Search Results (2,083)

Hydrogels and nanocomposite−enhanced hydrogels, owing to their high−water content, excellent biocompatibility, and mechanical flexibility, have demonstrated broad application prospects in tissue engineering, drug delivery, and flexible electronics. With the continuous advancement of computational power, molecular dynamics (MD) simulations have increasingly become an important tool for characterizing nanocomposite materials and hydrogel systems. This approach enables the capture of structural evolution at the atomic/molecular scale and provides mechanistic insights into deformation behaviors and interaction mechanisms under external stimuli such as mechanical force, temperature, and electric fields. This review is organized around the central framework of “structural construction–interfacial regulation−responsive behavior–dynamic evolution”, and systematically summarizes the recent progress in the application of molecular dynamics and multiscale simulation methods to hydrogels and nanocomposite hydrogels. The systems discussed mainly include synthetic polymer-based hydrogels, natural polymer−based hydrogels, peptide/protein−based hydrogels, and nanocomposite hydrogels. Particular emphasis is placed on modeling strategies and force−field selection principles for describing atomic interactions in various nanocomposite hydrogel systems. In addition, the important applications of multiscale simulation strategies in elucidating the interfacial behavior of hydrogels and the mechanisms underlying their dynamic responses under nonequilibrium conditions are also discussed. Finally, future development trends are outlined, including multiscale coupled simulations, closed−loop correction between experiments and simulations, and data−driven modeling strategies for the precise design and performance prediction of complex hydrogel systems. Full article

To date, Lyme vaccine development has largely overlooked the vaccinee’s human leukocyte antigen (HLA) genetic makeup on which antibody production critically depends. Here, we evaluated in silico the predicted binding affinities of 192 HLA-II alleles with all 15-mer peptide sequences of five Borrelia burgdorferi proteins to identify peptides with strong binding affinity, as they would be the best candidates for antibody production in response to vaccination. We found the following: (a) 226 of the 1067 peptides tested (21.2%) were found to bind strongly to HLA-II molecules; (b) decorin-binding protein A had the greatest number of strongly binding peptides; and (c) 69 HLA-II alleles (primarily of the DRB1 gene) bound with strong affinity to peptides from Borrelia burgdorferi proteins. Finally, we tested for possible susceptibility to autoimmunity by any one of the 226 peptides above by searching for their occurrence in ~84,000 proteins of the human proteome and found overlap with only two 8-mer peptide sequences (embedded within the 226 15-mer peptides), neither of which was characterized by strong binding to HLA-I, suggesting a reduced likelihood of autoimmunity. These findings emphasize the importance of a personalized vaccine approach based on the vaccinee’s human leukocyte antigen genetic makeup and offer specific vaccine-candidate peptides that are predicted to maximize vaccine effectiveness and safety. The results of this computational study provide novel directions for future development of Lyme vaccines. Full article

Melanoma is characterized by a high mutational burden making it an established model for studying tumor neoantigens and developing strategies for personalized immunotherapy. In this study, a reproducible bioinformatics pipeline was developed and implemented for the identification and prioritization of candidate neoantigens derived from non-synonymous somatic mutations in melanoma, using genomic data from the MSK-IMPACT cohort (mel-mskimpact-2020; n = 696) and comparative reference information from TCGA-SKCM. From the somatic mutation annotation file (MAF), 16,311 non-synonymous mutations were filtered, from which 50,480 mutant 8–11-mer peptides were generated using a sliding-window approach centered on the mutated position. Peptide–HLA class I binding affinity was predicted using MHCflurry 2.0 across six representative alleles (HLA-A*02:01, HLA-A*24:02, HLA-B*35:01, HLA-B*39:05, HLA-C*04:01, and HLA-C*07:02). Candidate prioritization was initially based on predicted binding percentile (rank ≤ 2), identifying 12,209 peptide–HLA combinations with high predicted binding affinity. To refine candidate selection, additional computational analyses were incorporated, including proteasomal cleavage prediction using NetChop 3.1 and estimation of T-cell epitope immunogenicity using the Immune Epitope Database (IEDB) immunogenicity predictor. Furthermore, a direct comparison between mutant (MUT) and corresponding wild-type (WT) peptides was performed using Δaffinity and Δrank metrics to evaluate the predicted impact of somatic mutations on HLA binding. The analysis revealed a predominance of peptides associated with the HLA-B locus, particularly the allele HLA-B*35:01, among the interactions with the lowest predicted binding percentiles. Several high-ranking peptide candidates were derived from genes with known roles in melanoma biology, including PLCG2, GATA3, AKT1, PTEN, PTCH1, and SMO. Overall, the integrative computational framework implemented in this study enables the systematic prioritization of candidate neoantigens derived from non-synonymous mutations in melanoma. This pipeline provides a reproducible strategy for exploring tumor neoantigen repertoires and may serve as a foundation for subsequent experimental validation and for studies related to neoantigen-based immunotherapies and immunopeptidomics. Full article

