Search Results (4,678)

Cancer vaccines have emerged as a class of therapeutics designed to harness the immune system to stimulate durable anti-tumor responses with lower systemic toxicity than conventional therapies. Many platforms have been explored, including protein, peptide, DNA, RNA, and cell-based vaccines. Within this landscape, peptide vaccines remain a promising approach. Most clinical trials have examined peripheral immune responses and clinical outcomes, but there is growing interest in the vaccine site microenvironment (VSME) as a window to understand local immune activation and its implications for systemic immunity and tumor control. Studies of the VSME have investigated the effects of adjuvants, local immune cell dynamics, and their correlation with systemic responses and outcomes. Local adjuvants typically enhance immune cell infiltration, though there are concerns regarding VSME sequestration or dysfunction of immune cells, which could impact systemic efficacy. Repeated vaccination at a single site may improve antigen presentation and immune responses, but factors such as injection site location may be linked to variability in clinical outcomes. Current studies are limited by substantial variability in sampling, timing, and analyses used in VSME assessment. This limits the comparability of findings and broader inferences regarding the influence of vaccine site dynamics on therapeutic efficacy. Standardized VSME assessment as part of future vaccine trials may improve evaluation of immune responses and provide a more consistent surrogate for vaccine effectiveness. This refinement may inform optimal vaccine strategies and further support the development of next-generation cancer immunotherapies. Full article

Androgenetic alopecia (AGA) is a prevalent, multifactorial hair disorder affecting a substantial portion of both males and females, with significant psychosocial consequences. Over the past decade, regenerative medicine has reshaped AGA treatment, offering biologically driven alternatives to conventional pharmacological and surgical therapies. Among these, Autologous Micrografting Technology (AMT) (Regenera Activa^® by Rigenera^® Technology, Barcelona, Spain) emerged 10 years ago as a notable innovation leveraging the body’s intrinsic regenerative potential through micrografts derived from a healthy scalp tissue. This review provides a comprehensive overview of the pathophysiology of AGA—including genetic, hormonal, and inflammatory contributors—and evaluates the clinical efficacy, safety, and mechanistic basis of AMT in comparison with other regenerative strategies such as platelet-rich plasma, adipose-derived mesenchymal stem cells, and exosome-based treatments. Clinical studies demonstrate that AMT yields significant short-term improvements in hair density and thickness with favorable safety outcomes. Moreover, advancements in device technology and treatment protocols have enhanced consistency and reproducibility. As multimodal and personalized approaches gain traction in hair restoration, AMT is a minimally invasive point-of-care procedure within the evolving regenerative landscape. Future studies are warranted to optimize treatment algorithms, extend follow-up data, better define patient selection criteria for maximizing outcomes with AMT, and expand the indication of autologous micrografting technology. Full article

Purpose: Telehealth (TH) offers promising solutions for enhancing the management of chronic obstructive pulmonary disease (COPD), particularly in resource-limited or remote settings. However, regulatory uncertainty remains a significant barrier to adopting and integrating TH technologies into routine care. This systematic review aims to evaluate the role of regulatory guidelines in implementing and adopting TH solutions for COPD care and to identify key barriers and facilitators shaping these regulatory efforts. Methods: Following PRISMA guidelines, a comprehensive search of five databases up to 18 October 2025 (PubMed, Web of Science, Scopus, CINAHL, and JSTOR) and grey literature was conducted. Studies and governmental reports were included if they examined regulatory frameworks, stakeholder perspectives, or implementation challenges related to TH in COPD care. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) tool. Narrative and data synthesis were employed. Results: From 343 identified records, 33 sources (18 peer-reviewed studies and 15 governmental/organizational reports) met the inclusion criteria. Findings revealed wide disparities in the existence, specificity, and enforcement of TH regulatory guidelines across countries. Developed nations often had more structured yet nonspecific frameworks, while emerging health systems, such as Saudi Arabia, exhibited fragmented but evolving regulatory landscapes. Common barriers included unclear stakeholder roles, inadequate funding, technological limitations, and resistance to organizational change. Conclusions: Clear, inclusive, and context-sensitive regulatory guidelines are essential to support the successful integration of TH in COPD care. Enhanced regulatory clarity can improve patient trust, engagement, and adherence by addressing safety, accountability, and accessibility concerns. Future research should focus on stakeholder-informed policies that reflect the practical realities of healthcare delivery in both developed and emerging systems. Full article

