Ultrasound in Medicine & Biology

Background: Late gadolinium enhancement (LGE) quantification by cardiovascular magnetic resonance is central to risk stratification in hypertrophic cardiomyopathy (HCM), yet conventional techniques require contour tracing and region-of-interest (ROI) placement, which may reduce reproducibility and increase analysis time. We developed a novel visual standardized approach, the Visual Standardized Quantification of LGE (VISTAQ), that does not require myocardial contouring, arbitrary ROI positioning, or dedicated post-processing software. Methods: In this multicenter, multivendor retrospective study, LGE images from 400 patients (100 prior myocardial infarction, 250 HCM, 50 other non-ischemic heart diseases) were analyzed. VISTAQ subdivides each myocardial segment into transmural mini-segments and classifies LGE visually using predefined criteria, expressing global LGE burden as the percentage of positive mini-segments. Reproducibility was assessed in 250 patients across different observer expertise levels using intraclass correlation coefficients (ICC) and Bland?Altman analysis. In 100 HCM patients, VISTAQ was compared with conventional methods (mean+2SD, +5SD, +6SD, FWHM, visual thresholding). Prognostic performance was evaluated in 250 HCM patients over a median 5-year follow-up. Results: VISTAQ demonstrated excellent intra- and inter-observer reproducibility (ICC up to 0.98 and 0.97, respectively), consistent across disease subtypes. Compared with conventional techniques, VISTAQ showed similar ICC to FWHM but significantly lower net and absolute inter-observer differences (median absolute difference 1.3%). Mean+2SD markedly overestimated LGE, whereas mean+6SD slightly underestimated LGE compared with VISTAQ, mean+5SD, FWHM, and visual thresholding. Analysis time was substantially shorter with VISTAQ (median 105 vs. 375 seconds, p<0.0001). During follow-up, 21 hard cardiac events occurred in HCM population. An LGE threshold >10% predicted events with higher accuracy using VISTAQ (AUC 0.90; sensitivity 85%; specificity 94%) compared with mean+6SD (AUC 0.75; sensitivity 57%; specificity 93%). Conclusions: VISTAQ provides highly reproducible, time-efficient LGE quantification without dedicated software and demonstrates non-inferior prognostic discrimination in HCM compared with conventional threshold-based techniques.

Modelling of hemodynamics in the circle of Willis (CoW) depends on vascular segmentation, which may vary based on imaging modality. Computed tomography angiography (CTA) is commonly used in clinic but involves radiation and injection of contrast agents, whereas magnetic resonance angiography (MRA) offers a non-invasive alternative. This study aims to compare CoW morphology and modelled cerebral perfusion pressure of CTA and MRA segmentations, validating if MRA can replace CTA in modelling workflows. CTA and time-of-flight MRA (TOF-MRA) of the CoW was performed in 19 patients undergoing elective aortic arch surgery (67{+/-}7 years, 8 women). The CoW was semi-automatically segmented based on signal intensity thresholding. A TOF-MRA threshold was optimized against the CTA segmentation, using the CTA as reference standard. Computational fluid dynamics (CFD) modelling with boundary conditions based on subject-specific flow rates from 4D flow MRI simulated cerebral perfusion pressure in the segmented geometries. A baseline simulation and a unilateral brain inflow simulation, i.e., occlusion of a carotid, were carried out. Linear mixed models indicated there was no effect of choice of modality on either average arterial lumen area (CTA - TOF-MRA: -0.2{+/-}1.3 mm2; p=0.762) or baseline pressure drops (0.2{+/-}1.9 mmHg; p=0.257). In the unilateral inflow simulation, we found no difference in pressure laterality (-6.6{+/-}18.4 mmHg; p=0.185) or collateral flow rate (10{+/-}46 ml/min; p=0.421). TOF-MRA geometries can with signal intensity thresholding be matched to produce similar morphology and modelled cerebral perfusion pressure to CTA geometries. The modelled pressure drops over the collateral arteries were sensitive to the segmentation regardless of modality.

