Search Results (192)

Background/Objectives: Glioblastoma (GBM) is an aggressive brain tumor known for its profound heterogeneity and treatment resistance. Dysregulated complement signaling and epigenetic alterations have been implicated in GBM progression. This study identifies kynureninase (KYNU), a key enzyme in the kynurenine pathway, as a novel regulator of complement components and investigates its interaction with histone deacetylase 6 (HDAC6) in the context of therapeutic targeting. Methods: KYNU expression, and its association with complement signaling in GBM, were analyzed using publicly available datasets (TCGA, GTEx, HPA). Pathway enrichment was performed via LinkedOmics. In vitro studies in GBM cell lines (U87, U251, T98G) assessed the effects of KYNU silencing and treatment with an HDAC6 inhibitor (tubastatin) and a BET inhibitor (apabetalone) on gene expression and cell viability. Results: Bioinformatic analyses revealed significant overexpression of KYNU in GBM tissues compared to normal brain tissue. KYNU expression was positively associated with genes involved in complement and coagulation cascades. In vitro experiments demonstrated that KYNU silencing reduced the expression of C3, C3AR1, and C5AR1 and suppressed GBM cell viability. Treatment with tubastatin, while reducing viability, paradoxically upregulated complement genes, suggesting potential limitations in therapeutic efficacy. However, this effect was mitigated by KYNU knockdown. Combined treatment with apabetalone and tubastatin effectively suppressed KYNU expression and enhanced cytotoxicity, particularly in cells with high complement expression. Conclusions: Our findings establish the KYNU–HDAC6–complement axis as a critical regulatory pathway in GBM. Targeting KYNU-mediated complement activation through combined epigenetic approaches—such as HDAC6 and BET inhibition—represents a promising strategy to overcome complement-driven resistance in GBM therapy. Full article

Background/Objectives: Cardiac aging involves the progressive structural and functional decline of the myocardium. Endurance training is a well-recognized non-pharmacological intervention that counteracts this decline, yet the molecular mechanisms driving exercise-induced cardiac rejuvenation remain inadequately elucidated. This study aimed to identify key effector genes and regulatory pathways by integrating human cardiac aging transcriptomic data with multi-omic exercise response datasets. Methods: A systems biology framework was developed to integrate age-downregulated genes (n = 243) from the GTEx human heart dataset and endurance-exercise-responsive genes (n = 634) from the MoTrPAC mouse dataset. Thirty-seven overlapping genes were identified and subjected to Enrichr for pathway enrichment, KEA3 for kinase analysis, and ChEA3 for transcription factor prediction. Candidate effector genes were ranked using ToppGene and ToppNet, with integrated prioritization via the FLAMES linear scoring algorithm. Results: Pathway enrichment revealed complementary patterns: aging-associated genes were enriched in mitochondrial dysfunction and sarcomere disassembly, while exercise-responsive genes were linked to protein synthesis and lipid metabolism. TTN, PDK family kinases, and EGFR emerged as major upstream regulators. NKX2-5, MYOG, and YBX3 were identified as shared transcription factors. SMPX ranked highest in integrated scoring, showing both functional relevance and network centrality, implying a pivotal role in mechano-metabolic coupling and cardiac stress adaptation. Conclusions: By integrating cardiac aging and exercise-responsive transcriptomes, 37 effector genes were identified as molecular bridges between aging decline and exercise-induced rejuvenation. Aging involved mitochondrial and sarcomeric deterioration, while exercise promoted metabolic and structural remodeling. SMPX ranked highest for its roles in mechano-metabolic coupling and redox balance, with X-inactivation escape suggesting sex-specific relevance. Other top genes (e.g., KLHL31, MYPN, RYR2) form a regulatory network supporting exercise-mediated cardiac protection, offering targets for future validation and therapy. Full article

