Search Results (47)

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

Immunization coverage is a key public health indicator reflecting healthcare accessibility and socio-economic conditions. This study employs Principal Component Analysis (PCA) to construct composite development scores and analyze their relationship with immunization coverage for measles and diphtheria-tetanus-pertussis (DTP) vaccines across 195 countries (2000–2021). The analysis comprises a training period (2000–2015) for score development and a test period (2016–2021) for validation. Variables were selected based on correlation with immunization coverage and standardized before PCA extraction. PC1, the principal component explaining the largest variance, was identified as a key indicator of development disparities. Findings reveal that higher PC1 scores (lower socio-economic development) are associated with reduced immunization rates, while lower PC1 scores (higher socio-economic development) correspond to greater coverage, a trend consistent across both periods. Geospatial analysis highlights stark disparities, particularly in sub-Saharan Africa and South Asia, whereas North America, Europe, and East Asia maintain significantly higher coverage. These results provide policy-relevant insights, demonstrating the utility of PCA-derived scores for resource allocation and targeted interventions. Full article

Background: Reusable, machine-processable clinical decision support system (CDSS) rules have not been widely achieved in the medical informatics field. This study introduces the process, results, challenges faced, and lessons learned while converting the United States of America Centers for Disease Control and Prevention (CDC)-recommended immunization schedules (2022) to machine-processable CDSS rules. Methods: We converted the vaccination schedules into tabular, charts, MS Excel, and clinical quality language (CQL) formats. The CQL format can be automatically converted to a machine-processable format using existing tools. Therefore, it was regarded as a machine-processable format. The results were reviewed, verified, and tested. Results: We have developed 465 rules for 19 vaccines in 13 categories, and we have shared the rules via GitHub to make them publicly available. We used cross-review and cross-checking to validate the CDSS rules in tabular and chart formats. The CQL files were tested for syntax and logic with hypothetical patient HL7 FHIR resources. Our rules can be reused and shared by the health IT industry, CDSS developers, medical informatics educators, or clinical care institutions. The unique contributions of our work are twofold: (1) we created ontology-based, machine-processable, and reusable immunization recommendation rules, and (2) we created and shared multiple formats of immunization recommendation rules publicly which can be a valuable resource for medical and medical informatics communities. Conclusions: These CDSS rules can be important contributions to informatics communities, reducing redundant efforts, which is particularly significant in resource-limited settings. Despite the maturity and concise presentation of the CDC recommendations, careful attention and multiple layers of verification and review are necessary to ensure accurate conversion. The publicly shared CDSS rules can also be used for health and biomedical informatics education and training purposes. Full article

Background: Children with disabilities, particularly in low- and middle-income countries (LMICs), face heightened risks of vaccine-preventable diseases due to a range of systemic and social barriers. Although immunization is a fundamental human right and a proven public health intervention, this vulnerable group is often overlooked in policy and practice. Understanding the factors compromising vaccine equity for these children is critical to reducing zero-dose prevalence and improving health outcomes. Methods: This scoping review examined peer-reviewed, gray literature from 2010 to 2024. Searches were conducted in PubMed, Google Scholar, and relevant organizational reports (WHO, UNICEF). Studies addressing children with disabilities and focusing on immunization barriers, interventions, or lessons learned were selected. English-language publications were screened in title/abstract and full-text stages. Key data extracted included population, barriers, and immunization outcomes. Since this review focused on articles in English, this is a key limitation. Results were synthesized thematically to identify recurring patterns and to guide improved interventions and policies. Results: Twelve articles met the inclusion criteria. Key barriers identified were inadequate healthcare infrastructure, insufficient provider training, limited follow-up services in rural regions, societal stigma, and pervasive misconceptions around both disability and vaccines. Factors such as maternal education, logistical support for caregivers, and using low-sensory, inclusive vaccination settings were consistently linked with better outcomes. Effective strategies included mobile vaccination units, tailored interventions (e.g., distraction or sedation techniques), school-based immunization programs, and robust community engagement to address stigma. Lessons learned underscored the importance of flexible, individualized care plans and empowering families through transparent communication. Conclusions: Children with disabilities continue to experience significant gaps in immunization coverage, driven by intersecting barriers at the individual, health system, and societal levels. Scaling tailored interventions, inclusive policies, strengthened infrastructure, and ongoing research can help ensure these children receive equitable access to life-saving vaccinations. Full article

