Search Results (301)

Background: mRNA vaccines, first approved in December 2020, have been used globally to prevent infectious diseases, and those for treating cancers are being developed. Safety-related labelling changes of Comirnaty and Spikevax were made in June 2025; however, concerns remain. This study assessed the potential risks associated with mRNA vaccines on the indications previously approved, utilizing Real-World Data (RWD) of Adverse Events Following Immunization (AEFIs) derived from the Vaccine Adverse Event Reporting System (VAERS) and Academic Literature Databases (ALD). Methods: A Disproportionality Analysis (DPA) was performed using the Reporting Odds Ratio (ROR) and the Bayesian Confidence Propagation Neural Network (BCPNN) algorithm on spontaneous case reports from VAERS. Statistical positive signals were cross-validated with literature case reports from ALD to provide more comprehensive medical descriptions and clearer causal assessments, and compared with safety information documented in clinical trials and on vaccine labelling. Time-to-onset, stratified, and immunization schedule analyses were conducted to characterize the safety profiles of mRNA vaccines. Results: In total, 5,040,725 spontaneous case reports and 4,387 literature case reports were analyzed. In both VAERS and ALD, new signals involving blood and lymphatic system disorders (e.g., thrombotic thrombocytopenic purpura) and ear and labyrinth disorders (e.g., deafness) were detected from Comirnaty as Designated Medical Events (DMEs), while blood and lymphatic system disorders (e.g., thrombotic thrombocytopenic purpura) from Spikevax in ALD only. No new signals were detected from other vaccines on the DMEs list. In VAERS, Serious Adverse Events (SAEs) were more common in females, while death risk was higher in males. In ALD, SAEs were more common in males for most mRNA vaccines, except Comirnaty. Medical history emerged as a key risk factor for SAEs, particularly among older adults. Conclusions: Statistically significant safety signals were detected across all mRNA vaccines based on five-year cumulative RWD, indicating the need of intensified monitoring of specific populations, including older adults and individuals with medical histories, alongside further optimization of vaccination strategies. Full article

The construction cost of an offshore wind farm collection system accounts for 15–30% of the total investment, and its efficient design is crucial to the economy; however, traditional methods in large-scale scenarios suffer from slow convergence and local optimization problems. In this study, we propose an upper and lower topology optimization framework based on the sled dog optimizer (SDO). The upper layer adopts polar coordinate partitioning combined with dynamic minimum spanning tree (DMST) to realize wind farm partitioning, and deals with the current-carrying capacity constraints and cable no-crossing requirements in a synchronized manner. The lower layer applies the SDO algorithm to optimize the topology structure within the partitioning range. The performance of genetic algorithm (GA), immunity algorithm (IA), particle swarm optimization (PSO), and SDO approaches is compared by the dynamic minimum spanning tree method through the case of an offshore wind farm with 62 wind turbines (WTs). The results show that the SDO-DMST framework significantly outperforms the comparison algorithms in terms of computational efficiency and cost optimization, and the proposed method can stably obtain high-quality cable topology solutions, which proves its superiority in unit group partitioning and cable routing co-optimization. In this paper, the SDO is introduced to collection system optimization for the first time, providing an efficient and robust design solution for large-scale offshore wind farms. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is challenging to treat with chemotherapy. Immunotherapy has shown moderate responses in inflammatory and immunosuppressive tumor environments. Hepatic cytochrome P450 monooxygenases (CYPs) play a crucial role in xenobiotic and drug metabolism, as well as lipid and steroid metabolism. We aimed to investigate whether CYP expression and various parameters of the innate and adaptive immune system are prognostic factors for the survival of HCC patients. Methods: HCC biopsies (n = 370) from The Cancer Genome Atlas (TCGA) database were analyzed using Kaplan–Meier statistics and the KMPlotter algorithm. Parameters such as immune cell infiltration, DNA mutation rates, and neoantigen load were selected for survival analysis and subjected to hierarchical cluster analysis. The expression of candidate CYP genes in tumors was compared to that in normal liver tissues. Furthermore, tumor infiltration of innate immune cells (basophilic and eosinophilic granulocytes, natural killer cells), adaptive immune cells (CD4+ memory and CD8+ cytotoxic T cells, regulatory T cells, type 1 and type 2 helper T cells), and mesenchymal stem cells was examined. Results: High expression of CYP19A1 and CYP26B1 was associated with shorter survival, whereas high expression of CYP3A5, CYP3A43, CYP7A1, and CYP27A1 was linked to longer survival. Mutation rates combined with CYP expression showed a correlation with five out of six CYP genes, while a high neoantigen load produced less definitive results. A specific cluster exhibiting high CYP expression and immune cell counts or mutation/neoantigen rates was associated with shorter survival, while another cluster was linked to longer survival. Conclusions: CYPs involved in the metabolic regulation of HCC, including CYP3A5, CYP3A43, CYP7A1, CYP19A1, CYP26B1, and CYP27A1, were found to have prognostic value for patient survival. Combined signatures that include CYP expression, mutational rates, and immune cell infiltration into tumors further enhanced the prognostic value for patient survival. This suggests that CYPs may influence the creation of a tumor-specific metabolic microenvironment that impacts immune functions. These combined signatures could be utilized for patient stratification to personalize tumor treatment and develop novel combination therapies aimed at optimizing treatment outcomes, such as combining transarterial chemoembolization (TACE) with immune checkpoint inhibitors. Full article

