Search Results (452)

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article

Background: Stannous ions and polyphenols are effective substances in preventive dentistry. The present study’s aim was to investigate whether a combination of these substance groups can achieve increased efficacy. Methods: Initial biofilm formation was performed on bovine enamel slabs, carried by 10 subjects intraorally. The subjects rinsed with tannic acid, SnCl₂, SnF₂, a combination (50:50) of tannic acid and SnCl₂, or a combination of tannic acid and SnF₂, with no rinsing in the negative control. Bacterial adherence, glucan formation (8 h, 48 h oral exposition,) and calcium release kinetics were measured (pH 2; 2.3; 3). Statistics were performed with the Kruskal–Wallis test (p < 0.05), Mann–Whitney U test (p < 0.05), and Bonferroni–Holm correction. Results: All rinsing solutions reduced bacterial adherence by more than 50%. Initial bacterial colonization and glucan formation was significantly reduced by SnF₂ and SnCl₂ as well as their combinations with tannic acid. The most significant reductions in calcium release at pH 2; 2.3; and 3 were obtained by SnF₂ and the combination of SnF₂ and tannic acid. At the acidic pH 2.0, SnF₂, SnCl₂, and tannic acid and SnF₂ showed significant protection compared to the control (p ≤ 0.01). TEM micrographs indicated that rinsing with SnF₂ and tannic acid leads to pronounced electron dense, thick pellicle layers. Conclusions: SnCl₂ and SnF₂, as well as their combinations with tannic acid, led to a reduction in initial bacterial colonization and glucan formation, showing an erosion-protective effect. These findings confirm the clinical applicability hitherto suspected by in vitro findings. Full article

Background/Objectives: The BRG1 loss-of-function (LOF) mutation is found in ~10% of non-small cell lung cancer (NSCLC) cases, but its role in lung tumorigenesis is unclear and so it is investigated in this study. Methods: BRG1-knockout (KO) lines were generated from various non-malignant, pre-malignant, and malignant human lung epithelium-derived cell lines using CRISPR. The effects of BRG1-KO on cell growth, the transcriptome, the methylome, and epigenetic therapy were compared with those of wild-type (BRG1-WT) isogenic controls using standard in vitro and in vivo assays. Results: The BRG1 protein was expressed in all non-/pre-malignant lung epithelial cells but lost in 47% (14/30) of NSCLC cell lines. BRG1-KO and cigarette smoke (CS) exposure individually transformed human bronchial epithelial cell lines (HBECs), as evidenced by anchorage-independent growth. BRG1-KO and CS produced additive to synergistic effects on sensitivity to transformation that differed across HBECs. RNA-seq analysis revealed that BRG1-KO significantly changed the expression of over 8500 genes on average, impacting lung development, function, damage repair, and cancer pathways, including axonal guidance, pulmonary wound healing, and epithelial-to-mesenchymal transition (EMT). BRG1-KO also led to the hypermethylation of >47,000 promoter CpGs within ~9500 genes on average in different HBECs, including silencing of epithelial genes involved in EMT and tumor suppressor genes. BRG1-KO also moderately increased the in vitro and in vivo sensitivity of NSCLC cells to some epigenetic drugs. Conclusions: BRG1-LOF is frequent in NSCLC; can drive the transformation of lung epithelial cells such that they acquire properties of pre-malignant cells, indicating a potential role in lung cancer initiation; and sensitizes lung tumors to epigenetic therapy. Full article