Background/Objectives: Hand, foot, and mouth disease (HFMD) is a major public health concern primarily caused by human enterovirus A71 (EV-A71), coxsackievirus A16 (CVA16), coxsackievirus A6 (CVA6), and certain coxsackievirus B serotypes. Currently available EV-A71 vaccines lack cross-protective efficacy against other serotypes, highlighting the urgent need for multivalent and broadly effective enterovirus vaccines. Methods: Immunoinformatics approaches were used to predict highly immunogenic B-cell and T-cell epitopes, which were assembled to construct a novel multivalent epitope vaccine, rCV-A3V, followed by in silico validation. Recombinant protein expression was confirmed by Western blotting and immunofluorescence assays. The immunogenicity was evaluated in Balb/c mice following intranasal immunization. Results: A preliminary safety evaluation demonstrated that the rCV-A3V vaccine was well tolerated in the mouse model, with no abnormal changes in body weight observed after immunization. In addition, the target protein was successfully expressed. Intranasal immunization induced a strong Th1-biased immune response, robust serum neutralizing and IgG antibody responses, and pronounced mucosal immunity, including elevated sIgA and IgG levels in nasal lavage fluid, sIgA in feces, and substantial sIgA responses in milk. Dominant epitope peptides were also identified. Conclusions: The intranasal live attenuated rCV-A3V vaccine successfully induced humoral, mucosal, and cellular immune responses against EV-A71, CVA16, CVA6, and CVB3, demonstrating broad immunogenicity. These findings provide experimental evidence supporting its potential as a candidate vaccine for HFMD. Full article

Antimicrobial resistance (AMR) represents a critical global health challenge, with methicillin-resistant Staphylococcus aureus (MRSA) posing a significant threat in both hospital-acquired and community-associated infections. Research has demonstrated that biofilm formation is a key factor contributing to drug resistance in MRSA. In this study, we investigated an fungus, Fusarium oxysporum BPF55, isolated from Blaps rynchopetera, which inhibits MRSA biofilm formation. The aim of this research was to identify the fungal strain and comprehensively characterize its genomic features, as well as to evaluate its anti-MRSA biofilm potential. Whole-genome sequencing revealed a genome size of 50,097,681 base pairs, a GC content of 47.36%, and 16,507 predicted coding genes. AntiSMASH analysis identified 56 secondary metabolite biosynthetic gene clusters, including those involved in the synthesis of various natural products such as terpenes, non-ribosomal peptides, and polyketides. Using UPLC-MS/MS, 15 compounds were annotated from the ethyl acetate extract. Molecular docking studies demonstrated that four compounds exhibit varying affinities for SarA and AgrA, two key proteins involved in MRSA biofilm formation. Overall, these findings suggest that the fungus F. oxysporum BPA55 produces a variety of secondary metabolites and contains bioactive compounds with potential anti-MRSA biofilm activity. Full article

