Search Results (1,035)

The association between COVID-19 vaccination and thromboembolic events has garnered significant research attention, particularly with the advent of vaccines based on adenoviral vectors, including AstraZeneca’s and Johnson & Johnson’s vaccines. This review underscores the uncommon occurrence of venous thromboembolism (VTE), arterial thromboembolism (ATE), and vaccine-induced thrombotic thrombocytopenia (VITT) following COVID-19 vaccination. Although these complications are extremely rare compared to the heightened risk of thrombosis from COVID-19 infection, elements like age, biological sex, type of vaccine and underlying health conditions may contribute to their development. In addition, rare renal complications such as acute kidney injury and thrombotic microangiopathy have been documented, broadening the spectrum of potential vaccine-associated thrombotic manifestations. Current guidelines emphasize early detection, individualized risk assessment, and use of anticoagulation therapy to mitigate risks. Despite these events, the overwhelming majority of evidence supports the continued use of COVID-19 vaccines, given their proven efficacy in reducing severe illness and mortality. In addition, recent comparative data confirm that mRNA-based vaccines are associated with a significantly lower risk of serious thrombotic events compared to adenoviral vector platforms. Ongoing research is essential to further refine preventive and therapeutic strategies, particularly for at-risk populations. Full article

Recent outbreaks of highly pathogenic human RNA viruses from probable zoonotic origin have highlighted the relevance of epidemic preparedness as a society. However, research in vaccinology and virology, as well as epidemiologic surveillance, is often constrained by the biological risk that live virus experimentation entails. These also involve expensive costs, time-consuming procedures, and advanced personnel expertise, hampering market access for many drugs. Most of these drawbacks can be circumvented with the use of pseudotyped viruses, which are surrogate, non-pathogenic recombinant viral particles bearing the surface envelope protein of a virus of interest. Pseudotyped viruses significantly expand the research potential in virology, enabling the study of non-culturable or highly infectious pathogens in a safer environment. Most are derived from lentiviral vectors, which confer a series of advantages due to their superior efficiency. During the past decade, many studies employing pseudotyped viruses have evaluated the efficacy of vaccines or monoclonal antibodies for relevant pathogens such as HIV-1, Ebolavirus, Influenza virus, or SARS-CoV-2. In this review, we aim to provide an overview of the applications of pseudotyped viruses when evaluating the neutralization capacity of exposed individuals, or candidate vaccines and antivirals in both preclinical models and clinical trials, to further help develop effective countermeasures against emerging neutralization-escape phenotypes. Full article

Gene therapy is a groundbreaking strategy in regenerative medicine, enabling precise cellular behavior modulation for tissue repair. In situ nucleic acid delivery systems aim to directly deliver nucleic acids to target cells or tissues to realize localized genetic reprogramming and avoid issues like donor cell dependency and immune rejection. The key to success relies on biomaterial-engineered delivery platforms that ensure tissue-specific targeting and efficient intracellular transport. Viral vectors and non-viral carriers are strategically modified to enhance nucleic acid stability and cellular uptake, and integrate them into injectable or 3D-printed scaffolds. These scaffolds not only control nucleic acid release but also mimic native extracellular microenvironments to support stem cell recruitment and tissue regeneration. This review explores three key aspects: the mechanisms of gene editing in tissue repair; advancements in viral and non-viral vector engineering; and innovations in biomaterial scaffolds, including stimuli-responsive hydrogels and 3D-printed matrices. We evaluate scaffold fabrication methodologies, nucleic acid loading–release kinetics, and their biological impacts. Despite progress in spatiotemporal gene delivery control, challenges remain in balancing vector biocompatibility, manufacturing scalability, and long-term safety. Future research should focus on multifunctional “smart” scaffolds with CRISPR-based editing tools, multi-stimuli responsiveness, and patient-specific designs. This work systematically integrates the latest methodological advances, outlines actionable strategies for future investigations and advances clinical translation perspectives beyond the existing literature. Full article

