Citations

ABSTRACT Condition Overview Clonal hematopoiesis (CH) refers to the presence of somatic variants in hematopoietic stem and progenitor cells (HSPC) that result in expansion over time. Diagnosis CH of indeterminate potential (CHIP) is operationally defined as pathogenic variants in oncogenic driver genes occurring in HSPCs at variant allele frequencies ≥ 2%. Clinical Associations CH is associated with increased risk for progressive cytopenias (also called clonal cytopenia of undetermined significance), hematological (predominantly myeloid but also lymphoid) neoplasms, cytosis (including monocytosis), and nonhematological conditions such as atherosclerotic cardiovascular and cerebrovascular disease. CH is linked to numerous other diseases including venous thromboembolism, type 2 diabetes mellitus, chronic obstructive pulmonary disease, osteoporosis, and gout, with a potential protective impact in Alzheimer's disease (AD). Management Recommendations CH detection is becoming increasingly common due to the ubiquitous use of somatic and germline sequencing in clinical practice, particularly, in oncology. The clinical implications of CH are most relevant in therapy‐related myeloid neoplasms (t‐MN), with antecedent CH clones in genes such as TP53, PPM1D, and/or CHEK2 having a clear selection advantage. Furthermore, genetic predisposition to CH has provided some clarity on the origin and evolution of CH. We are currently defining the role for CH assessment in individuals with persistent (≥ 4 months) unexplained cytopenias, in patients with malignancies prior to adjuvant cytotoxic chemotherapy and/or radiation or radionuclide therapy, screening prior to autologous hematopoietic stem cell transplantation or chimeric antigen receptor T cell (CAR‐T) therapy, and to work‐up potentially germline mosaic variants.

Abstract Mitochondrial creatine kinase (MtCK) is a key enzyme in energy buffering and homeostasis in cells. It catalyzes transfer of phosphoryl group from ATP to creatine. Overexpression of MtCK occurs in many cancer cells to meet elevated energy demands, which is associated with poor prognosis. This suggests that MtCK may be a promising target for cancer therapeutics. We sought to discover first-in-class selective inhibitors of MtCK with diverse mechanisms of action using high-throughput screening, biochemical characterization and cryo-EM studies. Through these studies, we identified diverse types of compounds that modulate activity of MtCK in vitro , including fast-equilibrium and time-dependent orthosteric and allosteric inhibitors. Select hits were subjected to in vitro enzymatic and binding assays to assess MtCK inhibition and binding. A subset of inhibitors was advanced into structural studies using cryo-EM resulting in the molecular structure of MtCK with and without bound substrates in complex with an allosteric uncompetitive inhibitor that we discovered. These studies identified compound’s unique binding pocket on MtCK and established the molecular steps manifesting in the apparent uncompetitive mode of inhibition. We demonstrate that the compound stabilizes an active site loop in a closed conformation restricting access of the creatine substrate to and the release of phosphocreatine product from its binding pocket. These findings establish chemical tools suitable for validation of MtCK as a promising breast cancer target with high therapeutic potential and build a foundation for future structure-guided optimization of the hit compounds of MtCK we identified and de novo rational design of novel MtCK inhibitors. Abstract Figure