Background: Extensive-stage small cell lung cancer (ES-SCLC) carries a poor prognosis, with fewer than 7% of patients surviving for five years. While immune checkpoint inhibitors (ICIs) combined with platinum–etoposide have reshaped first-line treatment, no head-to-head trials exist comparing available regimens, leaving the optimal therapeutic choice undefined. Methods: A systematic literature search identified phase III randomized controlled trials (RCTs) evaluating first-line ICI-based regimens in ES-SCLC. Individual patient data (IPD) were reconstructed from Kaplan–Meier curves using the IPDfromKM algorithm. Indirect treatment comparisons were performed using Cox proportional hazards models, with chemotherapy alone as the common comparator. The indirect comparison of tiragolumab efficacy was unanchored due to the design of the SKYSCRAPER-02 study. Restricted mean survival time (RMST), truncated at 27 months, was calculated as an additional measure of treatment effect. Results: Six RCTs were included. All ICI-based regimens improved overall survival (OS) compared to chemotherapy alone, except ipilimumab (HR 0.97, 95% CI 0.86–1.09). Serplulimab demonstrated the most favorable OS benefit (HR 0.55, 95% CI 0.48–0.64; RMST 16.73 months), representing a gain of approximately 4 months over chemotherapy alone and 1.8–2.3 months over atezolizumab and durvalumab. The addition of tiragolumab to atezolizumab plus chemotherapy yielded no significant advantage over atezolizumab alone. Conclusions: This IPD-based indirect comparison suggests that serplulimab plus chemotherapy may offer the most favorable OS estimates S benefit among first-line ICI regimens for ES-SCLC, while durvalumab and atezolizumab remain effective standards of care. These findings are hypothesis-generating and highlight the need for prospective head-to-head comparative studies. Full article

Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy in which radiotherapy plays a uniquely central role compared with other gastrointestinal cancers. Definitive chemoradiotherapy (dCRT) is widely used as a curative treatment; however, a substantial proportion of patients develop residual or recurrent disease, creating a complex clinical scenario that requires tailored salvage strategies. Salvage esophagectomy offers the potential for long-term survival but remains technically demanding and is associated with significant morbidity because of radiation-induced tissue damage. Less invasive local therapies, such as endoscopic submucosal dissection and photodynamic therapy, may provide effective treatment in selected patients, although their indications are limited by tumor characteristics and post-radiation fibrosis. In addition, immune checkpoint inhibitors have demonstrated promising efficacy in advanced ESCC and may represent a potential therapeutic option in the salvage setting. For patients who are not candidates for curative treatment, palliative esophageal stenting remains an important option for symptom relief, although prior radiotherapy may increase the risk of treatment-related complications. Given the diversity of available treatment modalities and their associated risks, a multidisciplinary and individualized treatment approach is essential. Further prospective studies are warranted to optimize treatment algorithms and improve outcomes in patients with ESCC after dCRT. Full article