Background: Hepatocellular carcinoma remains a global health challenge with high mortality rates. The tumor immune microenvironment significantly impacts disease progression and survival. However, traditional analyses predominantly focus on single immune genes, overlooking the critical interplay among multiple immune gene signatures. Our study explores the prognostic significance of chemokine (C-C motif) ligand 5 (CCL5) expression and associated immune genes through an innovative combination of Autoregressive Integrated Moving Average (ARIMA) and Convolutional Neural Network (CNN) models. Methods: A time series dataset of CCL5 expression, comprising 230 liver cancer patients, was analyzed using an ARIMA model to capture its temporal dynamics. The residuals from the ARIMA model, combined with immune gene expression data, were utilized as input features for a CNN to predict survival outcomes. Survival analyses were conducted using the Cox proportional hazards model and Kaplan–Meier curves. Furthermore, the ARIMA-CNN framework’s results were systematically compared with traditional median-based stratification methods, establishing a benchmark for evaluating model efficacy and highlighting the enhanced predictive power of the proposed integrative approach. Results: CNN-extracted features demonstrated superior prognostic capability compared to traditional median-split analyses of single-gene datasets. Features derived from CD8⁺ T cells and effector T cells achieved a hazard ratio (HR) of 0.7324 (p = 0.0008) with a statistically significant log-rank p-value (0.0131), highlighting their critical role in anti-tumor immunity. Hierarchical clustering of immune genes further identified distinct survival associations. Notably, a cluster comprising B cells, Th2 cells, T cells, and NK cells demonstrated a moderate protective effect (HR: 0.8714, p = 0.1093) with a significant log-rank p-value (0.0233). Conversely, granulocytes, Tregs, macrophages, and myeloid-derived suppressor cells showed no significant survival association, emphasizing the complex regulatory landscape within the tumor immune microenvironment. Conclusions: Our study provides the first ARIMA-CNN framework for modeling gene expression and survival analysis, marking a significant innovation in integrating temporal dynamics and machine learning for biological data interpretation. This model offers deeper insights into the tumor immune microenvironment and underscores the potential for advancing precision immunotherapy strategies and identifying novel biomarkers, contributing significantly to innovative cancer management solutions. Full article

Thymic epithelial tumors (TETs) are a diverse group of rare thymic tumors that arise from thymic epithelial cells. The rarity of these tumors has limited therapeutic advancements due to difficulty to enroll patients into Phase II and III clinical trials. Historically surgery, radiotherapy, and chemotherapy have been the mainstay therapeutic options for these patients with the development of new therapeutics hindered by the rarity, histological and molecular heterogeneity, and lack of actionable mutations. However, more recently, innovations in immunotherapy, next-generation tyrosine kinase inhibitors, and hyperthermic intrathoracic chemotherapy (HITHOC) have transformed the therapeutic landscape with more promising therapies currently under investigation. In this review we evaluate the histology and molecular subtypes of TETs, and discuss the therapeutic landscape including the current standard-of-care regimen as well as drugs that are currently in clinical trials. Full article

This research examines the time–frequency co-movement patterns among the Johannesburg Stock Exchange (JSE) size-based indices, utilizing daily data covering the period from November 2016 to December 2024. To conduct the analysis, three sophisticated wavelet techniques are applied: the Maximal Overlap Discrete Wavelet Transform (MODWT), the Continuous Wavelet Transform (WTC), and the Wavelet Phase Angle (WPA) model. Subsequently, the Multivariate Generalized Autoregressive Conditional Heteroscedasticity–Asymmetric Dynamic Conditional Correlation (MGARCH-DCC) model is employed to evaluate the robustness of the findings. The results reveal that the co-movement among the JSE size-based indices is influenced by investment holding periods and prevailing market conditions. Notably, a lead–lag relationship is identified, indicating that a single size-based index often drives the co-movement of the others. These findings carry important implications for investors, policymakers, and portfolio managers. Investors should account for optimal holding periods to avoid increased correlation and reduced diversification benefits. Policymakers are advised to mitigate financial market uncertainty, particularly during bearish phases, to manage excessive index co-movement. Portfolio managers must integrate both holding periods and market conditions into their investment strategies. This research offers a novel contribution to the South African investment landscape by providing practical and risk-mitigating insights into the role of JSE size-based indices within diversified portfolios—a topic that has received limited attention despite its growing relevance. Full article