The continuous emergence of SARS-CoV-2 variants highlights the need for novel antiviral agents with favorable safety profiles. Marine microalgae constitute a valuable source of bioactive compounds, including antiviral peptides. Building on previous in silico identification of peptides derived from the marine microalga Phaeodactylum tricornutum with predicted activity against SARS-CoV-2, this study evaluated the antiviral capacity of peptide fractions generated by enzymatic hydrolysis and separated by molecular weight (10–30, 5–10, 3–5, and <3 kDa) in human alveolar epithelial A549 cells infected with the SARS-CoV-2. Cytotoxicity analyses, assessed using MTT and resazurin assays, revealed a moderate, concentration-dependent reduction in metabolic activity while maintaining overall cell viability within an acceptable range for antiviral evaluation, with higher-molecular-weight fractions (10–30 and 5–10 kDa) displaying the most stable profiles. Antiviral activity was assessed by flow cytometry following post-infection treatment. Lower-molecular-weight fractions (3–5 and <3 kDa) showed early reductions in infection at low concentrations but exhibited variable responses. In contrast, the 10–30 and 5–10 kDa fractions showed more robust, dose-dependent inhibition at medium and high concentrations, reducing infection levels to levels close to those observed in uninfected controls. Comparative analysis with the reference antiviral drug lopinavir demonstrated that peptide fractions exhibit lower cytotoxicity while retaining antiviral activity under equivalent experimental conditions. Overall, these results indicate that antiviral efficacy is strongly influenced by peptide molecular weight and consistency of response. This work provides experimental in vitro validation of P. tricornutum–derived peptide fractions as marine antiviral candidates and supports the integration of in silico and functional approaches for marine drug discovery. Full article

Background/Objectives: Coronaviruses are a family of positive-sense RNA viruses that cause respiratory and gastrointestinal disease in mammals and birds. Their zoonotic nature and high mutability make them a pandemic threat. UNICOR-v is a pre-pandemic, pan-coronavirus vaccine composed of an adjuvanted mix of twelve synthetic peptides originating from conserved regions within Nsp12 and M coronavirus proteins containing clusters of predicted T-cell epitopes. Here, we evaluate the immunogenicity of UNICOR-v and its efficacy against Middle East Respiratory Syndrome-related coronavirus (MERS). Methods: Animals were vaccinated with an adjuvanted equimolar mix of UNICOR-v. Humoral and cellular immunogenicity were assessed 28 days later through ELISA and FLUOROSpot. Vaccine efficacy was assessed in a DPP4 knock-in (HDPP4-KI) mouse model where mice were challenged post-vaccination with a lethal or non-lethal dose of MERS-CoV-MA. Results: Vaccination with UNICOR-v induced high IgG titers in both mice and rabbits and cellular secretion of pro-inflammatory cytokines. Vaccination with UNICOR-v, or passive serum transfer, significantly reduced viral lung titers 4 days post-infection compared to placebo. Vaccination induced lower immune cell infiltration in the alveolar space and increased repair of the cells lining the major airways in vaccinated mice, translating to increased survival rate compared to placebo. Conclusions: These data demonstrate the ability of conserved T-cell epitopes to protect against MERS-CoV infection, supporting further characterization of the breadth of protection of UNICOR-v against other coronaviruses that affect humans and livestock, following a One Health approach to control this highly zoonotic family of viruses. Full article

Foot-and-mouth disease virus (FMDV) is a highly contagious picornavirus that affects cloven-hoofed animals and carries significant economic implications for the global livestock industry. FMDV features two Leader (L) protein isoforms, Lab and Lb, differing at their amino termini by 28 amino acids (La region). Currently, the activity of La protein sequences has not been investigated. To address this issue, the comparison study of biological and functional roles of Lab and Lb was performed as the La region alone did not independently perform protein function. We found that Lab and Lb significantly regulated FMDV replication and pathogenicity, and their coexistence afforded optimal FMDV properties. Subsequently, we observed that both L isoforms cleaved eukaryotic translation initiation factor 4G (eIF4G) I, suppressed type I and type III interferon (IFN) expression, and exhibited marked cytotoxicity, indicating that they were all key components in FMDV’s antagonism of host antiviral defenses. Finally, the subcellular distribution of Lab and Lb was detected. Despite dual localization in cytoplasmic and nuclear compartments, both isoforms displayed different spatial distribution patterns, and Lb induced more pronounced morphological changes to host cells than Lab. Furthermore, bioinformatics predicted that the La region might contain a non-classical secretory signal peptide, potentially facilitating Lab distribution to the cell membrane or extracellular space. Collectively, the primary encoding role of La region was to control the intracellular distribution of L protein, as opposed to regulating its functional activity. This study may help to deepen our understanding of why FMDV encoded two isoforms of L protein. Full article