The high case fatality rate, cross-species transmission, and ongoing evolution of H5 avian influenza viruses pose an imminent threat of an influenza pandemic, particularly with the currently predominant clade 2.3.4.4b lineage. Existing seasonal influenza vaccines and licensed H5 vaccines provide limited cross-protection against H5 viruses, underscoring an urgent need for the development of broadly protective H5 vaccines. In this study, we analyzed all human-infected H5 hemagglutinin (HA) sequences using bioinformatics approaches and subsequently designed a novel H5 influenza vaccine through algorithm optimization. The predicted structure of this vaccine closely resembles that of the wild-type H5 HA trimer. In animal studies, the algorithm-optimized H5 mRNA vaccine not only induced high levels of neutralizing antibodies against multiple clade 2.3.4.4b H5 viruses but also elicited cross-neutralizing antibodies against clade 2.3.4.4 and clade 2.2.1 H5 viruses, as well as robust cellular immune responses. These findings highlight the potential of algorithm-based approaches in developing broadly protective vaccines against pandemic viruses and suggest that this vaccine candidate could serve as a strategic stockpile for preventing H5 influenza pandemics. Full article

Chronic hand eczema (CHE) is a persistent and relapsing inflammatory dermatosis characterized by substantial functional impairment, psychosocial distress, and occupational disability. Although epidemiologically common and clinically burdensome, CHE has long suffered from nosological ambiguity, frequently interpreted as a localized manifestation of atopic dermatitis, psoriasis, allergic contact dermatitis, or cumulative irritant dermatitis. The recent regulatory approval of topical delgocitinib, a pan-Janus kinase (JAK) inhibitor specifically indicated for moderate-to-severe CHE inadequately controlled by topical corticosteroids, has reshaped both therapeutic strategy and conceptual framing of the disease. The introduction of a targeted therapy dedicated to CHE has reinforced its clinical identity while simultaneously highlighting its internal biological heterogeneity. Beneath the umbrella term “chronic hand eczema” lie distinct phenotypes characterized by variable barrier dysfunction, immune polarization, and environmental interaction. This review integrates current knowledge on epidemiology, pathophysiology, diagnostic stratification, therapeutic algorithms, phase III registrative evidence, emerging real-world data, and the central role of barrier restoration. Particular attention is devoted to the hand as a specialized barrier organ and to the interplay between inflammation and epidermal structural integrity. In the era of targeted therapy, precise diagnostic framing and barrier-oriented management are indispensable to optimize outcomes. Full article

Liver involvement in pediatric rheumatologic diseases is an increasingly recognized but often underappreciated clinical issue. Systemic autoimmune and autoinflammatory disorders including juvenile idiopathic arthritis, systemic lupus erythematosus, juvenile dermatomyositis, systemic vasculitis, and mixed connective tissue disease can all affect hepatic structure and function. The mechanisms of injury are multifactorial, encompassing immune-mediated inflammation, macrophage activation, drug-induced toxicity, and metabolic alterations. Hepatic manifestations range from asymptomatic transaminase elevations to fulminant liver failure, frequently influenced by immunosuppressive therapy and comorbid infections. Early recognition through routine biochemical monitoring, imaging, and targeted autoantibody testing is essential for differentiating primary disease activity from treatment-related injury. Timely, multidisciplinary management involving pediatric rheumatology and hepatology teams can prevent progression to chronic liver disease and optimize outcomes. This review summarizes the current understanding of hepatic pathology in pediatric rheumatology, highlighting diagnostic algorithms, monitoring strategies, and emerging therapeutic considerations. Full article

One of the complications of both surgical and non-surgical treatment of fractures is the development of non-union. The 5–10% incidence of non-union quoted in the international literature is thought to be an underestimate of the real magnitude of this clinical problem. The etiology of atrophic non-union is multifactorial, involving biological, mechanical, infectious, and host-related factors. Much of the evidence regarding its pathogenesis is heterogenous and largely hypothesis generating. This heterogenicity has contributed to the wide range of treatment strategies used to address an atrophic non-union, with variable success rates. This structured narrative review summarizes current insights into the pathogenesis of atrophic non-union, including the inflammatory and immune response, the role of mesenchymal stem cells, bone morphogenetic protein, and the mechanisms of remodeling and angiogenesis. It also outlines an algorithmic approach to management, including the exclusion of occult infection, assessment of mechanical stability, optimization of modifiable host factors, and, finally, a graded approach to enhance the biological and mechanical environment of the non-union. Full article

