Search Results (406)

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

The objective of this study was to compare the proportion of explained genetic variance (EXGV) and the reliability of genomic breeding values (GBVs) predictions for milk yield (MY), fat yield (FY), protein yield (PY) fat percentage (FP), protein percentage (PP), and somatic cell score (SCS) in Holstein cattle. Three types of genomic information were evaluated. (a) SNP-ALL: this analysis included 88,911 single nucleotide polymorphisms (SNP) from 8290 animals. (b) HAP-PSEUDOSNP: haplotypes, defined based on high linkage disequilibrium (LD, r² ≥ 0.80) between SNPs, which were encoded as pseudo-SNPs, with a total of 35,552 pseudo-SNPs and 8331 animals included. (c) SNP-HAP: analysis using only individual SNPs included in the haplotypes (without recoding); for this analysis, 33,010 SNPs and 8192 individuals were retained. All analyses were conducted using the single-step genome-wide association study method implemented in the BLUPF90 software package. The results showed that the inclusion of SNPs with high LD (SNP-HAP) increases the reliability of GBVs’ predictions compared to the SNP-ALL analysis; average reliability increased between 0.05 and 0.11. Moreover, the SNP-HAP analysis resulted in a twofold increase in the EXGV for all traits, likely due to increased estimates of individual marker effects compared to the SNP-ALL analysis. 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/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

Fetal loss, the spontaneous termination of pregnancy between day 42 and 260 of gestation, is poorly understood. Impacts of fetal loss include loss of production, increased health risk, and economic loss. The aims of this study were to identify loci associated with fetal loss in Holstein heifers and primiparous cows to facilitate the selection of reproductively efficient cattle and identify the genetic causes of fetal loss. A genome-wide association analysis (GWAA) compared 5714 heifers that calved at term (controls) to 416 heifers that experienced fetal loss (cases), and for primiparous cows, 2519 controls were compared to 273 cases. The efficient mixed-model association eXpedited approach in the SNP and Variation Suite (v 9.1) statistical software was used with additive, dominant, and recessive inheritance models for the GWAA. In heifers, 16 loci were associated (FDR < 0.05) with fetal loss in the recessive model. In primiparous cows, there were 44 loci associated (FDR < 0.05) with fetal loss in the recessive model. No loci associated with fetal loss were shared between cows and heifers or in the additive and dominant models. These results improve the characterization of genetic factors contributing to fetal loss in Holstein heifers and primiparous cows and provide targets for genomic selection. Full article

Background/Objectives: Periodontal disease is a condition marked by the destruction of tooth-supporting tissues, driven by an exaggerated immune response to an unbalanced dental biofilm. Conventional treatments struggle due to antimicrobial resistance and the biofilm’s protective extracellular matrix. This study evaluates the potential of bacteriophages as an innovative strategy for managing periodontal disease. Methods: This research employed a qualitative approach using Discursive Textual Analysis, with IRAMUTEQ version 0.8 alpha 7 (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires) software. The search was conducted in the Orbit Intelligence and PubMed databases, for patents and scholarly articles, respectively. The textual data underwent Descending Hierarchical Classification, Correspondence Factor Analysis, and Similarity Analysis to identify core themes and relationships between words. Results: The analysis revealed an increase in research and patent filings concerning phage therapy for periodontal disease since 2017, emphasizing its market potential. The primary centers for intellectual property activity were identified as China and the United States. The study identified five focus areas: Genomic/Structural Characterization, Patent Formulations, Etiology, Therapeutic Efficacy, and Ecology/Phage Interactions. Lytic phages were shown to be effective against prominent pathogens such as Fusobacterium nucleatum and Enterococcus faecalis. Conversely, the lysogenic phages poses a potential risk, as they may transfer resistance and virulence factors, enhancing pathogenicity. Conclusions: Phage therapy is a promising approach to address antimicrobial resistance and biofilm challenges in periodontitis management. Key challenges include the need for the clinical validation of formulations and stable delivery systems for the subgingival area. Future strategies, such as phage genetic engineering and data-driven cocktail design, are crucial for enhancing efficacy and overcoming regulatory hurdles. Full article