Background/Objectives: Carotenoids in citrus are vital nutritional compounds and precursors of the stress hormone abscisic acid (ABA). SNF1-related kinases (SnRKs)—key regulators of plant stress signaling that phosphorylate is targeting proteins for post-transcriptional regulation—mediate ABA signaling through its subfamily SnRK2-phosphatase type-2C (PP2C)-PYR1-LIKE (PYL) cascades. This study aims to identify the SnRK-PP2C-PYL family members and decipher their underlying post-transcriptional regulatory mechanisms which control carotenoid metabolism in Citrus sinensis for improved nutrition and stress resilience. Methods: SnRK, PP2C, and PYL were identified by integrated HMMER-blastp-CDD pipeline in the Citrus genome. Using two carotenoid-divergent cultivars, ‘Newhall’ (yellow) and ‘Cara Cara’ (red, hyperaccumulating linear carotenoids), we conducted spatiotemporal expression profiling and integrated transcriptomic and metabolomic data via Weighted Gene Co-expression Network Analysis (WGCNA) to identify modules correlated with accumulation. Results: We identified 26 CsSnRKs (1 SnRK1, 7 SnRK2, 18 SnRK3), 57 CsPP2Cs, and 7 CsPYLs in Citrus sinensis. Despite a >26-fold difference in linear carotenoids, structural gene expression was similar among cultivars, strongly implicating post-transcriptional control. WGCNA identified a key turquoise module highly correlated with linear carotenoid content. This module contained phosphorylation-related genes (CsSnRK1/3.5/3.6/3.16, CsPP2C14/15/33/35/38/40/43/56, and CsPYL6), biosynthetic genes (CsPSY1, CsZISO, and CsZDS), and candidate transcription factors. Network analysis predicted that CsSnRKs, CsPP2Cs, and CsPYLs regulate phytoene-derived carotenoid biosynthesis. Conclusions: We propose a novel phosphorylation-mediated post-transcriptional regulatory network in carotenoid accumulation. This mechanism bridges ABA signaling and metabolic adaptation, providing crucial molecular targets for engineering nutrient-dense and climate-resilient citrus varieties. Full article

Long noncoding RNAs (lncRNAs) have emerged as key regulators of gene expression during seed development and physiology. This review examines the diverse roles of lncRNAs in key stages of seed development, including embryogenesis, maturation, dormancy, germination, and aging. It integrates the current understanding of the biogenesis and classification of lncRNAs, emphasizing their functional mechanisms in seeds, particularly those acting in cis and trans. These mechanisms include the scaffolding of polycomb and SWI/SNF chromatin remodeling complexes, the guidance of RNA-directed DNA methylation, the ability to function as molecular decoys, and the modulation of small RNA pathways via competitive endogenous RNA activity. This review highlights the regulatory influence of lncRNAs on abscisic acid (ABA) and gibberellin (GA) signaling pathways, as well as light-responsive circuits that control dormancy and embryonic root formation. Endosperm imprinting processes that link parental origin to seed size and storage are also discussed. Emerging evidence for epitranscriptomic modifications, such as m6A methylation, and the formation of LncRNA–RNA-binding protein condensates that maintain resting states and coordinate reserve biosynthesis are also reviewed. Advances in methodologies, including single-cell and spatial transcriptomics, nascent transcription, direct RNA sequencing, and RNA–chromatin interaction mapping, are expanding the comprehensive lncRNA landscape during seed development and germination. These advances facilitate functional annotation. Finally, possible translational research applications are explored, with a focus on developing lncRNA-based biomarkers for seed vigor and longevity. Full article

Monochromatic mode-filtering-based scattering suppression techniques have been shown to be applicable to all commonly used forms of far- and near-field antenna and RCS measurement techniques. Traditionally, the frequency-domain mode-filtering technique takes a far-field pattern, either measured directly or obtained using a suitable near-field to far-field transformation, as its starting point. The measurement is required to be conducted such that the antenna under test (AUT) is positioned offset from the origin of the measurement coordinate system. This physical offset introduces a phase taper across the AUT pattern and results in far greater interference occurring between the direct and indirect parasitically coupled spurious scattered signals. The method is very general and can be applied to all forms of near- or far-field measurements. However, for the case of a spherical near-field measurement (SNF) approach, it is somewhat cumbersome and tedious as first we must perform a probe-corrected spherical near-field to far-field transformation, which itself involves the computation of a complete set of spherical mode coefficients, and then after the displacement has been applied to the far-electric-fields, a second spherical wave expansion and summation is required to implement the mode-filtering procedure. While this data processing chain has been widely deployed and exhaustively validated, it requires passing through the asymptotic far-field, which inevitably results in additional computational effort, as well as incurring some loss of information, which can impose limitations on further near-field applications. This paper introduces an alternative, novel, rigorous algorithm that applies the displacement of the AUT directly using the vector addition theorem for spherical waves. An efficient implementation has been developed, and it is shown that the new, rigorous algorithm for the translation and filtering can be easily implemented directly within the data processing chain of any standard spherical near-field transformation algorithm, avoiding the need to first transform to the asymptotic far-field and also removing the need for a secondary spherical mode expansion and secondary spherical mode summation. While the vector addition theorem required for the spherical near-field to far-field transformation (SNFFFT) algorithm has been described in detail in the open literature, its implementation has been limited to the case of impinging waves and positive z-directed translations where the magnitude of the displacement is necessarily larger than the minimum sphere radius (MRE). In the current paper, the addition theorem will be derived in a new form that allows the translation to be applied in any desired direction, without the need for additional rotations, as well as being valid for solutions for waves transitioning through the sphere and applicable for the case where the magnitude of the translation is smaller or larger than the radius of the minimum sphere. Full article

