European Journal of Nuclear Medicine and Molecular Imaging

Background and Purpose: 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (static PET) has mixed specificity and sensitivity in targeting epileptic zones in the noninvasive stage of epilepsy surgery evaluations. We compared the signal quality of static PET compared to a method of interictal dynamic PET (iD-PET). Materials and Methods: We calculated the signal quality of static PET and iD-PET obtained from a cohort of patients with focal epilepsy. We developed a Bayesian regional estimated signal quality (BRESQ) technique to objectively compare signal-to-noise ratios (SNRs) by region of interest (ROI) within subjects. Results: Adjusted for ROI size and neighboring regions, iDPET was superior to sPET with probability >95% in 8/36 regions; >90% in 21/36 regions; >80% in 29/36 regions. The top five regions with the largest adjusted SNR differences (greatest magnitude of iDPET superiority) were the Temporal Mesial (Left and Right), Occipital Lateral (Left and Right), and the Left Frontal Inferior Base. Conclusions: We found that iDPET yielded a superior SNR in most ROI. BRESQ offers a scalable and generalizable method to quantify signal quality between brain mapping modalities.

BackgroundQuantitative cerebral blood flow (CBF) measured with [15O]water positron emission tomography (PET) is the reference standard for quantifying brain perfusion. However, clinical interpretation of individual CBF measurements is limited by the absence of large normative datasets accounting for physiological variability across the adult lifespan. Long-axial field-of-view PET enables high-sensitivity quantitative [15O]water perfusion imaging without arterial blood sampling, allowing normative characterization of cerebral perfusion at unprecedented scale. The aim of this study was to establish normative and covariate-adjusted models of cerebral blood flow across the adult lifespan using total-body [15O]water PET. MethodsQuantitative CBF measurements were obtained in 302 neurologically healthy adults (age 21-86 years) using total-body [15O]water PET. Linear mixed-effects models were used to evaluate the effects of age, sex, body mass index (BMI), and blood hemoglobin concentration on CBF and to generate normative prediction models across the adult lifespan. Between-subject and within-subject variability were estimated from repeated scans in a subset of participants (n=51). ResultsMean grey matter CBF was 46.1 mL/(min*dL), with substantial inter-individual variability but high within-subject reproducibility (intraclass correlation coefficients 0.78-0.89). Advancing age was associated with a decline in CBF of approximately 7% per decade (p_FDR < 10-12). Higher BMI was associated with lower CBF (approximately -6% per 10 kg/m2; p_FDR < 0.01). Women exhibited higher CBF than men (approximately 7.5%), but this difference was largely explained by lower blood hemoglobin concentration in women. Covariate-adjusted models were used to generate normative predictions and prediction intervals describing expected CBF across adulthood. ConclusionThis study establishes a normative database of quantitative cerebral blood flow across the adult lifespan using high-sensitivity [15O]water PET. Age, BMI, and hemoglobin are major determinants of inter-individual variability in CBF. The resulting generative models provide a quantitative reference framework for interpreting cerebral perfusion measurements and may enable automated detection of abnormal brain perfusion in clinical PET imaging.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[-&gt;]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

PURPOSEGlycolytic production of HDO from the metabolism of perdeuterated glucose provides a means for metabolic imaging with 2H MRI. The present study compared HDO production from a cost-efficient [2,3,4,6,6-2H5]glucose with [2H7]glucose in vitro and in vivo. METHODS2H NMR spectroscopy was performed to measure glucose consumption, lactate, and HDO production in the SFxL glioblastoma cell line. In vivo studies in healthy mice using 2H magnetic resonance spectroscopy were performed at 11.1 T after administering a bolus of either metabolic contrast agent. In vivo metabolite levels were quantified using unlocalized and slice-selective localized spectra. RESULTSOur in vitro results demonstrated similar glucose consumption and HDO production kinetics, although significant differences in lactate labeling were observed. The in vivo study showed comparable glucose consumption and HDO production kinetics following tail-vein bolus administration of either metabolic contrast agent, while lactate was not detected in the brain. CONCLUSION[2,3,4,6,6-2H5]glucose shows comparable HDO production to [2H7]glucose, while offering lower cost and reduced spectral complexity. These findings place [2,3,4,6,6-2H5]glucose as an alternative to [2H7]glucose for HDO-based DMI studies.

