Magnetic Resonance Imaging

Alzheimer's disease (AD) pathology involves amyloid deposition, reactive gliosis, and localized tissue alterations that coexist within the same brain regions, creating heterogeneous microstructural environments within individual imaging voxels. Conventional diffusion MRI averages these environments into aggregate measures, potentially obscuring their distinct contributions. Frequency-dependent multidimensional MRI ({omega}MD-MRI) resolves distributions of water components with different diffusion length scales, anisotropies, and relaxation properties, providing sensitivity to microstructural restriction, heterogeneity, and shape-size correlations within a voxel. Whether these measurements reveal microstructural complexity associated with AD pathology remains unclear. Here, we performed {omega}MD-MRI on ex vivo brain specimens from approximately 8-month-old 5xFAD and wild-type mice and interpreted the imaging findings alongside complementary histology. {omega}MD-MRI revealed widespread but spatially nonuniform differences between 5xFAD and wild-type brains. Measurements sensitive to microstructural restriction, heterogeneity, and shape-size correlations consistently indicated greater microstructural heterogeneity in 5xFAD brains, with the most prominent differences in the hippocampal formation and major cerebral white matter tracts. Complementary qualitative histology demonstrated extensive amyloid deposition and glial activation in affected regions, while overall cytoarchitecture and myelin organization remained largely preserved. Thus, the {omega}MD-MRI abnormalities occurred in tissue characterized by multiple coexisting pathological and relatively preserved microstructural environments rather than widespread structural degeneration. These findings demonstrate that {omega}MD-MRI can reveal the spatial and microstructural heterogeneity associated with amyloid pathology and provide a more comprehensive characterization of AD-related tissue alterations.

Purpose: The primary objective of the Stanford Knee Osteoarthritis PET/MRI Evaluation (SKOPE) study is to develop and evaluate a multimodal, dynamic [18F]NaF PET-MRI framework for characterizing whole-joint physiology and its relationship to osteoarthritis (OA) risk, pain, and disease progression. Specifically, we aim to integrate dynamic PET with quantitative and anatomical MRI, to characterize structural, compositional, and metabolic features across the knee and surrounding musculoskeletal system, evaluate acute tissue responses to exercise, and identify imaging biomarkers associated with OA risk, pain, and disease progression. Methods: The SKOPE study includes multimodal PET-MRI of the knee and surrounding musculoskeletal tissues, with imaging of the knee, tibia, ankle, thigh, hip, pelvis, and lumbosacral spine. Dynamic [18F]NaF PET is combined with conventional anatomical MRI and quantitative MRI techniques, including quantitative double-echo steady-state (qDESS) T2 mapping of cartilage, Dixon fat-fraction imaging, ultrashort echo time (UTE) T2* mapping of short-T2 tissues, UTE imaging of tibial bone, and zero echo time (ZTE) imaging for bone morphology and pseudo-CT generation. Additional MRI sequences characterize muscle composition, bone and joint anatomy, intervertebral discs, and regional vascular anatomy. Selected scans are acquired before and after a standardized exercise protocol to assess the acute physiological response of the joint. Automated segmentation is used to generate subject-specific masks of muscles, bones, vertebrae, and intervertebral discs. A subset of the MRI protocol is repeated at 1- and 2-year follow-up to assess longitudinal changes. Expected Impact: By combining dynamic bone metabolic imaging with quantitative measures of cartilage, menisci, muscle, bone, fat, vascular structures, and the spine and hip, the SKOPE protocol provides a whole-joint and multijoint framework for studying the structural, metabolic, and physiological processes associated with OA and pain. Exercise and longitudinal imaging further enable assessment of acute tissue responses and changes over time, supporting the development of quantitative imaging biomarkers for OA risk, pain, and disease progression.