Background: Preeclampsia (PE) is a hypertensive disorder of pregnancy associated with oxidative stress and mitochondrial dysfunction. NRF1 and NRF2 are transcription factors that regulate mitochondrial activity and antioxidant defense. This study investigated their expression patterns in placentas from preeclamptic and severe preeclamptic pregnancies by immunohistochemical and bioinformatical methods. Methods: Placentas from 40 healthy controls, 40 PE, and 40 sPE patients were analyzed by histological and immunohistochemical techniques. Protein–protein interaction networks for NRF1, NRF2, and PE-related proteins were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape software, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis performed via ShinyGO, with significance set at false discovery rate (FDR) < 0.05. Results: NRF1 expression was significantly decreased in PE and sPE groups compared to controls, with notably negative staining in syncytial knots and fibrinoid areas. Conversely, NRF2 expression significantly increased, showing intense positivity in syncytiotrophoblasts, stromal cells, and vascular structures. Pathway analysis revealed that decreased NRF1 expression was associated with glutathione metabolism, hypoxia inducible factor-1 (HIF-1) signaling, and AMP-Activated Protein Kinase (AMPK) signaling pathways. Increased NRF2 expression was associated predominantly with inflammatory and immune response pathways, including AGE-RAGE signaling and pathogen–response pathways. Conclusions: Differential expressions of NRF1 and NRF2 in preeclamptic placentas reflect distinct yet interconnected responses to oxidative stress and inflammation. These transcription factors have potential clinical relevance as biomarkers for PE severity assessment and as targets for future therapeutic interventions. Full article

The fields of medical diagnostics, nephrology, and the sequencing of cellular genetic material are pivotal for precise quantification of kidney diseases. Single-cell sequencing, enhanced by automation and software tools, enables efficient examination of biopsies at the individual cell level. This approach shows the complex cellular mosaic that shapes organ function. By quantifying gene expression following injury, single-cell analysis provides insight into disease progression. In this review, new developments in single-cell analysis methods, spatial integration of single-cell analysis, single-nucleus RNA sequencing, and emerging methods, including expression quantitative trait loci, whole-genome sequencing, and whole-exome sequencing in nephrology, are discussed. These advancements are poised to enhance kidney disease diagnostic processes, therapeutic strategies, and patient prognosis. Full article

Background/Objectives: Targeted gene sequencing (TGS) for Comprehensive Genomic Profiling (CGP) use in sarcomas has recently increased in clinical practice. We report on TGS real-world data over a period of 3 years (2022–2025) at the IRCCS Istituto Ortopedico Rizzoli, with the aim of identifying potential actionable targets and providing therapeutic indications for advanced sarcoma patients. Methods: We analyzed 22 advanced sarcoma patients by using the VariantPlex Pan Solid Tumor kit panel, including 185 genes. In nine cases, saliva samples for germinal DNA analysis were available. Sequencing was performed on the NextSeq-500 Platform and analyzed with Archer Analysis software. The Cancer Genome Interpreter and OncoKB Database tools were used to find potential actionable targets. Results: We found the most frequent genetic variants, including missense, deletion, duplication, and delins, in the NOTCH4, AR, BARD1, MUC16, and ROS1 genes. Copy Number alterations affected the CDKN2A, CDKN2B, TP53, RHOA, MYC, CCND3, and DDR2 genes mainly in osteosarcoma samples. In four patients, longitudinal analyses of subsequent lesions showed the maintenance of most genomic alterations and enrichment in missense or splice variants in PMS2, SMARCA4, ARID1A, AKT1, BMPR1A, and PTEN, indicating the occurrence of tumor evolution. Germline variants subtraction identified the specific somatic tumor mutations. Advantages and disadvantages of our approach were considered in order to refine the analysis setting and better select possible actionable targets. Conclusions: Early access to genomic analyses, routine germline assessment, and broad gene panels would help in identifying possible targeted drugs with sufficient evidence of activity beneficial to each patient. In the clinical management of advanced sarcoma patients, when analyzing cost-effectiveness and sustainability, the role of the Molecular Tumor Board in the governance of the complexity introduced by mutational oncology should be considered. Full article

Objective: Growth traits are important economic characteristics in livestock. Genetic polymorphism has great influences on the improvement of goat growth traits. As an important member of the myogenic regulatory factor (MRFs) family, MyoG gene polymorphisms can alter the growth characteristics in goats. In this study, we aimed to investigate the regulation mechanism of the MyoG gene promoter region from the perspective of single nucleotide polymorphisms (SNPs) and transcription factors. Methods: Genomic DNA sequencing was carried out to detect SNPs in the −1000 bp upstream to 300 bp downstream of the MyoG gene promoter region in 224 Guizhou White goats (Capra hircus), and the genetic parameters of novel SNPs were calculated. The association between SNPs and growth traits, comprising body weight, body length, body height, chest circumference and cannon circumference, were analyzed using one-way ANOVA by IBM SPSS 23.0 software according to the general linear model. Transcription factor binding sites in the promoter region of the MyoG gene before and after mutation were predicted using bioinformatics software programs. Results: Four SNPs, including g.–709C>T, g.–461G>T, g.–377G>T and g.–249G>A, were identified in the 1 246 bp promoter region of the MyoG gene in Guizhou White goats. Based on χ² test, the g.–709C>T and g.–461G>T loci were consistent with Hardy–Weinberg equilibrium, while two other SNPs were deviated from Hardy–Weinberg equilibrium in Guizhou White goats. Association analysis revealed that the body weight of those with the CT genotype at the g.–709C>T locus was greater than of those with the CC and TT genotypes in Guizhou White goats (p < 0.05). At the g.–461G>T locus, the body weight of individuals with the GG genotype was significantly higher than that of those with GT genotype (p < 0.01). The body length of individuals with the GG genotype formed by the g.–249G>A locus was significantly higher than that of those with the GA genotype (p < 0.01). Online software programs found that four SNPs within the promoter region of the MyoG gene changed some transcription factor binding sites. Conclusions: Mutations of the MyoG gene promoter region may have a significant regulatory effect on the growth traits of Guizhou White goats. The small sample size may be one of the limitations for this study; nevertheless, these findings could provide a theoretical basis for further exploring the relationship between the four SNPs studied and the growth traits in Guizhou White goats, as well as the promoter function of the MyoG gene. Full article