Background. Parkinson's disease (PD) diagnosis remains delayed and suboptimally accurate, largely due to clinical overlap with atypical parkinsonian syndromes and the lack of reliable biomarkers. Here, we evaluated the performance of a previously patented 6-metabolites blood biomarker (6M-BB) for the differential diagnosis of PD and its translation to clinical IVDr NMR platform. Methods. Patient serum samples from de novo PD (n=30), multiple system atrophy (MSA, n=30), progressive supranuclear palsy (PSP, n=30), Alzheimer's disease (AD, n=33), and healthy individuals (n=29), were profiled by 1H NMR and classified using the 6M-BB. For clinical use, we rebuilt the model on absolute concentrations acquired on a Bruker Avance IVDr 600 MHz system. Results. The 6M-BB validation yielded 0.902 AUC and 87.9% accuracy for PD vs. HC (sensitivity 86.7%, specificity 89.3%), with an overall accuracy of 82.6% across all groups. The IVDr-based refit achieved 0.878 AUC (overall accuracy 77%). Adding VLDL-5 free cholesterol (V5FC) and citrate markedly improved performance to 0.959 AUC, with 94.9% accuracy for PD vs. HC (sensitivity 96.7%, specificity 93.1%) and 84.9% when MSA/PSP were included. Conclusion. The externally validated 6M-BB has demonstrated its robustness for the differential diagnosis of PD compared to other parkinsonian syndromes at de novo stage. Its successful transfer to a fully automated, standardized IVDr machine, with gains from V5FC and citrate, supports the feasibility and promising potential for clinical implementation, justifying future prospective multicenter studies.

Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-related errors. This study developed the large language model (LLM)-based automated Quality Assurance (QA) for auto-contouring (LAQUA) system using a multimodal LLM, Gemini 2.5 Pro, and evaluated its feasibility as a clinical primary screening tool to streamline the QA workflow. Methods: Twenty male pelvic CT scans from an open dataset were utilized. Three distinct auto-contouring software packages (OncoStudio, RatoGuide prototype and syngo.via) were evaluated. Auto-contouring results for each slice were exported as PDF images with overlaid contours and input into Gemini 2.5 Pro. The LLM was instructed to rate the contour quality on a 5-point clinical scale (5: Optimal; 4: Acceptable; 3: Suboptimal; 2: Unacceptable; redraw from scratch; 1: Unacceptable; organ not detected). Using evaluations by two board-certified radiation oncologists as ground truth, Spearman's rank correlation coefficients ({rho}) and weighted kappa coefficients ({kappa}) were calculated. Additionally, to assess screening performance, sensitivity and specificity were calculated by dichotomizing the scores into "Pass" and "Fail" using two different cutoffs (scores [&ge;] 3 and [&ge;] 4 as "Pass"). Finally, the alignment of the rationales provided by the LLM with the auto-contouring quality was evaluated by two board-certified radiation oncologists. This was conducted using a Likert scale assessing four domains (error detection, hallucination, clinical relevance, and anatomical understanding), each scored out of 2 points. Results: The LAQUA system demonstrated moderate to strong agreement with expert judgments across all evaluated organs ({rho}: 0.567 - 0.835; quadratic weighted {kappa} : 0.639 - 0.804), with the rectum showing the highest correlation. Regarding screening performance, a cutoff of [&ge;]3 as "Pass" achieved the highest sensitivity and specificity in specific subgroups, but with wide 95% confidence intervals (CIs). A cutoff of [&ge;]4 as "Pass" narrowed the CIs, yielding the highest sensitivity in the rectum (0.976) and the highest specificity in the left femoral head (0.933). Qualitatively, the LLM's rationales achieved an overall mean score of 1.70 {+/-} 0.48 (out of 2), with 155 of 291 outputs receiving perfect scores across all criteria. Conclusions: The LAQUA system demonstrated substantial agreement with expert evaluations in AI-based auto-contouring quality assessment. While potential overestimation bias (risk of missing "Fail" cases) warrants caution, the observed sensitivity suggests its feasibility as a primary screening QA tool to efficiently filter acceptable contours, thereby reducing the clinical workload.