Background: Endometrial cancer (EC) remains a major gynecologic malignancy with limited biomarkers for risk stratification. While killer cell lectin-like receptor G2 (KLRG2) exhibits oncogenic properties in other cancers, its clinical significance and mechanistic roles in EC are unknown. This study aims to systematically characterize KLRG2 expression in EC, evaluate its prognostic significance, decipher underlying molecular mechanisms, and explore its role in tumor immune microenvironment regulation. Methods: We performed integrated multi-omics analyses using TCGA-UCEC (n = 552), GTEx, and GEO cohorts (GSE106191), complemented by qPCR validation (14 EC vs. 14 normal samples). Prognostic models were constructed via Cox regression and time-dependent ROC analysis. Epigenetic regulation was assessed through methylation profiling (UALCAN/MethSurv), and immune correlations were evaluated using TIMER/ESTIMATE algorithms. Results: KLRG2 was significantly overexpressed in EC tissues compared to normal endometrium (p < 0.001), validated by immunohistochemistry and qPCR. High KLRG2 expression independently predicted worse overall survival (HR = 3.08, 95% CI = 1.92–4.96) and progression-free interval (HR = 1.98, 95% CI = 1.37–2.87). Furthermore, elevated KLRG2 levels were significantly associated with advanced-stage disease (p < 0.001), deep myometrial invasion (p < 0.05), and high-grade histology (p < 0.001). Mechanistically, promoter hypomethylation was associated with KLRG2 overexpression (p < 0.001), while hypermethylation at three CpG sites (cg04915254, cg04520485, cg23104233) correlated with poor prognosis. Functional enrichment linked KLRG2 to cell cycle checkpoints and G Protein-Coupled Receptor signaling. Immune profiling revealed cytotoxic lymphocyte depletion (CD8+ T cells: Spearman’s ρ = −0.247, p < 0.001; NK CD56bright cells: Spearman’s ρ = −0.276, p < 0.001) and Th2 polarization (Spearman’s ρ = 0.117, p = 0.006). Conclusions: This comprehensive EC study establishes KLRG2 as a dual diagnostic/prognostic biomarker and immunomodulatory target. These findings provide a rationale for developing KLRG2-directed therapies to counteract tumor-intrinsic proliferation and microenvironmental immune suppression. Future single-cell analyses are warranted to dissect KLRG2-mediated tumor-immune crosstalk. Full article

Phosphomannomutase 2 (PMM2) catalyzes the interconversion of mannose-6-phosphate and mannose-1-phosphate, a key step in the biosynthesis of GDP-mannose for N-glycosylation. Its deficiency is the most common cause of congenital disorders of glycosylation (CDGs), accounting for the subtype known as PMM2-CDG. PMM2-CDG is a rare autosomal recessive disease characterized by multisystemic dysfunction, including cerebellar atrophy, peripheral neuropathy, developmental delay, and coagulation abnormalities. The disease is associated with a spectrum of pathogenic missense mutations, particularly at residues involved in dimerization and catalytic function (i.e., p.Phe119Leu and p.Arg141His). The dimerization of PMM2 is considered essential for enzymatic activity, although it remains unclear whether this supports structural stability alone, or whether both subunits are catalytically active—a distinction that may affect how mutations in each monomer contribute to overall enzyme function and disease phenotype. PMM2 has a paralog, phosphomannomutase 1 (PMM1), which shares substantial structural similarity—including obligate dimerization—and displays mutase activity in vitro, but does not compensate for PMM2 deficiency in vivo. To investigate potential heterodimerization between PMM1 and PMM2 and the effect of interface mutations over PMM2 dimer stability, we first assessed the likelihood of their co-expression using data from GTEx and the Human Protein Atlas. Building on this expression evidence, we modeled all possible dimeric combinations between the two paralogs using AlphaFold3. Models of the PMM2 and PMM1 homodimers were used as internal controls and aligned closely with their respective reference biological assemblies (RMSD < 1 Å). In contrast, the PMM2/PMM1 heterodimer model, the primary result of interest, showed high overall confidence (pLDDT > 90), a low inter-chain predicted alignment error (PAE∼1 Å), and robust interface confidence scores (iPTM = 0.80). Then, we applied PISA, PRODIGY, and mmCSM-PPI to assess interface energetics and evaluate the impact of missense variants specifically at the dimerization interface. Structural modeling suggested that PMM2/PMM1 heterodimers were energetically viable, although slightly less stable than PMM2 homodimers. Interface mutations were predicted to reduce dimer stability, potentially contributing to the destabilizing effects of disease-associated variants. These findings offer a structural framework for understanding PMM2 dimerization, highlighting the role of interface stability, paralogs co-expression, and sensitivity to disease-associated mutations. Full article