Cardiac arrhythmias remain a major source of morbidity and mortality, often stemming from molecular and structural abnormalities that are insufficiently addressed by current pharmacologic and interventional therapies. Gene therapy has emerged as a transformative approach, offering precise and durable interventions that directly target the arrhythmogenic substrate. Across the spectrum of inherited and acquired arrhythmias—including long QT syndrome, Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, atrial fibrillation, and post-infarction ventricular tachycardia—gene-based strategies such as allele-specific silencing, gene replacement, CRISPR-mediated editing, and suppression-and-replacement constructs are showing growing translational potential. Advances in delivery platforms, including cardiotropic viral vectors, lipid nanoparticle-encapsulated mRNA, and non-viral reprogramming tools, have further enhanced the specificity and safety of these approaches. Additionally, innovative applications such as biological pacemaker development and mutation-agnostic therapies underscore the versatility of genetic modulation. Nonetheless, significant challenges remain, including vector tropism, immune responses, payload limitations, and the translational gap between preclinical models and human electrophysiology. Integration of patient-derived cardiomyocytes, computational simulations, and large-animal studies is expected to accelerate clinical translation. This review provides a comprehensive synthesis of the mechanistic rationale, therapeutic strategies, delivery platforms, and translational frontiers of gene therapy for cardiac arrhythmias. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

Gaucher disease (GD) is an autosomal recessive disorder caused by the deficient activity of the lysosomal enzyme glucocerebrosidase (GCase). Although enzyme replacement therapy (ERT) remains the standard of care for non-neuropathic GD patients, its high cost significantly limits accessibility. To enhance production efficiency, we developed a lentiviral system encoding a codon-optimized GCase gene driven by the human elongation factor 1a (hEF1α) promoter for stable production in human cell lines. A functional lentiviral vector, LV_EF1α_GBA_Opt, was generated at a titer of 7.88 × 10⁸ LV particles/mL as determined by qPCR. Six transduction cycles were performed at a multiplicity of infection of 30–50. The transduced heterogeneous human cell population showed GCase-specific activity of 307.5 ± 53.49 nmol/mg protein/h, which represents a 3.21-fold increase compared to wild-type 293FT cells (95.58 ± 16.5 nmol/mg protein/h). Following single-cell cloning, two clones showed specific activity of 763.8 ± 135.1 and 752.0 ± 152.1 nmol/mg/h (clones 15 and 16, respectively). These results show that codon optimization, a lentiviral delivery system, and clonal selection together enable the establishment of stable human cell lines capable of producing high levels of biologically active, synthetic recombinant GCase in vitro. Further studies are warranted for the functional validation in GD patient-derived fibroblasts and animal models. Full article

The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article

Rhipicephalus microplus is an important biological vector as it transmits several pathogens, including Babesia bovis, the causative agent of bovine babesiosis. The available strategies for controlling B. bovis are limited, resulting in substantial challenges for both animal health and livestock management. Infection of the tick midgut is the essential first step for the transmission cycle of B. bovis, yet this process remains largely unexamined. To better understand the first step of tick infection, this study employed a proteomic approach to identify a midgut protein that responds to B. bovis infection. We then used RNA interference for gene silencing to determine if the protein is essential for R. microplus infection. The protein we identified, Rm24, is twofold upregulated in the tick midgut during B. bovis infection. We silenced the gene encoding Rm24 and examined the effect of reduced expression on both tick fitness and B. bovis infection. Our results indicated that silencing the Rm24 gene impacted the survivability of adult female ticks, which exhibited a significant reduction in viability as compared to the control and non-injected groups. Importantly, we found that suppressing the gene encoding Rm24 led to a significant decrease in the number of engorged female ticks infected, with only 15% of female ticks testing positive for B. bovis kinetes as compared to over 50% in the control groups. We also detected a significant reduction in vertical transmission of B. bovis to larval progenies. These findings suggest that the Rm24 protein is critical for infection by B. bovis and could serve as a promising target for future transmission-blocking strategies. Full article

Background: Endometriosis is a chronic gynecological condition marked by ectopic endometrial-like tissue, leading to inflammation, pain, and infertility. Diagnosis is often delayed by up to 10 years. Identifying non-invasive biomarkers could facilitate earlier detection. MicroRNAs, known for their stability in biological fluids and role in disease processes, have emerged as potential diagnostic tools. This pilot study investigated whether serum miRNA profiling can differentiate endometriosis from other causes of chronic pelvic pain. Methods: Serum samples from 52 patients (36 with laparoscopically confirmed endometriosis and 16 controls) treated for chronic pelvic pain at a University Endometriosis Centre were analyzed. High-throughput miRNA sequencing was performed. Feature selection reduced 4285 miRNAs to the 20 most informative MiRNAs. Machine learning models, including logistic regression, decision tree, random forest, and support vector machine, were trained and evaluated. Results: Among the tested machine learning models, support vector machine achieved the best overall performance (accuracy 0.71, precision 0.80), while logistic regression and random forest showed the highest AUC values (0.84 and 0.81, respectively), indicating strong diagnostic potential of serum miRNA profiling. Conclusions: This study demonstrates the feasibility of using serum miRNA profiling combined with machine learning for the non-invasive classification of endometriosis. The identified miRNA signature shows strong diagnostic potential and could contribute to earlier and more accurate detection of the disease. Full article

