Citations

Variants in TMEM106B and GRN , which encode lysosomal proteins, interact through unknown mechanisms to increase the risk of age-related cognitive decline and neurodegeneration. Here, we show that these variants converge on a single molecular intermediate: the cleaved intra-lysosomal fibril core of TMEM106B, a precursor to amyloid fibrils that accumulate in the aging brain. A protein-coding TMEM106B risk variant (p.T185) drives fibril core accumulation by impairing its degradation and GRN risk variants amplify this effect. Mice over-expressing the fibril core develop hallmarks of neurodegeneration, and cryo-electron tomography reveals intra-lysosomal fibrils in cultured neurons, mice, and diseased human brain. In GRN -mutation carriers, in whom fibril burden is greatest, fibrils extrude through ruptured lysosomal membranes. These findings identify intra-lysosomal TMEM106B fibrillization as a convergent neurodegeneration mechanism and potential therapeutic target.

Abstract Background Radiomics extracts quantitative imaging features from computed tomography (CT) data for clinical decision‐making. However, variations in acquisition parameters—particularly x‐ray tube voltage (kV)—introduce non‐biological variability in attenuation values, limiting the reproducibility of radiomic features across scanners, protocols, and institutions. Purpose To develop and evaluate a CT dAta harmoNiZAtion framework based on deep learNed vIrTual monoEnergetic imaging (TANZANITE), which leverages the keV flexibility of virtual monoenergetic images (VMIs) to enable cross‐kV scan translation and radiomics harmonization. Methods TANZANITE is a model hub consisting of multiple pre‐trained convolutional neural networks (CNNs), each designed to translate VMIs from 1 keV level to another. Phantom‐based calibration was first used to determine energy‐equivalent (Eff_E) keV levels corresponding to each tube potential (e.g., Eff_E(A) keV for source kV and Eff_E(B) keV for target kV). A CNN trained using 69 120 patches from seven patient cases to map VMIs from Eff_E(A) to Eff_E(B) was selected from the TANZANITE hub and applied directly to clinical CT images acquired at the source kV. This harmonized the images to match the attenuation characteristics of the target kV setting. Evaluation was conducted on independent dual‐energy CT datasets acquired at 100/Sn150 kV. Regions of interest (ROIs) were placed in the kidney, liver, and spine to assess CT number consistency and radiomic feature reproducibility. The concordance correlation coefficient (CCC) was calculated across 93 non‐shape radiomic features. Results After TANZANITE processing with 100 kV images, CT numbers in evaluated organs closely matched the Sn150 kV reference values in four testing patient cases. For example, mean kidney CT numbers changed from 320 HU (100 kV) to 156 HU (TANZANITE), approximating the Sn150 kV value of 160 HU. Similar changes were observed in the liver (157–105 HU vs. 104 HU reference) and spine (45–22 HU vs. 19 HU reference). Radiomic reproducibility improved substantially across organs: mean CCC increased from 0.590 to 0.995 in the liver, 0.300 to 0.970 in the kidney, and 0.630 to 0.968 in the spine. Post‐TANZANITE, over 98% of features exceeded the stability threshold (CCC ≥ 0.900) in all three representative organs. Conclusion TANZANITE provides a flexible, image‐domain harmonization framework by learning the keV‐to‐keV translation in the VMI domain and applying pre‐trained CNNs to clinical kV images. It improves CT number consistency and organ‐specific radiomic reproducibility without requiring raw projection data or scanner‐specific training. This approach supports consistent quantitative imaging across multiple‐kV acquisition protocols, enhancing radiomics reliability in clinical settings.

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.

Abstract Adoptive T-cell therapies using tumour-specific T-cell receptors (TCRs) are limited by competition with endogenous receptors, which impairs efficacy and poses risks of off-target autoreactivity. Here we present a CRISPR-based platform that completely and selectively eliminates both endogenous TCR-α and -β chains without affecting introduced transgenic TCRs, irrespective of codon optimization. This approach achieves >90% deletion efficiency in Jurkat and primary human T cells, markedly enhancing the expression, pairing fidelity, and functional potency of transgenic receptors. Using a clinically relevant HLA-A*02:01-restricted DMF5 TCR, we show that dual TCR ablation boosts antigen-specific activation and cytotoxicity in vitro and significantly enhances tumor clearance in vivo in human immune system (HIS) mice, while preventing graft-versus-host disease (GVHD). Targeted locus amplification revealed that CRISPR-induced double-strand breaks did not alter lentiviral integration profiles, confirming genomic safety. Extending this approach to four insulin-reactive TCRs demonstrated that removal of endogenous receptors increased transduction efficiency and functional activity, with one (1E6) showing selective activation and infiltration of stem cell–derived islet grafts (SC-islets) in vivo . This study establishes a universal, safe, and scalable genome-editing platform for generating functionally precise human T cells. By integrating cancer immunotherapy and autoimmune disease modelling within a single framework, it provides a strong preclinical rationale for dual endogenous TCR removal as a route to improved specificity, safety, and therapeutic efficacy in TCR-based cell therapies.

Abstract Cytokine receptor–like factor 2 rearrangement (CRLF2r), an aberration causing a Philadelphia chromosome–like gene expression profile and a poor prognostic marker in B-cell acute lymphoblastic leukemia (B-ALL), endows relative chemotherapeutic resistance. Extramedullary disease (EMD), including both central nervous system (CNS) and non-CNS involvement, can also be associated with worse outcomes. The relationship between CRLF2r and EMD is unknown. Based on an initial observation of patients with relapsed/refractory (R/R) CRLF2r B-ALL and EMD, a single-center retrospective review was conducted to evaluate the incidence of EMD in patients with CRLF2r B-ALL. Across 177 children, adolescents, and young adults with R/R B-ALL, 21 (11.9%) harbored CRLF2r, of whom 19 (90.5%) had EMD at some point during their treatment course. In contrast, only 78 of the 156 patients (50%) without CRLF2r ever had EMD (P = .0003). In the CRLF2r cohort, 8 (38.1%) presented with EMD at diagnosis: 7 with CNS and 1 with non-CNS EMD. At relapse, 17 (80.9%) had EMD: 4 (19%) had CNS EMD, 9 (42.9%) had non-CNS EMD, and 4 (19%) had both. Isolated EMD was observed in 2 patients (11.8%) at first relapse and in 3 (17.6%) with multiple relapses. Given these findings, further study to evaluate this potential association between EMD and CRLF2r in R/R B-ALL is indicated.