Lung cancer is responsible for 2.21 million annual cancer cases and is the leading worldwide cause of cancer-related deaths. Specifically, lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype resulting from genetic causes; LUAD has a 15% patient survival rate due to it commonly being detected in its advanced stages. This study aimed to identify a novel biomarker signature of early-stage LUAD utilizing gene expression analysis of human lung tissue samples. Using 22 pairs of LUAD and matched normal lung microarrays, 229 differentially expressed genes were identified. These genes were networked for their protein–protein interactions, and 44 hub genes were determined from protein essentiality. Survival analysis of 478 LUAD patient samples identified four statistically significant candidates. These candidate genes’ expression profiles were validated from GTEx and TCGA (347 normal, 483 LUAD samples); immunohistochemistry validated the subsequent protein presence. Through intensive bioinformatic identification and multiple validations of the four-biomarker gene signature, AGER, MGP, and PECAM1 were identified as downregulated in LUAD; SLC2A1 was identified as upregulated in LUAD. These four biologically significant genes are involved in tumorigenesis and poor LUAD prognosis, meriting their use as a clinical biomarker signature and therapeutic targets for early-stage LUAD. Full article

Background/Objectives: TIGD1 (Trigger Transposable Element Derived 1) is a recently identified oncogene with largely unexplored biological functions. Emerging evidence suggests its involvement in multiple cellular processes across cancer types. This study aimed to perform a comprehensive pan-cancer analysis of TIGD1 to evaluate its expression patterns, diagnostic utility, prognostic value, and association with immunotherapy response and drug resistance. Methods: Transcriptomic and clinical data from TCGA and GTEx were analyzed using various bioinformatic tools. Expression profiling, survival analysis, immune correlation studies, gene set enrichment, single-cell sequencing, and drug sensitivity assessments were performed. Results: TIGD1 was found to be significantly upregulated in various tumor types, with notably high expression in colon adenocarcinoma. Elevated TIGD1 expression was associated with poor prognosis in several cancers. TIGD1 levels correlated with key features of the tumor immune microenvironment, including immune checkpoint gene expression, TMB, and MSI, suggesting a role in modulating anti-tumor immunity. GSEA and single-cell analyses implicated TIGD1 in oncogenic signaling pathways. Furthermore, high TIGD1 expression was linked to resistance to several therapeutic agents, including Zoledronate, Dasatinib, and BLU-667. Conclusions: TIGD1 may serve as a promising diagnostic and prognostic biomarker, particularly in colon, gastric, liver, and lung cancers. Its strong associations with immune modulation and therapy resistance highlight its potential as a novel target for precision oncology and immunotherapeutic intervention. Full article