Vaccination represents a critical preventive strategy in the current global healthcare system, serving as an indispensable intervention against diverse pathogenic threats. Although conventional immunization relies predominantly on hypodermic needle-based administration, this method carries substantial limitations, including needle-associated fear, bloodborne pathogen transmission risks, occupational injuries among healthcare workers, waste management issues, and dependence on trained medical personnel. Microneedle technology has emerged as an innovative vaccine delivery system, offering convenient, effective, and minimally invasive administration. These microscale needle devices facilitate targeted antigen delivery to epidermal and dermal tissues, where abundant populations of antigen-presenting cells, specifically Langerhans and dermal dendritic cells, provide robust immunological responses. Multiple research groups have extensively investigated microneedle-based vaccination strategies. This transdermal delivery technique offers several advantages, notably circumventing cold-chain requirements and enabling self-administration. Numerous preclinical investigations and clinical trials have demonstrated the safety profile, immunogenicity, and patient acceptance of microneedle-mediated vaccine delivery across diverse immunization applications. This comprehensive review examines the fundamental aspects of microneedle-based immunization, including vaccination principles, transcutaneous immunization strategies, and microneedle-based transdermal delivery—including classifications, advantages, and barriers. Furthermore, this review addresses critical technical considerations, such as treatment efficacy, application methodologies, wear duration, dimensional optimization, manufacturing processes, regulatory frameworks, and sustainability considerations, followed by an analysis of the future perspective of this technology. Full article

Background: Recommendations from a trusted healthcare provider have been shown to be the most effective intervention for encouraging patients to be vaccinated. However, providers have reported feeling less prepared to address vaccination questions and having less time to discuss vaccines with patients than before the COVID-19 pandemic. Providers may benefit from a brief update about the available influenza vaccines and vaccination guidelines. Academic detailing is an evidence-based approach for preparing healthcare providers to discuss getting vaccinated with patients. Methods: An academic detailing presentation was developed using influenza statistics, vaccination recommendations, and recent local and national immunization rate data. Academic detailing was conducted with physicians and community pharmacy personnel in Yakima County, Washington, between November 2023 and January 2024. Yakima County is designated as a medically underserved area due to a lack of providers. A pre-detailing survey was conducted to evaluate participant knowledge of current ACIP recommendations and gather opinions about local resident vaccination barriers. A post-detailing survey was conducted to gather participants’ opinions about the value of detailing. Results: Prior to the training, 73% of providers believed it was important to discuss influenza vaccination with patients, but only 52% felt confident in combating misinformation. Healthcare providers believed misinformation and vaccine hesitancy are the most common barriers for Yakima County patients, but recent survey results showed that online scheduling systems, long wait times, and limited appointment hours were the predominant issues reported locally. Two out of 12 community pharmacy personnel and zero resident physicians correctly named all three preferentially recommended influenza vaccines for patients 65 years and older. Overall, 96% of detailing participants reported that the session was valuable, 87% believed it would help them combat vaccine misinformation, and 65% reported planning to have more conversations with patients about influenza vaccination after participating. Conclusion: Physicians and community pharmacy immunizers found the influenza vaccines academic detailing to be valuable. Staying up to date on vaccination guidelines can prepare providers to be confident in having informed conversations with patients about getting vaccinated. Full article

Ulcerative colitis is a chronic relapsing–remitting and potentially progressive form of inflammatory bowel disease in which there is extensive inflammation and mucosal damage in the colon and rectum as a result of an abnormal immune response. MV130 is a mucosal-trained immunity-based vaccine used to prevent respiratory tract infections in various clinical settings. Additionally, MV130 may induce innate immune cells that acquire anti-inflammatory properties and promote tolerance, which could have important implications for chronic inflammatory diseases such as ulcerative colitis. This work demonstrated that the prophylactic administration of MV130 substantially mitigated colitis in a mouse model of acute colitis induced by dextran sulphate sodium. MV130 downregulated systemic and local inflammatory responses, maintained the integrity of the intestinal barrier by preserving the enterocyte layer and goblet cells, and reduced the oedema and fibrosis characteristic of the disease. Mechanistically, MV130 significantly reduced the infiltration of neutrophils and pro-inflammatory macrophages in the intestinal wall of the diseased animals and favoured the appearance of M2-polarised macrophages. These results suggest that MV130 might have therapeutic potential for the treatment of ulcerative colitis, reducing the risk of relapse and the progression of disease. Full article