Methods: DigiCog is a single-center cross-sectional study conducted within the Luxembourgish Predi-COVID cohort (NCT04380987). Participants aged 25-65 years, with and without persistent COVID-19 symptoms, are invited to participate. Cognitive assessments are performed during face-to-face sessions by trained nurses and neuropsychologists using both the VMTech device and standardized neuropsychological tests. Additional data on PCC symptom status, CR, sociodemographic characteristics, fatigue, and psychological factors are also collected. Agreement between digital and standard cognitive assessments will be evaluated using Cohen's kappa coefficient, with sensitivity, specificity, and receiver operating characteristic analyses as secondary measures. Cognitive performance will be compared between participants with and without PCC, and associations with CR proxies will be explored.

Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to predict IC response and OS in LANPC. Pretreatment MRI and WSIs from 404 patients with LANPC were retrospectively collected to construct a multi-task model (MoEMIL) for the simultaneous prediction of early IC response and OS. MoEMIL employed multi-instance learning to process WSIs, PyRadiomics and a convolutional neural network (ResNet50) to extract MRI features, and fused multimodal features through a multi-gate mixture-of-experts architecture. Clustering-constrained attention multiple instance learning and gradient-weighted class activation mapping were applied for visualization and interpretation. MoEMIL effectively stratified patients into good and poor IC response groups, achieving areas under the curve of 0.917, 0.869, and 0.801 in the train, validation, and test sets, respectively, and outperformed the deep learning radiomics model, the pathomics model and TNM staging. The model also stratified patients into high- and low-risk OS groups (P < 0.05). MoEMIL shows promise as a decision-support tool for early IC response prediction and prognostication in LANPC. Author SummaryWe have developed a deep learning model that integrates two types of medical images, including magnetic resonance imaging (MRI) and digital pathological slices, to simultaneously predict response to induction chemotherapy and prognosis in patients with locally advanced nasopharyngeal carcinoma. Current treatment decisions primarily rely on traditional tumor staging (TNM), which often fails to comprehensively reflect the complexity of the disease. Our model, named MoEMIL, was trained and tested on data from 404 patients across two hospitals and consistently outperformed both single-model approaches and TNM staging methods. By identifying patients who exhibit poor response to induction chemotherapy or higher prognostic risk, our tool can assist clinicians in achieving personalized treatment, enabling intensified management for high-risk patients and avoiding unnecessary side effects for low-risk patients. Additionally, we visualize the models reasoning process through heat map generation, which highlights the image regions exerting the greatest influence on prediction outcomes. This work represents a step toward more precise treatment for nasopharyngeal carcinoma; however, larger-scale prospective studies are required before the model can be integrated into routine clinical practice.

Alzheimer s disease (AD) and other tauopathies are characterized by the hyperphosphorylation of tau (pTau), leading to its aggregation in the brain, a process strongly predictive of neurodegeneration and future cognitive decline. Currently, tau positron emission tomography (PET) is the only validated method for detecting tau aggregates in vivo. However, its high cost, invasiveness, and limited accessibility restrict its use in clinical settings and preclude large-scale screening. Moreover, existing plasma biomarkers that quantify the level of pTau at specific sites (e.g., pTau217) have limited specificity for confirming AD-related tau aggregation, partly due to the heterogeneous and irregular phosphorylation patterns of pTau. Besides, the concentration of pTau is frequently elevated in the context of isolated amyloid-{beta} pathology, which is less strongly associated with cognitive decline in the absence of aggregated tau. There is therefore an urgent need for a reliable and scalable blood-based biomarker of tau pathology. A key mechanism underlying AD tau pathology is the ability of pathologically active pTau (PA pTau) to bind to and seed normal tau, facilitating prion-like propagation of insoluble tau aggregates. Here, we assessed the diagnostic performance of the VeraBIND Tau assay, the first functional assay to detect PA pTau seeding activity in plasma. Seventy-nine cognitively unimpaired (CU) and 66 cognitively impaired older adults underwent blood sampling, cognitive assessment, amyloid-PET or cerebrospinal fluid (CSF) analysis, and [18F]-MK6240 tau-PET imaging. Plasma pTau217 concentrations were quantified using the Lumipulse platform (Fujirebio). The VeraBIND Tau assay isolated PA pTau from plasma and evaluated its ability to bind recombinant normal tau using a tagged-tau chemiluminescent readout. VeraBIND Tau demonstrated 94.2% sensitivity and 96.1% specificity for predicting tau-PET positivity (AUC=0.97). It outperformed plasma pTau217 in CU individuals (PPV=85.9%), regardless of the pTau217 threshold used (maximal PPV of 57.5% using the 0.256pg/mL pTau217 threshold). This higher VeraBIND Tau diagnostic accuracy was driven by early tau-PET stages (Braak-like tau-PET stages 1-3; AUC=0.96 vs. 0.74 for pTau217, p=0.003). Moreover, both cross-sectional values and annual changes in VeraBIND Tau were significantly correlated with cognitive performance and entorhinal tau-PET signal (all absolute Spearman r[&ge;]0.23, p<0.05). These findings highlight the strong potential of VeraBIND Tau as a scalable and accurate screening tool to detect AD tau pathology in the general population. The assay may also help enrich clinical trials with tau-PET positive CU individuals, enhance clinical diagnostic workflows and support monitoring of tau-targeted therapies. Future work should evaluate its utility in optimizing triage and early-intervention strategies.