The SAFE MRI ECMO (NCT05469139) study established the safety of ultra-low-field 64mT MRI in patients receiving extracorporeal membrane oxygenation (ECMO) in the setting of intensive care and demonstrated that these images were highly sensitive in detecting acquired brain injuries. This retrospective analysis of prospectively collected observational data sought to expand on these findings in light of the crucial need for neurological monitoring while patients receive ECMO by evaluating the feasibility of volumetric analyses derived from ultra-low-field MR images. T2-weighted scans from thirty patients who received ultra-low-field MRI while undergoing ECMO at Johns Hopkins Hospital were analyzed using a volumetric pipeline to determine whole brain volume and volumes of total grey matter, total white matter, subcortical grey matter, ventricles, left hemisphere, right hemisphere, telencephalon, left and right lateral ventricles, the total intracranial volume, and the cerebellum. Segmented brain volumes in patients undergoing ECMO were comparable to measurements obtained using conventional field and ultra-low-field MRI in the absence of ECMO instrumentation. The subgroup analysis demonstrated subtle volumetric differences between patients supported with venoarterial ECMO and those receiving venovenous ECMO. These data provide the first evidence that ultra-low-field MRI provides volumetric measurements comparable to conventional field-strength MRI, even in the presence of ECMO circuitry, supporting its feasibility for neuroimaging in critically ill patients.

Background | Angina with nonobstructive coronary arteries (ANOCA) is a heterogeneous condition encompassing distinct endotypes representing different underlying pathophysiological mechanisms. Endothelial dysfunction is considered a central hallmark of ANOCA. However, studying patient-derived endothelial cells (ECs) remains challenging due to the limited availability of disease-specific endothelial samples. We therefore aimed to assess the feasibility of isolating and culturing ECs from catheterization material obtained during routine coronary function testing in ANOCA patients. Methods | Catheterization material was collected from 79 ANOCA patients (84% female, age 58{+/-}10 years) undergoing coronary function testing. ECs were isolated, expanded and characterized using immunostaining, flow cytometry, gene expression profiling and functional assays. Results | EC isolation was successful in 43% of cases and resulted in 34 primary EC cultures that were expanded up to passage 10. Isolation success was independent of clinical or procedural characteristics. Isolated cells exhibited typical EC morphology and expressed EC markers confirmed by immunostaining, flow cytometry and gene expression analyses. EC marker gene expression remained largely stable over passages. However, stress- and defense-related gene expression programs increased over time, while proliferation-related processes decreased. Functional assays demonstrated that the coronary catheterization-derived ECs showed typical properties of wound healing, angiogenesis, activation responses upon stimuli and monocyte adhesion. Conclusions | This study demonstrates the feasibility of isolating and expanding ECs directly from catheterization material collected during routine coronary function testing in ANOCA patients. These patient-derived ECs retain characteristic endothelial features and functionality. This approach offers primary EC cultures to study the mechanisms underlying endothelial dysfunction in ANOCA.

Computer vision and deep learning techniques, including convolutional neural networks (CNNs) and transformers, have increased the performance of medical image classification systems. However, training deep learning models using medical images is a challenging task that necessitates a substantial amount of annotated data. In this paper, we implement data augmentation strategies to tackle dataset imbalance in the VinDr-SpineXR dataset, which has a lower number of spine abnormality X-ray images compared to normal spine X-ray images. Geometric transformations and synthetic image generation using Generative Adversarial Networks are explored and applied to the abnormal classes of the dataset, and classifier performance is validated using VGG-16 and InceptionNet to identify the most effective augmentation technique. Additionally, we introduce a hybrid augmentation technique that addresses class imbalance, reduces computational overhead relative to a GAN-only approach, and achieves ~99% validation accuracy with both classifiers across all three case studies. Keywords: Data augmentation, Generative Adversarial Network, VGG-16, InceptionNet, Class imbalance, Computer vision, Spine X-ray, Radiology.

