Citations

The classification and management of central nervous system (CNS) tumours have undergone substantial transformation over the past two decades, with the recognition of an expanding set of molecularly defined disease entities. Genome-wide DNA methylation profiling has emerged as an important complement to histopathology and targeted molecular testing, providing an epigenetic framework that captures tumour lineage, biological state and large-scale genomic alterations within a single assay. Accumulating evidence demonstrates that methylation-based approaches improve diagnostic precision, resolve biologically heterogeneous entities and support clinically meaningful risk stratification across a broad spectrum of CNS tumours. Methylation profiling has also increasingly been applied to outcome prediction, liquid biopsy-based analyses and the resolution of diagnostically challenging or misclassified cases. In addition, advances in multi-omic integration, computational inference from routinely acquired clinical data and evolving implementation frameworks are extending the clinical applicability of epigenetic profiling. This review synthesises the current state of DNA methylation profiling within the diagnostic and clinical framework of CNS tumours and considers how emerging technologies and practice models are shaping its ongoing integration into neuro-oncological care.

Background Photophobia is experienced by most individuals with migraine, and it is frequently the most bothersome symptom other than headache. Light is not perceived uniformly; spectral composition and intensity influence discomfort and can exacerbate symptoms. While monochromatic green light shows therapeutic potential, it appears visually unnatural and is impractical for daily use. This study aimed to determine whether spectrally tailored white light, more visually acceptable in everyday environments, reduces light sensitivity among those with migraine. Methods Twenty adults (18–44 years) with episodic migraine completed two research sessions: one during an acute migraine attack and one between migraine attacks. Participants were exposed to four spectrally distinct white light conditions (red-, green-, cyan-, and blue-enriched) in a randomized order delivered at ten intensity levels (50–590 lux). After each exposure, visual discomfort was rated on a 0–10 scale. Pupil size was measured using a pupillometer. Repeated-measures ANOVAs examined the effects of light spectrum, intensity, and migraine state on visual discomfort and pupil response. Results Migraine attack status significantly increased visual discomfort ( F (1,19) = 25.84, p < .001, η 2 = .576). White light spectrum ( F (3,57) = 12.47, p < .001, η 2 = .396) and light intensity ( F (9171) = 124.30, p < .001, η 2 = .867) were associated with intensity of visual discomfort. Post-hoc analyses indicated that visual discomfort increased with increasing light intensity, especially at higher light intensities. Among all tested spectra, green-enriched white light resulted in the least visual discomfort; blue-, red-, and cyan-enriched white light produced significantly higher discomfort (all p < .001). Pupil size varied by spectrum ( F (3,57) = 14.13, p < .001, η 2 = .426), with green-enriched white light producing the largest pupil size. Conclusion Spectral composition, light intensity, and migraine state jointly influence both subjective and physiological responses to light. Across lighting spectra, lower light intensity was consistently associated with reduced visual discomfort, indicating greater visual comfort at lower intensities. These results identify green-enriched white light at lower intensities as a visually acceptable, migraine-conscious lighting option with potential for real-world application.

Background and Aims: Liver sinusoidal endothelial cells (LSECs) acquire a proinflammatory phenotype in metabolic dysfunction–associated steatohepatitis (MASH), characterized by enhanced expression of adhesion molecules and inflammatory mediators, a process termed lipotoxic endotheliopathy. However, the molecular drivers of this transformation remain incompletely defined. Approach and Results: We employed complementary models of lipotoxicity and MASH, including palmitate-treated primary human LSECs (in vitro), cultured precision-cut liver slices (PCLS) from MASH mice and normal human liver treated with MASH-inducing media (ex vivo), and diet-induced MASH models (in vivo). GeoMx digital spatial profiling of human MASH livers revealed enriched proinflammatory and fibrogenic signaling between LSECs and myeloid cells. Among the top upregulated genes and ligand–receptor pairs was ICAM1 , whose expression correlated with disease severity and was increased in human MASH by immunostaining. ATAC-seq on primary mouse LSECs showed enhanced chromatin accessibility at the Icam1 promoter in MASH. Chromatin immunoprecipitation confirmed enrichment of the active epigenetic mark H3K27ac and BRD4 binding at the ICAM1 promoter in human LSECs under lipotoxic stress. Mechanistically, we identified the GSK3β/c-Jun/BRD4 axis as a key regulator of ICAM1 induction in LSECs. In vivo, ICAM1-neutralizing antibody or endothelial-specific epigenetic suppression reduced hepatic inflammation, injury, and fibrosis in MASH mice. Conclusions: ICAM1 is epigenetically upregulated in LSECs during lipotoxic stress and promotes myeloid cell recruitment in MASH. Targeting the epigenetic regulation of ICAM1 may offer a novel therapeutic strategy for treating human MASH.