Background/Objectives: Epitope-based matching has emerged as a refined approach for assessing donor–recipient compatibility in renal transplantation. However, limited data are available on HLA Class I epitope distribution among Indian patients, particularly from northern India, where substantial allelic diversity is known to influence immunological risk. Methods: This retrospective analysis evaluated HLA Class I single-antigen bead (SAB) antibody data from 218 consecutive renal-transplant candidates who tested positive for anti-HLA antibodies between July 2018 and September 2024. HLA Class I epitopes were identified and analyzed using MATCH IT Antibody Software (Immucor, version 1.5.0). Demographic variables and sensitization history (previous transplant, transfusion, pregnancy) were reviewed. Results: A total of 504 distinct epitopes were identified, with 65GK and 163LG emerging as the most frequent motifs. The predominance of these epitopes mirrors the high prevalence of alleles such as HLA-A*24 and HLA-B*35 reported in North-Indian populations. The data suggest a strong influence of regional allele architecture on the immunogenic epitope landscape. Conclusions: This study provides the first baseline characterization of HLA Class I epitope distribution among northern-Indian renal-transplant candidates. The findings emphasize the need for establishing population-specific HLA epitope databases and highlight the potential of epitope-based matching to enhance donor selection and minimize immunological risk in Indian transplantation programs. Full article

Ethylparaben (EtP) is an emerging pollutant that is widely found in the environment, particularly in agricultural landscapes. With the extensive contamination of agricultural soils and irrigation waters, there is a rising concern about their potential impact on crop yields. To provide some of the first evidence that EtP may be more than just an agricultural contaminant, but a potential pollutant, we evaluated the systemic toxicities and cellular responses triggered by EtP in seed roots of Daucus carota, Lycopersicum esculentum, and Cucumis sativus, and in bulb roots of Allium cepa, at environmentally relevant concentrations of 1, 10, 100, and 1000 ng·L⁻¹. The seeds and bulbs remained in contact with the concentrations for 7 days. Distilled water and Tween 80 at 1000 ng·L⁻¹ were used as negative controls. The results were subjected to Kruskal–Wallis analysis of variance followed by Dunn’s test (p ≤ 0.05). In all plants, all concentrations significantly altered the activity of catalase, ascorbate peroxidase, guaiacol peroxidase, and superoxide dismutase. In carrot (10, 100, and 1000 ng·L⁻¹), tomato (1000 ng·L⁻¹), and cucumber (all concentrations), such concentrations caused lipid peroxidation, leading to the accumulation of hydrogen peroxide, as well as hydroxyl and superoxide radicals in the cells. These oxidants caused a delay in the progression of the cell cycle and alterations to the mitotic spindle in the root meristems, significantly inhibiting root growth in the plants evaluated. Recurrent contamination with EtP can potentially harm soil quality, posing a risk to both agricultural productivity and the environment. Full article

Sericulture sustains rural livelihoods in Asia, Africa, and Latin America, where it provides income for women, elderly workers, and smallholder households. Yet this sector faces a critical technological divide: traditional reeling methods remain labor-intensive and uncompetitive, while industrial innovations advance along trajectories that are poorly suited to low-resource contexts. This article presents a patent landscape of silk-reeling technologies retrieved from Espacenet and PATENTSCOPE (2000–2024), comprising 212 unique records. Patents were evaluated against six criteria: resource efficiency, knowledge accessibility, durability and reparability, context adaptability, equity and inclusion, and by-product valorization. This review reveals a strong industrial bias, with most patents emphasizing energy-intensive steaming, mechanized feeding, and digital control, while only a small fraction addresses rural conditions or social inclusion. Current innovations therefore tend to marginalize traditional producers from emerging bio-based value chains. This study contributes to discussions on how technological design can support rural sericulture, highlighting the need for resource-efficient, modular, and socially inclusive solutions. Future research should extend patent analysis to mulberry cultivation, silkworm breeding, and by-product recovery to fully integrate sericulture into the circular bioeconomy. Full article

