vaccination Attitudes Examination (VAX) scale, vaccine knowledge items, and sources of information, was administered to students in nursing (n = 205), pediatric nursing (n = 46), and midwifery (n = 21). Statistical analyses included descriptive statistics, ANOVA, post hoc tests, and Mann–Whitney U tests. Results: Among the 272 participants, 20.6% reported refusing at least one recommended vaccine, and 18.4% delayed vaccination for non-medical reasons. Vaccine knowledge and confidence increased significantly with academic progression (p < 0.001). Midwifery students showed both the highest concern for long-term vaccine effects and the greatest confidence in vaccine safety. Institutional and scientific sources were the most trusted, though traditional and non-institutional media also influenced perceptions, particularly among midwifery students. Conclusions: Despite high COVID-19 vaccine uptake, VH persists among health professional students. Discipline-specific patterns highlight the need for early, targeted educational strategies to enhance vaccine literacy and reduce hesitancy. Tailored training may empower future professionals to become informed and credible advocates for vaccination. Full article

Background: Pharmacy technicians are increasingly involved in immunization services, enhancing vaccine accessibility and reducing pharmacies’ workload. This scoping review aims to (1) provide a comprehensive overview of pharmacy technicians’ involvement in immunization services across various healthcare settings and countries, and (2) conduct a comparative analysis of training curricula for pharmacy technicians on immunization. Methods: A scoping review was conducted following the Arksey and O’Malley framework. A comprehensive search of the PubMed and Scopus databases was performed using keywords and MeSH terms such as “pharmacy technician(s)”, “immunization”, “vaccination”, “role”, and “involvement”. Studies included assessed pharmacy technicians’ roles in vaccine administration, training, and public health outcomes. Descriptive and thematic analyses were used to synthesize the findings. In addition, a supplementary analysis of immunization training curricula was conducted, reviewing programs from different countries to identify similarities, differences, and gaps in course structure, content, and delivery formats. Lastly, a comprehensive toolkit was developed, offering guidelines intended to facilitate the implementation of immunization training programs. Results: A total of 35 articles met the inclusion criteria, primarily from the United States of America (n = 30), Canada (n = 2), Ethiopia (n = 1), Denmark (n = 1) and United Kingdom (n = 1). The findings indicate that pharmacy technicians contribute significantly to vaccine administration, patient education, and workflow optimization, particularly in community pharmacies. The COVID-19 pandemic accelerated their involvement in immunization programs. Key challenges include regulatory barriers, a lack of standardized training, and resistance from other healthcare professionals. Facilitators include legislative support (e.g., the PREP Act), structured training programs, and collaborative pharmacist–technician models. Conclusions: Pharmacy technicians can play a vital role in expanding immunization services, improving vaccine uptake, and reducing pharmacist workload. Addressing regulatory inconsistencies, enhancing training, and fostering interprofessional collaboration are crucial for their effective integration of immunization programs. Since immunization by pharmacy technicians is not yet allowed in many EU countries, this review will provide a foundational basis to address their potential to support the healthcare workforce and improve access to immunization services. Full article

High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. Models were trained and validated on both original and synthetically balanced and augmented data. The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. The key predictors identified include timely receipt of birth and week six vaccines, the child’s age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps. Full article

Background/Objectives: Freund’s adjuvants induce different immunomodulatory effects, but their underlying molecular mechanisms are unclear. In this study, we investigated whether the immune-stimulating effects of the complete Freund’s adjuvant (CFA) involve the mechanisms of trained immunity (TI). Methods: We examined bone marrow cells (BMCs) isolated from CFA-immunized A/J mice to address this question. Incomplete Freund’s adjuvant (IFA) and Mycobacterium tuberculosis var. bovis Bacillus Calmette-Guérin (BCG) served as negative and positive controls, respectively. We evaluated cytokine profiles, metabolic, and epigenetic changes. Results: First, BMCs from all groups except saline showed varied levels of IL-1β, IL-6, and TNF-α. But expression of CCL5 and CXCL10 was significantly elevated only in the CFA and BCG groups. Transcriptionally, significant elevations were noted for TNF-α and IL-1β in the CFA and BCG groups, whereas CXCL10, IL-6, and IL-10 were upregulated in the CFA and BCG groups, respectively. Second, while BMCs from the BCG group expressed the markers of both the M1 and M2 macrophages, no clear trends were noted in the CFA and IFA groups. Third, cell lysates from the CFA group revealed metabolic reprogramming in the BMCs. Specifically, we observed an increased level of lactate, indicative of aerobic glycolysis, which is implicated in TI, and this was also detected in the IFA group. Fourth, epigenetic analysis revealed histone enrichment in the promoter region of TNF-α, in the CFA group, but to a lesser degree than the BCG group. However, no epigenetic changes were observed in the IFA group. Conclusions: Our data provide new insights into the mechanisms of Freund’s adjuvants and the immunomodulatory effects of CFA could involve the features of TI. Full article

Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune evasion, making it an attractive yet underexplored target for drug repurposing. In this study, we combined machine learning, molecular dynamics, and molecular docking to identify potential PLpro inhibitors in existing drugs. We performed long-timescale molecular dynamics simulations on PLpro–ligand complexes at two known binding sites, followed by structural clustering to capture representative structures. These were used for molecular docking, including a training set of 127 compounds and a library of 1107 FDA-approved drugs. A random forest model, trained on the docking scores of the representative conformations, yielded 76.4% accuracy via leave-one-out cross-validation. Applying the model to the drug library and filtering results based on prediction confidence and the applicability domain, we identified five drugs as promising candidates for repurposing for COVID-19 treatment. Our findings demonstrate the power of integrating computational modeling with machine learning to accelerate drug repurposing against emerging viral targets. Full article

A novel therapeutic approach, inverse vaccination, is being developed to combat autoimmune diseases and other inflammatory conditions. It aims to educate the immune system to recognize self-components as innocuous and stop reacting against them. Inverse vaccination, also referred to as tolerogenic vaccination, introduces autoantigens into the immune system to induce immune tolerance to the nominal antigen. In contrast to conventional vaccination, which aims to train the immune system to identify a pathogen as a potential threat that needs to be eradicated, inverse vaccination is designed to educate the immune system to recognize that an antigen is harmless and, consequently, extinguish the inflammatory attack of the tissues that contain the autoantigen. This article discusses recent progress in using inverse vaccination to design therapeutic interventions in several autoimmune diseases by deprivation of co-stimulatory signaling, tagging autoantigens to trigger immune tolerance in the liver, and mRNA vaccination. Also discussed is a tolerogenic feedback loop implicating B lymphocytes in inverse vaccination. Full article

Background/Objectives: Mobile healthcare units (MHUs) comprise flexible, ambulatory healthcare teams that deliver community care services, particularly in underserved or remote areas. In Greece, MHUs were pivotal in epidemiological surveillance during the COVID-19 pandemic and are now evolving into a sustainable and integrated service for much-needed community-based healthcare. To support this expanded role, targeted, competency-based training is essential; however, this can pose challenges, especially in coordinating synchronous learning across geographically dispersed teams and in ensuring engagement using an online format. Methods: A nationwide, online training program was developed to improve the knowledge of the personnel members of the Hellenic National Public Health Organization’s MHUs. This program was structured focusing on four core themes: (i) prevention–health promotion; (ii) provision of care; (iii) social welfare and solidarity initiatives; and (iv) digital health skill enhancement. The program was implemented by the University of Crete’s Center for Training and Lifelong Learning from 16 January to 24 February 2025. A multidisciplinary team of 64 experts delivered 250 h of live and on-demand educational content, including health screenings, vaccination protocols, biomarker monitoring, chronic disease management, treatment adherence, organ donation awareness, counseling on social violence, and eHealth applications. Knowledge acquisition was assessed through a pre- and post-training multiple-choice test related to the core themes. Trainees’ and trainers’ qualitative feedback was evaluated using a 0–10 numerical rating scale (Likert-type). Results: A total of 873 MHU members participated in the study, including both healthcare professionals and administrative staff. The attendance rate was consistently above 90% on a daily basis. The average assessment score increased from 52.8% (pre-training) to 69.8% (post-training), indicating 17% knowledge acquisition. The paired t-test analysis demonstrated that this improvement was statistically significant (t = −8.52, p < 0.001), confirming the program’s effectiveness in enhancing knowledge. As part of the evaluation of qualitative feedback, the program was positively evaluated, with 75–80% of trainees rating key components such as content, structure, and trainer effectiveness as “Very Good” or “Excellent.” In addition, using a 0–10 scale, trainers rated the program relative to organization (9.4/10), content (8.8), and trainee engagement (8.9), confirming the program’s strength and scalability in primary care education. Conclusions: This initiative highlights the effectiveness of a structured, online training program in enhancing MHU knowledge, ensuring standardized, high-quality education that supports current primary healthcare needs. Future studies evaluating whether the increase in knowledge acquisition may also result in an improvement in the personnel’s competencies, and clinical practice will further contribute to assessing whether additional training programs may be helpful. Full article

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article

Tuberculosis (TB) is a major global health threat, with the current Bacillus Calmette–Guérin (BCG) vaccine having limited efficacy against adult pulmonary disease. Trained immunity (TI) is a form of innate immune memory that enhances antimicrobial defense. It is characterized by the epigenetic and metabolic reprogramming of innate immune cells and holds promise as a promising approach to prevent TB. In this study, we investigated the capacity of heparin-binding hemagglutinin (HBHA), a methylated antigen of Mycobacterium tuberculosis, to induce TI in murine RAW264.7 macrophages, human-derived THP-1 macrophages, and human peripheral blood mononuclear cells (hPBMCs). HBHA-trained macrophages exhibited the enhanced expression of pro-inflammatory cytokines (IL-1β, IL-6, TNF-α) following secondary lipopolysaccharide stimulation. The epigenetic profiling indicated elevated levels of H3K4me1 and H3K4me3 histone marks at cytokine gene loci. Further, metabolic analysis revealed heightened lactate production and the increased expression of glycolytic enzymes. Functionally, HBHA-trained macrophages exhibited improved control of intracellular mycobacteria, as evidenced by a significant reduction in colony-forming units following BCG infection. These findings elucidate that HBHA induces a functional TI phenotype via coordinated epigenetic and metabolic changes, and suggest HBHA may serve as a valuable tool for studying TI and its relevance to host defense against mycobacterial infections, pending further in vivo and clinical validation. Full article