Ovarian cancer (OC), the third most common gynecologic malignancy, exhibits distinct metabolic alterations that could enable early detection via liquid biopsy. We developed an advanced machine learning pipeline integrating lipidomics (HPLC-MS, positive/negative ion modes) and NMR-based metabolomics to analyze plasma samples from 229 subjects, including 103 serous OC patients, 107 benign cases, and 19 healthy controls. By systematically evaluating feature selection methods and machine learning architectures, we identified optimal biomarker combinations for OC detection. Convolutional Neural Network (CNN) model based on Mann–Whitney-selected features demonstrated strong discriminatory power (81% accuracy) in distinguishing malignant from benign cases, while Extreme Gradient Boosting (XGBoost) combined with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) achieved exceptional performance (96% accuracy) in differentiating benign from control samples. For multiclass classification, XGBoost with Kruskal–Wallis-selected features achieved 77% accuracy, while one-versus-one CNN models utilizing Mann–Whitney-selected features attained 78% accuracy, demonstrating optimal performance among tested approaches. The complementary strengths of deep learning and ensemble methods underscore their potential for tailored diagnostic applications. While clinical implementation requires further standardization, these findings provide both a methodological framework for metabolic biomarker discovery and biological insights into OC pathophysiology, paving the way for integrated multi-omics approaches in gynecologic oncology. Full article

The synthesis of novel biodegradable polymers as non-viral vectors remains one of the challenging tasks in the field of gene delivery. In this study, the synthesis of the polysaccharide-g-polypeptide copolymers, namely, hyaluronic acid-g-polylysine (HA-g-PLys), using a copper-free strain-promoted azide-alkyne cycloaddition reaction was proposed. For this purpose, hyaluronic acid was modified with dibenzocyclooctyne moieties, and poly-L-lysine with a terminal azido group was obtained using ring-opening polymerization of N-carboxyanhydride of the corresponding protected amino acid, initiated with the amino group azido-PEG₃-amine. Two HA-g-PLys samples with different degrees of grafting were synthesized, and the structures of all modified and synthesized polymers were confirmed using ¹H NMR and FTIR spectroscopy. The HA-g-PLys samples obtained were able to form nanoparticles in aqueous media due to self-assembly driven by electrostatic interactions. The binding of DNA and model siRNA by copolymers to form polyplexes was analyzed using ethidium bromide, agarose gel electrophoresis, and SybrGreen I assays. The hydrodynamic diameter of polyplexes was ˂300 nm (polydispersity index, PDI ˂ 0.3). The release of a model fluorescently-labeled oligonucleotide in the complex biological medium was significantly higher in the case of HA-g-PLys as compared to that in the case of PLys-based polyplexes. In addition, the cytotoxicity in normal and cancer cells, as well as the ability of HA-g-PLys to facilitate intracellular delivery of anti-GFP siRNA to NIH-3T3/GFP+ cells, were evaluated. Full article

Usher syndrome, a rare genetic disorder causing both hearing and vision loss, presents significant diagnostic and therapeutic challenges due to its complex genetic basis. The identification of reliable biomarkers for early detection and intervention is crucial for improving patient outcomes. In this study, we present a machine learning-based hybrid sequential feature selection approach to identify key mRNA biomarkers associated with Usher syndrome. Beginning with a dataset of 42,334 mRNA features, our approach successfully reduced dimensionality and identified 58 top mRNA biomarkers that distinguish Usher syndrome from control samples. We employed a combination of feature selection techniques, including variance thresholding, recursive feature elimination, and Lasso regression, integrated within a nested cross-validation framework. The selected biomarkers were further validated using multiple machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines, demonstrating robust classification performance. To assess the biological relevance of the computationally identified mRNA biomarkers, we experimentally validated candidates from the top 10 selected mRNAs using droplet digital PCR (ddPCR). The ddPCR results were consistent with expression patterns observed in the integrated transcriptomic metadata, reinforcing the credibility of our machine learning-driven biomarker discovery framework. Our findings highlight the potential of machine learning-driven biomarker discovery to enhance the detection of Usher syndrome. Full article

