Search Results (6,093)

This review aims to provide a broad, multidisciplinary perspective on how dynamic genomics and systems biology are transforming modern healthcare, with a focus on cancer especially liver cancer (HCC). It explains how integrating multi-omics technologies such as genomics, transcriptomics, proteomics, interactomics, metabolomics, and spatial transcriptomics deepens our understanding of the complex tumor environment. These innovations enable precise patient stratification based on molecular, spatial, and functional tumor characteristics, allowing for personalized treatment plans. Emphasizing the role of regulatory networks and cell-specific pathways, the review shows how mapping these networks using multi-omics data can predict resistance, identify therapeutic targets, and aid in the development of targeted therapies. The approach shifts from standard, uniform treatments to flexible, real-time strategies guided by technologies such as liquid biopsies and wearable biosensors. A case study showcases the benefits of personalized therapy, which integrates epigenetic modifications, checkpoint inhibitors, and ongoing multi-omics monitoring in a patient with HCC. Future innovations, such as cloud-based genomic ecosystems, federated learning for privacy, and AI-driven data analysis, are also discussed to enhance decision-making and outcomes. The review underscores a move toward predictive and preventive healthcare by integrating layered data into clinical workflows. It reviews ongoing clinical trials using advanced molecular and immunological techniques for HCC. Overall, it promotes a systemic, technological, and spatial approach to cancer treatment, emphasizing the importance of experimental, biochemical–functional, and biophysical data-driven insights in personalizing medicine. Full article

Recent advances in molecular pathology, driven by integrated and comprehensive diagnostic approaches, have significantly advanced precision oncology. By leveraging multiomics technologies, molecular pathology enables the simultaneous assessment of genomic alterations, transcriptomic profiles, proteomic activity, and metabolic states integrated with conventional pathological evaluation to better explain tumour biology and behaviour. Large-scale international consortia, including The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumour Analysis Consortium (CPTAC) have systematically demonstrated the value of harmonised multiomics analyses in defining tumour subtypes, uncovering functional dependencies, and generating clinically actionable insights. Evidence from coordinated precision oncology initiatives, such as the National Cancer Institute—Molecular Analysis for Therapy Choice (NCI-MATCH) trial further indicates that treatment strategies guided by molecular pathology profiling are associated with improved clinical outcomes, including progression-free survival in molecularly selected patient populations. Consequently, molecularly stratified treatment approaches are increasingly required in routine clinical practice to enable targeted therapies for selected tumour entities. Integration of molecular data with functional and clinical outcomes has further facilitated the detection of emerging mechanisms of therapeutic resistance and heterogeneous treatment responses. Importantly, studies have shown that reliance on genomic analysis alone is insufficient to achieve optimal targeted therapy, underscoring the need for multi-layered molecular interrogation. This review highlights the biological and clinical relevance of multiomics integration, emphasising its critical role in comprehensive morpho-molecular tumour assessment and functional analyses while providing clinicians with a practical framework for interpreting integrated molecular diagnostics and addressing the methodological and translational challenges that must be overcome to enable broader implementation of precision oncology in routine practice. Full article

Porcine epidemic diarrhea virus (PEDV) is a major cause of fatal diarrhea in piglets. The continuous emergence of new variants, driven by recombination and mutation, poses a persistent global threat to the swine industry, resulting in significant economic losses. Therefore, ongoing surveillance of PEDV evolution is critical. In this study, we isolated a novel PEDV strain, designated PEDV/FJLY202201, from experimental intestinal samples collected from a diarrheal piglet in Fujian, China, and sequenced its complete genome. Complete genome analysis, phylogenetic analysis, and recombination analysis were conducted. Results showed that PEDV/FJLY202201 was a recombinant strain derived from two recombination events between G2a and G2b strains, with three breakpoints located in the ORF1b, Domain 0 (D0) and S2 subunit, respectively. Notably, multiple mutations were identified in the S2 subunit, a finding that has been rarely reported before. Furthermore, following challenge with the PEDV/FJLY202201 strain, 3-day-old piglets exhibited severe diarrhea, sustained a 30.35% weight loss, and reached 100% mortality, collectively demonstrating its high virulence. These data reveal the complex evolution of PEDV/FJLY202201 and provide a foundation for a better understanding of the genetic evolution and molecular pathogenesis of PEDV. Full article

