Citations

Abstract Objective . Penile vibro-elastography (PVE) is complicated by reverberant shear wavefields from the cylindrical geometry of the corpus cavernosum (CC), which bias shear wave speed (SWS) estimates. This study evaluated local phase velocity imaging with wavefield-based filtering (LPVI-WBF) for CC stiffness assessment in post-prostatectomy erectile dysfunction (ED), compared against phase gradient (PG) and time-of-flight (TOF) methods. Approach . Six patients with post-prostatectomy ED underwent PVE at four longitudinal visits (902 acquisitions). SWS was estimated in flaccid and erect states at 100, 150, and 200 Hz using LPVI-WBF, PG, and TOF. Wavefield composition was quantified via the standing wave index. Simulations evaluated accuracy and frequency stability under clean and reverberant conditions. Main results . Reverberant components dominated penile acquisitions, with traveling wave energy absent in 59% of acquisitions and mean standing wave fraction of 57 ± 17%. LPVI-WBF was the only method producing physiologically consistent estimates, with erect SWS exceeding flaccid (3.59 ± 0.48 vs 3.25 ± 0.58 m s −1 , p < 0.0001). PG and TOF yielded reversed results ( E < F, p < 0.0001). The dispersion exponent was near-zero for LPVI-WBF ( α = 0.048, p = 0.21), consistent with weakly viscoelastic tissue, while PG ( α = 0.184) and TOF ( α = 0.321) showed significant frequency dependence from reverberant contamination. Simulations confirm PG accuracy in clean wavefields but collapse under reverberant ones. Significance . LPVI-WBF produced SWS estimates consistent with expected CC biomechanics across all patients and frequencies, a criterion PG and TOF failed to satisfy. Phantom validation and multi-directional wavefield simulation are needed to establish accuracy.

Abstract Background Genetic testing is increasingly used in presurgical evaluation, but the yield of resection across germline genetic epilepsies remains uncertain. Methods We conducted a systematic review of MEDLINE (PubMed) and Scopus and added cases from three institutional cohorts and the Pediatric Epilepsy Research Consortium (PERC) databases, including individuals with pathogenic/likely pathogenic germline variants besides tuberous sclerosis and neurofibromatosis who underwent resection or laser ablation. Etiologies were grouped into biologically informed categories (GATORopathies, vascular, overgrowth, CNVs, channelopathies, synaptopathies, other). Primary outcome was seizure freedom (Engel I) at last follow-up. Group comparisons used Fisher’s exact and Kruskal–Wallis tests (α=0.05; Bonferroni when applicable). Prespecified sensitivity analyses stratified by lesional status, excluded GATORopathies, and restricted to literature-only cases. Results We included 223 literature cases (64 studies), 35 institutional cases, and 11 PERC cases (n=269). Median follow-up was 24 months (IQR 12–48.0). Seizure freedom was achieved in: vascular disorders 14/19 (73.6%), GATORopathies 79/120 (67.5%), CNVs 18/31 (66.7%), overgrowth 7/13 (53.8%), other 16/25 (41.7%), channelopathies 13/43 (33.3%), and synaptopathies 4/18 (22.2%) (overall p<0.001). Among cases with known imaging, 208/253 (82.2%) had epileptogenic MRI lesions, including 64% of channelopathies and 80% of the synaptopathies. In univariate contrasts (each category vs all others), odds of seizure freedom were higher for vascular disorders (2.66-fold, 95% CI 0.87–9.72) and GATORopathies (2.46-fold, 95% CI 1.46–4.18), and lower for synaptopathies (∼4.2-fold lower, OR 0.24, 95% CI 0.05– 0.78) and channelopathies (∼4.5-fold lower, OR 0.22, 95% CI 0.09–0.48). Direction and magnitude were consistent across prespecified sensitivity analyses (lesional-only, literature-only, exclusion of GATORopathies). Conclusions Resective surgeries can be effective in germline genetic epilepsies, but outcomes vary by pathway. Disorders with discrete, lesional substrates (GATORopathies, vascular) show the highest likelihood of seizure freedom, whereas channelopathies and synaptopathies, despite the presence of MRI lesions, have substantially lower yields even in lesional cases. Prospective, genotype-aware surgical registries with standardized reporting (EEG, imaging, pathology) and time-to-event outcomes are needed to refine the selection of surgical candidates and quantify seizure and non-seizure-related outcomes.