Abstract Background: ST-elevation myocardial infarction (STEMI) is reported to be a leading cause of mortality worldwide. While cardiac troponins are the gold standard for myocardial injury detection but creatine kinase-MB (CK-MB) and total creatine phosphokinase (CPK) retain prognostic use in resource-limited settings. Objective: To evaluate the prognostic significance of admission CK-MB and CPK levels in STEMI patients and to assess their association with hematological parameters for integrated risk stratification. Methods: This cross-sectional study enrolled 15 consecutive STEMI patients from the Punjab Institute of Cardiology, Lahore, during January 2024. Comprehensive laboratory analysis including cardiac biomarkers (CK-MB, CPK, troponin-I, LDH), complete blood count, renal function, serum electrolytes, and metabolic parameters, was performed on admission. Pearson correlation and comparative statistical analyses were also conducted to assess the relationships between cardiac biomarkers and hematological indices. Results: The cohort includes 15 patients (mean age 50.1 +/- 12.2 years; 73.3% male). Cardiac biomarker elevation was prevalent: CK-MB was elevated in 12/15 (80%), CPK was elevated in 12/15 (80%), with concordant elevation in 11/15 (73.3%), which indicates extensive myocardial necrosis. Troponin-I showed the highest elevation rate at 13/15 (86.7%). Hematological abnormalities included anemia (60%), WBC elevation (53.3%), and RBC reduction (40%). Random glucose averaged 150.80 +/- 63.55 mg/dL, with 66.7% highlighted the hyperglycemia. Remarkably, electrolyte balance was preserved in all of the patients (0% sodium, potassium, and bicarbonate abnormalities), indicating maintained homeostasis. Pearson correlation analysis revealed a significant correlation between CK-MB and CPK (r = 0.615, p = 0.0126), while correlations between cardiac biomarkers and hematological parameters were weak (p > 0.05). Risk stratification identified 53.3% of patients as high-risk who required intensive management. Conclusions: CK-MB and CPK demonstrate significant concordance and retain prognostic value in STEMI patients, particularly in resource-limited settings where troponin access may be constrained. While troponin-I remains the most sensitive biomarker, combined assessment of conventional cardiac enzymes supports reliable evaluation of myocardial injury. Hematological parameters reflect systemic response but show limited correlation with cardiac biomarkers.

Background: Hereditary transthyretin amyloidosis (hATTR) manifests as cardiomyopathy and/or polyneuropathy. The V142I variant predominantly causes cardiac disease in African Americans, though neurological involvement may be underrecognized. We characterized neuropathy documentation and treatment patterns in a predominantly V142I cohort. Methods: Retrospective review of 54 hATTR patients at a major academic medical center. Neuropathy was classified as: objective (abnormal EMG), possible polyneuropathy (documented symptoms suggestive of polyneuropathy), symptoms only (neuropathic symptoms without specialist evaluation), or unclear. Treatment with stabilizers (tafamidis, acoramidis, diflunisal) and gene silencers (patisiran, vutrisiran, eplontersen) was assessed. Results: Of 54 patients (88.9% African American, 85.2% V142I), 51 (94.4%) had confirmed cardiac involvement. Among cardiac patients, 40/42 eligible (95.2%) received stabilizers. Overall, 16 patients (29.6%) received gene silencers, with 13 (24.1%) receiving both a stabilizer and gene silencer concurrently. Possible neuropathy (objective, possible polyneuropathy, or symptoms) was documented in 30 patients (55.6%). Gene silencer use was highest among those with objective neuropathy (8/17, 47.1%) versus symptoms only (1/10, 10.0%). All three patients without confirmed cardiac disease received gene silencers. Conclusions: In this V142I-predominant cohort with 94.4% cardiac involvement, stabilizer use was high (95.2%) among eligible patients. Over half had possible neuropathy based on clinical documentation, though EMG completion was limited (57.4%). Gene silencer use was associated with objective neuropathy documentation and non-cardiac phenotype. These findings support systematic neurological assessment in hATTR, even when cardiac disease predominates.

