Bioimpacts最新文献

Introduction: A new era of regenerative medicine has been ushered in by the combination of tissue engineering and genetic engineering, offering unprecedented opportunities to address the growing demand for functional tissue replacements. This narrative review explores cutting-edge approaches in cell manipulation-based tissue engineering through the lens of genetic engineering, highlighting the transformative potential of this synergy.

Methods: We critically examine the application of advanced genetic engineering techniques, including CRISPR-Cas9, TALENs, and synthetic biology, in modifying cellular behaviors and functions for tissue engineering. The review encompasses a diverse range of engineered tissues, from cartilage and bone to cardiac, neural, skin, and vascular constructs, elucidating how genetic manipulation enhances their functionality and physiological relevance. We further investigate the integration of these genetic approaches with emerging technologies such as 3D-bioprinting, microfluidics, and smart biomaterials, which collectively expand the horizons of complex tissue fabrication.

Results: The review delves into pioneering trends, including in vivo genetic engineering for tissue regeneration and the development of patient-specific engineered tissues, discussing their implications for personalized medicine. We address the field's challenges, including long-term genetic stability, scalability, and off-target effects, while also considering the ethical implications and evolving regulatory landscape of genetically engineered tissues. Emerging technologies in genetic engineering, including base editing and synthetic genetic circuits, have been explored for their potential to create "smart" tissues capable of dynamic environmental responses. The review also highlights the synergistic potential of combining genetic engineering with stem cell technologies to enhance tissue functionality and immunological compatibility.

Conclusion: This comprehensive review concludes by underscoring the transformative impact of genetic engineering on cell manipulation-based tissue engineering. While significant challenges persist, the rapid advancements in this field herald a future where genetically tailored, functional tissue constructs could revolutionize regenerative medicine, offering new hope for addressing critical unmet medical needs.

Introduction: Breast cancer (BC) is a devastating condition with high morbidity and mortality rates in females. Autophagy is an early-stage cell response against stressful conditions. Emerging data have revealed the autophagy-angiogenesis interaction in terms of tumor development and metastasis.

Methods: Here, the angiogenesis behavior of human MDA-MB-231 cells was monitored after modulation of autophagy response in the presence of free 3-methyladenine (3-MA), metformin (Met), or drug-loaded exosomes (3-MA@Exos and Met@Exos). Orthotopic transplantation was done using human BC cell-laden alginate/gelatin (Alg/Gel) microspheres in mice after treatment with Met and/or 3-MA.

Results: Met, and/or Met@Exos increased the cell migration rate and promoted human endothelial cell migration compared to the control cells (P<0.05). However, these features were blunted in 3-MA and 3-MA@Exos groups (P<0.05). Flow cytometry analysis revealed that the drug loading into Exos did not influence internalization capacity or cell survival (P>0.05). ELISA revealed that vascular endothelial growth factor (VEGF) levels were reduced in Met and 3-MA-treated cells, with more pronounced reductions in the free 3-MA groups. Real-time PCR analysis showed diminished expression of several angiogenesis-related genes, except for platelet endothelial cell adhesion molecule-1 (PECAM-1) in the Met@Exos, 3-MA, and 3-MA@Exos groups. Met treatment increased the metastasis and tumor formation in mice mammary glands after orthotopic transplantation of BC tumoroids.

Conclusion: These data indicate that autophagy modulation can alter the angiogenesis and metastatic behavior of human BC cells in vitro and in vivo. Exos are valid bio-shuttles for the delivery of autophagy modulators in CSC-targeted therapies.

Breast cancer (BCA) remains the most prevalent cancer globally and the leading cause of cancer-related mortality among women, with rising incidence rates driven by genetic, lifestyle, and environmental factors. Early detection through precise screening is essential to improve prognosis and survival; yet, challenges persist, especially in resource-limited areas. Recent advances in Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, have illustrated significant potential to enhance breast cancer screening, diagnosis, and treatment personalization. This review highlights the multifaceted role of AI in BCA management, encompassing its applications in image-based screening modalities, genomic and immunologic profiling, and drug discovery. AI-driven approaches offer diagnostic accuracy, cost-effectiveness, time-saving, and individualized treatment regimens. Despite promising developments, further research is crucial to overcome current challenges and regulatory hurdles in clinical settings. This article highlights the positive aspects of AI technologies in advancing BCA care and the importance of continued interdisciplinary research to optimize their implementations in breast cancer workflows.

Introduction: African swine fever (ASF) continues to be a significant threat to the global livestock industry due to its severe impact on pig populations. Currently, there are no approved therapeutic agents for the virus, and biosecurity measures such as culling have led to substantial economic losses. In light of its effects on food security and the economy, our study aims to identify potential antiviral compounds from marine fungal metabolites that target the dUTPase enzyme of the African swine fever virus (ASFV).