Background: Microarray patches (MAPs) are innovative, needle-free vaccine delivery systems, suitable for administration by minimally trained health care workers or trained community health workers. Their introduction may transform immunization programmes, particularly for vaccines where high coverage is required for population immunity, such as measles, and where vaccine delivery is challenging, such as in low- and middle-income countries. Recognizing the need to understand how best to tailor these products to reflect country priorities, workshops on measles and rubella MAPs (MR-MAPs) were conducted in multiple regions to collect insights on needs and preferences from relevant stakeholders at country level. Methods: The CAPACITI Innovation Framework was used to structure stakeholder discussions in nine countries in the period from August 2022 to July 2023. The discussions, building on the findings from a situation analysis on the barriers related to measles and rubella vaccine delivery, followed the four-step process outlined in the framework. Results: Key barriers hindering delivery of measles and rubella vaccines across the countries were in the categories of human resource management, service delivery, and demand generation. MR-MAP attributes that stakeholders believed would reduce or eliminate these barriers included ease of preparation and administration, improved thermostability, fewer (ancillary) components, and single-dose presentation. Some attributes such as the site of administration, wear time, and storage volume could exacerbate certain barriers. Based on an understanding of key barriers, product attributes, and underserved populations, stakeholders identified several potential use cases for MR-MAPs: (i) delivery at a fixed health post, (ii) delivery through outreach sessions conducted by health workers, and (iii) administration by community health workers. To enable robust national decision making about the introduction of MR-MAPs and successful implementation, global and national evidence on feasibility and acceptability of MR-MAPs should be generated. To prepare for the potential introduction of MR-MAPs, immunization programmes should evaluate their immunization policies based on their preferred use cases and modify them if needed, for example, to enable community health workers to administer vaccines, along with making programmatic adjustments to waste management and training. Conclusions: MR-MAPs have the potential to reduce key barriers to MR delivery. Yet, their future impact depends on the ability of global stakeholders to steer the development of MR-MAPs to be responsive to country needs and preferences. The generation of evidence to enable robust decision making, timely modification of vaccine policies, and addressing programmatic considerations will be key to successful uptake. Full article

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST−BST–XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model’s reliability and an average accuracy of 97.21% was recorded for the ChST−BST–XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development. Full article

This article presents an Integrative Model of Sustainable Health Decision-Making and a toolkit to equip U.S. Extension professionals with knowledge and skills to engage in adult immunization education. The objective was to reduce mistrust and increase willingness and confidence toward delivering vaccination education. The model was developed through an explanatory parallel mixed methods design. Data collection included a needs assessment survey, interviews, workshops, and Neuromarketing message testing. The resulting toolkit was pilot tested before final delivery. Four key needs were identified: tailoring trainings based on Extension roles, prioritizing preserving community trust and professional credibility, establishing connections with medical experts, and strengthening Science Media Literacy skills to counter misinformation and communicate emerging science. Correlations among constructs supported an integrated model focused on a professional development core of Science Media Literacy, Motivational Interviewing, and Neuromarketing Science that strengthens communication relationships between priority populations and trusted partners. The model and work described in this article can serve as a general framework for engaging key influencers in communities in communication education intended to promote sustainable well-being, such as increasing vaccine uptake. Full article

Respiratory tract infections (RTIs) are among the most common and important problems in clinical medicine, making antibiotics the gold standard therapeutic option regardless of their frequent viral etiology. Their excessive and inappropriate use contributes to the rapid rise of antibiotic resistance and underscores the need for alternative strategies, especially when dealing with recurrent RTIs. Prevention is the ideal alternative, but specific vaccines targeting a wide range of respiratory pathogens are scarce. MV130 is a sublingual bacterial vaccine that induces trained immunity and provides non-specific protection against respiratory pathogens in various clinical settings according to the concept of TIbV (Trained Immunity-based Vaccine). A retrospective real-world study (RWS) was conducted to evaluate the annual incidence of RTIs and the consumption of antibiotics before and after the administration of MV130, using data sourced from the medical records of 599 patients (186 children and 413 adults) who suffered from recurrent RTIs. The median number of infectious episodes in children was significantly reduced by more than 70% from 5 episodes (interquartile range (IQR) 4.0–6.0) to 1 (IQR, 0.0–2.0) (p < 0.001) after MV130. Similarly, in adults, the median number of episodes before MV130 immunization was 5 (IQR, 4.0–6.0), which dropped by more than 80% to 1 (IQR, 0.0–1.0) during the year following MV130 immunization (p < 0.001). The median number of antibiotic courses also significantly decreased for both children and adults by over 80% (p < 0.001). This RWS showed that MV130 is an effective strategy for the prevention of respiratory infections and the reduction of associated antibiotic consumption. Full article

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing data for COVID-19 drug detection and vaccine advancement, while artificial intelligence-based models are used for these purposes. Models based on artificial intelligence are used to evaluate and recognize the best candidate targets for future therapeutic development. Artificial intelligence-based strategies can be used to address issues with the safety and efficacy of COVID-19 vaccine candidates, as well as issues with manufacturing, storage, and logistics. Because antigenic peptides are effective at eliciting immune responses, artificial intelligence algorithms can assist in identifying the most promising COVID-19 vaccine candidates. Following COVID-19 vaccination, the first phase of the vaccine-induced immune response occurs when major histocompatibility complex (MHC) class II molecules (typically bind peptides of 12–25 amino acids) recognize antigenic peptides. Therefore, AI-based models are used to identify the best COVID-19 vaccine candidates and ensure the efficacy and safety of vaccine-induced immune responses. This study explores the use of artificial intelligence-based approaches to address logistics, manufacturing, storage, safety, and effectiveness issues associated with several COVID-19 vaccine candidates. Additionally, we will evaluate potential targets for next-generation treatments and examine the role that artificial intelligence-based models can play in identifying the most promising COVID-19 vaccine candidates, while also considering the effectiveness of antigenic peptides in triggering immune responses. The aim of this project is to gain insights into how artificial intelligence-based approaches could revolutionize the development of COVID-19 vaccines and how they can be leveraged to address challenges associated with vaccine development. In this work, we highlight potential barriers and solutions and focus on recent improvements in using artificial intelligence to produce COVID-19 drugs and vaccines, as well as the prospects for intelligent training in COVID-19 treatment discovery. Full article