BACKGROUNDMild traumatic brain injury (mTBI) is a signature injury in civilian and military populations that remains invisible to detection by conventional radiological methods. Diffusion MRI has been identified as a potential clinical tool for revealing subtle microstructural alterations associated with mTBI. OBJECTIVEThis study evaluates whether a comprehensive and powerful diffusion MRI (dMRI) technique called mean apparent propagator (MAP) MRI can detect sequelae of mTBI. METHODSWe analyzed data from 417 participants of the GE/NFL prospective mTBI study which included 143 matched controls (mean age, 21.9 {+/-} 8.3 years; 76 women) and 274 patients with acute mTBI and GCS [&ge;]13 (mean age, 21.9 {+/-} 8.5 years; 131 women). All participants underwent MRI exams at up to four visits including structural high-resolution T1W, T2W, FLAIR-T2W, and dMRI, in addition to clinical assessments of post-concussive physical symptoms (RPQ-3), psychosocial functioning and lifestyle symptoms (RPQ-13), and postural stability (BESS). The dMRI data for each subject were co-registered across all visits and analyzed using the MAP-MRI framework to measure and map the distribution of net microscopic displacements of diffusing water molecules in tissue and ultimately compute the microstructural MAP-MRI tissue parameters including propagator anisotropy (PA), Non-Gaussianity (NG), return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-to-plane probability (RTPP). We quantified voxel-wise and region-of-interest (ROI)-based changes in these parameters across all four visits. RESULTSMAP-MRI parameter values were within the expected ranges and showed relatively little variation across visits. We found no significant differences in the longitudinal trajectories of these parameters between mTBI patients and controls. At acute post-injury timepoints, RPQ-3 and RPQ-13 scores were increased in mTBI patients relative to controls, while BESS scores were not significantly different between groups. Analysis of dMRI metrics and clinical mTBI markers showed significant correspondence between MAP-MRI metrics in cortical gray matter, caudate and pallidum and BESS scores. CONCLUSIONWe developed and tested a state-of-the-art quantitative image processing pipeline for sensitive analysis and detection of subtle tissue changes in longitudinal clinical diffusion MRI data. The absence of a significant statistical difference between populations in the dMRI parameters in this study suggests that the mTBI corresponded to acute post-injury clinical symptoms but that the injury was not severe enough to cause detectable microstructural damage/alterations, and that increased diffusion sensitization combined with improved analysis techniques may be needed. CLINICAL IMPACTThese findings suggest that acute mTBI (GCS[&ge;]13) may not be detectable with diffusion MRI. TRIAL REGISTRATIONClinicalTrials.gov NCT02556177

Purpose: Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries. Methods: From hybrid MPI/CT imaging (N=43,099) across 15 sites, we included 4,552 most relevant patients with 1) no prior coronary artery disease; 2) AI-derived mild CAC scores (1-99); and 3) normal perfusion (stress total perfusion deficit <5%). The independent associations between AI-identified proximal CAC and major adverse cardiovascular events (MACE) and all-cause mortality (ACM) were evaluated using multivariable Cox regression, likelihood ratio test (LRT), and continuous net reclassification index (NRI). Results: Among the patients with mild CAC and normal perfusion (mean age 65{+/-}12 years, 51% male), 1,730 (38%) had proximal CAC. Over 3.6 (inter-quartile interval 2.1, 5.2) years follow up, 599 (13%) and 444 (10%) patients had MACE or ACM, respectively. Proximal CAC was associated with an increased risk of MACE (adjusted hazard ratio [HR] 1.24, 95% CI 1.03-1.48, P=0.02) and ACM (adjusted HR 1.25, 95% CI 1.01-1.53, P=0.04) after the adjustment of CAC score and density, clinical risk factors, and perfusion deficit. Proximal CAC improved the risk stratification of MACE (LRT P=0.02; NRI 12%) and ACM (LRT P=0.04; NRI 12%). Conclusion: In patients with mild CAC and normal perfusion, AI detection of proximal CAC identified a higher-risk group for adverse outcomes, highlighting its prognostic utility.