Cross-sectional studies suggest associations between physical exercise and white matter in older adults, but evidence from randomized controlled trials is scarce. Neurite orientation dispersion and density imaging indices are biophysically informed metrics of white matter microstructure. Here, we tested whether a remotely delivered, 8-week multicomponent physical exercise intervention impacts neurite density (NDI) and orientation dispersion (ODI) across major white matter tracts in older adults. This secondary analysis of a randomized controlled trial included participants with available diffusion MRI data (n = 66; age: 66.4 {+/-} 3.6 y; 43 females). Participants were randomized to a multicomponent exercise (PAG, n = 34) or an active control (CON, n = 32) intervention. Intervention effects on NDI/ODI were tested using linear mixed-effects and Bayesian multilevel models adjusted for age and sex. A significant Timepoint x Group interaction was observed for NDI (p = 0.003) but not for ODI (p = 0.785), further confirmed in Bayesian analyses for 22 white matter tracts, indicating a greater increase in NDI in the PAG. The standardized composite VO2max score increased from pre- to post-intervention within the PAG, although the Timepoint x Group interaction was not significant (p = 0.079). Across all participants, pre-to-post changes in mean NDI were positively correlated with changes in VO2max, but this association did not differ between groups. Our results indicate that white matter microstructure remains responsive to short-term, multicomponent physical exercise in older adults.

Background: Acute phase response is an early immunometabolic response to brain injuries, primarily coordinated by the liver via the activation of acute phase proteins. These immune responses can be both beneficial, promoting tissue repair, and detrimental, exacerbating neurological deficits, if not properly controlled. Despite the central role of the liver in immunometabolism, how hepatic metabolism dynamically adapts to traumatic brain injury remains under explored, primarily due to limited liver-specific modalities that can assess metabolic pathways in vivo. 13C MRI utilizing hyperpolarized 13C-pyruvate can assess key regulatory enzyme activities in hepatic metabolism. Methods: Rats with controlled cortical impact were studied in vivo using hyperpolarized [1-13C]pyruvate and [2-13C]pyruvate under fed and fasted conditions 3-4 days after injury. Hyperpolarized 13C products, including [13C]bicarbonate from [1-13C]pyruvate and [5-13C]glutamate, [1-13C]acetyl-L-carnitine, and [2-13C]phosphoenolpyruvate from [2-13C]pyruvate, were evaluated to assess mitochondrial and gluconeogenic metabolism. In parallel, liver tissues were collected following [U-13C3]pyruvate injection for NMR isotopomer analysis of phosphoenolpyruvate, glucose, and glutamate. Results: While no metabolic differences were detected under fed condition, [13C]bicarbonate and [2-13C]phosphoenolpyruvate increased after brain injury under fasted condition, indicating an upregulation of the hepatic gluconeogenic pathway after injury. 13C NMR of liver tissue extracts from injured rats showed an elevated [2,3-13C2]glutamate-to-[4,5-13C2]glutamate ratio and increased 13C-labeling in phosphoenolpyruvate than controls, confirming enhanced hepatic gluconeogenic pathway. Conclusion: This study demonstrates that hepatic acute phase response to brain injuries can be monitored in vivo by hyperpolarized pyruvate, which may be further utilized for longitudinal immunometabolic evaluation of the liver during pathogenesis and therapeutic interventions.

Diffusion-weighted MRI, beyond the commonly used diffusion tensor framework, offers a unique window into tissue microstructure in vivo, yet its clinical adoption has remained limited. Major barriers include the complexity of diffusion MRI sequence design, lengthy acquisition protocols, and the challenges associated with robust estimation of high-dimensional microstructural model parameters. Here, we address these limitations by combining optimised diffusion encoding with state-of-the-art simulation-based inference, establishing a clinically feasible framework for multi-compartment diffusion modelling. We validate the approach through i) in-depth in silico experiments and ii) in vivo studies made up of both human and rodent data. The resulting microstructural metrics are robust, reproducible across healthy individuals and show significant spatial associations with brain-wide expression patterns of cell-specific genes. Requiring less than 10 minutes of acquisition time, this framework substantially lowers the barriers to advanced microstructural imaging, a prerequisite step toward its eventual evaluation for the diagnosis, stratification, and monitoring of brain disorders.