Extramedullary relapse (EMR) of acute lymphoblastic leukemia (ALL) after allogeneic hematopoietic stem cell transplantation (Allo-HSCT) represents a clinically and biologically distinct entity compared with medullary relapse, characterized by marked heterogeneity, compartmental immune escape mechanisms, and generally poor prognosis. EMR arises at the intersection of clonal resistance, evolutionary disease adaptation, and heterogeneous distribution of the graft-versus-leukemia effect, resulting in evolutionary trajectories that are often dissociated between medullary and extramedullary compartments. In the absence of prospectively validated therapeutic algorithms, EMR management requires a structured and adaptive approach based on multidimensional assessment integrating leukemia biology, disease burden and anatomical distribution, bone marrow minimal residual disease (MRD) status, and immune reconstitution. Therapeutic strategies include local treatments, targeted agents, immunotherapies, and immunomodulatory interventions, applied within a dynamic sequence tailored to treatment response. Follow-up plays a central role as an active tool for prognostic stratification and clinical decision-making, enabling early detection of systemic progression and optimization of the timing of consolidative strategies, including second Allo-HSCT in selected patients. An integrated and biologically driven management of post-Allo-HSCT EMR is essential to improve outcomes in this high-risk clinical setting. Full article

Coronavirus Disease 2019 (COVID-19) and other lower respiratory tract diseases (LRTDs), including bacterial pneumonia and acute respiratory distress syndrome, share overlapping clinical features but arise from distinct pathophysiological mechanisms. The molecular signatures that distinguish these diseases remain insufficiently characterized in African populations, where genetic background, endemic infections, and environmental exposures may substantially shape immune responses. We integrated spatially resolved single-cell transcriptomic profiles from lung autopsy specimens of 30 Malawian patients, including 10 with COVID-19, 12 with other LRTDs, and 8 non-LRTD controls. In total, 61,391 cells representing 15 cell types and 36,602 gene expression features were analyzed. Using an integrated machine learning framework that combined nine feature-ranking algorithms with incremental feature selection, we identified potential molecular signatures that could discriminate among disease states within this cohort. The optimal classification models achieved weighted F1 scores greater than 0.94, demonstrating a robust capacity to differentiate COVID-19 from other LRTDs in our dataset. Notably, the macrophage-associated state in COVID-19 was dominated by an IFN-γ response with upregulation of CD163 and HLA-DQA2, contrasting sharply with the type I/III interferon signature reported in European cohorts. In addition, we observed cell-type-specific COVID-19 signatures, including downregulation of CAV1 in AT1 cells, consistent with epithelial damage; dysregulation of SFTPC in AT2 cells, suggesting surfactant dysfunction; and upregulation of NFKBIA in neutrophils, indicating altered inflammatory regulation. Gene Ontology enrichment further revealed universal disruption of protein synthesis machinery, along with cell-type-specific alterations in immune activation, epithelial repair, and inflammatory signaling pathways. Full article

Treatment-resistant schizophrenia (TRS) remains a significant clinical challenge due to limited therapeutic options and a poor understanding of its underlying biology. Recent findings suggest that immune system dysregulation may play a critical role in the pathophysiology of TRS. This systematic review aimed to synthesize current evidence on immunological abnormalities associated with TRS, with a focus on inflammatory markers, immune cell profiles, and the role of autoantibodies, and to explore their potential utility in diagnosis and treatment. A systematic review of the literature was conducted in accordance with PRISMA guidelines, incorporating clinical, molecular, and translational studies on immunological markers in patients with TRS. Included studies assessed cytokine levels, immune cell phenotypes, autoantibodies, genetic factors, and the effects of immunomodulatory therapies. Emphasis was placed on findings differentiating TRS from treatment-responsive schizophrenia. TRS is associated with distinct immune profiles, including elevated IL-6, IL-8, TNF-α, and sCD25 levels, overexpression of CD33 on monocytes and expansion of CD123+ plasmacytoid dendritic cells. Autoantibodies, particularly those targeting glutamatergic receptors, are more prevalent in TRS and decrease with clozapine treatment. Predictive models using IgM autoantibodies and genetic variants show promise for early identification of at-risk individuals. Emerging immunomodulatory treatments such as rituximab, levamisole, and senolytics are under investigation, offering potential for personalized strategies. Immunological dysfunction represents a reproducible and biologically relevant feature of TRS. Integration of immune biomarkers into clinical practice may enhance diagnostic precision and inform personalized therapeutic approaches. Future research should prioritize standardized biomarker protocols and longitudinal studies to validate causal associations and optimize treatment algorithms. Full article