Background and Objectives: Normal appearing white matter (NAWM) may already harbor subtle microstructural alterations not yet visible on conventional MRI. Quantitative Multi-Parametric Mapping (qMPM) such as Magnetization Transfer saturation (MTsat), longitudinal relaxation rate (R1), and Proton Density (PD) offer new possibilities for analyzing NAWM which are sensitive to demyelination, axonal loss, and edema. We aimed to characterize these alterations within white matter hyperintensities (WMH) and the perilesional NAWM (pNAWM), to gain insights into the underlying process of lesion progression. We also investigated their association with cerebrovascular risk factors (CVRF) and long-term cognitive performance. Methods: This investigation included the cerebral MRI data of 245 participants from the prospective Berlin Longterm Observation of Vascular Events (BeLOVE) study. Furthermore, 121 participants cognitive performance was evaluated at baseline and longitudinally at 2 years follow-up using Montreal Cognitive Assessment (MoCA). Regions of interest (ROIs) of WMH, pNAWM at 1, 2, 3 mm were assessed in comparison to the mirrored contralesional white matter (cWM). Linear mixed effects models were employed to demonstrate the pairwise comparisons between each region using estimated marginal means and the association of MPM metrics with CVRFs. Linear regression was used to assess the association with cognitive performance. Results: In 245 participants, (mean age 62 years, SD: 12 years; 29.8% females), MPM metrics demonstrated a clear spatial gradient of microstructural injury. MTsat and R1 values were lower in WMH compared to cWM (lower case Greek beta = -0.48 (-0.52 - -0.44) and lower case Greek beta = -0.07 (-0.08 - -0.06), p<0.001, respectively) and showed gradual recovery with increasing distance indicating a microstructural gradient in pNAWM. Conversely, PD values were higher in WMH and decreased peripherally (lower case Greek beta = 2.32 (2.05 - 2.61, p<0.001). No substantial associations were found between MPM parameters and CVRFs in our cohort. At baseline and 2-year follow-up, cognitive performance was associated with higher pNAWM R1 values, whereas MTsat were only moderately associated. Discussion: Quantitative MPM reliably detects microstructural alterations not only within WMH, but also in pNAWM, confirming the high sensitivity of qMPM to subtle tissue pathology and support its utility as a promising biomarker for longitudinal studies and monitoring therapeutic effects.

BACKGROUND: Guidelines recommend aortic valve replacement (AVR) in patients with severe aortic regurgitation (AR) based on progressive changes in left ventricular (LV) function or size. We aimed to reassess the clinical relevance of current guideline recommendations pertaining to traditional echocardiographic measurements in routine practice. METHODS: Retrospective analysis of patients with severe AR who underwent serial echocardiographic follow-up over at least 18 months. The composite outcome was symptom-driven AVR, acute heart failure hospitalization, or death. We used a joint modelling approach to handle within-subject correlation and censoring. RESULTS: The cohort consisted of 140 patients, with a median follow?up of 93 months (interquartile range 58?130). LV end-systolic (LVESD) and fractional shortening (FS) showed a small but statistically significant longitudinal trend, while LVEDD did not. Changes in all three parameters in parallel joint models adjusted for age and gender were consistently associated with increased risk of the composite event. Each 1?mm increase in LVESD and LVEDD was associated with a 6% and 5% increase in risk, respectively; each 1% decrease in FS corresponded to a 12% increase in risk. Only 8 (5.7%) of patients were predicted to exceed the guideline-recommended LVEDD threshold of 65 mm over 10 years. Age at onset was also a significant risk factor, with each decade increasing risk by 65% for each of the three parallel joint models. CONCLUSIONS: LV parameters show modest changes over time, despite holding strong prognostic value in patients with severe AR. LVEDD, while associated with overall risk, does not predictably or significantly dilate over time in most patients. AVR decisions should be based on comprehensive clinical and volumetric assessment rather than waiting for simple linear progression to guideline cutoffs.

Background: To investigate the safety and efficacy of CTguided lung nodule localization needles for the preoperative localization of small pulmonary nodules. Methods: A retrospective study was conducted on 102 patients with a total of 113 small pulmonary nodules who underwent preoperative localization at Jinan Fourth People's Hospital from January 2024 to December 2025. Nodule diameter and depth, localization time, the number of pleural punctures, the localization success rate, and postoperative complications (hook dislodgement, hemorrhage, and pneumothorax) were recorded. All patients underwent video assisted thoracoscopic surgery (VATS) after localization. Results: The mean nodule diameter was 0.97{+/-}0.36 cm, the mean depth was 1.26{+/-}0.48 cm, and the mean localization time was 9.8{+/-}3.65 minutes. The hook dislodgement rate was 0.98% (1/102), the intrapulmonary hemorrhage rate was 14.71% (15/102), and the pneumothorax rate was 16.67% (17/102). All pulmonary nodules were successfully resected by VATS at 73.82{+/-}13.83 minutes after localization, and no severe complications occurred. Conclusions: The use of a CTguided lung nodule localization needle for the preoperative localization of small pulmonary nodules decreases the time needed for intraoperative nodule detection and operation time. This strategy is a simple, safe, and accurate preoperative localization method that is worthy of increased clinical use.

