influenza vaccine

Single-component self-assembling protein nanoparticles displaying stabilized prefusion-closed hemagglutinin trimers for influenza vaccine development.

Zhang Yi-Nan YN, Zhu Xueyong X, Braz Gomes Keegan K, Lee Yi-Zong YZ et al.

Current influenza vaccines primarily target hemagglutinin (HA), the major viral surface glycoprotein and principal determinant of neutralizing antibody (NAb) responses. However, antigenic drift and shift, together with HA's intrinsic metastability and low-pH sensitivity, limit broad and durable vaccine protection. Here, we stabilize HA in its prefusion-closed conformation through structure-guided amino acid substitutions. Targeting a conserved residue in the HA2 central triple helix-N95 in influenza A and Q95 in influenza B-provides a core design principle for modulating HA metastability across influenza A subtypes and both influenza B lineages, although the effects vary across viral groups. Using H1 CA09 and H3 HK68 as representative strains, we display stabilized HA trimers on 24-mer ferritin and 60-mer multilayered single-component self-assembling protein nanoparticles (SApNPs). In mice, HA-presenting SApNPs exhibit prolonged retention in lymph node follicles and elicit more robust germinal center responses compared with soluble trimers. Stabilized HA trimers and SApNPs induce functional antibody responses and confer varying levels of protection against homologous, heterologous, and cross-lineage viral challenges. Glycan modification enhances NAb induction or protection in some settings. Together, these findings provide mechanistic insights into HA metastability and establish a rational design framework for next-generation HA-based influenza vaccines.

Predictive value of the prognostic nutritional index and aggregate index of systemic inflammation for pulmonary complications in children with influenza.

Wu Yuanyuan Y, Zhang Jianjian J, Ke Xuejia X, Pan Junping J et al.

Pulmonary complications drive morbidity in pediatric influenza, but early risk stratification remains challenging. This study evaluated the predictive utility of the prognostic nutritional index (PNI) and the aggregate index of systemic inflammation (AISI)-accessible biomarkers of immune-nutritional and inflammatory status-for pulmonary complications in pediatric influenza. This retrospective study analyzed 248 pediatric patients with influenza admitted between January 2022 and December 2024. PNI and AISI were calculated from admission laboratory data. Multivariable logistic regression and receiver operating characteristic (ROC) curves were utilized to evaluate the independent and combined predictive performance of PNI and AISI for the development of pulmonary complications. Pulmonary complications developed in 86 patients (34.7%). These patients exhibited significantly lower PNI and higher AISI than those without complications (both P<0.001). Furthermore, PNI correlated inversely, and AISI positively, with disease severity markers including C-reactive protein (CRP), procalcitonin, fever duration, and hospital length of stay (all P<0.001). Multivariable analysis identified prolonged fever, decreased PNI, and elevated AISI as independent predictors of pulmonary complications. The combined PNI-AISI model showed higher apparent discriminatory performance than PNI or AISI alone [the area under the receiver operating characteristic curve (AUC): 0.913, sensitivity: 87.2%, specificity: 85.2%]; however, further validation is required to confirm its generalizability. Decreased PNI and elevated AISI independently predict pulmonary complications and disease severity in pediatric influenza. Integrating these indices provides a highly accurate, accessible, and physiologically grounded tool for early risk stratification, optimizing clinical decision-making.

Influenza vaccination in children with underlying conditions: affirming effectiveness and confronting gaps.

Geissler Kimberley H KH, Peacock-Chambers Elizabeth E

Therapeutic melanoma vaccines: Platforms, neoantigen strategies, and emerging combination immunotherapies.

Mhanna Daniel D, Salman Bilal B, El Hakim Rawad R, Aridi Lea L et al.

Melanoma has emerged as a major focus of cancer immunotherapy research because of its highly immunogenic nature and responsiveness to immune-based treatments. Therapeutic melanoma vaccines are designed to stimulate tumor-specific immune responses through the delivery of Tumor-Associated Antigens (TAAs), Tumor-Specific Antigens (TSAs), and personalized neoantigens. This narrative review provides an overview of current melanoma vaccine strategies, including peptide-based vaccines, dendritic cell vaccines, nucleic acid-based platforms such as mRNA, DNA, and viral vector vaccines. Recent advances in vaccine engineering and tumor genomics have accelerated the development of personalized neoantigen vaccines capable of targeting mutations unique to individual tumors. In parallel, Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being incorporated into neoantigen identification pipelines to improve epitope prediction and optimize vaccine design. Combination strategies involving Immune Checkpoint Inhibitors (ICIs), particularly anti-PD-1 and anti-CTLA-4 therapies, have further enhanced interest in melanoma vaccines by helping overcome tumor-induced immune suppression and augment T-cell activation. In addition to reviewing vaccine mechanisms and emerging technologies, this manuscript examines the evolving clinical trial landscape through analysis of melanoma vaccine studies registered on ClinicalTrials.gov. Although many studies have reported encouraging safety and immunogenicity findings, challenges related to tumor heterogeneity, immune evasion, biomarker selection, and manufacturing complexity continue to limit widespread clinical implementation. Ongoing advances in computational immunology, biomaterial engineering, and precision oncology are expected to further refine melanoma vaccine development and improve therapeutic efficacy. Collectively, these innovations may help establish melanoma vaccines as an increasingly important component of future personalized cancer immunotherapy strategies.