Sodium magnetic resonance imaging (23Na MRI) provides a unique opportunity to probe ionic microenvironments in neural tissue because sodium ions play central roles in membrane electrophysiology, ion transport, and cellular homeostasis. Unlike conventional proton ({superscript 1}H) MRI, which primarily reflects water distribution and tissue structure, {superscript 2}3Na MRI is sensitive to ionic compartmentation and quadrupolar interactions arising from the spin-3/2 nature of the sodium nucleus. However, sodium MRI remains technically challenging due to intrinsically low signal sensitivity and rapid biexponential relaxation, particularly when imaging small biological systems. Here, we establish a high-field multinuclear MRI platform for imaging human cerebral organoids at 14 Tesla. Cerebral organoids derived from human induced pluripotent stem cells provide a simplified three-dimensional neural tissue model that enables investigation of ionic microenvironments without vascular or systemic confounds. Using a dual-tuned {superscript 1}H/{superscript 2}3Na radiofrequency coil, we performed co-registered structural, diffusion, and sodium imaging of individual fixed organoids. High-resolution {superscript 1}H MRI (33-100 m) revealed pronounced microstructural heterogeneity, while multi-echo {superscript 2}3Na MRI (300-400 m) enabled voxel-wise characterization of quadrupolar relaxation behavior. Bi-exponential analysis of the sodium signal decay identified distinct relaxation components (T2*short {approx} 1 ms and T2*long {approx} 12 ms) and revealed spatial heterogeneity in sodium microenvironments across the organoid tissue. These results demonstrate the feasibility of quantitative sodium relaxometry in cortical organoids and establish a multinuclear imaging platform for investigating ionic microenvironment dynamics in three-dimensional neural tissue models.

Multimodal medical image analysis exploits complementary information from multiple data sources (e.g., multi contrast Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), and Positron Emission Tomography (PET)) to enhance diagnostic accuracy and support clinical decision making. Central to this process is the learning of robust representations that capture both modality invariant and modality specific features, which can then be leveraged for downstream tasks such as MRI segmentation and normative modeling of population level variation and individual deviations. However, learning robust and generalizable representations becomes particularly challenging in the presence of missing modalities and heterogeneous data distributions. Most existing methods address this challenge primarily from a statistical perspective, yet they lack a theoretical understanding of the underlying geometric behavior such as how probability mass is allocated across modalities. In this paper, we introduce a generalized geometric perspective for multimodal representation learning grounded in the concept of barycenters, which unifies a broad class of existing methods under a common theoretical perspective. Building on this barycentric formulation, we propose a novel approach that leverages generalized Wasserstein barycenters with hierarchical modality specific priors to better preserve the geometry of unimodal distributions and enhance representation quality. We evaluated our framework on two key multimodal tasks brain tumor MRI segmentation and normative modeling demonstrating consistent improvements over a variety of multimodal approaches. Our results highlight the potential of scalable, theoretically grounded approaches to advance robust and generalizable representation learning in medical imaging applications.

