Citations

Abstract Hairy cell leukemia (HCL) and HCL variant (HCLv) are distinct, rare, and chronic splenic B-cell lymphomas/leukemias that partially overlap in clinicopathologic presentation but differ in genetic basis, prognosis, and management. HCL is caused by the BRAF-V600E kinase–activating mutation in >95% of the patients, usually has excellent responses to chemotherapy with purine analogues, and is also amenable to BRAF inhibitor–based targeted treatments. In contrast, HCLv lacks BRAFV600E mutation, requires combined therapy with purine analogues in addition to rituximab, and generally shows less durable responses. Here, an international team of hematologists, experts on these rare diseases, was convened by the Hairy Cell Leukemia Foundation to update the previous guidelines (published in 2017) by providing a summary of current methods to diagnose and manage patients with HCL and HCLv as well as a prospective on newer targeted therapies to further improve outcomes.

Abstract Objectives The study aimed to compare long-term outcomes of the David and Yacoub valve-sparing aortic root replacement (VSARR) procedures in a nationwide, low-volume setting. Methods All patients undergoing elective VSARR in Denmark between January 2010 and April 2022 were identified using the Danish registries. The primary end-point was all-cause death. Secondary end-points included stroke, reoperation, a composite of death or reoperation, and long-term echocardiographic outcomes. Analyses were performed in both crude and propensity score-matched (PSM) populations and compared with outcomes from a control group comprised of all elective composite root replacement procedures performed at the contributing centres in the study period. Results A total of 160 patients underwent VSARR (median age; David vs Yacoub; 49.7 [SD 14.1] years vs 51.5 [SD 14.3] years; P = .42) with the David (n = 92, 57.5%; median follow-up 8.2 [SD 3.6] years) or Yacoub (n = 68, 42.5%; median follow-up 7.1 [SD 2.4] years) procedure. Ten-year all-cause death was similar (David 9.3% [95% CI: 2.3-15.2] vs Yacoub 8.8% [95% CI: 3.6-20.6]; log-rank P = .54). Reoperation rates (log-rank P = .12), stroke incidence (P = .41), and the composite end-point of death or reoperation (log-rank P = .63) were comparable, with consistent findings in the PSM cohort. Long-term echocardiographic follow-up demonstrated no difference in recurrent regurgitation, stenosis, or valve-related complications. Compared to elective composite root replacement (CRR) (n = 486), VSARR demonstrated lower rate of all-cause death (P = .027) and stroke (P = .026), while reoperation rate was higher (13.8% vs 4.3%; P < .001). However, the risk of reoperation was comparable between procedures when accounting for competing risk of death (log-rank P = .17). Conclusions Long-term outcomes following elective VSARR using the David and Yacoub procedure were comparable. VSARR was associated with lower risk of all-cause death and stroke compared to elective CRR, supporting the two VSARR procedures as durable options when performed in experienced centres with careful patient selection, even in low-volume settings.

Abstract Advances in biomarker technology, digital cognitive assessments, and amyloid‐targeting therapies have redefined the opportunities for accurate and early diagnosis and care of Alzheimer's disease (AD). These advances also create new possibilities for intervention before the onset of cognitive impairment. This paradigm shift has increased the focus on Stages 1 and 2 of AD, in which individuals are cognitively unimpaired but exhibit biological evidence of disease. While early identification of AD offers an opportunity to intervene early to delay progression and preserve quality of life, it also presents complex challenges related to communicating diagnostic results to patients and their families, contextualizing the cost effectiveness of early diagnosis and treatment, and implementation of and equitable access to treatments. Recent, successfully enrolled, preclinical AD trials highlight the complex strategies required to identify asymptomatic, biomarker‐positive individuals on a large scale, and demonstrate critical knowledge gaps in inclusion, follow‐up, and long‐term outcome measurement. The Spring 2025 Alzheimer's Association Research Roundtable (AARR) meeting brought together academics, clinicians, industry, and regulatory leaders to exchange perspectives on current challenges, key learnings, and potential strategies for identifying and treating individuals in very early stages of AD, effectively and safely. This paper presents key takeaways from the Spring 2025 AARR meeting. Highlights New criteria for Alzheimer's disease (AD) enable early identification of AD pathology. Blood tests and digital cognitive assessment tools may facilitate early and personalized care. Plasma biomarkers may be scalable in clinical settings but need confirmation or follow‐up assessments in those individuals with equivocal results. Early therapy shows best results in people with lower initial amyloid and tau burden.