Yaks (Bos grunniens) are a predominant livestock species on the Tibetan Plateau, known for their adaptability to the cold and dry climate typical of this region. This study investigates the association of two SNPs within the ATF6 gene (Chr3:9812652G>T (CM016692.1) and Chr3:9900243C>T (CM016692.1)) with key milk-quality traits in yaks. Due to the low frequency of TT homozygotes (<5%), analysis focused on major genotypes: GG vs. GT and CC vs. CT. Results from the general linear models revealed that the g.3_9812652G>T variant was significantly associated with increased levels of casein, protein, acidity, and solid-not-fat (SNF) in GT individuals (p < 0.01). No significant differences were observed for lactose, urea, citric acid, or fat. For g.3_9900243C>T, CT individuals showed higher casein, protein, SNF, and citric acid levels compared to CC (p < 0.05). These results suggest both SNPs are linked to key milk traits, especially protein, casein, and SNF. The g.3_9812652G>T variant had a stronger and more consistent effect, indicating it may play a larger role in milk composition regulation. Overall, ATF6 is a promising candidate gene for marker-assisted selection (MAS) to improve milk quality in yaks. Further studies in larger and more diverse populations are needed to confirm these findings and explore the gene’s functional role. Full article

Background/Objectives: DGAT1 p. K232A (rs109234250) is a well-established causal variant influencing milk fat and protein content in dairy cattle, but it is often absent from commercial genotyping arrays. PPP1R16A rs109146371 frequently appears as a top signal in genome-wide association studies (GWAS) for milk traits. This study aimed to evaluate the linkage disequilibrium (LD) between these two variants in Japanese Holsteins and assess whether rs109146371 exerts an independent effect on milk traits. Methods: A total of 256 Japanese Holstein cows were genotyped for DGAT1 p. K232A and PPP1R16A rs109146371 using TaqMan SNP assays. LD statistics (r², D′) were computed, and linear mixed-effects models were used to evaluate associations with 305-day milk yield, fat percentage, protein percentage, and solids-not-fat (SNF) percentage. Likelihood ratio tests were conducted to assess the independence of SNP effects. Results: Strong LD was observed between DGAT1 p. K232A and PPP1R16A rs109146371 (r² = 0.91, D′ = 0.9962). Both SNPs showed significant associations with all milk production traits; however, model comparisons indicated that rs109146371 did not improve model fit when K232A was included, suggesting no independent effect. Conclusions: PPP1R16A rs109146371 serves as a proxy for DGAT1 K232A rather than an independent determinant of milk traits. Full article

This study investigated the effects of lactation period, foaling month and number, mare age, and regional factors on the quality parameters, amino acid composition, fatty acid profile, and nutritional indices of Kazakh mare’s milk under pasture conditions. A total of 240 milk samples were collected from Almaty and Zhambyl regions during the summer and autumn lactation periods. Standard physicochemical analyses determined fat, protein, casein, TS, and SNF contents, while amino acids were quantified via HPLC and fatty acids by GC. Significant seasonal differences were observed: summer milk contained higher PUFA (18.29%) and n-3 (5.71%) levels and exhibited lower SFA and AI values, indicating superior nutritional quality. Milk from younger mares (4 to 6 years) showed elevated essential amino acids and better lipid health indices compared to older mares. Zhambyl region samples had higher unsaturated fatty acids and SNF, while Almaty milk exhibited higher SFA and casein content. Amino acid profiling revealed that summer milk was enriched in glutamic acid, aspartic acid, serine, and histidine, whereas autumn milk contained more valine, leucine, methionine, and cysteine. PCA revealed distinct clustering based on season, mare age, and foaling period, confirming their substantial roles in shaping milk composition. These findings highlight that mare age, lactation period, and foaling timing significantly affect the nutritional quality of the mare’s milk. These results provide valuable insights for optimizing milk production and kumys fermentation strategies under traditional pasture-based systems. Full article