Background and ObjectivesIntramedullary spinal cord tumors (IMSCTs) are rare, and the extent of surgical resection may influence overall survival (OS). Gross total resection (GTR) may offer superior outcomes compared to subtotal resection (STR) or biopsy. Our study seeks to quantify the benefits of resection extent on OS in patients with spinal gliomas (SGs). MethodsA systematic review was conducted using the following databases: Scopus, Embase, and PubMed. Studies reporting OS in patients who underwent GTR, STR, or biopsy for low- or high-grade SG. We used a random-effects model to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs); this was performed separately for low-grade (WHO grade I-II) and high-grade (III-IV) SGs. Subgroup analysis was performed for radiotherapy. I2 statistic and Cochrans Q tests evaluated study heterogeneity, Eggers and funnel plot asymmetry tests assessed publication bias, and Risk Of Bias In Non-randomized Studies of Exposure (ROBINS-E) evaluated individual study bias. ResultsIn a pooled analysis of 5 studies, GTR was not associated with improvement in OS compared to STR or biopsy in high grade SGs (HR=0.48, 95% CI: 0.19 -1.26). However, low-grade SGs revealed significant benefit in overall survival with GTR (HR=0.27, 95% CI: 0.15-0.46). Patients treated with radiotherapy were associated with worse outcomes following GTR in low-grade SGs (HR=1.48, 95% CI: 1.30-1.69) but no survival differences in high-grade SGs (HR=1.21, 95% CI: 0.52-2.83). ROBINS-E determined only 1 study with high risk of bias. ConclusionGTR for intramedullary spinal gliomas may not confer a significant benefit in overall survival for high-grade lesions but may provide benefit in lower grades. Radiotherapy confers a worse survival in lower-grade tumors, potentially due to their infiltrative nature. Future studies should stratify outcomes based on tumor biology, as well as follow functional outcomes overtime.

Epilepsy is a chronic neurological disorder requiring multi-faceted management, including seizure detection, syndrome diagnosis, prognostication, antiseizure medication recommendation, epileptogenic zone localization, and surgical outcome prediction. Although numerous deep learning approaches have been developed for individual tasks, these models are typically siloed and modality-specific (e.g., EEG for seizure detection, MRI for localization), failing to reflect the multidisciplinary nature of real-world epilepsy care, where epileptologists, neuroradiologists, neurosurgeons, neuropsychologists and neuropsychiatrists jointly interpret heterogeneous evidence to guide decisions. In this work, we propose a clinical guideline-grounded hybrid multi-agent framework for holistic epilepsy management. Heterogeneous patient data is processed through modality-specific discriminative and generative models, where textual interpretations from generative agents are combined with structured predictions from discriminative models as auxiliary guidance. This aggregated evidence is passed to a central orchestrating agent grounded in international epilepsy guidelines, which evaluates multi-modal findings within structured clinical pathways and performs iterative cross-agent coordination for evidence-informed decision-making. We evaluate our framework across two datasets spanning six epilepsy management tasks and also introduce a publicly available multi-modal, multi-task epilepsy benchmark. Results demonstrate that integrating discriminative evidence with guideline-grounded generative coordination yields more reliable and comprehensive decisions compared to conventional LLM-based and task-specific baselines. Our dataset and code is available at URL.

BackgroundGallium (Ga) is a promising anti-tumor agent; however, its precise molecular targets in osteosarcoma remain debated. While current paradigms largely attribute its toxicity to reactive oxygen species (ROS) and ferroptosis, understanding its true mechanism is essential for overcoming therapeutic resistance. This highlights the need for interdisciplinary approaches, such as metabolomics, to unveil novel vulnerabilities in cancer metabolism. MethodsWe employed an interdisciplinary strategy utilizing high-resolution liquid chromatography-mass spectrometry (LC-MS) metabolomics and 13C2-glutamine stable isotope tracing in osteosarcoma cells to elucidate the cytotoxic mechanisms of gallium nitrate. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) was utilized for elemental mapping, and in silico modeling was applied to evaluated metal binding dynamics. Furthermore, synergistic effects were tested by combining gallium with the DNA-damaging agent cisplatin. ResultsOur metabolic profiling revealed a profound bifurcation characterized by the systemic depletion of glycolysis and pentose phosphate pathway intermediates, coupled with a novel ribonucleotide accumulation bottleneck. The observed distinct signature strongly implicated ribonucleotide reductase (RNR) as the primary enzymatic target. In silico modeling and SEM-EDS visually and thermodynamically confirmedthat gallium acts as a structural decoy for iron within the RNR active site. The co-localization induces functional iron starvation rather than canonical ferroptosis. Furthermore, isotope tracing confirmed that elevated ROS is a consequence of overall metabolic failure, not the primary driver of cell death. Crucially, gallium functioned as a metabolic DNA repair inhibitor, synergizing potently with cisplatin to prevent the repair of platinum-induced DNA lesions. ConclusionsGallium selectively sensitizes highly proliferative sarcoma cells by disrupting RNR-mediated DNA precursor synthesis, while sparing normal osteoblasts. Leveraging metabolomics to uncover this state of functional iron starvation provides a rational, interdisciplinary framework for developing gallium-based combination therapies designed to break platinum resistance in clinical oncology.