The bacillus Calmette–Guérin (BCG) is an attenuated bacterium derived from virulent Mycobacterium bovis. It is the only licensed vaccine used for preventing severe forms of tuberculosis in children. Besides its specific effects against tuberculosis, BCG administration is also associated with beneficial non-specific effects (NSEs) following heterologous stimuli in humans and mice. The NSEs from BCG could be related to both adaptive and innate immune responses. The latter is also known as trained immunity (TI), a recently described biological feature of innate cells that enables functional improvement based on metabolic and epigenetic reprogramming. Currently, the mechanisms related to BCG-mediated TI are the focus of intense research, but many gaps are still in need of elucidation. This review discusses the present understanding of TI induced by BCG, exploring signaling pathways that are crucial to a trained phenotype in hematopoietic stem cells and monocytes/macrophages lineage. It focuses on BCG-mediated TI mechanisms, including the metabolic-epigenetic axis and the inflammasome pathway in these cells against intracellular pathogens. Moreover, this study explores the TI in different immune cell types, its ability to protect against various intracellular infections, and the integration of trained innate memory with adaptive memory to shape next-generation vaccines. Full article

It is essential to use highly antigenic epitope areas, since the development of peptide vaccines heavily relies on the precise design of epitope regions that can elicit a strong immune response. Choosing epitope regions experimentally for the production of the SARS-CoV-2 vaccine can be time-consuming, costly, and labor-intensive. Scientists have created in silico prediction techniques based on machine learning to find these regions, to cut down the number of candidate epitopes that might be tested in experiments, and, as a result, to lessen the time-consuming process of their mapping. However, the tools and approaches involved continue to have low accuracy. In this work, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and long short-term memory (LSTM) for the classification of peptides into epitopes or non-epitopes. Numerous transfer learning strategies were utilized, and the fine-tuned method gave the best result, with an AUC of 0.979, an f1 score of 0.902, and 95.1% accuracy, which was far better than the performance of the model trained from scratch. The experimental results obtained show that this model has superior performance when compared to other methods trained on IEDB datasets. Using bioinformatics tools such as ToxinPred, VaxiJen, and AllerTop2.0, the toxicities, antigenicities, and allergenicities, respectively, of the predicted epitopes were determined. In silico cloning and codon optimization were used to successfully express the vaccine in E. coli. This work will help scientists choose the best epitope for the development of the COVID-19 vaccine, reducing cost and labor and thereby accelerating vaccine production. Full article

Introduction: We have previously shown that trained-immunity-based vaccines, namely TIbV, significantly reduce the rate of recurrent infections, both of the respiratory tract (RRTI) and urinary tract infections (RUTI) in SAD patients on disease-modifying drugs (DMARDs). Objective: We evaluated the frequency of RRTI and RUTI from 2018 to 2021 in those SAD patients that received TIbV until 2018. Secondarily, we evaluated the incidence and clinical course of COVID-19 in this cohort. Methods: A retrospective observational study was conducted in a cohort of SAD patients under active immunosuppression immunized with TIbV (MV130 for RRTI and MV140 for RUTI, respectively). Results: Forty-one SAD patients on active immunosuppression that were given TIbV up to 2018 were studied for RRTI and RUTI during the 2018–2021 period. Approximately half of the patients had no infections during 2018–2021 (51.2% no RUTI and 43.5% no RRTI at all). When we compared the 3-year period with the 1-year pre-TIbV, RRTI (1.61 ± 2.26 vs. 2.76 ± 2.57; p = 0.002) and RUTI (1.56 ± 2.12 vs. 2.69 ± 3.07; p = 0.010) episodes were still significantly lower. Six SAD patients (four RA; one SLE; one MCTD) with RNA-based vaccines were infected with SARS-CoV-2, with mild disease. Conclusions: Even though the beneficial protective effects against infections of TIbV progressively decreased, they remained low for up to 3 years, with significantly reduced infections compared to the year prior to vaccination, further supporting a long-term benefit of TIbV in this setting. Moreover, an absence of infections was observed in almost half of patients. Full article