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors primarily involving the adrenal medulla and its associated paraganglia, with heterogeneous clinical behavior and complex molecular drivers. This study aimed to characterize DNA methylation and gene expression patterns in PPGLs to understand the molecular differences between tumor subtypes and malignancy. We performed an integrative analysis of DNA methylation (Illumina EPIC 850K) and gene expression profiles (Affymetrix microarrays) in 24 PPGLs, comparing these with The Cancer Genome Atlas (TCGA) data, to delineate cluster- and malignancy-specific epigenetic patterns. Comparison between pseudohypoxic Cluster I and kinase-signaling Cluster II tumors revealed 13 differentially methylated CpG sites, with a specific CpG within DSCAML1 showing hypermethylation in Cluster II accompanied by increased expression, suggesting context-dependent gene body methylation effects. Benign versus malignant comparisons identified 101 differentially methylated CpGs, including hypermethylated CpG in BAIAP2L1 and hypomethylated CpG in SHANK1 in malignant tumors. Pathway enrichment of differentially methylated genes revealed alterations in Notch signaling, adherens junctions, cytoskeletal regulation, and intracellular transport. Gene expression analysis demonstrated partial overlap between clusters, with malignant tumors exhibiting distinct transcriptional profiles involving RNA processing, metabolism, and adhesion pathways. Correlation between methylation and expression was generally limited, emphasizing that methylation-dependent gene regulation is a locus-specific and context-dependent regulation. These findings illustrate a complex interplay between epigenetic modifications and transcriptional programs in PPGLs, enhancing our understanding of molecular heterogeneity and tumor classification, and identifying candidate biomarkers and therapeutic targets for malignant progression. Full article

Objective: To develop and validate a genetic diagnostic model for colorectal cancer (CRC). Methods: First, differential expression genes (DEGs) between colorectal cancer and normal groups were screened using the TCGA database. Subsequently, a two-sample Mendelian randomization analysis was performed using the eQTL genomic data from the IEU OpenGWAS database and colorectal cancer outcomes from the R12 Finnish database to identify associated genes. The intersecting genes from both methods were selected for the development and validation of the CRC genetic diagnostic model using nine machine learning algorithms: Lasso Regression, XGBoost, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT). Results: A total of 3716 DEGs were identified from the TCGA database, while 121 genes were associated with CRC based on the eQTL Mendelian randomization analysis. The intersection of these two methods yielded 27 genes. Among the nine machine learning methods, XGBoost achieved the highest AUC value of 0.990. The top five genes predicted by the XGBoost method—RIF1, GDPD5, DBNDD1, RCCD1, and CLDN5—along with the five most significantly differentially expressed genes (ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) in the GSE87211 dataset, were selected for the construction of the final colorectal cancer (CRC) genetic diagnostic model. The ROC curve analysis revealed an AUC (95% CI) of 0.9875 (0.9737–0.9875) for the training set, and 0.9601 (0.9145–0.9601) for the validation set, indicating strong predictive performance of the model. SHAP model interpretation further identified IFITM1 and DBNDD1 as the most influential genes in the XGBoost model, with both making positive contributions to the model’s predictions. Conclusions: The gene expression profile in colorectal cancer is characterized by enhanced cell proliferation, elevated metabolic activity, and immune evasion. A genetic diagnostic model constructed based on ten genes (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) demonstrates strong predictive performance. This model holds significant potential for the early diagnosis and intervention of colorectal cancer, contributing to the implementation of third-tier prevention strategies. Full article

Rotavirus (RV) is the primary cause of severe gastroenteritis in young children, yet the long noncoding RNA (lncRNA) regulatory landscape governing the host response remains largely unmapped. To address this gap, the present study performed an integrated transcriptomic analysis of mRNA and lncRNA expression profiles in RV-infected MA104 cells at 24 h post-infection. Deep sequencing identified 11,919 high-confidence lncRNAs, revealing a massive transcriptional shift: 3651 mRNAs and 4655 lncRNAs were differentially expressed, with both populations predominantly upregulated. Functional enrichment analysis confirmed the strong activation of key innate immunity pathways, including the RIG-I-like receptor, Toll-like receptor, and TNF signaling pathways. Conversely, fundamental metabolic pathways were found to be suppressed. Crucially, the analysis of lncRNA targets highlighted their involvement in coordinating the host antiviral defense, particularly through transregulation. Experimental validation confirmed the significant upregulation of key immune-related mRNAs (OASL and C3) as well as two novel lncRNAs (lncRNA-6479 and lncRNA-4290) by qRT-PCR. The significant upregulation of OASL and C3 was validated at the protein level, confirming the biological relevance of the transcriptomic data. This study provides a foundational, genome-wide resource, identifying novel lncRNA targets for future mechanistic investigation into host–RV interactions. Full article