Methods: We screened 4,683 marine fungal metabolites using a series of virtual screening techniques. These included ADMET profiling to assess drug-likeness, consensus molecular docking to predict preferred docking poses and rank the docking scores, 300 ns molecular dynamics (MD) simulations to determine stability, principal component analysis (PCA) to verify simulation convergence, and MMPB(GB)SA analysis to estimate binding affinity.

Results: Of the 4,683 compounds, 328 passed our ADMET filter, and the 10 highest-scoring ligands from molecular docking were evaluated for stability and binding affinity against both swine and ASFV dUTPase. Among the candidates, tricycloalternarene C (M1421), derived from Alternaria sp., emerged as a promising candidate. It exhibited excellent drug-likeness, stability, and binding affinity comparable to the three control compounds, while showing less affinity towards the swine dUTPase.

Conclusion: Tricycloalternarene C holds potential as a selective inhibitor of ASFV dUTPase. We recommend further experimental validation to confirm its efficacy as an antiviral agent against African swine fever.

Radiation therapy, chemotherapy, and surgery have been the standard cancer treatment approaches for many years. Even with these treatments, the majority of tumors still have a dismal prognosis. With complete remission rates ranging from 65% to 90% in the crucial CD19-CART trials, chimeric antigen receptor T-cell (CART) therapy has revolutionized the treatment paradigm for pediatric patients with relapsed/refractory B-cell acute lymphoblastic leukemia (B-ALL). Hematological tumors have responded well to CART. The first CART was authorized by the FDA in 2017 to treat B-ALL. The FDA authorized CART to treat B-cell lymphoma in October of that year. In recent years, research has focused on CART to increase and improve the therapeutic effect. New toxicity profiles and treatment constraints have also surfaced with this new medicine, calling for cooperative group trials, new management strategies, and toxicity consensus grading systems. The introduction of CART treatment for pediatric B-cell ALL will be the main topic of this article, along with previous and ongoing trials. We will also talk about CART therapy trials for various pediatric cancers. Safe procedures and close observation are essential since CART treatment has the potential to cause serious toxicities.

Oral chemotherapy offers an attractive alternative to conventional intravenous administration by providing high patient compliance and improved treatment adherence. However, several challenges, like poor drug solubility, enzymatic degradation, and extensive first-pass metabolism, have significantly limited the oral bioavailability of chemotherapeutic agents. Recently, polymeric nanoparticles (PNPs) have become an alternative strategy to overcome these challenges and revolutionize the oral chemotherapeutic approach. PNPs offer unique advantages, including drug protection from harsh gastrointestinal conditions, controlled release profiles, and enhanced mucosal adhesion, which collectively improve drug absorption and therapeutic efficacy. Additionally, surface-modified PNPs can bypass efflux transporters such as P-glycoprotein and promote receptor-mediated endocytosis to achieve targeted delivery and minimize systemic toxicity. While these advancements highlight the transformative potential of PNPs in oral chemotherapy, potential clinical challenges such as scalability, reproducibility, and regulatory hurdles must be addressed to enable successful clinical translation. The present review comprehensively explores the role of PNPs in enhancing the oral delivery of cancer therapeutics, emphasizing strategies to improve drug stability, prolong gastrointestinal retention, and facilitate efficient cellular uptake. The advancements discussed herein underscore the transformative potential of PNPs as a pivotal approach for improving oral chemotherapy outcomes and expanding therapeutic possibilities in cancer management.

The tumor microenvironment (TME), comprising malignant and non-transformed cells like immune cells, endothelial cells, and cancer-associated fibroblasts, significantly affects tumor growth and progression. Tumor cells manipulate the TME by releasing chemokines and inhibitory cytokines, reprogramming surrounding cells to support their survival and evade immune detection. Innate immune cells within the TME play dual roles, either promoting or inhibiting tumor progression, impacting immunotherapy outcomes. Recent studies highlight the influence of innate immune cells in shaping the TME and the pivotal role of tumor-derived microRNAs (miRNAs) in modulating these cells. miRNAs regulate gene expression and enhance tumor immune evasion, angiogenesis, drug resistance, and invasion. Their tumor-specific expression patterns suggest potential as biomarkers and therapeutic targets. This study focuses on how miRNAs affect innate immune cells like macrophages, dendritic cells, myeloid-derived suppressor cells, and natural killer cells, contributing to immunosuppressive or immunogenic environments. Understanding miRNA-mediated interactions between cancer and immune cells opens new possibilities for improving targeted immunotherapy and advancing cancer treatments.