Background: Hippocampal sclerosis (HS) is a hallmark of mesial temporal lobe epilepsy (MTLE). However, genetic studies on MTLE patients with HS (MTLE-HS) remain limited, especially in East Asian populations. This study aimed to identify genetic variants associated with MTLE-HS and elucidate their biological relevance through integrative genomic and transcriptomic analyses. Methods: We conducted a genome-wide association study (GWAS) on 157 Korean epilepsy patients, including 52 MTLE-HS subjects and 105 non-acquired focal epilepsy individuals without HS as controls. The splicing and expression quantitative trait locus (sQTL and eQTL, respectively) effects of significant variants were analyzed using GTEx datasets. Transcriptomic data from the hippocampi of MTLE-HS subjects and an epilepsy mouse model were examined to assess TMEM14A expression. Gene correlation enrichment analysis was performed to investigate potential associations with epilepsy-related phenotypes. Results: The GWAS identified rs6924849, located downstream of TMEM14A, as significantly associated with MTLE-HS. The sQTL analysis revealed that rs6924849 induces abnormal TMEM14A splicing in hippocampal tissue. Transcriptomic analyses showed reduced TMEM14A expression in MTLE-HS hippocampi, while mice with pilocarpine-induced epilepsy exhibited a transient increase in TMEM14A expression during the acute phase post-status epilepticus. Gene correlation enrichment analyses linked TMEM14A to seizure-related phenotypes in both humans and mice. Conclusions: This study identifies rs6924849 as a novel genetic variant associated with MTLE-HS in an East Asian population. The dysfunctional splicing and altered expression of TMEM14A may contribute to the neuronal loss characteristic of HS, as TMEM14A regulates apoptosis. These findings emphasize the potential role of TMEM14A in MTLE-HS pathogenesis from genomic and transcriptomic perspectives. Full article

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, necessitating the identification of novel biomarkers for improved prognosis and diagnosis. This study investigates the role of epoxide hydrolase 4 (EPHX4), a member of the epoxide hydrolase family, in LUAD. Using data sourced from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, which were subsequently validated by the Gene Expression Omnibus (GEO), we analyzed levels of EPHX4 expression, mutation, and methylation in tumors versus normal tissues. Our findings revealed a significant upregulation of EPHX4 in LUAD tissues compared to normal lung tissues (p < 0.001), correlating with poorer overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Furthermore, EPHX4 exhibited considerable diagnostic potential, as demonstrated by an area under the curve (AUC) of 0.854 in a Receiver Operating Characteristic (ROC) analysis. Notably, EPHX4 expression was associated with immune infiltration, specifically Th2 cells, neutrophils, and macrophages, along with immune checkpoint molecules including PD-L1, PD-L2, and TIM-3. Additionally, EPHX4 was involved in pivotal tumor-associated pathways, particularly cell cycle regulation. In conclusion, an elevated EPHX4 expression is indicative of poorer prognosis in LUAD and may play a role in immune evasion and cell cycle dysregulation, highlighting its potential as a promising biomarker for the diagnosis and prognostic prediction of LUAD. Full article

This study systematically analyzed the expression and clinical significance of Hydroxysteroid 17-beta dehydrogenase type 10 (HSD17B10) in 33 cancers by integrating TCGA, GTEx, and other multi-omics databases. HSD17B10 was highly expressed in 14 cancers, like GBM and LGG, but low in 5, such as KIRC. Its expression correlated closely with overall survival (OS) and disease-free survival (DFS). In GBM-LGG, LGG, and other cancers, high HSD17B10 expression was linked to lower survival rates, indicating that it could be an independent prognostic marker. HSD17B10 also had a two-way relationship with the tumor’s immune microenvironment. In cancers such as GBM-LGG, high expression correlated positively with immune/stromal scores. However, in most cancers like LUAD, it was negatively associated with B- and T-cell infiltration. Epigenetic analysis showed that low methylation in the HSD17B10 promoter region might drive its high expression in tumors such as SARC, and specific methylation sites (e.g., CG26323797) were significantly related to patient survival. Functional enrichment analysis revealed that HSD17B10 participated in tumor progression by regulating oxidative phosphorylation, mitochondrial metabolism, and RNA methylation. Single-cell and spatial transcriptome data further demonstrated that HSD17B10 had a cell-type-specific expression pattern in colorectal cancer. This study provides a theoretical basis for HSD17B10 as a pan-cancer prognostic marker and therapeutic target. Full article