Background: The Bacille Calmette–Guérin (BCG) vaccine is part of the Extended Programme on Immunization (EPI) and as such is generally administered at birth. The global introduction of BCG not only protected many vaccinated infants against severe complications of tuberculosis but also resulted in markedly reduced overall childhood mortality. Studies in human adults determined that BCG vaccination induces epigenetic reprogramming of innate immune cells (also known as trained immunity) and can also enhance T cell responses to both mycobacterial and non-mycobacterial antigens. Goal and Methods: The current study tested the hypothesis that BCG immunization similarly impacts the functionally distinct infant immune system. Towards this goal, we applied RNA sequencing to assess transcriptome changes in circulating CD4⁺ T cells of Malawian infants prior to and 2 to 13 weeks after BCG immunization. Results: In the first three months of life, transcriptome changes of infant CD4 T cells implied a functional shift towards T helper 1 and Th17 immunity. Vaccination with BCG resulted in additional modulation of the CD4 T cell transcriptome and differentially expressed genes could be linked to metabolomic function. Conclusions: These findings are consistent with data reported in BCG vaccinated adults and contribute to the understanding of molecular changes in infant CD4 T cells that may explain the improved capacity of the infant immune system to respond to pathogens after BCG vaccination. Full article

Background/Objectives: Vaccine immunogenicity is often suboptimal in vulnerable populations such as the elderly, infants, and individuals in low- and middle-income countries. One contributing factor may be pre-existing immunomodulatory conditions, including helminth infections. This study investigates the impact of Fasciola hepatica (F. hepatica) derived molecules on the early humoral response to the COVID-19 mRNA vaccine Comirnaty^® in a mouse model. Methods: BALB/c mice were pretreated with a F. hepatica protein extract (FH) or complete Freund’s adjuvant (CFA) prior to vaccination. Cytokine production and antibody responses were assessed at 0, 14, and 21 days post-vaccination (dpv) through serum analysis and ex vivo splenocyte stimulation with the SARS-CoV-2 receptor-binding domain (RBD) or LPS. Results: At 0 dpv, FH-treated mice showed increased serum IL-10, while CFA treatment induced IL-12. FH- but not CFA-treated splenocytes secreted IL-10 upon RBD or LPS stimulation. At 21 dpv, FH-treated mice lacked IFN-γ production but maintained IL-10 and showed elevated IL-4, consistent with a Th2-skewed profile. Although total anti-RBD IgG levels were similar between groups, FH-treated mice exhibited reduced IgG avidity and a higher IgG1/IgG2 ratio. CFA-treated mice showed delayed avidity maturation. Conclusions: Prior exposure to F. hepatica antigens can modulate the early immune response to Comirnaty^®, affecting both cellular activation and antibody quality. This altered response may reflect a reduced early protective capacity of the vaccine, which might need to be considered when designing or evaluating vaccination strategies using mRNA vaccines in helminth-endemic regions. Full article

The World Health Organization (WHO) Global Benchmarking Tool (GBT) classifies regulatory systems into four maturity levels, with Maturity Level 3 (ML3) signifying a stable and effective regulatory environment. As of January 2025, eight African nations—Egypt, Ghana, Nigeria, Rwanda, Senegal, South Africa, Tanzania, and Zimbabwe—have attained ML3 status, marking a significant milestone in the continent’s regulatory landscape. Achieving ML3 confers critical benefits, including reducing substandard and falsified medicines, which enhances public health safety and fosters trust in healthcare systems. This progress encourages local manufacturing, diminishing reliance on imported medicines and promoting economic development. Furthermore, ML3 NRAs are better equipped to address public health emergencies, enabling swift approvals for vaccines and therapeutics while upholding safety standards. Nonetheless, challenges persist, including fragmented regulatory systems, the prevalence of counterfeit medicines, and limited resources. Overcoming these hurdles necessitates enhanced organizational capacity, investments in training, and the promotion of collaboration among NRAs. There is an urgent call for greater political commitment and resource allocation to strengthen regulatory systems across Africa. Achieving and maintaining ML3 status is essential for enhancing medicine regulation, supporting local manufacturing, and improving public health outcomes across the continent. While progress has been made, sustained efforts are crucial to tackling existing challenges and harnessing the full potential of advanced regulatory frameworks. Full article