Abstract Background Tau‐PET tracers are essential for visualizing pathology in Alzheimer's (AD). High detectability to tau is crucial for early detection and monitoring of tau deposition. This study compares the noise to dynamic range ratio (NRR) of [ 18 F]FTP, [ 18 F]MK6240, [ 18 F]PI2620, and [ 18 F]RO948, cross‐sectionally and longitudinally. Methods 460 individuals from the HEAD study (23 cognitively unimpaired (CU) young, 249 CU old and 188 cognitively impaired) underwent [ 18 F]FTP and [ 18 F]MK6240 tau‐PET scans; 94 additionally received [ 18 F]PI2620 and [ 18 F]RO948. A subset of 28 individuals (15 CU and 13 CI) underwent [ 18 F]FTP and [ 18 F]MK6240 follow‐up scans (1.5 ± 0.1 years later). Annual change was measured as [(followup‐baseline)/time between scans]. Noise was calculated as the standard deviation (SD) of CU Aβ‐ participants aged ≤65 (SD CUAβ‐≤65 ). The dynamic range was calculated as the SD across all subjects (SD range ). A lower the NRR (=SD CUAβ‐≤65 /SD range ) indicates more detectability. Analyses were performed at both region‐of‐interest and voxel‐wise levels. Results Across the whole cohort, [ 18 F]MK6240 exhibited lower NRR than [ 18 F]FTP in all regions. Voxel‐wise, differences were most pronounced in the frontal medial temporal regions (Figure 1). Within the four‐tracers subset, [ 18 F]PI2620 and [ 18 F]FTP showed the highest NRR in Braak II, followed by [ 18 F]RO948 and [ 18 F]MK6240. In Braak IV‐VI, [ 18 F]MK6240 consistently demonstrated the lowest values, followed by [ 18 F]PI2620, [ 18 F]FTP and [ 18 F]RO948. Similarly, in metatemporal‐ROI and Braak III, [ 18 F]MK6240 remained the lowest, however followed by [ 18 F]FTP, [ 18 F]PI2620 and [ 18 F]RO948 (Figure 2). Lower [ 18 F]MK6240 NRR was due to larger SD range , while [ 18 F]FTP's lower values stemmed from smaller SD CUAβ‐≤65 . Conversely, high [ 18 F]PI2620 and [ 18 F]RO948 values are driven by larger SD CUAβ‐≤65 . Longitudinal analyses further confirmed that [ 18 F]MK6240 exhibited the lowest NRR across the entire brain, including AD‐related regions, with voxel‐wise differences mainly in the temporal and parietal lobes. Conclusion Tau‐PET tracers exhibit significant variability in detectability. [ 18 F]MK6240 consistently demonstrated lower NRR, implying better detectability across all regions, cross‐sectionally and longitudinally. Despite differences in NRR, [ 18 F]FTP, [ 18 F]RO948 and [ 18 F]PI2620 followed similar pattern. These results provide insights into the differential tracer detectability, helping guide their optimal use in detecting and tracking tau pathology. Ongoing follow‐up scans will further clarify longitudinal tracer detectability and its implications for tau progression in AD.

Abstract Background Neuroimaging data typically arrives as Digital Imaging and Communications in Medicine (DICOM) files, batched by exam. We use exam or study to denote a single imaging session of a participant, containing up to ∼40 series (the generic term for scans and derived products). Different series have different properties and applications, making their labeling complex. Filenames often change in transit, and Series Description tags (0008, 103E) are arbitrary, operator‐entered, and unreliably coupled to the series type. Furthermore, label preferences vary from site to site, making the application of a common classification system mandatory for multisite studies. Here we describe the system that we have developed and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) and SCAN. Method Although Series Description is unreliable, DICOM includes other tags that parameterize MR pulse sequences and suggest their purpose. The mapping from these features to types is difficult to explicitly specify, especially since different manufacturers and protocols use different conventions. Thus we developed a machine learning classifier using 4060 semiautomatically labeled head and spinal series from 1.5 to 7T MR scanners, including both site‐specific and standard protocols designed to study a wide range of neurological conditions, including dementia, hydrocephalus, and multiple sclerosis. An 80%/20% split, stratified by type, was used for the training/test sets. After initial training, a few cases in the training set with high errors due to rarity were augmented and the model retrained. Performance was evaluated on the clean test set. Result The taxonomy is presented in Table 1. The classifier correctly identified 807 out of 812 (99.4%) series, with the error cases coming from outside the ADNI or SCAN protocols (Table 2). The confusion matrix is shown in Figure 1. Conclusion We implemented an automatic series type classifier with labels chosen to be generally useful for the brain MRI community. Some errors were noted, but the test set was deliberately challenging and the errors appear to be ones that could easily be made by a human. The classifier is expected to have good tolerance for as yet unseen head MR protocols but is likely unsatisfactory for body MRI.