Dynamic positron emission tomography (PET) with long axial field-of-view (LAFOV) scanners enables multi-organ imaging and kinetic quantification beyond static (late-phase) imaging; however, the long times typically required for dynamic acquisitions remain clinically impractical. This study evaluates a deep learning (DL) framework to enable abbreviated dynamic PET acquisitions, comparing single-time-window (STW, early dynamic data only) and dual-time-window (DTW, early dynamic data plus a late 5-min static frame) protocols with early dynamic scan durations of 5-30 min and dose levels ranging from 360 MBq to 18 MBq. Seventeen 60-min dynamic [18F]FDG datasets were first motion-corrected using a staggered FALCON pipeline and then used to train and test a spatiotemporal DL model for autoregressive frame prediction. Performance was assessed across the full quantitative workflow, from DL-predicted frames and time-activity curves to organ-based kinetic modeling and voxel-wise parametric imaging in multiple tissues and two patient cohorts. DTW protocols consistently outperformed STW, better preserving late-phase kinetics. For a 15-min early dynamic scan, adding a late 5-min scan reduced mean absolute Ki difference from 23% (STW) to 17% (DTW) in the liver and from 26% to 15% in the thalamus. DTW + DL further reduced errors to [≤]10% in the liver, thalamus, and breast lesion, and 16% in muscle. Our recommended protocol, 15-min early dynamic scan plus a 5-min late scan with DL, remained robust to up to a 5-fold dose reduction (~74 MBq). Overall, these findings support DL-enabled abbreviated, low-dose dynamic LAFOV PET as a clinically feasible approach for accurate kinetic quantification

Diffusion models achieve high reconstruction quality in low-dose computed tomography (LDCT), but their iterative sampling trajectories impose substantial computational costs. Unlike unconditional generation, paired LDCT reconstruction starts from an image that already contains the anatomy and spatial structure of the standard-dose CT (SDCT) target; reconstruction primarily requires correcting dose-related noise and artifacts. We therefore introduce Residual Endpoint Flow Matching (REFM), an LDCT reconstruction method that learns to transport an LDCT image directly toward its paired SDCT endpoint rather than defining a noise-to-image trajectory. REFM predicts the residual velocity along linear interpolations between both images and supports single-step and multi-step reconstruction using the same trained network. We evaluate five model capacities using 1 to 50 Euler steps against deterministic U-Net and diffusion-based baselines. Across all REFM variants, one-step inference consistently provides the highest reconstruction quality. On the TCIA validation set, REFM Base achieves 50.98 dB PSNR and 0.9865 SSIM at 94.54 fps, compared with 50.92 dB, 0.9847, and 9.26 fps for DDPM-10. REFM Small retains 50.71 dB while increasing throughput to 198.56 fps. Without fine-tuning, REFM Base also matches the 25-step DDPM baseline on the external Mayo Clinic dataset, although DDPM remains stronger on synthetically degraded CRLM images. Thus, our results show that exploiting paired anatomical correspondence enables diffusion-level LDCT reconstruction with a single step reconstruction.

Study design Retrospective cohort study. Objectives To correlate Hounsfield unit (HU) values, using elliptical regions of interest (ROI), that can be easily defined in routine clinical practice with magnetic resonance imaging (MRI) T2-hyperintense area fraction, as a surrogate for paraspinal muscle fat infiltration and to establish specific HU screening thresholds that may be applied with standard picture archiving and communication system (PACS). Methods We included 136 patients (71 men; 61.0 {+/-} 15.4 years) who underwent preoperative computed tomography (CT) and MRI within an 8-week period. Elliptical ROI HU values were measured at L2/3 and L4/5 for erector spinae, multifidus, and psoas major. MRI T2-hyperintense area fraction (Otsu thresholding) served as the fat infiltration reference. Linear mixed-effects (LME) models were used to assess the HU-T2 association and level-specific receiver operating characteristic (ROC) analyses (lower HU value side; n=136 per muscle-level) to identify thresholds for [≥]30% and [≥]50% infiltration criteria. Results Intraclass coefficients = 0.709 (HU) and 0.857 (T2 fraction); Goutallier weighted kappa = 0.579. In the overall LME, {beta} was -0.880 HU per 1% T2-fraction increase (95% confidence interval -0.935 to -0.825; marginal R2 =0.502); the association was steeper in multifidus ({beta} = -1.020) than in erector spinae ({beta} = -0.753). Psoas major (R = -0.226) was excluded from ROC analyses. Difference between L2/3 and L4/5 HU cutoffs was ~20 HU. The [≥]50% criterion revealed higher discrimination. Conclusions Elliptical ROI-based HU measurements may reliably screen paraspinal muscle fat infiltration in erector spinae and multifidus using standard PACS. Specific thresholds may allow practical preoperative evaluation without additional costs or radiation.