Abstract Monoclonal gammopathy of undetermined significance (MGUS) is a necessary precursor condition to multiple myeloma (MM). Given the role of autophagy in modulating MM risk, we investigated whether genetic variation in autophagy‐related genes influences susceptibility to MGUS. We analyzed the association of 34,042 common autophagy‐related single nucleotide polymorphisms (SNPs) with MGUS across six independent cohorts, five from Europe and one from North America, comprising 2317 MGUS cases and 282,358 controls. We also assessed their impact on immune parameters, including absolute counts of 91 blood‐derived immune cell subsets and 103 circulating immunological proteins. Meta‐analysis revealed a genome‐wide significant association between the ULK4 rs6599175C allele and increased MGUS risk ( p = 3.35 × 10 −8 ). Carriers of this allele showed reduced counts of memory B cell subsets (IgM+CD38+CD27+ and IgD+IgM+CD27+; p = .0038 and p = .0056, respectively) and natural effector B cells (CD24+CD38+IgD+IgM+ cells; p = .0060). Although these associations were not statistically significant after multiple testing correction, they suggest a role of ULK4 in early B‐cell differentiation. Additionally, the CDKN2A rs2811710 variant showed a suggestive association with MGUS risk ( p = 2.17 × 10 −4 ), affecting transcription factor binding involved in B‐cell proliferation and differentiation, although it lacked association with immune markers. In conclusion, we confirm a genome‐wide significant association of the ULK4 locus and MGUS risk, supporting its role in early B‐cell differentiation, and identify CDKN2A as a candidate susceptibility locus warranting further investigation.

Dominant-inactivating mutations in the colony stimulating factor-1 receptor ( CSF1R ) cause CSF-1R–related leukoencephalopathy (CRL), an adult-onset neurodegenerative disease that is modeled in the Csf1r +/– mouse. CRL is caused by microglial dysfunction. However, the primary microglial deficit is unknown. To address this question, we employed single-nucleus RNA sequencing of brains from young Csf1r +/– mice without pathological or behavioral alterations. Reduction of CSF-1R signaling caused metal ion accumulation in brain macrophages, with concomitant activation of cell death and stress response pathways in oligodendrocytes and neuronal subpopulations. Reduction of metallothionein 1 ( Mt1 ) and 3 ( Mt3 ) gene expression was a common feature in glial and neuronal cells of Csf1r +/– mice. Overexpression of Mt1 restored metal ion homeostasis, normalized ROS production in microglia, and prevented the development of behavioral deficits, while Mt3 deletion had disease-enhancing effects. These findings demonstrate CSF-1R regulation of metal ion homeostasis via metallothioneins in the brain.

ABSTRACT Brain lesions classically cause contralateral sensorimotor and postural deficits, attributed to the decussation of descending neural pathways. However, recent findings reveal that, beyond neural mechanisms, contralateral effects can also be mediated by the neuroendocrine system via humoral pathways. This raises the possibility that the brain regulates left- and right-sided peripheral processes through hypothalamic neurohormones released into the bloodstream. For such spatially targeted endocrine signaling to occur, hemisphere-specific neural activity must be encoded into side-specific hormonal output—requiring a lateralized organization of hypothalamic neuroendocrine systems. Here, we report molecular asymmetries in the rat hypothalamus that support this mechanism. Transcriptomic analysis revealed asymmetric expression of eleven neurohormonal genes, including Gnrh1 , Cck , and Trh , along with distinct left-right side-specific gene co-expression networks. Chemogenetic stimulation of Arg-vasopressin neurons in vasopressin-hM3Dq-mCherry transgenic rats produced generalized changes in these networks—predominantly in the right hypothalamus—suggesting that hypothalamic neurohormonal circuits function as integrated, lateralized ensembles. Stereological analysis revealed asymmetric coordination of vasopressin neurons within the paraventricular nucleus, with the left rostral region decoupled from the right rostral and caudal subregions. Functionally, gonadotropin-releasing hormone, cholecystokinin-8, and thyrotropin-releasing hormone—administered intracisternally in rats with complete spinal cord transection—elicited side-specific peripheral responses, measured as hindlimb postural asymmetry in a binary left–right output model. These neurohormonal effects were therefore transmitted via the humoral route. These findings suggest that multiple hypothalamic neurohormones and their integrated networks are asymmetrically organized, and that their lateralization may be necessary for hemisphere-specific hormonal regulation of peripheral systems.

ABSTRACT This international, multidisciplinary consensus report represents the first effort to systematically define and characterize fatty pancreas. A key outcome of this endeavor was the recommendation to adopt “fatty pancreas” as the standardized and inclusive term to describe all forms of fat accumulation in the pancreas. This terminological consensus provides a critical foundation for unified reporting and clinical communication. Another major contribution of the report is the consensus on diagnostic imaging findings, which was based on radiological and endoscopic modalities. The proposed criteria aim to enhance consistency in clinical assessment and support the development of standardized research protocols. In addition to establishing terminology and diagnostic frameworks, the report also synthesizes current knowledge across a wide range of relevant domains. These include the etiology and epidemiology of fatty pancreas, as well as its associations with alcohol consumption, smoking, acute and chronic pancreatitis, pancreatic exocrine insufficiency, type 2 diabetes mellitus, and surgical outcomes. The potential links between fatty pancreas and neoplastic conditions such as intraductal papillary mucinous neoplasms and pancreatic cancer are also addressed, alongside the current understanding of its metabolic implications (beta‐cell function and glucose homeostasis) and treatment strategies. Throughout the consensus process, a consistent theme emerged: the limited availability of high‐quality, prospective clinical data. Therefore, many of the recommendations in this report are based on expert consensus rather than strong empirical evidence. As such, the statements require rigorous prospective validation before they can be adopted into routine clinical practice. This underscores a critical need for further research, particularly studies aimed at clarifying causal relationships, validating diagnostic tools, and determining the clinical relevance of fatty pancreas across diverse patient populations. This report serves as both a summary of our current understanding and a roadmap for future investigations, aiming to close existing knowledge gaps and guide evidence‐based clinical practice in this emerging field.