Abstract The prevalence of steatotic liver disease (SLD) is rising globally, presenting a significant public health challenge. While non-contrast computed tomography (CT) scans hold promise for the opportunistic detection of SLD, their full potential has not been systematically explored. Clinical indicators, such as serum data, are crucial for monitoring the progression of SLD. We introduce a novel Multimodal AI for Opportunistic Steatotic Liver Disease Screening (MAOSS), which integrates demographic data, serum tests, conventional CT biomarkers, and deep learning-derived CT image features to enhance model performance and explainability. MAOSS employs an innovative learning strategy, trained on a large-scale dataset (n = 1,783) encompassing both histopathologically-confirmed SLD and radiologically-reported steatosis cases. The model underwent extensive retrospective validation using steatosis grades derived from both histology and MRI-PDFF across internal and external datasets (n = 913), as well as real-world patient data (n = 18,504). It accurately detected mild to severe steatosis (≥ S1) with AUCs ranging from 0.903 to 0.929. Notably, MAOSS significantly improved the overall AUC for radiologists (n = 11) from 0.709 to 0.798 (p < 0.001). Furthermore, MAOSS was applied to optimize the standard clinical pathway for a simulated primary risk screening of SLD patients (n = 1,192), resulting in the identification of 31% more patients at risk of steatohepatitis and liver fibrosis progression, highlighting its potential to enhance SLD detection and management.

Introduction: Abdominal aortic aneurysms (AAA) are common among individuals over age 65 with a prevalence of 9%. Most AAAs are small and require surveillance imaging to monitor size and growth rates. Tools capable of predicting growth rates would be clinically useful. Hypothesis: We investigated how computational hemodynamics derived from CT angiography (CTA) differentiated slow and rapid-growing AAAs. Methods: Using readily available computing power in the clinical workflow, 3D models were generated for 25 subjects with AAA from available 3D CTA data. AAA growth rates were categorized as slow ( less than 5 mm/year) or rapid (greater than 5 mm/year) based on serial imaging. A geometrical analysis was performed using 7 indices (Volume, Surface Area, Length, Max/Min radius, Expansion Ratio [Mean radius/vessel radius], Proximal vessel area, Voronoi diagram-based metrics) for each AAA. Computational hemodynamics were performed using commercial software (ANSYS-Fluent). Computational hemodynamics were completed for all cases (see Fig. 1). In-house software assessed wall shear stress-(WSS) and vortex-based hemodynamics. Support vector machine (SVM) was run with cross-validation (50 iterations) to assess predictive strength using these 16 parameters. Results: Among 25 AAAs studied, 7 were rapid and 18 were slow-growing. The combination of proximal vessel area, spatiotemporally averaged WSS, and spatially averaged OSI provided the best accuracy for growth differentiation. The area under receiving operating curve (AUROC) and total accuracy are 0.770 and 0.765, respectively, with 48% and 86% of rapid and slow-growing AAAs correctly identified, respectively. Conclusions: Computational hemodynamics appears feasible with reasonable growth rate prediction. With optimization, this tool may enable resource utilization refinement for imaging surveillance strategies by distinguishing rapid from slow-growing AAAs.

AbstractBackgroundAPOE ε4 is the strongest genetic risk factor for late‐onset Alzheimer’s disease (AD). The contribution of microglial APOE4 to AD pathogenesis is unknown, although APOE has the most enriched gene expression in neurodegenerative microglia (MGnD).MethodGeneration of CX3CR1‐CREERT2 mice crossed to APOE‐KI(E3 and E4)fl/fl: APP/PS1 and APOE‐KI(E3 and E4)fl/fl: P301S mice. Microglia and astrocytes were isolated and sequenced using either bulk RNA‐seq (MGnD‐Paradigm) or single‐cell RNA‐seq. Crosstalk between cells was determined using the NichenetR database and validated using 1) recombinant Lgals3 intracranial injection into APP/PS1 mice and 2) adoptive transfer of microglia, from APOE3‐KI, APOE4‐KI, and APOE4‐cKO mice challenged with labeled apoptotic neurons, into WT mice and isolation of astrocytes. Validation in the brain of AD donors carrying the APOE e3/3 and e3/4 alleles.ResultHere we show, in mice and in humans, a negative role of microglial APOE4 in the induction of MGnD response to neurodegeneration. Deletion of microglial APOE4 restores MGnD phenotype, associated with neuroprotection in P301S tau transgenic mice, and decreases pathology in APP/PS1 mice. MGnD‐astrocyte crosstalk associated with b‐amyloid (Ab) plaque encapsulation and clearance is mediated via Lgals3 signaling following microglial APOE4 deletion. In the brain of AD donors carrying the APOE ε4 allele, we found a sex‐dependent reciprocal induction of AD risk factors associated with suppression of MGnD genes in females, including LGALS3, as compared to APOE ε3/3 carriers. Mechanistically, APOE4‐mediated induction of ITGB8‐TGFb signaling impairs MGnD response via upregulation of TGFb‐mediated microglial homeostatic checkpoints, including INPP5D. Microglial deletion of Inpp5d restores MGnD‐astrocyte crosstalk and facilitates plaque clearance in APP/PS1 mice. Genetic and pharmacological blocking of ITGB8‐TGFb signaling enhanced MGnD response associated with increased plaque clearance in AD mice.ConclusionWe identify the microglial APOE4‐ITGB8‐TGFb pathway as a negative regulator of microglia‐astrocyte response to AD pathology, and restoring MGnD phenotype via blocking ITGB8‐TGFb signaling may provide new molecular targets to modulate and restore functional microglia in AD.