Background: Hippocampal sclerosis (HS) is a common cause of drug-resistant focal epilepsy (DRFE) and amenable to neurosurgical treatment. Detection relies on MRI but can be challenging. 7 Tesla (T) ultra-high field MRI and automated MRI post-processing tools have independently been shown to improve radiological diagnosis of HS. However, combining these approaches remains underexplored. This study evaluated whether AID-HS, a tool for HS detection developed using 3T MRI, generalises to 7T MRI data. Methods: We collated a dataset of paired 3T and 7T T1-weighted MRI from four epilepsy centres, including 23 patients with HS, 39 healthy controls, and 23 individuals with focal cortical dysplasia as disease controls. Histopathology served as the gold standard for defining HS where available (n=7), otherwise radiological findings (n=16). AID-HS was applied to images acquired at both field strengths, and sensitivity and specificity for detection and lateralisation of HS were compared. Additionally, agreement of hippocampal features across 3T and 7T was evaluated. Results: We found no evidence of a difference in performance of AID-HS between 3T and 7T. Sensitivity for detection of unilateral HS was 63% (12/19) at 3T and 68% (13/19) at 7T (McNemar's exact test p=1.0). Specificity in controls was 97% (60/62) at 3T and 100% (62/62) at 7T (p=0.5). Bilateral HS was correctly flagged in 3 of 4 cases using feature-based criteria, with high specificity in controls. Quantitative hippocampal features showed moderate to good agreement across field strengths (ICC 0.70 to 0.98), with small differences observed for volume and thickness estimates. Conclusion: AID-HS provides robust detection and lateralisation of HS across multiple 7T MRI centres, highlighting its potential to enhance lesion detection. Future work is needed to investigate whether models trained on 7T data can leverage the improved image quality for further gains in HS detection performance.

Objectives: Transcranial magnetic stimulation (TMS) is widely used to probe corticospinal excitability by eliciting motor evoked potential (MEP)s in targeted muscles, with MEP characteristics such as magnitude and latency reflecting the physiological state of the pathways being stimulated. Although numerous studies have examined MEP reliability in upper extremity muscles, less is known about the reliability of this measurement across the lower extremity. We hypothesized that inter-session, test-retest reliability of MEPs recorded simultaneously from multiple lower-limb muscles, from a single TMS location, would differ by muscle, stimulation intensity, and quantification method. Materials and Methods: Ten healthy participants (5 males, 5 females) completed three TMS sessions separated by atleast one week. At each session, the stimulation hotspot was identified using a five-location virtual grid anchored at the vertex, with electromyography (EMG) recorded from all eight muscles of interest at each grid location; the grid location producing the largest and most consistent MEPs in the tibialis anterior (TA), the primary target muscle, was selected as the stimulation site and held constant across all three sessions. MEPs were then recorded bilaterally from the TA, soleus, rectus femoris, and biceps femoris muscles at two stimulation intensities (110% and 120% resting motor threshold (RMT)). MEP size was quantified using mean rectified magnitude and peak-to-peak amplitude, and inter-session reliability was assessed using intraclass correlation coefficients (ICC). Bland-Altman analysis was used to characterize the range of measurement variability across all eight muscles. Results: MEP size differed across sessions, and reliability varied by muscle, intensity, and quantification method. The highest reliability was observed in the right TA, the muscle used to establish the stimulation hotspot, using mean rectified magnitude at 120% RMT. Reliability was comparatively lower in the seven non-target muscles recorded from the same fixed stimulation site, indicating that MEP consistency was not uniform across the lower-limb musculature. Conclusions: MEP reliability in the lower extremity depends heavily on the muscle, stimulation intensity, and quantification method used, and is highest in the muscle for which the stimulation site was optimized. These findings support the interpretation that coil positioning targeted to a specific muscle yields more consistent responses in that muscle than in others recorded from the same fixed site, and underscore the importance of careful muscle selection and hotspot optimization when designing TMS protocols for longitudinal or clinical lower-limb research.

Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System IISDS, an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [≥]0.011 Dice score, reducing lesion volume estimation error by [≥]0.3 average symmetric surface distance (ASSD), and increasing classification performance by [≥]0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation.

Automated lesion segmentation is increasingly central to PSMA PET/CT interpretation, supporting staging, treatment planning, and response assessment at a scale that outpaces available nuclear-medicine expertise. However, automated PSMA-PET/CT whole-body lesion segmentation models are trained on images alone, with no knowledge of where in the body prostate metastases actually tend to occur. Radiologists use clinical domain knowledge of metastatic spread, but its absence in machine learning models produces false positives in anatomically implausible locations and missed lesions in high-risk sites such as the liver. In this work, we explore whether population-level spatial knowledge of metastatic spread can be used to augment deep learning segmentation predictions, and how such a prior should be fused with a network's output, without additional training. We build a data-driven metastasis atlas from 375 expert-annotated whole-body PSMA PET/CT scans and investigate its fusion with a trained segmentation network under a Bayesian framework, in which prediction probabilities from an nnU-Net-based lesion segmentation model serve as the likelihood and the data-driven atlas as the prior. Because metastases occupy only a small fraction of whole-body voxels, the atlas's peak probability is too low, and standard power-scaled or naive Bayesian pooling references lack the tools to deal with this shortcoming. This causes these standard fusion strategies to fail and, in the naive Bayesian case, to sharply degrade performance. We instead derive a calibrated, background-referenced log-odds fusion, one of many possible approaches to combine a population atlas with a deep learning model's predictions, distinct from classical multi-atlas label fusion in that it fuses a single population prior with a trained network's softmax rather than combining several registered atlases. Furthermore, this approach is neutral outside atlas support by construction, reduces exactly to the baseline network when unweighted, and requires no retraining. This atlas fusion significantly improved mean Dice over the baseline nnU-Net on a disjoint internal test set ($+0.011$, Holm-adjusted $p=0.021$) and on an independent external cohort ($+0.0129$, Holm-adjusted $p=3.8\times10^{-16}$), with lesion sensitivity improving from 0.849 to 0.861 internally and Dice improving over baseline in every stratified anatomic region, including the rare, high-risk sites motivating this work, while naive Bayesian pooling degrades performance sharply and power-scaled pooling underperforms it throughout. Our findings suggest that population-level spatial priors can meaningfully augment deep learning predictions in whole-body oncologic segmentation, provided the fusion rule is calibrated to where the prior actually carries signal.

Rib-cage morphology is a determinant of thoracic biomechanics, ventilation, and injury response, yet statistical shape models (SSMs) of the rib cage have relied on small cohorts (~100s of individuals) imaged by clinical computed tomography, which over-represents injury and disease. We constructed a surface-based SSM of the complete 24-rib cage from 26,275 standardised whole-body magnetic resonance imaging (MRI) scans of adults aged 19-74 years from the population-based German National Cohort (NAKO). Ribs were segmented with a deep-learning pipeline (a rib-extended SPINEPS model), reconstructed as per-rib surface meshes, and brought into dense vertex-wise correspondence by Gaussian-process morphable registration in Scalismo; the aligned ensemble was summarised by generalised Procrustes analysis and principal component analysis (PCA). Fourteen per-rib geometric descriptors provided a quantitative cross-walk between the abstract PCA modes and named shape features, and associations with sex, age, body size and composition (including body-fat percentage), and smoking exposure were estimated by multivariable regression with Benjamini-Hochberg false-discovery-rate control. Shape variation was strongly concentrated: 28 modes captured 95% of the total variance, and the first three alone accounted for 69.4% (PC1, 42.6%; PC2, 16.3%; PC3, 10.5%) and admitted consistent anatomical readings - a sexually dimorphic axis (PC1), a slender-versus-stout body-habitus contrast (PC2), and a free-rib-size axis at ribs 11-12 (PC3). The sexes were nearly fully separated along PC1 (Cohen's d = 2.52). Body mass and body-fat percentage were the dominant modifiable correlates of rib-cage shape, whereas the association with cumulative smoking exposure was comparatively small. The model is released as a population-representative geometric reference for benchmarking and morphing donor-derived finite-element human-body models and for further large-cohort shape analysis.