Background: Administration of PARP inhibitors against breast and ovarian cancers with BRCA1 and BRCA2 mutations has shown clinical benefits in patients. However, these agents are also toxic and have a narrow therapeutic index. Objectives: In this work, we aimed to identify membrane proteins that are specifically upregulated in these cancers. Methods: We interrogated public datasets to analyze genes upregulated or downregulated when these mutations were present, compared with wild-type cancers. Surface protein expression and functional annotation analyses were also performed. Results: In breast cancer, we identified 11 upregulated and 44 downregulated transcripts in BRCA1-mut, while 10 upregulated and 57 downregulated transcripts were identified in BRCA2-mut cancers. In ovarian cancer, 79 transcripts were upregulated and 123 were downregulated in BRCA1-mut cancers, while five were upregulated and seven were downregulated in BRCA2-mut tumors. Regarding the biological function related to these genes, in BRCA1-mutated ovarian cancers, the main functions of upregulated genes included MHC assembly or regulation of the interferon gamma pathway; in BRCA2-mut ovarian cancers, regulation of phosphorylation and signaling; in BRCA1-mut breast cancers, cell damage repair and angiogenesis; and finally, in BRCA2-mut breast cancers, cytokine production and T-cell migration. Genes expressed in the surface membrane or extracellular matrix and related to patient outcomes included B3GNT7 and CTSV in BRCA2-mut breast cancers, exhibiting detrimental prognoses. CD6, CXCL9, and CXCL13 were associated with favorable outcomes in BRCA1-mutant ovarian cancers. The last three genes were also correlated with the infiltration of effector T cells and dendritic cells in ovarian tumors. Conclusions: In summary, we identified deregulated candidate genes that could be used as therapeutic targets. Full article

The Epstein–Barr virus (EBV) is the first human herpesvirus identified as an oncogenic agent, with approximately 95% of adults worldwide being latently infected. EBV infection is associated with multiple diseases, including nasopharyngeal carcinoma, Hodgkin’s lymphoma, infectious mononucleosis, and multiple sclerosis. Given significant EBV-associated disease burden, developing effective vaccines against EBV remains a priority. In this review, we first presented the current understanding of EBV biology and pathogenesis, focusing on its biological structure and immune evasion mechanisms, and discussed key viral antigens—including gp350, gp42, gH/gL, and latency proteins—as potential targets for EBV vaccine development. We also summarized recent advances in various EBV vaccine platforms, including subunit, viral vector-based, nanoparticle-based, and mRNA vaccines, and discussed the related preclinical and clinical evidence, although no effective EBV vaccine has been approved for clinical use yet. In summary, this review provides an overview of the current landscape in EBV vaccine research, and sheds new light on developing new therapeutic approaches against EBV-associated diseases. Full article

Evaluating the development process of mosquito species under the influence of temperature is essential for understanding their ecology and geographical distribution, as well as assessing their potential as vectors of pathogens. Aedes (Protomacleaya) terrens, a species recognized for its susceptibility and competence in transmitting the chikungunya virus, serves as a relevant model for research in this context. This study aimed to analyze the influence of temperature on egg hatching and the development cycle of this species to expand knowledge on its biology and implications for public health. During the experiment, 800 eggs were used, collected through 10 ovitraps in a forest remnant located in Uruaçu, Goiás, Brazil. The total number of eggs was divided into four groups, exposed to constant temperatures of 15 ± 2 °C, 20 ± 2 °C, 25 ± 2 °C, and 30 ± 2 °C. After hatching, first-instar larvae were individually separated and monitored daily under controlled conditions until adult emergence. The highest hatching rate occurred at 25 °C, showing an optimal point around 27 °C. Throughout development, temperature significantly reduced the duration of each stage, with the fastest complete cycle at 30 °C, a difference of approximately 10–12 days when compared to 20 °C and approximately 47 days when compared to 25 °C. These results offer valuable insights into the temperature sensitivity of Ae. terrens across its developmental stages, suggesting that each stage has its own optimal temperature. Thus, small variations in responses to environmental conditions and differentiation between sexes may become more pronounced throughout development. In this sense, temperature can affect not only the development and survival of dipterans but also the capacity for virus transmission, as the pathogen influences the reproduction rate and longevity of the vectors. Full article