Abstract Background Decreased kidney function, indicated by a lower estimated glomerular filtration rate (eGFR), may affect interpretation of Alzheimer's disease (AD) blood biomarkers, including phosphorylated tau ( p ‐tau) and amyloid‐beta (Aβ) peptides, due to altered clearance. However, there is limited data available to determine the influence of eGFR on these biomarkers and whether it differs by biomarker or assay type, especially in racially diverse studies of community‐based older adults. Methods We used data from 557 participants in the Mayo Clinic Study of Aging (Rochester, MN n = 182; age 79±8 years; 36.3% Female, White participants) and the UMMC MIND Center‐Mayo Clinic Study of Aging (Jackson, MS n = 375; age 69±8 years; 73% Female 27% Black participants). eGFR was determined using the 2021 CKD‐EPI Creatinine equation. Plasma biomarkers were measured using the Lumipulse immunoassays for p ‐tau217, Aβ42, and Aβ40 (Fujirebio Diagnostics; n = 420); or the mass spectrometry‐based PrecivityAD2 assay (C2N Diagnostics; n = 253) for p ‐tau217, np‐tau217, Aβ42, and Aβ40. Common ratio values were calculated. Aβ42/40 measures were inverted so higher values are worse for all biomarkers. Log base‐2 transformed regression analysis related eGFR to each single analyte and ratio; coefficients (95% CI) reflect estimated doubling in biomarker per doubling in eGFR. Group specific [MN White ( n = 182), MS White ( n = 224), MS Black ( n = 151) participants] estimates are reported, as the distribution of social determinants of health can vary across populations and locations. Results Lower eGFR was associated with higher (worse) AD‐plasma biomarkers, and group stratified estimates were mostly consistent (Figure 1). Ratios of biomarkers diminished but did not entirely alleviate associations with eGFR. For example, MN White participants’ C2N p ‐tau217 association of ‐1.47 (‐1.94, ‐1.00), was attenuated to ‐0.67 (‐1.11, ‐0.22), when using p ‐tau217/np‐tau217. Conclusions Kidney function may be associated with AD‐plasma biomarker values, even with common ratio corrections. There was not strong evidence for highly differential associations of eGFR by race/site categories, though sample sizes are currently limited. The implications for diagnosis and risk classification need to be explored further to support health equity. Optimal consideration of comorbidities on blood biomarker concentrations must continue to be informed by investigation in large, racially and ethnically diverse, community‐based studies.

Abstract Background Toxicities to organs at risk adjacent to the target volume represent a critical limitation to dose escalation in radiotherapy. While external beam therapy with accelerated ions allows for a reduced dose to surrounding healthy organs, its biological effectiveness can be markedly higher than that of conventional x‐rays and should be considered during treatment planning. Current biophysical models possess limitations in computing in vivo normal tissue complication probability (NTCP) curves for clinically relevant ion beams. Purpose This study introduces and validates a novel computational framework capable of predicting NTCP curves for serial organs following ion exposures. Methods The proposed framework integrates the linear‐quadratic model, the critical volume NTCP model, a novel two‐stage fitting process of fractionated photon NTCP data, histological data, and the Mayo Clinic Florida Microdosimetric Kinetic Model (MCF MKM) of relative biological effectiveness. The model was applied to predict the NTCP for the spinal cord myelopathy of rats exposed at different depths along the spread‐out Bragg peaks (SOBPs) of protons, helium, carbon, and oxygen ions. Radiation transport simulations were conducted to assess the microdosimetric pattern of energy deposition at the subcellular scale. Results The two‐stage photon fitting process yielded a α photons /β photons ratio of 2.3 ± 0.6 Gy and NTCP model parameters for calculations considering either endothelial or glial cells as the primary targets for the spinal cord myelopathy. Without using any ion data for calibration, the predicted NTCP showed an overall good agreement with the corresponding animal data. The model successfully reproduced the experimentally observed trends in the NTCP dependence with respect to fractionation, ion type, and exposure position along the SOBPs. Conclusions The model's ability to predict the NTCP curves for ion exposures, based solely on photon in vivo data, provides mechanistic insights and can guide preclinical investigations into the biological effects of different ions and fractionation schemes.