Plant-derived extracellular vesicles (PDEVs) are rapidly gaining popularity in cosmetics and regenerative medicine due to their biocompatibility, natural origin and promising bioactive properties. Nevertheless, the absence of standardized guidelines for their characterization has resulted in an inconsistent, unregulated landscape. This compromises product reproducibility, consumer safety, and scientific credibility. Here, a comprehensive set of minimal characterization guidelines for PDEVs is proposed to include physical and chemical profiling, molecular marker identification, cargo analysis, and stability assessment under storage and formulation conditions. Functional validation through cellular uptake assays, activity tests, and advanced in vitro or ex vivo models that replicate realistic skin exposure scenarios is pivotal. Requirements for transparent labelling, reproducible sourcing, batch-to-batch consistency, and biological activity substantiation to support claims related to skin regeneration, anti-aging, and microbiome modulation are also required. By establishing a harmonized baseline for quality and efficacy evaluation, these guidelines aim to elevate the scientific standards and promote the safe, ethical, and effective use of PDEV-based ingredients in cosmetic and biomedical applications. Full article

Recent advancements in remote sensing and geospatial processing tools have ushered in a new era of mapping and monitoring landscape changes across various scales. This progress is critical for understanding and anticipating the underlying drivers of environmental change. In particular, large-scale Land Use and Land Cover (LULC) mapping has become an indispensable tool for territorial planning and monitoring. This study aims to map and evaluate LULC changes in the El Jadida region of Morocco between 1985 and 2020. Utilizing multispectral Landsat imagery, we applied and compared three supervised machine learning classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NNET). Model performance was assessed using statistical metrics, including overall accuracy, the Kappa coefficient, and the F1 score. The results indicate that the RF algorithm was the most effective, achieving an overall accuracy of 90.3% and a Kappa coefficient of 0.859, outperforming both NNET (81.3%; Kappa = 0.722) and SVM (80.2%; Kappa = 0.703). Analysis of explanatory variables underscored the decisive contribution of the NDWI, NDBI, and SWIR and thermal bands in discriminating land cover classes. The spatio-temporal analysis reveals significant urban expansion, primarily at the expense of agricultural land, while forested areas and water bodies remained relatively stable. This trend highlights the growing influence of anthropogenic pressure on landscape structure and underscores its implications for sustainable resource management and land use planning. The findings demonstrate the high efficacy of machine learning, particularly the RF algorithm, for accurate LULC mapping and change detection in the El Jadida region. This study provides a critical evidence base for regional planners to address the ongoing loss of agricultural land to urban expansion. Full article

Guizhou Province in China exhibits a distinctive agroforestry mosaic landscape with frequent human activity in forested areas. This region experiences recurrent forest fires, characterized by significant difficulties in suppression and high risks. Research on the prediction of forest fire occurrences holds crucial practical significance in terms of enhancing regional forest fire prevention capabilities. However, the current fire risk forecasting methods in the area consider only meteorological factors, neglecting firebrands and fuel conditions, which results in deviations between forecasted and actual fire occurrences. Therefore, this study proposes a novel fire occurrence prediction method that utilizes the ignition component (IC) from the National Fire Danger Rating System (NFDRS) to characterize the weather–fuel complex while integrating the firebrand occurrence probability to construct a predictive model. The applicability and accuracy of this method are also evaluated. The results show that, firstly, the probability of at least one daily forest fire occurrence in the study area can be expressed as a nonlinear function based on the IC. Secondly, as time progresses, the correlation between the forest fire occurrence probability and the IC shows a decreasing trend, although the differences across different time spans are not statistically significant. Thirdly, when a 5-year time span is adopted, the error in calculating the forest fire occurrence probability based on the IC is significantly lower than at other time spans. Finally, a predictive model for the forest fire occurrence probability based on the IC is established, where P = (100*IC)/(4.06 + IC), with a mean absolute error (MAE) of 4.83% and mean relative error (MRE) of 14.87%. Based on this research, the IC enables the calculation of forest fire occurrence probabilities, assessment of fire risk ratings, and guidance for fire preparedness and planning. This work also provides theoretical support and a methodological reference for conducting forest fire probability studies in other regions. Full article