Background/Objectives: SMARCA4-deficient or SMARCA4-mutated cancers are rare but highly aggressive tumors with poor differentiation, resistance to conventional treatments, and limited clinical guidance. While thoracic SMARCA4-deficient undifferentiated tumors are relatively well described, the full spectrum of SMARCA4-altered cancers across different organs and their therapeutic responses remains poorly understood. This study aimed to systematically review published case reports and case series to clarify the clinical characteristics, molecular features, treatment patterns, and survival outcomes of SMARCA4-altered malignancies. Methods: We conducted a systematic review of case reports and case series published between 2015 and 2025 using PubMed, Embase, and Web of Science. Eligible studies included adult patients with immunohistochemically or genetically confirmed SMARCA4-deficient or SMARCA4-mutated tumors. Key clinical, pathological, molecular, therapeutic, and outcome-related data were extracted. Descriptive statistics were used, and exploratory subgroup analyses were performed based on tumor type and treatment modality. The review protocol was registered in PROSPERO (CRD420251088805). Results: A total of 109 studies reporting 160 individual patients were included. Most tumors arose in the thorax (40.0%), followed by gastrointestinal (17.5%) and gynecologic sites (15.6%). The median age was 58 years, with a male predominance (70.0%) and frequent smoking history (44.4%). Platinum-based chemotherapy was administered in 62.5% of cases, and immune checkpoint inhibitors (ICIs) were used in 25.6%. Among ICI-treated patients, partial responses or stable disease were observed in 80.5%. The median progression-free survival (PFS) was 4.0 months, and the median overall survival (OS) was 5.0 months. Conclusions: SMARCA4-altered cancers are clinically and molecularly diverse but uniformly aggressive, with limited therapeutic benefit from conventional chemotherapy. Immune checkpoint inhibitors may offer improved outcomes in select patients, particularly those with thoracic tumors. Early molecular profiling, rare tumor registries, and biomarker-driven trials are crucial for guiding future treatment strategies. Full article

Older adults, aged 65 years and older, develop and experience frailty at different rates. Yet, this heterogeneity is not well understood, nor are the factors, such as geographical residence, that influence different frailty trajectories and subsequent healthcare outcomes. We aim to identify factors that impact older adult frailty trajectories, skilled nursing facility (SNF) placement, and death. Medicare beneficiaries ≥ 65 years from the National Health and Aging Trend Study (2011–2021) with complete data using Fried’s frailty phenotype on ≥ 2 occasions (n = 6082) were included in the analysis. Rural/urban residence was defined using Office of Management and Budget criteria. Latent class growth analysis (LCGA) helped identify four frailty trajectories: improving, stable, mildly worsening, and drastically worsening. Cox proportional hazard analysis and logistic regression determined the association of social determinants of health (sex, race/ethnicity, education and income level, healthcare and transportation access, and social support) on death and SNF admission, respectively. The mean age was 75.12 years (SE 0.10); 56.4% female, 18.6% (n = 1133) rural residence. In the overall sample, 1094 (23.0%) older adults were classified as robust, 3242 (53.0%) as pre-frail, and 1746 (24.0%) as frail. Urban residence did not modify the relationship between frailty trajectories and SNF placement, nor did geographic residence on death. Higher income was associated with lower odds of a worse frailty trajectory, SNF admission, and a lower hazard of death, all reaching statistical significance. Future work should examine the factors that influence older adult participation in research and the impact of standardizing the definition of geographic rurality on older adult frailty and health outcomes. Full article