Muscle architecture shapes muscle function and changes with age, growth, training and disease, yet quantifying three-dimensional (3D) muscle architecture in vivo remains challenging. We introduce a hybrid fascicle tractography approach for freehand 3D ultrasound data that accurately reconstructs 3D muscle fascicles with respect to an objective, anatomically relevant coordinate system defined by the muscle's central aponeurosis. The hybrid approach combines Hessian-based fascicle detection with wavelet-based refinement to generate volumetric fascicle orientations. In a synthetic dataset with known ground truth, fascicle orientations and lengths were estimated with errors of [≤]2{degrees} and ~1.5%, respectively. In vivo, the approach detected physiologically plausible fascicle lengthening in the human tibialis anterior following a passive plantar flexion rotation, whereas diffusion tensor imaging of the same muscle did not. The proposed method enables anatomically relevant, objective and non-invasive quantification of 3D muscle architecture in vivo, providing a practical framework for applications in clinical and applied muscle physiology.

Background & Aims: Fatigue is a central symptom of chronic liver disease (CLD), substantially impacting health-related quality of life (HRQoL). This study aimed to further understand CLD symptomatology, including fatigue, and its impact on HRQoL from a patient perspective. Methods: Abbott Global Assessment of Patients unmet needs (aGAP) was a multinational, cross-sectional survey in adults with compensated CLD in China, India and Mexico, conducted between July and November 2024. Adult participants who self-reported that they had physician-diagnosed CLD and were experiencing fatigue completed a quantitative survey to assess symptom burden and included three HRQoL patient-reported outcome (PRO) questionnaires (Patient-Reported Outcomes Measurement Information System [PROMIS]-29+2, Work Productivity and Activity Impairment - Specific Health Problem version 2.0 [WPAI: SHP], Multidimensional Fatigue Inventory [MFI]). Results: Overall, 505 participants (China: 200; Mexico: 105; India: 200) completed the study. Participants reported that their CLD-related fatigue sometimes, often or always affected their self-esteem/confidence (45.1%) and ability to maintain or acquire new employment (38.6%). Most participants reported moderate (51.3%) or serious (26.9%) fatigue, with 33.5% experiencing fatigue every day or almost every day. Many participants felt their social life was negatively impacted by their fatigue (47.3%) and that there were related financial difficulties (53.9%). Use of validated PRO tools demonstrated severe fatigue (MFI: overall mean [SD] 13.9 [3.4] general fatigue and 13.4 [3.6] physical fatigue) as well as substantial levels of work and activity impairment (WPAI: SHP overall mean [SD] 53.0 [26.4]) and high levels of anxiety, pain interference, depression and sleep interference (PROMIS T-scores [≥]54). Conclusions: Fatigue has a substantial impact on HRQoL among adults with CLD across several countries, highlighting a global unmet need for targeted interventions to effectively identify and manage the condition.

Importance: Evidence on GLP-1/GIP therapies is largely derived from trials enrolling selected populations or medical records that miss utilization outside healthcare channels. No nationally representative cohort has characterized real-world uptake, indications, and access. Objective: To characterize GLP-1/GIP prevalence, indication, clinical profile, and access. Design: Prospective cohort study with three GLP-1/GIP surveillance waves (March 2024, December 2024, October 2025). Setting: The Understanding America Study, an address-based, nationally representative panel of approximately 15,000 US adults aged 18+ years initiated in 2014. Participants: UAS participants responding to at least one surveillance wave (n=9150). Exposures: GLP-1/GIP use status (never vs any use, comprising current and former use), self-reported primary indication (diabetes, weight loss, or other), and access pathway (traditional vs non-traditional). Main Outcomes and Measures: Survey-weighted prevalence of GLP-1/GIP use, overall and by indication and access pathway; sociodemographic, cardiometabolic, treatment, and access characteristics; and smartwatch-derived resting heart rate, heart rate variability, maximum activity heart rate, step count, and sleep duration and variability. Results: Among n=9150 adults (1274 with any use; 60.9% female; median age 53 years), weighted prevalence increased 46%, from 8.2% (March 2024) to 12.0% (October 2025) representing 32 million. Weight-loss indications grew, reaching nearly half of use (4.1% to 5.6%); diabetes-indicated use was stable (5.3% to 5.4%). Users carried high cardiometabolic burden (obesity, 68.2%; diabetes, 53.6%) but diverged by indication: diabetes-indicated users were older (median, 59 vs 49 years), whereas weight-loss-indicated users were more often female (69.9% vs 51.3%) and healthier. One in three users (~9 million) had non-traditional access, especially in weight-loss-indicated users, of whom 33% had no conventional prescription; 41% used compounding, online, or foreign pharmacies; and, 43% lacked coverage. Non-traditional users were five times as likely to report an unlisted, likely compounded formulation (19.8% vs 4.1%). All p<0.05. Conclusions and Relevance: Real-world GLP-1/GIP use has grown rapidly and diversified substantially in indication, access, and population profile. One in 3 users obtained treatment through nontraditional channels largely invisible to claims data, raising long-term safety, efficacy, and coverage questions. GLIMMER provides a public, nationally representative longitudinal evidence base for future payer and provider decisions.