Myelodysplastic neoplasms (MDS) are characterized by remarkable heterogeneity in clinical manifestations, posing significant management challenges arising due to genetic plasticity. While the Revised International Prognostic Scoring System (IPSS-R) has traditionally stratified MDS into higher-risk (HR) and lower-risk (LR) categories, the recently developed Molecular International Prognostic Scoring System (IPSS-M) integrates molecular signatures and has further enhanced prognostic stratification. In LR-MDS, current therapeutic interventions remain non-curative and the goal of treatment is centered along three critical axes: reducing transfusion dependence, improving quality of life, and reducing the risk of progression to acute myeloid leukemia (AML). This review examines recent progress made in the therapeutic landscape of LR-MDS, with particular emphasis on the molecular basis of these novel agents that may have disease-modifying potential. We evaluate the clinical trials and targeted agents in the pipeline for treating LR-MDS, providing a comprehensive perspective where these treatment modalities are placed in the current standard of care and how these novel targets can shape future therapeutic innovations. Full article

Above-ground biomass in agroforestry refers to the total mass of living vegetation, primarily trees and shrubs, integrated into agricultural landscapes. It plays a key role in climate change mitigation by capturing and storing carbon. Accurate estimation of above-ground biomass in agroforestry systems requires effective drone deployment and sensor management. This study presents a detailed methodology for biomass estimation using Unmanned Aircraft Systems, based on an experimental campaign conducted in the Campania region of Italy. Multispectral drone platforms were used to generate calibrated reflectance maps and derive vegetation indices for biomass estimation in agroforestry landscapes. Integrating field-measured tree attributes with remote sensing indices improved the accuracy and efficiency of biomass prediction. Following the assessment of mission parameters, flights were conducted using a commercial drone to demonstrate consistency of results across multiple altitudes. Terrain-follow mode and high image overlap were employed to evaluate ground sampling distance sensitivity, radiometric performance, and overall data quality. The outcome is a defined process that enables agronomists to effectively estimate above-ground biomass in agroforestry landscapes using drone platforms, following the procedure outlined in this paper. Predictive performance was evaluated using standard model metrics, including R², RMSE, and MAE, which are essential for replicability and comparison in future studies. Full article

Woody plants, comprising forest and fruit tree species, provide essential ecological and economic benefits to society. Their genetic improvement is challenging due to long generation intervals and high heterozygosity. Genetic transformation, which combines targeted DNA delivery with plant regeneration from transformed cells, offers a powerful alternative to accelerating their domestication and improvement. Agrobacterium tumefaciens, Rhizobium rhizogenes, and particle bombardment have been widely used for DNA delivery into a wide variety of explants, including leaves, stems, hypocotyls, roots, and embryos, with regeneration occurring via direct organogenesis, callus-mediated organogenesis, somatic embryogenesis, or hairy root formation. Despite successes, conventional approaches are hampered by low efficiency, genotype dependency, and a reliance on challenging tissue culture. This review provides a critical analysis of the current landscape in woody plant transformation, moving beyond a simple summary of techniques to evaluate the co-evolution of established platforms with disruptive technologies. Key advances among these include the use of developmental regulators to engineer regeneration, the rise in in planta systems to bypass tissue culture, and the imperative for DNA-free genome editing to meet regulatory and public expectations. By examining species-specific breakthroughs in key genera, including Populus, Malus, Citrus, and Pinus, this review highlights a paradigm shift from empirical optimization towards rational, predictable engineering of woody plants for a sustainable future. Full article