Excess extravascular lung water (EVLW) is a common consequence for patients with congestive heart failure, inflammatory conditions, or acute respiratory distress syndrome. Computed tomography (CT) is widely used to assess EVLW, but with a higher radiation dose and logistical complexity. Lung ultrasound (LUS) using B-line artifacts demonstrates a reasonable correlation with EVLW, but user experience with the acquisition technique, equipment-dependent factors and the subjective analysis of the B-line artifacts results in significant inter-observer variability. We propose a technique to automatically detect lung ultrasound B-lines over heart cycles. The method first extracts B-mode image features from videos in a region of interest (ROI). A ResNet50-UNet network is then applied to segment regions of B-lines. This technique was performed on 8 videos (i.e., 4 patients versus 4 controls with the duration of each video 2–3 s, resolution 1024 × 758 pixels, and frame rate 28 fps). The training accuracy and lossvalue of proposed models were 96.5% and 0.05, respectively. The paired t-test statical analysis shows that there is significant difference (p-value = 0.0035) of total number of B-line between normal and lungs with edema. The proposed method improves the accuracy for counting the B-lines and reduces the labor work and intra-observer variability from different users.

Many lung diseases are associated with dramatic changes in mechanical properties of the lung. Chest radiographs and computed tomography (CT) are the most common clinical methods for evaluating lung disease, but they are expensive, use radiation, and do not measure lung stiffness. Shear wave elastography (SWE) uses ultrasound radiation force (URF) to generate wave propagation in the tissue. However, URF should not be applied to the lung, because there are air and blood in the alveoli. Relatively high-intensity ultrasound can cause alveolar hemorrhage and lung injury. We have developed a noninvasive, and clinically feasible technology, lung ultrasound surface wave elastography (LUSWE), that is capable of measuring lung elastic properties with speed and accuracy. LUSWE uses a handheld shaker to generate a local small harmonic vibration on the chest surface. The resulting wave propagates through the intercostal muscle and propagates on the lung. The surface wave speed is noninvasively measured using an ultrasound technique. LUSWE is safe because diagnostic ultrasound is only used to detect the wave propagation along the lung duo to mechanical wave generation on the chest. LUSWE may be used to assess multiple lung disorders such as lung fibrosis or lung edema.

The evaluation of diffuse lung disease invariably involves chest computerized tomography (CT). The detailed information obtained is often equivalent to that from lung biopsies. It, however, involves exposure to ionising radiation which limits its repeated application in monitoring and follow up. Ultrasound techniques offer potentially safer/cheaper alternatives and lung ultrasound surface wave elastography (LUSWE) is a novel, noninvasive, and clinically feasible technique to quantify lung stiffness, an important biomechanical property that changes in diffuse fibrotic and infiltrative lung diseases. We have developed a sensitive, accurate and reproducible technique to measure changes in lung surface stiffness by using surface wave transmission speed and demonstrated its value in detecting and staging the degree of fibrosis in diffuse interstitial lung diseases, specifically idiopathic pulmonary fibrosis and progressive systemic sclerosis associated lung disease, as well as changes in heart failure. The technique consists of ultrasound detection of the propagation speed of a wave generated by the vibrations from an indenting shaker in multiple intercostal spaces to provide a representative assessment of both lungs. It offers the prospect of simple, radiation-free, repetitive assessment of changes in acute and chronic diffuse lung diseases with the potential for easy and rapid bedside use, all important considerations in clinical practice.