Enterococcus spp. produce diverse bioactive molecules used for biotechnological purposes or as probiotic agents for livestock and human health. The main aim of this study was to decipher the genetic traits using whole-genome sequencing (WGS) of a bacteriocinogenic Enterococus faecium 1702 strain showing diverse probiotic traits. Genetic traits of the strain were determined by performing WGS using the NovaSeq6000 platform followed by consecutive sequence analysis using appropriate software. WGS showed that the genome of the E. faecium 1702 strain has a size of 2,621,416 bp, with a GC content of 38.03%. The strain belonged to the sequence type ST722 not known as a human clonal lineage. The strain was free of genes encoding clinically relevant antibiotic resistance; in addition, genes encoding sensu stricto virulence factors, plasmids, and prophages were not detected. Annotations through the Prokaryotic Genomes Automatic Annotation Pipeline (PGAP) tool revealed 2413 coding sequencing entries (CDC) out of 2521 predicted chromosomal genes. The functional annotation of the whole genome through the KEGG database using KofaScan revealed several genes related to several biological activities, including metabolic process, carbohydrate metabolism, amino acid metabolism, and nucleotide metabolism. The strain harbored three entero-bacteriocins (enterocins) encoded by entA, entB, and entX (enterocin X-alpha and X-beta) genes. Interestingly, the strain harbored the ansB, glsA, and arcA genes encoding L-asparaginase, L-glutaminase, and arginine deiminase, respectively, known for their anticancer activities. E. faecium 1702 harbored the gadB, gadC, and gadR genes implicated in gamma(γ)-aminobutyric acid (GABA) production, which is known for its analgesic, anti-anxiety, hypotensive, diuretic, and antidiabetic effects. The WGS findings and phenotypic traits of E. faecium 1702 revealed significant features that allow for its use as a probiotic or for biotechnological and pharmaceutical applications. Full article

Whole-genome studies in cattle play a key role in exploring both individual and population-level genetic variability. Recently, low-coverage whole-genome sequencing (0.5–2×) has been considered as an alternative to traditional approaches. Low-coverage whole-genome sequencing (lcWGS), which provides uniform coverage of the entire genome at relatively low cost, combined with subsequent imputation, enables the reconstruction of genotypes with high accuracy and density. lcWGS enables detection of rare and functionally important variants and provides exploratory potential for structural variation analysis; however, accurate SV imputation still presents significant challenges. The aim of this review is to analyze the potential and prospects of lcWGS as a tool for genomic selection and genetic studies in cattle. The review systematizes current advances in the application of lcWGS in cattle, focusing on imputation accuracy, factors affecting it, and the comparative efficiency of different software solutions. A literature survey was conducted using PubMed and Google Scholar databases, with preference given to original studies, systematic reviews, and large-scale projects addressing imputation accuracy, reference panel composition and size, cost-effectiveness, and practical applications of lcWGS in cattle genomics. Key factors influencing efficiency include sequencing depth, reference panel size and composition, as well as the choice of imputation algorithm. lcWGS represents a cost-effective and powerful alternative to traditional genome-wide approaches, capable of capturing rare and breed-specific variants; however, its application to structural variation still requires methodological improvement and integration with high-resolution reference pangenomes or long-read sequencing. Despite significant progress and the high potential of lcWGS in cattle genomics, several challenges and limitations remain, requiring further investigation and resolution to fully realize the advantages of this technology. Addressing these challenges will enable more efficient use of lcWGS for genetic research and accelerate genetic progress in cattle breeding. Full article