Background and Aims: Acute myocarditis (AM) is a T cell-mediated myocardial disease with clinical manifestations ranging from mild chest pain to cardiogenic shock. Reliable biomarkers to stratify patients and guide therapy are currently lacking. In particular, the extent of the dysregulation of inflammatory pathways, and the impact on myocardial dysfunction, remain elusive. Methods: Serum analyses were performed in prospectively recruited AM patients (n = 103) from two independent cohorts. Multimodal data integration combining profiling of cytokine and chemokine dysregulation with clinical biomarkers was used to define clinical phenotypes with distinct inflammatory signatures. Machine-learning and regression models were applied to determine biomarkers that indicate clinical severity. Results: Immuno-proteomic profiling revealed conserved inflammatory patterns across AM cohorts, dominated by T cell-related cytokines and chemokines. In addition, AM patients showed dysregulation of fibroblast-derived cytokines, including hepatocyte growth factor (HGF), bone morphogenic protein 4 (BMP4) and the BMP4 inhibitors Gremlin-1 (GREM1) and Gremlin-2 (GREM2). Data integration and unsupervised clustering revealed two immuno-clinical phenotypes, linking T cell activation and fibroblast dysregulation to disease severity. Machine learning-based analysis identified CXCL10, GREM2 and LVEF as critical parameters for stratifying disease severity. Conclusions: These findings highlight a systemic T cell activation signature as diagnostic hallmark of AM. In addition, dysregulation of fibroblast-derived tissue cytokines serves as an indicator for distinct immuno-clinical phenotypes in myocardial inflammatory disease. Thus, the clinically relevant link between T cell-driven immune activation, myocardial inflammation and fibroblast-driven remodelling provides a versatile set of parameters to identify severe manifestations of AM.

End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

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.

Background: Deep brain stimulation (DBS) is a treatment option for Parkinson disease (PD). However, the effect of DBS on the arterial pressure (AP) remains unexplored. We aimed to develop an artificial baroreflex system for treating orthostatic hypotension (OH) due to central baroreflex failure in patients with PD. To achieve this, we developed an appropriate algorithm after estimating the dynamic responses of the AP to DBS using a white noise system identification method. Methods: We randomly performed DBS while measuring the AP tonometrically in 3 trials involving 3 patients with PD treated with DBS. We calculated the frequency response of the AP to the DBS using a fast Fourier transform algorithm. Finally, the feedback correction factors were determined via numerical simulation. Results: The frequency responses of the systolic AP to random DBS were identifiable in all 3 trials, and the steady state gain was 8.24 mmHg/STM. Based on these results, the proportional correction factor was set to 0.12, and the integral correction factor was set to 0.018. The computer simulation revealed that the system could quickly and effectively attenuate a sudden AP drop induced by external disturbances such as head-up tilting. Conclusion: An artificial baroreflex system with DBS may be a novel therapeutic approach for OH caused by central baroreflex failure.