Background: Accurate preoperative prediction of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains challenging, particularly in clinically node-negative (cN0) patients, leading to potential overtreatment. We aimed to develop and validate a Transformer-based 2.5D deep learning model (ThyLNT) using preoperative computed tomography (CT) images for robust prediction of LNM and to explore its underlying biological basis through multi-omics analyses. Methods: A total of 1,560 PTC patients from six hospitals were retrospectively included. The Tongji Hospital cohort (n=1,010) was divided into training (70%) and internal validation (30%) sets, while five independent institutions served as external test cohorts. For each lesion, seven 2.5D slices were extracted and modeled using a DenseNet201 backbone. Slice-level features were integrated using a Transformer-based feature-level fusion strategy and compared with ensemble learning, multi-instance learning (MIL), and traditional radiomics approaches. Model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration analysis, decision curve analysis (DCA), and precision-recall curves. Multi-omics analyses, including bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and spatial metabolomics, were performed to investigate biological correlates. Results: The Transformer-based model consistently outperformed comparator models across cohorts. In the training and validation cohorts, ThyLNT achieved AUCs of 0.882 and 0.787, respectively, with external AUCs ranging from 0.772 to 0.827. Compared with ultrasound (US) and CT, ThyLNT showed superior predictive performance (all P < 0.001 in the validation cohort). Simulation analysis in cN0 patients suggested that ThyLNT could reduce unnecessary lymph node dissection (LND) from 52.16% to 4.88%. Transcriptomic analysis combined with WGCNA and correlation analysis identified VEGFA as the gene most strongly associated with ThyLNT prediction scores. Single-cell and spatial transcriptomic analyses suggested metastasis-related tumor microenvironment remodeling, while enrichment analysis of genes affected by virtual knockout of VEGFA indicated involvement of angiogenesis- and epithelial-mesenchymal transition (EMT)-related pathways. Spatial metabolomics further revealed coordinated lipid metabolic reprogramming in metastatic tissues. These findings suggest that ThyLNT provides robust predictive performance while capturing biologically relevant features associated with metastatic progression.

ObjectiveNicotinic acetylcholinergic receptors (nAChRs), comprising of and {beta} subunits are present in the brain and whole body. The less abundant 2-subunit is a fast-acting receptor subtype and plays an important role in cognition and learning. To understand cellular functional consequences, this study evaluated glucose metabolism using [18F]FDG PET/CT in 2 knockout (2KO) and 2 hypersensitive (2HS) mice. MethodsControl (CN; 4M, 4F), 2 knockout (2KO; 4M, 4F) and 2 hypersensitive (2HS; 4M,4F), 12-16 month old mice were used. Mice were fasted and injected with [18F]FDG (3-5 MBq) while awake. After 40 minutes they underwent whole body PET/CT. On a separate day, nicotine challenge [18F]FDG studies were done. Reconstructed images were analyzed to obtain standard uptake values (SUV) of [18F]FDG in brain and interscapular brown adipose tissue (IBAT). Statistical analysis was performed. ResultsThe 2HS male mice exhibited the largest brain increase in [18F]FDG compared to 2KO male mice. The rank order of brain [18F]FDG uptake in the 3 groups: 2HS[male]> CN[male]> 2KO[male]> CN[female]= 2KO[female][&ge;] 2HS[female]. Nicotine treatment reduced brain [18F]FDG uptake in all mice. Females had lower [18F]FDG uptake compared to males and were less sensitive to 2 nAChR. In the case of IBAT, 2KO mice had significantly higher baseline [18F]FDG uptake compared to the other two groups: 2KO[male]> 2KO[female]> 2HS[female]> 2HS[male]> CN[female]> CN[male]. Nicotine decreased IBAT in 2KO mice rather than increase as observed in CN and 2HS mice. Conclusions2 nAChRs plays a significant role in brain activation as exhibited by the increase in [18F]FDG in 2HS mice. In the absence of regulatory control by the 2 nAChR, the 2KO mice IBAT exhibited higher [18F]FDG IBAT compared to controls and 2HS mice. Female mice were less affected by nicotine compared to the male mice. Overall, 2 nAChRs played a significant role in glucose metabolism in the brain and IBAT.