Background/Objectives: Adults with congenital heart disease (CHD) have a substantially higher risk of ischemic stroke than the general population. Circulating biomarkers such as N-terminal pro B-type natriuretic peptide (NT-pro-BNP), high-sensitivity C-reactive protein (hs-CRP), and microalbuminuria have been associated with adverse cardiovascular outcomes in CHD, but their role in predicting cerebrovascular events remains uncertain. Methods: Prospective cohort study including 372 adults with CHD [median age 34 years (IQR 23–42); 57.8% male] followed at a tertiary center between 2017 and 2022. Baseline assessments included demographic characteristics, CHD anatomical complexity, cardiovascular risk factors, NT-pro-BNP, hs-CRP, lipid profile, and 24-h urinary albumin excretion. The primary endpoint was incident ischemic stroke during a median follow-up of 6.3 years (IQR 3.9–8.3). Univariable Cox proportional hazards models were used to identify predictors of stroke. Results: During follow-up, 13 patients (3.5%) experienced ischemic stroke. Patients with stroke were significantly older [51 (46–64) vs. 30 (23–40) years; p < 0.001] and had a higher prevalence of dyslipidemia (61.5% vs. 15.0%; p < 0.001). NT-pro-BNP levels were markedly higher in patients with stroke [369 (218–604) vs. 64 (21–172) pg/mL; p < 0.001]. No significant differences were observed between groups in renal function parameters, hs-CRP, thyroid-stimulating hormone, or urinary albumin excretion rate. In Cox analyses, older age and dyslipidemia were the strongest predictors of stroke (p < 0.001). Arterial hypertension, diabetes mellitus, and higher NT-pro-BNP levels were also associated with increased stroke risk (p < 0.05), whereas CHD anatomical complexity, NYHA functional class, and cyanosis were not. Conclusions: In adults with CHD, ischemic stroke was mainly associated with traditional cardiovascular risk factors and elevated NT-pro-BNP levels rather than anatomical disease complexity or functional status. Full article

Background: Tumor neoantigens are key targets for personalized vaccines and T-cell therapies, yet most pipelines focus on neoantigens derived from SNV/small indel and often yield a limited number of high-quality candidates. SVs are prevalent in tumors and can generate novel chimeric sequences and neopeptides, making them a promising additional source of neoantigens. However, SV-derived neoantigen prediction remains challenging due to breakpoint uncertainty, isoform-dependent coding inference, and limited integration of multi-dimensional evidence and reproducibility. Methods: We developed SVNeoPP (Structural Variant Neoantigen Prediction and Prioritization), an end-to-end workflow for SV-derived neoantigen analysis. SVNeoPP takes WGS and RNA-seq as inputs, performs SV calling and annotation, and reconstructs altered transcripts and coding sequences in a traceable, isoform-aware manner to generate candidate peptides. Candidates are prescreened by integrating antigen-processing features with HLA binding prediction, and then hierarchically filtered and prioritized based on transcript expression, LC–MS/MS proteomics evidence, immunogenicity predictions, and sequence similarity to experimentally validated neoantigen databases. SVNeoPP is implemented in Snakemake to enable modular extension, checkpoint-based restarts, and end-to-end reproducibility. Results: Using a hepatocellular carcinoma (HCC) multi-omics dataset as a proof of concept, we demonstrated the performance of SVNeoPP and obtained a high-priority shortlist of candidate peptides. Compared with other methods, SVNeoPP substantially expanded the candidate search space for SV-derived neoantigens and showed more favorable distributions of antigen-processing and HLA binding features. Conclusions: SVNeoPP provides a reusable, traceable, and interpretable multi-dimensional evidence-driven framework for SV-derived neoantigens. As a complementary module to SNV/small-indel pipelines, it broadens the neoantigen candidate repertoire and generates ranked candidates with interpretable evidence to facilitate downstream prioritization and decision-making. Full article