ABSTRACT Musculoskeletal infection (MSKI) remains a major problem after trauma and elective orthopedic surgery. Chronic MSKI is related to the formation of biofilm, which impairs diagnosis and effective treatments. Therefore, to understand and communicate global standards and best practices, the 2025 International Consensus Meeting (ICM) on MSKI created a Biofilm Section to address crucial aspects of biofilm biology pertaining to its mechanisms of drug resistance and immune evasion, and potential approaches to overcome them. This featured a 2‐year process, with final voting and discussion on May 8–10, 2025, in Istanbul, Turkey. This Consensus Article is the effort of the Biofilm Basic Mechanisms Workgroup, which interpreted the results on ICM questions related to (1) the infectious microenvironment; (2) appropriate inocula in preclinical research; (3) biofilm behavior in infected tissues; and (4) synergy within biofilms and with other comorbidities. Collectively, we find that this field has the necessary research tools to discover the pathophysiology of orthopedic implant‐associated biofilm development and maturation, perform clinically relevant studies in animal models, and elucidate mechanisms that allow opportunistic infections in compromised tissues and patients with other health issues.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background Plasma biomarkers of Alzheimer's disease (AD) pathology and neurodegeneration provide a non‐invasive method to assess brain pathology associated with dementia and cognitive decline. However, it remains unclear whether these biomarkers are differentially associated with changes in specific cognitive domains or how they relate to subsequent cognitive change in participants with or without cognitive impairment. Methods Plasma biomarkers were assayed in a subset of ARIC participants using Quanterix SiMoA technology on stored specimens collected in midlife (visit 3: 1993‐95, mean age: 59) and late‐life (visit 5: 2011‐13, mean age: 77). Biomarkers included amyloid‐β 42 to amyloid‐β 40 (Aβ42:Aβ40) ratio, phosphorylated tau at threonine 181 ( p ‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Factor scores for global cognition and cognitive domains (memory, language, executive function) were calculated from ten neuropsychological tests administered at visits 5 (2011‐13), 6 (2016‐17), 7 (2018‐19), 8 (2020), and 9 (2021‐22). Linear mixed effects models with multiple imputation of pre‐death cognitive change estimated the association between biomarkers in midlife and late‐life, and biomarker change from midlife to late‐life, with 5‐year change in late‐life global and domain‐specific cognition, adjusting for demographics, lifestyle and cardiovascular risk factors, and APOE‐ε4 status. Exploratory analyses examined effect modification of the biomarker‐global cognition associations by visit 5 adjudicated cognitive impairment. Results Among 1515 participants with plasma biomarkers (60.7% female, 25.5% identified as Black), 39% had late‐life cognitive impairment (Table 1). Higher midlife NfL and GFAP were associated with faster 5‐year declines in global cognition and language during late‐life (Table 2). Only midlife NfL was associated with faster late‐life memory decline. No midlife biomarkers were associated with executive function. In late‐life, lower Aβ42:Aβ40 and higher p ‐Tau 181, NfL, and GFAP were associated with faster declines in global cognition, memory, and language. Midlife to late‐life changes in p ‐Tau 181, NfL, and GFAP were associated with declines in global cognition and language. No consistent effect modification by cognitive impairment was observed (Figure 1). Conclusions Plasma biomarkers of AD pathology, neuronal injury, and astrogliosis, measured at different life epochs (midlife vs. late‐life) show distinct association patterns with subsequent changes in cognitive function across domains.

Abstract Background Precise cross‐tracer harmonization of tau PET imaging is essential for comparing tau burden across different tracers and studies. Here, we evaluated the performance of the universal tau PET scale (Uniτ) in harmonizing Flortaucipir, MK6240, PI2620, and RO948 tau PET images into a universal scale. Method We assessed 485 individuals across aging and AD spectrum scanned head‐to‐head with Flortaucipir and MK6240, and a subset of 90 individuals with additional PI2620 and RO948. We generated tau PET SUVRs using the inferior cerebellar gray matter as reference with a common 8mm FWHM. We estimated Uniτ parameters on a training set ( n = 200) by fitting a smoothed hyperbolic tangent equation to the Meta‐Temporal ROI anchored in the mean SUVR of young participants and the 90th percentile from cognitively impaired individuals. We compared tau positivity on the Uniτ scale with classifications from SUVRs (mean + 3 SD from Youngs). We applied the same equation to all brain voxels to generate Uniτ 3D images. Then, we extracted mean Uniτ values for key ROIs and correlated them with ROI‐based Uniτ values to evaluate voxel‐wise estimates. Result Using tracer‐specific parameters, Uniτ harmonized Flortaucipir, MK6240, PI2620, and RO948 to the same scale, aligning values near the identity line and confirming its applicability across tau PET tracers (Figure 1). For Flortaucipir, Uniτ tau positivity matched original SUVR classifications (ground truth), with only one mismatch for MK6240 (Figure 1). Applying the Uniτ transformation to all brain voxels effectively harmonized 3D images to a common scale, reducing visual variabilities (Figure 2). Furthermore, ROI‐based estimates and those extracted from the 3D Uniτ images were identical (Figure 3). Conclusion Our results indicate that tau PET tracers can be harmonized to a common scale using a large head‐to‐head dataset. The Uniτ scale can harmonize entire 3D tau PET images, suggesting that there is no need to use pre‐established ROIs, which would constrain the analysis to a few brain regions. Uniτ is freely accessible across platforms ( www.unitau.app ) for ROI and 3D tau PET harmonization.