Background. Videolaryngoscopy still requires adjuncts or hyperangulated rescue in a clinically important minority, and bedside screening discriminates modestly. Point-of-care ultrasound (POCUS) of the anterior airway is a promising alternative, but existing prediction models are opaque or assume a pre-specified functional form. We developed and internally validated a parsimonious, fully disclosed POCUS risk equation whose form is recovered from data and whose structural properties are machine-checked by formal proof - to our knowledge the first formally verified clinical risk predictor - following TRIPOD+AI 2024. Methods. In a prospective single-centre, single-operator cohort of 259 adults undergoing elective videolaryngoscopy (no-Easy airway 68/259, 26.3%), Sequentially Thresholded Least Squares with bootstrap stability selection (B=300) screened a 71-term library of nine POCUS features and retained a seven-term logistic equation; a two-term bootstrap-stable model was pre-specified as robustness analysis. Internal validation used 5x10 repeated cross-validation plus temporal and device hold-outs, with pre-specified overfitting and optimism assessments. Five behavioural properties of the deployed equation were machine-checked in Lean 4. Results. Two interactions met the |c|/sigma_c>2 stability criterion: skin-to-epiglottis x skin-to-hyoid-bone distance and tongue volume x sagittal tongue area. The seven-term equation reached a 5x10 cross-validated C-statistic of 0.966 (optimism-corrected 0.968) and held across temporal and device hold-outs (0.94-0.97). Calibration-in-the-large matched prevalence, with cross-validated slope 0.90 attenuating to 0.625 out-of-time; standard recalibration restored 0.92 without loss of discrimination. The pre-specified two-term robustness model reproduced this performance (C-statistic 0.964-0.968; events-per-parameter 34; shrinkage 0.99), confirming the result is not an artefact of the screening stage. Net benefit over a clinical baseline was positive across 10-50% thresholds. All five Lean 4 theorems compiled without sorry. Conclusions. A sparse, formally verified POCUS equation predicts difficult videolaryngoscopy with high internally validated discrimination and quantified, modest overfitting. Because the equation was developed in a single-operator cohort and its inputs are operator-dependent, external validation requires prior harmonisation of the measurement protocol and operator credentialing.

Abstract Objective: Glaucoma is a chronic disorder that impairs ocular health and may exacerbate ocular surface disease leading to tear film instability, dry eye symptoms and decreased quality of life. This study compared changes in tear quantity among glaucoma subjects living with and without diabetes mellitus, attending an eye clinic in Nigeria. Methods: A comparative cross sectional research design was used. 157 subjects which comprised 74 glaucoma subjects living with diabetes mellitus and 83 glaucoma subjects living without diabetes mellitus participated in the study. Tear quantity assessment included the Schirmer I test and tear meniscus height (TMH) measurement. Descriptive statistics, independent samples t-test and Chi-square test were used to examine the data at 0.05 level of significance. Results: Glaucoma subjects living with diabetes mellitus showed substantially decreased tear production (11.4 +/- 6.8 mm) compared with glaucoma subjects living without diabetes mellitus (19.6 +/- 9.6 mm; p < 0.001). Tear meniscus height in glaucoma subjects living with diabetes mellitus (0.8 +/- 0.3 mm) was significantly greater than in subjects living without diabetes mellitus (0.7 +/- 0.3 mm; p = 0.034). Conclusion: Diabetes mellitus dramatically deteriorates the ocular surface function in glaucoma subjects by decreasing tear production, altering the tear meniscus height and increasing the severity of ocular surface symptoms. Routine glaucoma care, especially in patients with diabetes mellitus, should include a full ocular surface evaluation including Schirmer I test, TBUT, TMH, and OSDI assessment to allow early detection and management of ocular surface disease, better treatment adherence, and improved visual outcomes. Keywords: Glaucoma, Diabetes Mellitus, Tear production, Tear Meniscus Height, Ocular Surface Disease.