This study investigates the immunomodulatory effects and underlying mechanisms of KEMPFPK, a peptide derived from bovine β-casein, using integrated in vitro, in silico, and in vivo approaches. In RAW264.7 macrophages, KEMPFPK enhanced proliferation, phagocytosis, and migration and selectively upregulated the chemokine MCP-1. Under LPS-induced inflammation, KEMPFPK suppressed pro-inflammatory cytokines (IL-1β, TNF-α) and NO production while promoting the anti-inflammatory cytokine IL-10. These effects were mediated through the inhibition of NF-κB and MAPK signaling pathways. Molecular docking predicted high-affinity binding of KEMPFPK to Toll-like receptors (TLR2 and TLR4), suggesting a potential mechanism for its immunomodulatory activity. In cyclophosphamide (CTX)-induced immunosuppressed mice, KEMPFPK administration restored immune organ indices, rebalanced serum cytokine levels, and modulated humoral immunity. Importantly, KEMPFPK was associated with a significantly reshaped gut microbiota profile, characterized by the promotion of beneficial genera (e.g., Ligilactobacillus, Adlercreutzia) and the suppression of opportunistic pathogens (e.g., Escherichia–Shigella). These findings establish KEMPFPK as a dual-phase immunomodulator and suggest that its effects may involve direct immune cell regulation coupled with indirect microbiota remodeling. This study provides a scientific foundation for the application of KEMPFPK in immunomodulatory functional foods. Full article

Background/Objectives: Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to sustain proliferation, survive under metabolic stress, and develop therapeutic resistance. While oncogenic signaling pathways regulating cancer metabolism have been extensively studied, increasing evidence indicates that non-coding RNAs (ncRNAs) play essential roles in coordinating metabolic adaptation. This review aims to synthesize current knowledge on long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) as important but relatively less characterized regulators of cancer metabolic adaptation and discuss their potential as biomarkers and therapeutic targets. Methods: We analyzed their roles across multiple types of cancer, prioritizing studies that integrate ncRNA profiling with metabolomics and mechanistic investigations, with particular attention to their diagnostic, prognostic, and predictive value. Results: LncRNAs and circRNAs regulate major metabolic pathways, including glycolysis, mitochondrial function, glutaminolysis, lipid metabolism, and redox balance. They act through transcriptional and epigenetic mechanisms, protein scaffolding, peptide encoding, and miRNA sponging, frequently converging on key regulators such as HIF-1α, c-Myc, p53, AMPK, and mTOR. However, many reported associations remain largely correlative, with limited integration of quantitative metabolic flux analyses and insufficient validation in physiologically relevant models. Conclusions: Although lncRNAs and circRNAs constitute an important context-dependent regulatory layer linking oncogenic signaling to metabolic reprogramming, future studies should combine ncRNA perturbation with stable isotope tracing, fluxomics, spatial metabolomics, long-read sequencing, and single-cell approaches to define causal and spatially resolved metabolic functions. Such integrative strategies may improve biomarker development and support ncRNA-informed, metabolism-oriented therapeutic interventions. Full article

Acute heart failure (AHF) is a leading cause of hospitalization and mortality worldwide. Despite advances in biomarker-based evaluation, accurate diagnostic and prognostic stratification remains challenging in everyday clinical practice. Growth differentiation factor-15 (GDF-15) has emerged as a biomarker associated with advanced disease profiles, poor outcomes and complex underlying pathophysiological processes in heart failure (HF). This study aimed to investigate the diagnostic and prognostic value of GDF-15 in the acute setting and to evaluate its incremental role within multimarker assessment strategies. In this prospective cohort study, 60 patients hospitalized with AHF and 42 control subjects were enrolled. Circulating levels of GDF-15, N-terminal pro–B-type natriuretic peptide (NT-proBNP), and high-sensitivity cardiac troponin I (hs-cTnI) were measured at admission. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and multivariable logistic regression. Prognostic value was evaluated for in-hospital, short-term, and one-year mortality, including multimarker models. GDF-15 levels were significantly elevated in AHF and demonstrated a favorable diagnostic profile, with high specificity and acceptable sensitivity. When integrated into multivariable diagnostic models, GDF-15 added significant value beyond established cardiac biomarkers. For prognosis, standalone biomarkers showed limited long-term discrimination, whereas multimarker approaches incorporating GDF-15 improved short-term mortality prediction and enhanced one-year risk stratification in patients with markedly elevated NT-proBNP levels. GDF-15 provides independent diagnostic and prognostic information in AHF and enhances multimarker strategies for comprehensive patient assessment, supporting its integration as a complementary biomarker in contemporary AHF evaluation. Full article