Background/Objectives: Diabetic kidney disease (DKD) is a common diabetic complication, driven by a multifactorial pathogenesis that includes various genetic components. However, the precise causative genes and their underlying biological pathways remain poorly understood. Methods: We performed a cross-tissue transcriptome-wide association study (TWAS) of DKD using expression quantitative trait loci (eQTL) data from 49 tissues in the Genotype—Tissue Expression (GTEx) version 8 (v8) resource. Five complementary analytical frameworks—sparse canonical correlation analysis (sCCA), functional summary-based imputation (FUSION), fine-mapping of causal gene sets (FOCUS), summary-data-based Mendelian randomization (SMR), and multi-marker analysis of genomic annotation (MAGMA)—were integrated to nominate candidate genes. Causal inference was refined using Mendelian randomization (MR), and biological significance was evaluated through pathway enrichment, protein interaction networks, and druggability profiling. Results: We identified 23 candidate genes associated with DKD risk, of which 13 were supported by MR analysis. Among these, 10 represent previously unreported susceptibility genes. Notably, four genes—HLA-DRB1, HLA-DRB5, NOTCH4, and CYP21A2—encode potentially druggable proteins, with HLA-DRB5 and CYP21A2 both qualifying as novel susceptibility genes and therapeutic targets. These genes converge on immune modulation, steroid biosynthesis, DNA repair, and transcriptional regulation—processes central to DKD pathogenesis. Conclusions: Our study represents the first systematic cross-tissue TWAS of DKD, revealing a prioritized set of genetically and functionally supported susceptibility genes. The identification of druggable targets among these genes provides critical insight into the mechanistic underpinnings of DKD and highlights their potential for future therapeutic development. These findings enhance our understanding of DKD pathophysiology and offer a foundation for precision medicine strategies in nephrology. Full article

Background: The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes. Methods: We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches—S-LDSC, MAGMA, and DESE—we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn’s disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data. Results: Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42–0.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, p = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and CREB1. Conclusions: Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases. Full article

Lung adenocarcinoma (LUAD) is driven by epidermal growth factor receptor (EGFR) mutations, making it a key therapeutic target. ADAM9, a member of the A disintegrin and metalloproteinase (ADAM) family, facilitates the release of growth factors and was implicated in activating the EGFR-mediated progression in several cancer types. In this study, we explored potential associations among ADAM9 single-nucleotide polymorphisms (SNPs), the EGFR mutation status, and the clinicopathological progression of LUAD in a Taiwanese population. In total, 535 LUAD patients with various EGFR statuses were enrolled, and allelic distributions of ADAM9 SNPs—located in promoter and intron regions, including rs78451751 (T/C), rs6474526 (T/G), rs7006414 (T/C), and rs10105311 (C/T)—were analyzed using a TaqMan allelic discrimination assay. We found that LUAD patients with at least one polymorphic G allele in ADAM9 rs6474526 had a lower risk of developing EGFR mutations compared to those with the wild-type (WT) TT genotype. Furthermore, G-allele carriers (TG + GG) of rs6474526 were associated with an increased likelihood of developing larger tumors (T3 or T4), particularly among patients with mutant EGFR. Conversely, in patients with WT EGFR, carriers of the T allele in rs10105311 had a lower risk of progressing to advanced stages (stage III or IV). Among females or non-smokers, G-allele carriers of rs6474526 demonstrated a higher risk of advanced tumor stages and distant metastases. In clinical data from the Genotype-Tissue Expression (GTEx) database, individuals with the polymorphic T allele in rs6474526 showed reduced ADAM9 expression in lung and whole blood tissues. Screening the genotype of rs6474526 in a set of LUAD cell lines revealed that cells carrying at least one minor G allele exhibited higher ADAM9 levels compared to those with the TT genotype. Additionally, analyses using TCGA and CPTAC databases revealed elevated ADAM9 expression in LUAD specimens compared to normal tissues. Elevated protein levels were correlated with advanced T stages, pathological stages, and worse prognoses. In summary, our results suggest that ADAM9 genetic variants of rs6474526 may affect ADAM9 expression and are associated with the EGFR mutation status. Both rs6474526 and rs10105311 were correlated with disease progression in LUAD patients. These variants could serve as potential biomarkers for predicting clinical outcomes. Full article