Background: Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Patient disposition following TBI has been shown to interact with factors such as age, sex, and injury severity to impact clinical outcomes. Discharge home is associated with better functional outcomes and lower mortality, while discharge to rehabilitation or long-term care facilities is linked to greater injury severity, older age, and higher comorbidity burden. The aim of this study was to further correlate clinical outcomes with discharge dispositions in patients with severe TBI. Methods: This is a retrospective study (2020–2023) of dispositions in patients with severe TBI with AIS (head) ≥ 3. We investigated the relationship between patient disposition and a range of clinical variables, using both parametric (ANOVA) and non-parametric (Kruskal–Wallis, Wilcoxon, Van der Waerden, Savage, Kolmogorov–Smirnov, and Cramer–von Mises) statistical tests. Variables significant in univariate analysis were entered into a multinomial logistic regression model, with discharge home as the reference group. Results: In a cohort of 824 patients, 25.1% were female (n = 207) and 74.9% were male (n = 617). The mean age was 64.1 years for females and 48.9 years for males. Those admitted for severe TBI were included in our analysis. Most patients were discharged home (52.8%), followed by death (12.4%), inpatient rehab (5.1%), and home with services (5.6%). Significant associations were found between disposition and sex, with both males and females most likely to be discharged home (p = 0.0174), as well as between disposition and injury type (p = 0.0186). Disposition was significantly associated with most major clinical variables: hospital length of stay (HLOS), vent days, Glasgow Coma Scale (GCS), and Injury Severity Score (ISS), with p-values < 0.0001 for ANOVA and non-parametric tests. Longer HLOS and ICULOS were associated with discharge to skilled nursing facilities (SNF) most frequently. Days on mechanical ventilation correlated most strongly with discharge to SNF. Lower GCS scores and higher AIS and ISS scores were linked to death or brain death. Prolonged EDLOS was predominantly associated with psychiatric admissions. Higher levels of ETOH were associated with discharge to police custody, followed by homelessness. Conclusions: Our study supports existing evidence that discharge disposition following severe TBI is influenced by several factors, such as injury severity, age, sex, and clinical variables, such as length of stay and ventilator days. Full article

This study synthesizes and comparatively investigates two squaric acid-based phthalocyanine-like dyes, SNF and its long-chain alkylated derivative LNF, to systematically elucidate the influence of peripheral hydrophobic groups on their third-order nonlinear optical (NLO) properties. The NLO characteristics were comprehensively characterized using femtosecond Z-scan and I-scan techniques at both 800 nm and 900 nm. Both dyes exhibited strong saturable absorption (SA), confirming their potential as saturable absorbers. Critically, the comparative analysis revealed that SNF exhibits a significantly greater nonlinear absorption coefficient (β) compared to LNF under identical conditions. For instance, at 800 nm, the β of SNF was approximately 3–5 times larger than that of LNF. This result conclusively demonstrates that the introduction of long hydrophobic alkyl chains attenuates the NLO response. Furthermore, I-scan measurements revealed excellent SA performance, with high modulation depths (e.g., LNF: 43.0% at 900 nm) and low saturation intensities. This work not only clarifies the structure–property relationship in these D-A-D dyes but also presents a clear strategy for modulating the NLO properties of organic chromophores for applications in near-infrared pulsed lasers. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Bone metastasis remains a significant cause of morbidity and diminished quality of life in patients with advanced breast, prostate, and lung cancers. Emerging research highlights the pivotal role of reversible epigenetic alterations, including DNA methylation, histone modifications, chromatin remodeling complex dysregulation, and non-coding RNA networks, in orchestrating each phase of skeletal colonization. Site-specific promoter hypermethylation of tumor suppressor genes such as HIN-1 and RASSF1A, alongside global DNA hypomethylation that activates metastasis-associated genes, contributes to cancer cell plasticity and facilitates epithelial-to-mesenchymal transition (EMT). Key histone modifiers, including KLF5, EZH2, and the demethylases KDM4/6, regulate osteoclastogenic signaling pathways and the transition between metastatic dormancy and reactivation. Simultaneously, SWI/SNF chromatin remodelers such as BRG1 and BRM reconfigure enhancer–promoter interactions that promote bone tropism. Non-coding RNAs, including miRNAs, lncRNAs, and circRNAs (e.g., miR-34a, NORAD, circIKBKB), circulate via exosomes to modulate the RANKL/OPG axis, thereby conditioning the bone microenvironment and fostering the formation of a pre-metastatic niche. These mechanistic insights have accelerated the development of epigenetic therapies. DNA methyltransferase inhibitors (e.g., decitabine, guadecitabine) have shown promise in attenuating osteoclast differentiation, while histone deacetylase inhibitors display context-dependent effects on tumor progression and bone remodeling. Inhibitors targeting EZH2, BET proteins, and KDM1A are now advancing through early-phase clinical trials, often in combination with bisphosphonates or immune checkpoint inhibitors. Moreover, novel approaches such as CRISPR/dCas9-based epigenome editing and RNA-targeted therapies offer locus-specific reprogramming potential. Together, these advances position epigenetic modulation as a promising axis in precision oncology aimed at interrupting the pathological crosstalk between tumor cells and the bone microenvironment. This review synthesizes current mechanistic understanding, evaluates the therapeutic landscape, and outlines the translational challenges ahead in leveraging epigenetic science to prevent and treat bone metastases. Full article