Background/Objectives: Sepsis is a leading cause of hospital mortality and represents a time-sensitive medical emergency. Current diagnostic strategies rely on clinical assessment, severity scores, biomarkers, and blood cultures. However, blood cultures require 24–72 h for pathogen identification and demonstrate limited sensitivity, while biomarkers such as procalcitonin and C-reactive protein lack optimal specificity. These limitations support the widespread empirical use of broad-spectrum antibiotics and highlight the need for rapid, sensitive, and culture-independent diagnostic tools. Methods: A narrative literature review was conducted using PubMed and Google Scholar, including 28 studies published over the past 10 years, encompassing observational and preclinical investigations. Current evidence on the application of Raman spectroscopy in sepsis was summarized, with a dual focus on pathogen identification and the assessment of the host response. Results: Raman spectroscopy has demonstrated the ability to detect early molecular alterations in circulating immune cells and mitochondrial redox status, potentially preceding conventional biomarkers. For pathogen identification, Raman techniques have achieved diagnostic accuracies comparable to automated systems, but with significantly shorter turnaround times. Integration with microfluidics, optical tweezers, and deep learning algorithms has further enhanced performance, although these applications remain largely experimental. Conclusions: Despite these promising results, the lack of methodological standardization, spectral overlap among phylogenetically related species, limited large-scale validation, and challenges in interpreting certain spectral signatures remain unresolved. Most available evidence originates from preclinical, single-center, and controlled studies, underscoring the need for prospective multicenter trials and harmonized protocols. Full article

Background: Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract infections in infants, seniors, and immunocompromised individuals, contributing substantially to the global disease burden. Given the limited preventive options available, developing an effective and safe vaccine remains a public health priority. Methods: An mRNA vaccine encoding the RSV PreF protein was designed and prepared. Antigen properties were evaluated in silico, and the coding sequence was optimized using NLP algorithms. The stability and translational efficiency of the mRNA constructs were verified through in vitro and in vivo assays, followed by immunogenicity evaluation of the formulated mRNA vaccines in a BALB/c mouse model. Results: The optimized mRNA showed predicted improvements in structural stability and a lower free energy state, which were associated with increased translational efficacy in vitro. Correct antigen conformation and retention of key epitopes were confirmed by intracellular staining followed by flow cytometry. A balanced Th1-biased immune response was induced in mice, characterized by high levels of neutralizing antibodies and antigen-specific T-cell immunity, along with enhanced memory T-cell proliferation and differentiation, indicating long-term immunological memory. Conclusions: A novel RSV PreF mRNA vaccine was successfully developed via optimization of protein structure and mRNA sequence. Superior immunogenicity was demonstrated in the BALB/c mouse model, together with promising potential in terms of vaccine safety and immunological persistence. These findings represent a promising step forward in the pursuit of an effective RSV vaccine and suggest the potential of the developed mRNA vaccine to induce substantial immune responses that may correlate with protection in future challenge studies. Full article

Intelligent parking systems have been recognized as a core technological intervention for resolving parking garage shortages and advancing traffic safety. Nevertheless, it remains challenging to generate a smooth, accurate, and optimal parking trajectory when employing conventional intelligent path optimization algorithms. Hence, building upon a newly designed optimization model for intelligent vehicle parking path planning, this study develops an improved immune moth–flame optimization algorithm (IIMFO). Specifically, aiming at the shortest path length and smooth enough trajectory, we leverage a cubic spline interpolation-driven path planning model to resolve the complex automatic parking trajectory optimization problem. To significantly strengthen the optimization effect, we introduce immune concentration selection, nonlinear decaying adaptive inertia weight adjustments, and elite opposition-based learning mechanisms to improve the immune moth–flame algorithm. Based on the evaluation results of the test functions, as well as the simulation and semi-automatic experiments of the real-world scenario of intelligent vehicle parking path optimization, the results indicate that the improved strategy can achieve better parking trajectories. Full article