Background: The persistently low 5-year survival rate for pancreatic cancer (PC) underscores the critical need for early detection. However, population-wide screening remains impractical. Artificial Intelligence (AI) models using electronic health record (EHR) data offer a promising avenue for pre-symptomatic risk stratification. Objective: To systematically review and meta-analyze the performance of AI models for PC prediction based exclusively on structured EHR data. Methods: We systematically searched PubMed, MedRxiv, BioRxiv, and Google Scholar (2010–2025). Inclusion criteria encompassed studies using EHR-derived data (excluding imaging/genomics), applying AI for PC prediction, reporting AUC, and including a non-cancer cohort. Two reviewers independently extracted data. Random-effects meta-analysis was performed for AUC, sensitivity (Se), and specificity (Sp) using R software version 4.5.1. Heterogeneity was assessed using I² statistics and publication bias was evaluated. Results: Of 946 screened records, 19 studies met the inclusion criteria. The pooled AUC across all models was 0.785 (95% CI: 0.759–0.810), indicating good overall discriminatory ability. Neural Network (NN) models demonstrated a statistically significantly higher pooled AUC (0.826) compared to Logistic Regression (LogReg, 0.799), Random Forests (RF, 0.762), and XGBoost (XGB, 0.779) (all p < 0.001). In analyses with sufficient data, models like Light Gradient Boosting (LGB) showed superior Se and Sp (99% and 98.7%, respectively) compared to NNs and LogReg, though based on limited studies. Meta-analysis of Se and Sp revealed extreme heterogeneity (I² ≥ 99.9%), and the positive predictive values (PPVs) reported across studies were consistently low (often < 1%), reflecting the challenge of screening a low-prevalence disease. Conclusions: AI models using EHR data show significant promise for early PC detection, with NNs achieving the highest pooled AUC. However, high heterogeneity and typically low PPV highlight the need for standardized methodologies and a targeted risk-stratification approach rather than general population screening. Future prospective validation and integration into clinical decision-support systems are essential. Full article

Background and Objective: Patients with stage III clear cell renal cell carcinoma (ccRCC) have a high risk for disease recurrence post-nephrectomy. To mitigate overtreatment, there is a pressing need to determine who benefits from immune checkpoint inhibition (ICI) around the time of surgical resection. We performed digital spatial analysis of both gene and protein expression in stage III ccRCC tumors, some of which had preoperative ICI exposure. Methods: Nephrectomy specimens from stage III ccRCC patients were analyzed using the Nanostring GeoMx Digital Spatial Profiler. Differential expression analysis was performed and validated using NCT02210117 trial data to identify genes associated with both ICI and clinical response. A gene score was then generated to predict overall survival in patients from The Cancer Genome Atlas (TCGA). Key Findings and Limitations: In a small cohort of 19 patients, RNA expression significantly differed based on preoperative ICI exposure and recurrence status—CD8+ effector and central-memory T-cell signatures were less prevalent in the treatment-naïve with recurrence group. Three out of four patients with preoperative immune checkpoint inhibition recurred. External validation yielded a four-gene set (GZMK, GZMA, ITGAL, and IL7R), where higher expression levels predicted better overall survival in the TCGA cohort (p = 0.005). Conclusions and Clinical Implications: Preoperative ICI favorably altered the tumor microenvironment to resemble that of treatment-naïve patients without recurrence but did not translate to improved survival. Upon external validation, the genes GZMK, GZMA, ITGAL, and IL7R were modifiable with ICI and associated with improved overall survival. Further investigation is needed to assess if patients with low baseline expression of these genes may benefit from ICI around the time of surgery. Full article