Personalized cancer vaccines are a key strategy for training the immune system to recognize and respond to tumor-specific antigens. Our earlier software release, AutoEpiCollect 1.0, was designed to accelerate the vaccine design process, but the identification of tumor-specific genetic variants remains a manual process and is highly burdensome. In this study, we introduce AutoEpiCollect 2.0, an improved version with integrated genetic analysis capabilities that automate the identification and prioritization of tumorigenic variants from individual tumor samples. AutoEpiCollect 2.0 connects with RNA sequencing and cross-references the resulting RNAseq data for efficient determination of cancer-specific and prognostic gene variants. Using AutoEpiCollect 2.0, we conducted two case studies to design personalized peptide vaccines for two distinct cancer types: cervical squamous cell carcinoma and breast carcinoma. Case 1 analyzed five cervical tumor samples from different stages, ranging from CIN1 to cervical cancer stage IIB. CIN3 was selected for detailed analysis due to its pre-invasive status and clinical relevance, as it is the earliest stage where patients typically present symptoms. Case 2 examined five breast tumor samples, including HER2-negative, ER-positive, PR-positive, and triple-negative subtypes. In three of these breast samples, the same epitope was identified and was synthesized by identical gene variants. This finding suggests the presence of shared antigenic targets across subtypes. We identified the top MHC class I and class II epitopes for both cancer types. In cervical carcinoma, the most immunogenic epitopes were found in proteins expressed by HSPG2 and MUC5AC. In breast carcinoma, epitopes with the highest potential were derived from proteins expressed by BRCA2 and AHNAK2. These epitopes were further validated through pMHC-TCR modeling analysis. Despite differences in cancer type and tumor subtype, both case studies successfully identified high-potential epitopes suitable for personalized vaccine design. The integration of AutoEpiCollect 2.0 streamlined the variant analysis workflow and reduced the time required to identify key tumor antigens. This study demonstrates the value of automated data integration in genomic analysis for cancer vaccine development. Furthermore, by applying RNAseq in a standardized workflow, the approach enables both patient-specific and population-level vaccine design, based on statistically frequent gene variants observed across tumor datasets. AutoEpiCollect 2.0 is freely available as a website based tool for user to design vaccine. Full article

This study theoretically explores the mechanism of action of Chicoric acid against influenza virus based on network pharmacology, molecular docking, and molecular dynamics simulation techniques, aiming to provide insights for the development of new veterinary drugs for influenza. Potential targets for influenza virus action were identified using the PharmMapper (i.e. Version 2017) server and disease databases including GeneCards and OMIM. The STRING online analysis platform and Cytoscape 3.9.1 software were employed to construct a protein–protein interaction (PPI) network of the target proteins, followed by topological analysis to screen for key targets. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets using the DAVID database. A “drug–target–pathway” network diagram was constructed using Cytoscape 3.9.1 software. Molecular docking was carried out with AutoDock 1.5.6 and PyMOL 2.5 software to identify dominant binding targets, followed by molecular dynamics simulation analysis. The results of network analysis showed that there were 31 potential targets of Chicoric acid; the protein interaction network suggested that UBC, UBA52, RPS27A, HCK, and CDKN1B may be the core targets of Chicoric acid; 55 cell biological processes were obtained by GO enrichment analysis, and 15 related signaling pathways were obtained by KEGG pathway enrichment analysis; molecular docking showed that UBC and UBA52 had a good affinity to Chicoric acid and may be the dominant target of Chicoric acid exerting its effect. Chicoric acid may play a role in antiviral activity by acting on the dominant protein of UBC and UBA52, thus achieving an anti-influenza virus effect. Full article

Reversine is a small-molecule Aurora kinase inhibitor known for its pro-apoptotic effects and potential to remodel chromatin architecture. Although its impact on mitotic regulation is established, its effects on telomere dynamics and nuclear organization in chronic myeloid leukemia (CML) remain unclear. This study aimed to investigate the effects of reversine on telomere architecture, genomic instability, and apoptosis in CML cell lines (K-562 and MEG-01). Reversine was applied at increasing concentrations, and cytotoxicity was assessed using caspase-3/7 activation assays. Quantitative PCR was used to measure AURKA and AURKB mRNA expressions. Three-dimensional telomere architecture was analyzed with TeloView^® v1.03 software after Q-FISH labeling to quantify telomere number, signal intensity, aggregation, nuclear volume, and a/c ratio. Reversine induced a dose- and time-dependent apoptotic response in both cell lines and significantly downregulated AURKA and AURKB expressions. Three-dimensional telomere analysis revealed a marked reduction in telomere number and aggregates, signal intensity, and nuclear volume. While reduced signal intensity may indicate telomere shortening, the concurrent decrease in aggregation and altered spatial parameters suggests telomeric reorganization rather than progressive instability. These features reflect structural nuclear remodeling and early apoptotic commitment. Differences between K-562 and MEG-01 responses underscore potential heterogeneity in telomere maintenance mechanisms. Reversine modulates genomic stability in CML cells through dual mechanisms involving Aurora kinase inhibition and telomere architecture remodeling. The integration of 3D telomere profiling highlights reversine’s potential as a therapeutic agent targeting nuclear disorganization and mitotic dysregulation in leukemia. Full article