Duchenne muscular dystrophy (DMD) is a severe X-linked neuromuscular disease (NMD) caused by loss-of-function mutations in the DMD gene. RNA-based therapies, especially antisense oligonucleotides (ASO)-mediated exon skipping and adenosine deaminase acting on RNA (ADAR)-guided RNA editing, have emerged as complementary approaches that modulate pre-mRNA splicing or correct transcripts without altering genomic DNA. Current phosphorodiamidate morpholino oligomer (PMO) drugs targeting exons 51, 53, and 45 provide mutation-class-specific benefit. At the same time, next-generation delivery strategies (e.g., peptide-conjugated PMOs (PPMOs), antibody–oligonucleotide conjugates (AOC), and endosomal-escape vehicles) aim to improve skeletal, cardiac, and diaphragm exposure. In parallel, RNA editing strategies offer a route to correct select nonsense or missense variants at the base level and may, in principle, restore near-native dystrophin expression. Meaningful translation of these modalities requires predictive large-animal models. A genome-edited microminipig (MMP) bearing DMD exon-23 mutations faithfully recapitulates hallmark features of human DMD. That includes early locomotor deficits, elevated serum creatine kinase (CK) and cardiac troponin T, progressive myocardial fibrosis, and a decline in left-ventricular ejection fraction (LVEF), while maintaining a manageable lifespan of approximately 30 months suitable for long-term studies. In particular, the MMP model provides a practical platform for addressing the persistent challenge of efficient therapeutic delivery to the heart and diaphragm through longitudinal dosing, imaging, and biopsy. In this review, we synthesize clinical progress in exon skipping, outline the promise of RNA editing, and integrate recent insights from Duchenne muscular dystrophy model for microminipigs (DMD-MMPs) as an advanced surrogate for preclinical development and translational evaluation. Full article

Influenza A virus remains a major cause of respiratory disease worldwide and poses a persistent challenge to vaccine development due to its rapid genetic evolution and antigenic variability. T-cell-based immunity has therefore gained increasing importance, as it can provide broader and more durable protection by targeting conserved viral regions. Accurate identification of T-cell epitopes (TCEs) is a fundamental requirement for epitope-based vaccine design and immunological research. Although numerous computational methods have been proposed, many existing approaches rely on handcrafted physicochemical features, which offer limited ability to capture contextual sequence dependencies. In this study, a transformer-based viroinformatics framework is proposed for the binary prediction of TCEs from Influenza A virus peptide sequences. The framework employs a pretrained Evolutionary Scale Modeling-2 (ESM-2) protein language model (PLM) to generate rich, contextualized embeddings directly from raw amino acid sequences, eliminating the need for manual feature engineering. These embeddings are processed using a lightweight attention-based transformer classifier to learn epitope-specific sequence patterns. The model achieves strong and stable predictive performance, attaining an accuracy of approximately 97% and an AUC close to 0.99 under stratified cross-validation. Ablation analysis further confirms that protein language model representations and self-attention contribute substantially to performance gains over classical machine learning baselines. To enhance practical reliability, Monte Carlo dropout is incorporated during inference to provide uncertainty-aware predictions, enabling differentiation between high-confidence and ambiguous peptide candidates. In addition, attention-based interpretability is used to identify residue-level contributions to model decisions, offering biologically meaningful insights into epitope recognition. Overall, this study demonstrates that PLMs combined with Transformer architectures provide an effective, interpretable, and a promising computational framework for Influenza A TCE discovery and vaccine research. Full article