Nanoplastics generated from plastic waste in our ecosystems are becoming increasingly prevalent as bulk plastics exposed to natural factors like water and sunlight fragment to the nanoscale over time. These incidental nanoplastics span a wide range of physicochemical properties, which makes studying nanoplastic interactions in biological systems difficult. Here, we characterized the behavior of incidental nanoplastics generated through mechanical abrasion within coacervate droplets to probe the surface properties of the nanoplastics. We used elastin-like polypeptides (ELPs) to create hydrophobic or charged coacervate microenvironments. Using optical microscopy and fluorescence quantification, we observed that nanoplastics made from polyethylene terephthalate (nPET), nylon 6 (nPA), and polystyrene (nPS) exhibited distinct partitioning behavior with more favorable interactions with hydrophobic droplets. This indicated that the hydrophobic polymer backbone was the predominate surface feature despite exposed functional groups of the incidental nanoplastics, in contrast to findings with model carboxylated latex nanospheres (nPS-COOH). Furthermore, the selective partitioning of incidental nanoplastics into the hydrophobic droplets was able to capture over 80% of nPET in solution, and after recovery of the protein droplet, was able to cumulatively capture over 75% of the nPET feedstock across multiple cycles. This work explores the nuanced surface characteristics of incidental nanoplastics, expands the application of coacervates as chemical probes, and demonstrates a biopolymer approach for effective nanoplastic removal.

Environmental degradation and accumulation of plastics results in micro- and nanoplastics (MNPLs) that are small enough to cross biological barriers, including the blood-brain barrier. Microglia, resident immune cells of brain, are critical regulators of neuroimmune homeostasis and represent a cellular target of nanoplastic exposure. In this study, we assessed the neurotoxic effects of two sizes of polystyrene nanoplastics (PS-NPs; 100 nm and 500 nm) using integrated in vivo and in vitro exposure and washout paradigms. In vivo exposure in mice (60 days; 0.15 or 1.5 mg/day) showed the accumulation of both PS-NP sizes in the cerebral cortex without histopathological damage. However, cortical microglia showed pronounced morphological remodeling, observed as increased expression of Iba1 and GFAP. Transcriptomic profiling of cortical tissue revealed a strong size-dependent response. The 100 nm PS-NP group revealed 18 DEGs (|log2FC| [&ge;] 2, padj < 0.05), whereas the 500 nm PS-NPs showed more than 4,000 DEGs, including upregulation of immune- and microglia-associated genes (CCL5, CXCL10, LCN2, LYZ2) and downregulation of synaptic and neuronal signaling genes (GRIN2B, SYN1, STX1B, MAP1B, ITPR1/2). In vitro assessment, using BV2 microglia cells, showed internalization of PS-NPs via the endolysosomal pathway, with strong co-localization to Rab7- and LAMP2-positive compartments and prolonged intracellular retention following exposure washout. Also, microglial activation markers (Iba1, CD68) exhibited a transient, size- and concentration-dependent increase, correlated with intracellular particle burden rather than cumulative exposure. Overall, these findings demonstrate that PS-NPs accumulate in brain, driving size-dependent microglia activation and transcriptomic reprogramming, even after cessation of exposure to PS-NPs. HighlightsO_LIPS-NPs (100 nm and 500 nm) reach mouse cerebral cortex following 60-day oral exposure. C_LIO_LIPS-NPs were internalized by microglia; accumulated in endolysosomal compartments. C_LIO_LIPS-NP exposure induced transient microglial activation without sustained cytotoxicity. C_LIO_LIMicroglial activation was correlated with intracellular PS-NPs burden. C_LIO_LITranscriptomics revealed disruption of neuroimmune and microglial regulatory pathways. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/712727v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1aba3eaorg.highwire.dtl.DTLVardef@1967641org.highwire.dtl.DTLVardef@12da637org.highwire.dtl.DTLVardef@1fb8441_HPS_FORMAT_FIGEXP M_FIG C_FIG