Background: Optical genome mapping (OGM) detects genome-wide structural variants (SVs), including balanced rearrangements and complex copy-number alterations beyond standard-of-care cytogenomic assays. In chronic lymphocytic leukemia (CLL), cytogenetic and genomic risk stratification is traditionally based on fluorescence in situ hybridization (FISH), karyotyping, targeted next-generation sequencing (NGS), and immunogenetic assessment of immunoglobulin heavy chain variable region (IGHV) somatic hypermutation status, each of which interrogates only a limited aspect of disease biology. Methods: We retrospectively evaluated fifty patients with CLL using OGM and integrated these findings with cytogenomics, targeted NGS, IGHV mutational status, and clinical time-to-first-treatment (TTFT) data. Structural variants were detected using OGM and pathogenic NGS variants were derived from a clinical heme malignancy panel. Clinical outcomes were extracted from the electronic medical record. Results: OGM identified reportable structural variants in 82% (41/50) of cases. The most frequent abnormality was del(13q), observed in 29/50 (58%) and comprising 73% (29/40) of all OGM-detected deletions with pathologic significance. Among these, 12/29 (42%) represented large RB1-spanning deletions, while 17/29 (58%) were focal deletions restricted to the miR15a/miR16-1 minimal region, mapping to the non-coding host gene DLEU2. Co-occurrence of adverse lesions, including deletion 11q/ATM, BIRC3 loss, trisomy 12, and deletion 17p/TP53, were recurrent and strongly associated with shorter TTFT. OGM also uncovered multiple cryptic rearrangements involving chromosomal loci that are not represented in the canonical CLL FISH probe panel, including IGL::CCND1, IGH::BCL2, IGH::BCL11A, IGH::BCL3, and multi-chromosomal copy-number complexity. IGHV data were available in 37/50 (74%) of patients; IGHV-unmutated status frequently co-segregated with OGM-defined high-risk profiles (del(11q), del(17p), trisomy 12 with secondary hits, and complex genomes whereas mutated IGHV predominated in OGM-negative or structurally simple del(13q) cases and aligned with indolent TTFT. Integration of OGM with NGS further improved genomic risk classification, particularly in cases with discordant or inconclusive routine testing. Conclusions: OGM provides a comprehensive, genome-wide view of structural variation in CLL, resolving deletion architecture, identifying cryptic translocations, and defining complex multi-hit genomic profiles that tracked closely with clinical behavior. Combining OGM and NGS analysis refined risk stratification beyond standard FISH panels and supports more precise, individualized management strategies in CLL. Prospective studies are warranted to evaluate the clinical utility of OGM-guided genomic profiling in contemporary treatment paradigms. Full article

Background/Objectives: Whole exome sequencing (WES) is an effective method for detecting disease-causing variants. However, copy number variation (CNV) detection using WES data often has limited sensitivity and high false-positive rates. Methods: In this study, we constructed a reference CNV set using chromosomal microarray analysis (CMA) data from 44 of 180 individuals who underwent WES and CMA and evaluated four WES-based CNV callers (CNVkit, CoNIFER, ExomeDepth, and cn.MOPS) against this benchmark. For each tool, we first defined software-specific problematic genomic regions across the full WES cohort and filtered out the CNVs that overlapped these regions. Results: The four algorithms showed low mutual concordance and distinct distributions in the problematic regions. On average, 2210 sequencing target baits (1.23%) were classified as problematic; these baits had lower mappability scores and higher coefficients of variation in RPKM than the remaining probes. After the supplementary filtration step, all tools demonstrated improved performance. Notably, ExomeDepth achieved gains of 14.4% in sensitivity and 7.9% in positive predictive value. Conclusions: We delineated software-specific problematic regions and demonstrated that targeted filtration markedly reduced false positives in WES-based CNV detection. Full article

Sphaerodactylidae play a crucial role in ecosystems, possessing significant ecological, scientific, and conservation value. They contribute to pest control and the maintenance of ecological balance, and also provide abundant materials for research in evolutionary biology and biodiversity. To refine the phylogenetic position of Teratoscincus scincus within the Sphaerodactylidae using mitogenomic data, this study sequenced the complete mitochondrial genome of T. scincus using the Illumina NovaSeq Xplus platform, and subsequently performed assembly, annotation, and analysis. The phylogenetic relationships of T. scincus within the Sphaerodactylidae were analyzed using 13 protein-coding genes (PCGs) from the mitochondrial genome via Bayesian inference (BI) and maximum likelihood (ML) methods. The complete mitochondrial genome of T. scincus is 16,943 bp in length and consists of 13 PCGs, 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loop). The base composition shows a distinct AT preference, with the highest A + T content (56.3%) found in the PCGs region. A phylogenetic tree was constructed based on the amino acid sequences of 13 PCGs from the mitochondrial genomes of nine Sphaerodactylidae species retrieved from GenBank and the newly sequenced T. scincus generated in this study. The results confirm that T. scincus belongs to the genus Teratoscincus within the family Sphaerodactylidae. Phylogenetic analysis reveals that T. scincus and Teratoscincus keyserlingii cluster into a monophyletic group, suggesting a close phylogenetic relationship. Additionally, the phylogenetic tree provides new molecular evidence for understanding the formation mechanism of Sphaerodactylidae diversity. This study not only enriches the mitochondrial genome database of Sphaerodactylidae but also lays an important foundation for subsequent research on the adaptive evolution and conservation biology of T. scincus. Full article