In today's rapidly advancing field of medical research, non-coding RNA (ncRNA) and nanomedicine have emerged as promising areas of study for therapeutic and diagnostic approaches. ncRNAs, previously considered "junk DNA" and hence insignificant, are now being documented for their remarkably extraordinary regulatory roles in gene expression and various cellular processes. These molecules acquire various forms, comprising microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and small interfering RNAs (siRNAs), each with its distinct functions. The enormous benefits of ncRNA therapies include ease of sequence design and creation, functional flexibility, charge and protection, and the opportunity for patient-specific management. Nanomedicine, on the other hand, combines nanotechnology and medicine through developing innovative solutions for disease treatment and diagnosis. This article provides an overview of the technical aspects and potential of commercializing the design and targeting of ncRNAs using nanocarriers and nano-delivery systems for miRNA delivery. Furthermore, the impact of nanomedicine on the healthcare industry, as well as its therapeutic and diagnostic applications, has been investigated. Overall, this study will provide insight into novel systems for the treatment and diagnosis of ncRNA.

Introduction: The Varicella-zoster virus (VZV) causes varicella (chickenpox) and herpes zoster (shingles), posing significant global health challenges. Despite existing vaccines, gaps in coverage and efficacy persist, necessitating novel vaccine designs. This study aimed to develop a multi-epitope vaccine targeting VZV using immunoinformatics and structural bioinformatics approaches.

Methods: MHC-I and MHC-II binding epitopes from VZV proteins (glycoprotein E, glycoprotein B, tegument protein IE63) were predicted using IEDB tools, prioritizing conserved epitopes with high binding affinity. A chimeric construct was engineered with 18 epitopes, adjuvants (β-defensin 3), and cell-penetrating peptides (HIV TAT), linked with GPGPG/AAY spacers. Antigenicity (VaxiJen), allergenicity (AlgPred), physicochemical properties (ProtParam), and solubility (SOLpro) were assessed. Tertiary structure modeling (GalaxyWEB) and refinement (GalaxyRefine) were performed. Docking (PatchDock) and dynamics simulations (GROMACS, 100 ns) evaluated TLR2-vaccine binding stability. Immune response was simulated (C-ImmSim), and codon optimization (JCAT) ensured E. coli expression compatibility.

Results: Non-allergenic, antigenic (VaxiJen score: 0.52), stable (instability index: 30.20), and soluble (GRAVY: -0.548). Molecular weight: 34 kDa; pI: 9.65. RMSD (3.8 nm) and RMSF analyses confirmed complex stability. Free energy landscape revealed low-energy binding states (0.3-1.8 kcal/mol). Simulated results showed robust IgG/IgM production, Th1 cytokines (IFN-γ, IL-2), and memory cell activation. Epitopes covered 100% of populations in Europe/North America and > 77% in Africa/South Asia.

Conclusion: The multi-epitope vaccine demonstrated strong immunogenicity, structural stability, and broad population coverage. Computational validation supports its potential as a candidate for preventing VZV infections, pending experimental verification in the future.

Introduction: Design and development of new drugs needs a huge amount of investment of time and money. The advent of machine learning and computational biology has led to sophisticated techniques for drug repositioning, i.e., recommending available drugs for new diseases or, more specifically, protein targets. However, there remains a critical need for improved synergy between these techniques to enhance their predictive accuracy and practical application in clinical settings.

Methods: This study presents a novel approach that integrates two methodologies: SLSDR, a sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection technique, and the iDrug method for drug repurposing which integrates different domains. SLSDR is a subspace learning algorithm based on matrix factorization, and iDrug is a matrix factorization-based drug repositioning method that integrates data from two different domains (drug-disease and drug-target domains). By leveraging SLSDR's ability to extract essential features from drug-disease and drug-target spaces, we enhance the iDrug objective function. Our approach includes constructing a drug-drug similarity matrix using a feature space derived from SLSDR, and target-target and disease-disease similarity matrices. This ensures a comprehensive representation of drug-disease and drug-target associations. We introduce a novel objective function that captures the nuanced interactions between drugs and diseases, considering the complex interrelationships among features within all the datasets.

Results: By integrating these components, our strategy offers a holistic solution for drug repositioning, optimizing the prediction process. In terms of prediction accuracy, AUC, AUPR and computing efficiency, the results indicate notable gains over the state of the art drug repurposing methods. Fig. 1, represents the comparison of the performance of the proposed method with existing approaches across various metrics.

Conclusion: The proposed matrix factorization based method for drug repurposing, benefits from integrating knowledge from two domains, drug-disease and drug-target domains, and also is capable of preserve the geometry of the data in both feature space, and s ample space. Comparing to existing state of the art methods, this shows accuracy improvement in drug repurposing.