Abstract INTRODUCTION The Centiloid scale is the standard for amyloid (Aβ) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge. METHODS This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalizing implausible longitudinal trajectories during training. The model was trained using data from 2,129 participants (7,149 Aβ positron emission tomography [PET] scans) in the Australian Imaging, Biomarkers and Lifestyle Study of ageing (AIBL)/Alzheimer's Disease Neuroimaging Initiative (ADNI) and validated using 15,807 Aβ PET scans from 10,543 participants across 10 external datasets. RESULTS DeepSUVR increased correlation between tracers, and reduced variability in the Aß‐negatives. It showed significantly stronger association with cognition, visual reads, neuropathology, and increased longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting small treatment related slowing of amyloid accumulation in the A4 study. DISCUSSION DeepSUVR substantially advances Aβ PET quantification, outperforming all standard approaches, which is particularly important for clinical decision making and to detect subtle or early changes in Aβ. Highlights Novel artificial intelligence (AI)‐method that penalizes biologically implausible longitudinal trajectories, enabling the model to learn standardized uptake value ratios (SUVR) correction factors without requiring longitudinal data at inference time. Improves Centiloid consistency across tracers and studies, significantly enhancing cross‐sectional and longitudinal amyloid positron emission tomography (PET) quantification. DeepSUVR‐derived Centiloids show stronger associations with cognition, visual reads, and neuropathology. Longitudinal variability is reduced three‐ to five‐fold, enabling more reliable tracking of amyloid accumulation and better detection of treatment effects. Novel reference and target masks derived from DeepSUVR replicate most of the model's performance, offering a practical alternative for integration into existing pipelines.

Abstract When using real-world data to construct an external comparator arm for a single-arm trial, there may be differences in how and when patients are assessed for disease between trial and real-world settings. Such differences can generate outcome measurement error when comparing time-to-event endpoints and lead to biased findings. Recent methods have been developed to mitigate measurement error bias in real-world endpoints; however, they rely on the existence of a validation sample, ie, data on a set of patients where both the “true” trial-like and “mis-measured” real-world measures are collected. We demonstrate how novel statistical methods can be leveraged as quantitative bias analyses (QBA) to contextualize real-world evidence findings when outcome measurement error is of concern, but validation samples are infeasible to collect. Quantitative bias analyses allow researchers to set plausible ranges for the amount of error when not directly measurable. We highlight how to conduct QBA with two recent methods, cumulative incidence curve correction and survival regression calibration, and illustrate how to generate plausible parameter values through simulation. We provide an illustrative QBA example in a cohort of real-world patients with newly diagnosed multiple myeloma and provide practical guidance to apply QBA for outcome measurement error and interpret results.

Background: Sinonasal undifferentiated carcinoma (SNUC) is an extremely rare, high-grade, and aggressive tumor of the sinonasal tract. Due to the rarity of this malignancy, current treatment guidelines are based on small and often/mainly single-center retrospective datasets. In the absence of a universally accepted standard of care for SNUC, treatment approaches vary across countries and institutions, reflecting real-world clinical practice. The primary aim of this study was to describe real-world treatment and outcomes for patients with confirmed SNUC. Methods: This was an international, multi-center, retrospective, observational cohort study that pooled patients into the largest SNUC dataset to date. Fifteen centers were enrolled to contribute data, including seven from Europe, four from the United States, three from the United Kingdom, and one from Canada. In the absence of a universally accepted standard of care for SNUC, treatment approaches varied across countries and institutions, reflecting real-world clinical practice. Patients included were those with histologically confirmed SNUC who were treated between 1997 and 2021. Results: This study yielded 485 patients treated for SNUC. The median age at diagnosis was 55.6 years (IQR: 44.5–67.6), and 63.7% were male. Most cases presented at advanced stages, with 70.8% as T4a or T4b. Overall survival (OS) outcomes were available for 412 patients, with a median follow-up of 26.0 months. The 5- and 10-year OS were 47.2% (95% CI: 40.8–53.3%) and 39.6% (95% CI: 32.5–46.6%), respectively. Advanced age, dichotomized T-stage (T4a/b vs. T1–3), M-stage, and orbital involvement were significant poor prognostic factors on univariable analysis (p’s < 0.01). On multivariable analysis, orbital involvement (HR: 2.73, 95% CI: 1.42–5.27, p = 0.003) and distance metastasis stage (HR: 3.00, 95% CI: 1.25–7.21, p = 0.014) were both independently associated with worse OS. Conclusions: This observational study presents the largest multi-center cohort analysis of SNUC to date, providing new insights into prognostic factors for a rare cancer treated at global centers of excellence. Orbital involvement and the presence of metastases are candidate independent risk factors associated with poorer OS.