Background: Prostate cancer enzalutamide treatment is approved at a standard dose of 160 mg daily. Concerns for real-world patients -- older and more fragile than those enrolled in clinical trials -- have prompted consideration of initiating treatment with lower doses, but the long-term efficacy of this approach remains unknown. We evaluate the long-term survival and longevity in patients treated with standard versus upfront low-dose enzalutamide. Methods: Retrospective analysis of 151 patients treated with enzalutamide (102 receiving 160 mg; 49 receiving [&le;]80 mg) between 2014--2021 at the Centre Hospitalier Universitaire de Martinique, with complete follow-up through end of life (98.7% completeness of follow-up). Primary outcomes were overall survival (OS), progression-free survival (PFS), and longevity (attained age). Results: Doses [&le;]80 mg were associated with longer median OS (36.3 vs. 20.7 months), improved restricted mean OS (difference of 0.7 years, p=0.05), and enhanced longevity (median 82.5 vs. 78.3 years, p=0.004). PSA response rate at 12 weeks was higher with lower-dose (71.4% vs. 48.8%, p=0.016). In multivariable models adjusted for prognostic factors, [&le;]40 mg compared with 160 mg was non-inferior regarding OS (HR=0.61, 95% CI 0.36--1.06), superior regarding PFS (HR=0.59, 95% CI 0.35--0.99), and superior regarding longevity (HR=0.48, 95% CI 0.28--0.84). Bone metastasis, poor performance status, PSA response, time to PSA nadir, and disease duration were independent predictors of outcomes. A post-hoc analysis revealed a strong association between dose and physician-prescribing profiles, ranging from "endorse-lowest-dose" to "never-deviate-from-full-dose". Conclusions: Lower doses of enzalutamide were non-inferior to full-dose. Dose-adapted strategies warrant further investigation.

Purpose: To evaluate the safety, procedural performance, sample recovery, and surgeon preference of an ophthalmic needle designed specifically for anterior chamber (AC) paracentesis. Methods: In this multicenter study, AC paracentesis was performed in clinic and operating-room settings using a 32-gauge x 4-mm needle with low dead space. The procedure was evaluated using a standardized physician survey. Prespecified outcomes included procedure-related adverse events (primary outcome), needle entry and handling, aspiration and sample recovery, comparative performance versus a 30-gauge needle, and physician preference for future use. Results: A total of 110 needle uses by eight surgeons were included. No ocular complications occurred, including lens or iris injury, hyphema, AC collapse, wound leak, hypotony, infection, or retinal complication, and no procedure required needle exchange or conversion to another device. Two technical events without ocular sequelae were noted, in which needle entry was partial thickness and did not reach the AC (1.8%; exact 95% CI, 0.2%-6.4%). Physicians rated needle entry, handling and sample recovery as good or excellent. Compared with a 30-gauge needle, the study needle was rated as at least comparable across all assessed domains. All surgeons rated it better or much better for intra-procedural safety and preferred it for future AC taps. Conclusions and Relevance: This short, 32-gauge low-dead-space ophthalmic needle demonstrated a favorable safety profile and was preferred over a 30-gauge needle by all surgeons. As aqueous humor liquid biopsy expands in clinical diagnostics and trials, an ophthalmic-specific needle design may help improve the consistency and safety of aqueous humor collection for molecular analysis and broader clinical use. Keywords: Anterior chamber paracentesis; Aqueous humor; Liquid biopsy; Low dead space; Ophthalmic needle