Lay Summary Accurately differentiating actual tumor progression from treatment-related changes, such as pseudoprogression or radiation-induced injury, remains one of the most critical diagnostic challenges in neuro-oncology for both primary and secondary brain tumors. Conventional MRI often lacks specificity because it primarily reflects blood-brain barrier disruption rather than viable tumor tissue. Consequently, advanced techniques like perfusion-weighted MRI and amino acid PET have gained significant attention. While perfusion-weighted MRI provides essential insights into blood flow and is widely available, accumulating evidence suggests that amino acid PET can be more reliable in identifying treatment-related changes. Since both modalities offer complementary information, there is an ongoing debate regarding their relative roles and the optimal way to integrate them into clinical practice. Combining these tools aims to provide more reliable diagnoses, ultimately preventing unnecessary interventions and ensuring that true tumor growth is detected as early as possible.

Abstract Context Patients with mild autonomous cortisol secretion (MACS) have higher rates of vertebral fractures compared to patients with non-functioning adrenal nodules, yet bone density is not always reduced. Objective To characterize the effect of MACS on bone density, metabolism, and microstructure as measured by high-resolution peripheral quantitative computed tomography (HR-pQCT). Design Cross-sectional study, 2019-2022 Setting Tertiary referral center Participants Seventy-five patients with MACS and 75 age-, sex-, and gonadal status–matched referent subjects. Measurements Bone turnover markers, dual-energy x-ray absorptiometry (DXA) scan, and HR-pQCT. Outcomes Areal and volumetric bone mineral density (aBMD, vBMD), trabecular bone score (TBS), microstructural parameters including trabecular bone volume to total volume (BV/TV) ratio. Results While no differences in aBMD were observed at any skeletal site, patients with MACS had decreased TBS (1.389 vs 1.475, P &lt; .001) and lower osteocalcin concentration (17.1 vs 19.5 ng/mL, P = .030) than referent subjects. On HR-pQCT at the tibia, patients with MACS had reduced trabecular BV/TV (0.220 vs 0.233, P = .015) compared to referent subjects. On multivariable analysis, MACS was associated with lower TBS (odds ratio [OR] per SD decrease 2.87 [1.65-5.00]), lower trabecular BV/TV at the radius (OR per SD decrease 2.58 [1.33-5.00]), and lower trabecular BV/TV at the tibia (OR per SD decrease 4.06 [2.10-7.86]). Conclusion Patients with MACS have microstructural deterioration that may not be evident on routine DXA aBMD testing.

Abstract Purpose To report historical patterns of pharmaceutical expenditures, to identify factors that may influence future spending, and to predict growth in drug spending in 2026 in the United States, with a focus on the nonfederal hospital and clinic sectors. Methods Historical patterns were assessed by examining data on drug purchases from manufacturers using the IQVIA National Sales Perspectives database. Factors that may influence drug spending in hospitals and clinics in 2026 were reviewed—including new drug approvals, patent expirations, and potential new policies or legislation. Focused analyses were conducted for biosimilars, cancer drugs, endocrine drugs, generics, specialty drugs, and vaccines. For nonfederal hospitals, clinics, and overall (all sectors), estimates for growth of pharmaceutical expenditures in 2026 were made based on a combination of quantitative analyses and expert opinion. Results In 2025, overall pharmaceutical expenditures in the US grew 12.7% compared to 2024, for a total of $915.2 billion. Utilization (a 9.3% increase) drove this increase, while growth due to new drugs (0.9%) and price increases (1.2%) had a modest impact. Tirzepatide was the new top drug in 2025, followed by semaglutide and apixiban. Drug expenditures were $42.9 billion (a 9.6% increase) and $190.5 billion (a 19.0% increase) in nonfederal hospitals and in clinics, respectively. In clinics, volume drove drug expense growth, contributing with a 13.6% increase, while price and new drugs each contributed with a 2.7% increase. In nonfederal hospitals, expenditure growth was driven by increases in volume (4.6%), new products (3.7%), and prices (1.5%). Several new drugs that will influence spending are expected to be approved in 2026. Specialty, endocrine, and cancer drugs will continue to drive expenditures. Conclusion For 2026, we expect overall prescription drug spending to rise by 10.0% to 12.0%, whereas in clinics and hospitals we anticipate increases of 14.0%-16.0% and 4.0%-6.0%, respectively, compared to 2025. These national estimates of future pharmaceutical expenditure growth may not be representative of any health system because of the myriad of local factors that influence actual spending

Abstract Advances in genomics, informatics, and artificial intelligence (AI) are reshaping preventive medicine by enabling risk-stratified, personalized approaches to disease prevention. This paper argues for a systemic transformation in US health care policy to support equitable access to personalized prevention. We critique the inefficiencies of current age-based screening paradigms and highlight the potential of integrating polygenic risk scores, multi-omic data, and AI-driven analytics to optimize preventive strategies. However, without concurrent innovation in financing models, these advances risk exacerbating existing health disparities. Drawing on lessons from the rare disease community, we propose policy solutions, including expanded, equitably funded health savings accounts, transparent pricing, and patient-centered decision-support tools, to democratize access to personalized prevention. We advocate for a healthcare ecosystem that aligns technological innovation with flexible, patient-directed funding mechanisms, enabling all persons, not just the well-resourced, to benefit from precision prevention. The Human Genome Project promised to unlock the complete genetic blueprint of humans to revolutionize medicine through personalized care, disease prevention, and new biotechnologies. Realizing this goal requires coordinated action across science, policy, and payment systems to ensure that personalized prevention becomes a standard, equitable component of modern health care.