Ras signaling regulates many cellular processes in cancer development. While well-known Ras-related oncogenes, such as KRAS, have been extensively explored, the role of other Ras-related genes in cancer remains poorly studied. Dexamethasone-induced Ras-related protein 1 (RASD1), a member of the Ras superfamily, is widely expressed across various tissues and is involved in inhibiting cell growth and inducing apoptosis, suggesting a potential role as a tumor suppressor. Here, we investigated RASD1 expression across multiple tissues and cancers, utilizing data from The Cancer Genome Atlas (TCGA), Human Protein Atlas, and Genotype-Tissue Expression (GTEx) databases. Our analysis revealed a significant downregulation of RASD1 mRNA expression in several cancer types compared to normal tissues, correlating with low levels of promoter methylation. Interestingly, high RASD1 expression correlated with a favorable prognosis in multiple cancers. Immune cell infiltration analysis indicated that elevated RASD1 expression is associated with an increased infiltration of CD4⁺ T cells and myeloid-derived dendritic cells in cancer. Furthermore, the expression of genes exhibiting similar expression patterns as RASD1 suggest that RASD1 may play a role in interleukin-4-mediated apoptosis and could regulate the transcription of the phosphatase and tensin homolog (PTEN) gene. Overall, these findings suggest that RASD1 may modulate immune signaling and tumor suppressive pathways. Full article

Background: The precise diagnosis and classification of acute myeloid leukemia (AML) has important implications for clinical management and medical research. Methods: We investigated the expression of protein-coding genes in blood samples from AML patients and controls using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Subsequently, we applied the feature selection method of the least absolute shrinkage and selection operator (LASSO) to select the optimal gene subset for classifying AML patients and controls as well as between a particular FAB subtype and other subtypes of AML. Results: Using LASSO method, we identified a subset of 101 genes that could effectively distinguish between AML patients and control individuals; these genes included 70 up-regulated and 31 down-regulated genes in AML. Functional annotation and pathway analysis indicated the involvement of these genes in RNA-related pathways, which was also consistent with the epigenetic changes observed in AML. Results from survival analysis revealed that several genes are correlated with the overall survival in AML patients. Additionally, LASSO-based gene subset analysis successfully revealed differences between certain AML subtypes, providing valuable insights into subtype-specific molecular mechanisms and differentiation therapy. Conclusions: This study demonstrated the application of machine learning in genomic data analysis for identifying gene subsets relevant to AML diagnosis and classification, which could aid in improving the understanding of the molecular landscape of AML. The identification of survival-related genes and subtype-specific markers may lead to the identification of novel targets for personalized medicine in the treatment of AML. Full article

Understanding the genetic regulation of gene expression and splicing in muscle tissues is critical for elucidating the molecular mechanisms of meat quality traits. In this study, we integrated large-scale whole-genome sequencing and strand-specific RNA-seq data from 582 F2 hybrid pigs (White Duroc × Erhualian) to characterize the expression and splicing quantitative trait loci (eQTLs/sQTL) in longissimus dorsi muscle. We identified 11,058 cis-eQTL-associated genes (eGenes) and 5139 cis-sQTL-associated genes (sGenes), of which 29% of eGenes and 80% of sGenes were previously unreported in the PigGTEx database. Functional analyses revealed distinct genomic features: eQTLs were enriched near transcription start sites (TSSs) and associated with active TSS-proximal transcribed regions and enhancers, whereas sQTLs clustered at splice junctions, underscoring their distinct roles in gene expression and splicing regulation. Colocalization analysis of e/sQTLs with GWAS signals prioritized PHKG1 as a key candidate gene (PPH4 > 0.9) for glycogen metabolism. Notably, we confirmed that an sQTL-driven alternative splicing event in exon 10 of PHKG1 was significantly correlated with phenotypic variation (R = −0.39, p = 9.5 × 10⁻²¹). Collectively, this study provides novel insights into the genetic regulation of gene expression and alternative splicing in porcine muscle tissue, advancing our understanding of the molecular mechanisms underlying economically important meat quality traits. Full article