Glioblastoma multiforme (GBM) is the most aggressive primary brain cancer (with a median survival time of 14.5 months), characterized by heterogeneity. Identifying prognostic molecular subtypes could provide a deeper exposition of GBM biology with potential therapeutic implications. In this study, we classified GBM into two prognostic subtypes, C1-GBM (n = 57; OS: 313 days) and C2-GBM (n = 109; OS: 452 days), using pathway-based signatures derived from RNA-seq data. Unsupervised consensus clustering revealed that only binary classification (cluster number, CN = 2; mean cluster consensus score = 0.84) demonstrated statistically prognostic differences. We characterized C1 and C2 based on oncogenic pathway and immune signatures. Specifically, C1-GBM was categorized as an immune-infiltrated “hot” tumor, with high infiltration of immune cells, particularly macrophages and CD4⁺ T cells, while C2-GBM as an “inherent driving” subtype, showing elevated activity in G2/M checkpoint genes. To predict the C1 or C2 classification and explore therapeutic interventions, we developed a neural network model. By using Weighted Correlation Network Analysis (WGCNA), we obtained the gene co-expression module based on both gene expression pattern and distribution among patients in TCGA dataset (n = 166) and identified nine hub genes as potentially prognostic biomarkers for the neural network. The model showed strong accuracy in predicting C1/C2 classification and prognosis, validated by the external CGGA-GBM dataset (n = 85). Based on the classification of the BP neural network model, we constructed a Cox nomogram prognostic prediction model for the TCGA-GBM dataset. We predicted potential therapeutic small molecular drugs by targeting subtype-specific oncogenic pathways and validated drug sensitivity (C1-GBM: Methotrexate and Cisplatin; C2-GBM: Cytarabine) by assessing IC₅₀ values against GBM cell lines (divided into C1/C2 subtypes based on the nine hub genes) from the Genomics of Drug Sensitivity in Cancer database. This study introduces a pathway-based prognostic molecular classification of GBM with “hot” (C1-GBM) and “inherent driving” (C2-GBM) tumor subtypes, providing a prediction model based on hub biomarkers and potential therapeutic targets for treatments. Full article

Respiratory syncytial virus (RSV) is among leading global causes of lower respiratory tract infections, yet data from Russia and other states of the Former Soviet Union (FSU) remain fragmented and structurally inconsistent. This systematic review aims to map and synthesize existing evidence on RSV epidemiology and genotypic distribution across the FSU. Published studies from eLIBRARY and PubMed databases queried for RSV prevalence data, together with public health surveillance datasets, were used to summarize RSV prevalence research across eight FSU countries. Random-effects meta-analysis across age strata showed high prevalence in children before 6 (21%) and a progressive decline with age, which is in agreement with global data. Prevalence estimates showed a high degree of variability partially explained by study scope and clinical presentation. We observed COVID-19-related seasonal disruptions of RSV seasonality, followed by gradual post-pandemic stabilization. Genotypic data reflects global trends with two cosmopolitan clades, A.D and B.D, and their descendants, dominating in the region. The review is limited by uneven geographical and temporal coverage, and scarce data on adults. The review provides the first integrated summary of RSV epidemiology across the FSU and underscores the need for expanded regional surveillance and genomic reporting. Full article

Klebsiella oxytoca has emerged as an important opportunistic pathogen in nosocomial infections, particularly during the COVID-19 pandemic, due to its capacity to acquire and disseminate resistance and virulence genes through horizontal gene transfer (HGT). This study presents a genome-based comparative analysis of K. oxytoca within the genus Klebsiella, aimed at exploring the evolutionary plausibility of outer membrane vesicle (OMV) associated processes in bacterial adaptation. Using publicly available reference genomes, we analyzed pangenome structure, phylogenetic relationships, and the distribution of mobile genetic elements, resistance determinants, virulence factors, and genes related to OMV biogenesis. Our results reveal a conserved set of envelope associated and stress responsive genes involved in vesiculogenic pathways, together with an extensive mobilome and resistome characteristic of the genus. Although these genomic features are consistent with conditions that may favor OMV production, they do not constitute direct evidence of functional OMV mediated horizontal gene transfer. Instead, our findings support a hypothesis generating evolutionary framework in which OMVs may act as a complementary mechanism to established gene transfer routes, including conjugation, integrative mobile elements, and bacteriophages. Overall, this study provides a genomic framework for future experimental and metagenomic investigations into the role of OMV-associated processes in antimicrobial resistance dissemination and should be interpreted as a recently identified evolutionary strategy inferred from genomic data, rather than a novel or experimentally validated mechanism. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article