Understanding the time course of Alzheimer's disease biomarkers of amyloid and tau pathology and their temporal relation to clinical symptoms is key to identifying optimal windows for disease intervention and planning future drug trials. The goal of this work was to determine the extent to which Sampled Iterative Local Approximation (SILA), an algorithm extensively validated for amyloid PET, is capable of modeling longitudinal tau (T) PET trajectories and estimating person-level tau positivity onset ages in two commonly analyzed brain regions and two tracers from two different cohorts. Methods: 385 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI; mean (SD) age = 73.4 (7.3) years) with longitudinal flortaucipir tau PET and 288 participants from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center (collectively referred to as WISC; mean (SD) age = 67.4 (6.7) years) with longitudinal MK-6240 tau PET were included in the study. Standard uptake value ratios (SUVRs) in the entorhinal cortex and a meta-temporal ROI were modeled with SILA separately, for each cohort and region. Forward and backward SUVR and T+/- prediction were characterized with ten-fold cross-validation and in-sample validation techniques. Accuracy of estimated T+ onset ages (ETOA) was characterized in T- to T+ converters. Differences in ETOA were tested between APOE-e4 carriers and non-carriers, as well as differences in time T+ between levels of cognitive impairment. Results: SILA was able to accurately estimate retrospective change in tau SUVR in the meta-temporal region regardless of age, sex, APOE-e4 carriage, tau SUVR, and dementia (p >0.05) whereas dementia was associated with model residuals in entorhinal cortex (p [&le;] 0.05; ADNI). In subsets of observed T- to T+ converters, the difference between "observed" and estimated meta-temporal T+ onset age [95% CI] was 0.12 [-0.27, 0.52] years for ADNI and -0.09 [0.93, 0.74] years for WISC. ETOA was significantly earlier, and odds of SILA-estimated T+ status were higher amongst APOE-e4 carriers (p <0.05) and those with dementia (p <0.05). Conclusions: Our results suggest SILA can be used to accurately model longitudinal tau PET trajectories and retrospectively estimate individual T+ onset ages in the meta-temporal region. The accuracy of SILA time estimates in entorhinal cortex worsened amongst those with dementia in ADNI suggesting entorhinal cortex may only be suitable for studying the temporal progression of tau during the preclinical time frame.

Importance: Glucose metabolic dysregulation in brain is a common feature of late-onset age-associated neurodegenerative disease (A2ND). Prior meta-analyses have identified disease-specific effects compared to healthy, unimpaired individuals. Yet, a unifying A2ND glucose dysregulation spatial signature remains undescribed. Objective: To determine the common signature of dysregulated glucose metabolism on FDG-PET using activation likelihood estimation (ALE) meta-analyses across A2ND. Data Sources: Searches were conducted using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane from inception through July 2025. The search terms included controlled vocabulary and keywords for four neurodegenerative diseases Parkinson Disease, Amyotrophic Lateral Sclerosis, Alzheimer Disease, and Multiple Sclerosis, Fluorodeoxyglucose F18, glucose, and positron-emission tomography (PET). Study Selection: Studies comparing adults with late-onset neurodegenerative diseases to non-diseased controls using FDG-PET to quantify brain glucose uptake and reporting whole-brain coordinate findings in either Talairach or Montreal Neurological Institute space were included. Data Extraction and Synthesis: Three researchers, assisted by an AI screening tool, screened 7275 potential titles and abstracts for inclusion. Full texts were then retrieved for potentially relevant articles and were evaluated by three researchers using prespecified inclusion/exclusion criteria. Main Outcomes and Measures: Cluster peak and subpeak coordinates, cluster-wise t- or Z- values, and annotations indicating the disease of interest, whether the outcome was for hyper- (disease group > control) or hypometabolism (disease group < control), were extracted from included texts and analyzed using ALE. Results: A total of 130 FDG-PET studies were included in the meta-analysis, with a combined sample of 5412 individuals with A2ND and 3549 controls. Meta-analyses revealed dysregulated glucose metabolism as a unifying feature across A2ND which included both hypo- and hypermetabolic patterns. Neuroanatomical metabolic pattern was unique and disease specific. Each A2ND metabolic phenotype was associated with unique and complex patterns of neurological functionalities. Conclusions and Relevance: These data demonstrate dysregulated glucose metabolism as a common A2ND feature, suggesting responsive remodeling of neural bioenergetics. While hypometabolism is a common research focus, due to functional relevance, hypermetabolism may reflect a compensatory, maladaptive, or neuroinflammatory signal, that requires focused investigation. A2ND prevention and treatment efficacy may depend on addressing bidirectional metabolic dysregulation in addition to disease-specific drivers of pathology.