Objective To evaluate the Cartilage Thickness Score (CTh-Score) as a quantitative measure of cartilage damage severity by assessing its association with three osteoarthritis (OA) milestones and comparing its performance with conventional morphometric measures (radiographic minimum joint space width (JSW) and regional average cartilage thickness). Methods Data were obtained from the Osteoarthritis Initiative (OAI) and the publicly available OAI CTh-Maps and CTh-Score dataset. Three matched case-control designs were used to represent major OA milestones: (i) incident radiographic OA onset, (ii) combined pain and structural progression, and (iii) knee replacement (KR) in the coming 2 years. Progression subjects were extracted from the FNIH Biomarkers Consortium cohort. Cases and controls were compared at 4 years (T-4Y), 2 years (T-2Y), and 0 years (T0) before the milestone. MRI-based CTh-Score and regional average cartilage thickness, as well as JSW, were analyzed cross-sectionally and longitudinally. Associations with case status were assessed using adjusted logistic regression models, and responsiveness was evaluated using longitudinal change and standardized response means. Results The onset cohort included 307 matched case-control pairs, the progression cohort 164 cases and 369 controls, and the KR cohort 81 cases and 324 controls. Across all three study designs, the CTh-Score significantly differentiated cases from controls at all timepoints. In the onset cohort, the CTh-Score was higher in future cases than controls at T-4Y (16.2 vs 12.6, p=0.007), T-2Y (23.5 vs 16.7, p<0.001), and T0 (39.8 vs 18.6, p<0.001), whereas JSW and regional thickness measures showed limited or later discrimination. Similar findings were observed for progression (43.2 vs 33.0 at T-4Y; p<0.001) and KR (55.4 vs 46.1 at T-4Y; p=0.02) cohorts. Longitudinally, CTh-Score changes differentiated cases from controls earlier and more consistently than JSW or regional average thickness, and its responsiveness was consistently the highest across OA milestones and time intervals. In adjusted models, the CTh-Score was independently associated with all outcomes at T-4Y and T-2Y, with odds ratios per standard deviation increase ranging from 1.3 to 2.2. Conclusion The CTh-Score captures high-resolution cartilage thickness patterns associated with OA onset, progression, and future knee replacement, outperforming conventional morphometric measures in early discrimination, responsiveness, and predictive association. These findings support CTh-Score as a sensitive quantitative marker of cartilage damage severity across the OA continuum.

Objectives: Among surgically resected non-small cell lung cancer (NSCLC) patients with similar stage and histopathological characteristics, there is variability in patient outcomes which highlights urgency of identifying biomarkers to predict recurrence. The goal of this study was to systematically develop a pre-surgical CT-based habitat-based radiomics classifier to predict recurrence-of-risk in NSCLC. Methods: This study included 293 NSCLC patients with surgically resected stage IA-IIIA disease that were randomly divided into a training (n = 195) and test cohorts (n = 98). From pre-surgical CT images, tumor habitats were generated using two-level unsupervised clustering and then radiomic features were calculated from the intratumoral region and habitat-defined subregions. Using ridge-regularized logistic regression, separate classifiers were developed to predict 3-year recurrence using intratumoral radiomics, habitat-based radiomics, and a combined model (intratumoral and habitat) which was generated using a stacked learning framework. For each classifier, probability of recurrence was calculated for each patient then numerous statistical and machine learning approaches were utilized to stratify patients for recurrence-free survival. Results: The combined radiomics classifier yielded a superior AUC (0.82) compared to the intratumoral (AUC = 0.75) and habitat radiomics (AUC = 0.81) models. When the classifiers were used to stratify high- versus low-risk patients utilizing a cut-point identified by decision tree analysis, high-risk patients were yielded the largest risk estimate (HR = 8.43; 95% CI 2.47 - 28.81) compared to the habitat (HR = 5.41; 95% CI 2.08 - 14.09) and intratumoral radiomics (HR = 3.54; 95% CI 1.45 - 8.66) models. SHAP analyses indicated that habitat-derived information contributed most strongly to recurrence prediction. Conclusions: This study revealed that habitat-based radiomics provided superior statistical performance than intratumoral radiomics for predicting recurrence in NSCLC.

Background: Paediatric pneumonia is a leading cause of childhood morbidity and mortality worldwide. Chest X-rays (CXR) are an important diagnostic tool in the diagnosis of pneumonia, but shortages in specialist radiology services lead to clinically significant delays in CXR reporting. The ability to communicate findings both to clinicians and laypersons allows MLLMs to be deployed throughout clinical workflows, from image analysis to patient communication. However, MLLMs currently underperform state-of-the-art deep learning classifiers. Objective: To evaluate the diagnostic accuracy of ensemble strategies with MLLMs compared to the baseline average agent for paediatric radiological pneumonia detection. Methods: We conducted a retrospective cohort study using paediatric CXRs from two independent hospital datasets totalling 2300 CXRs. Fifteen MedGemma-4B-it agents independently classified each CXR into five pneumonia likelihood categories. Majority voting, soft voting, and GPTOSS-20B aggregation were compared against the average agent performance. The primary metric evaluated was OvR AUROC. Secondary metrics included accuracy, sensitivity, specificity, F1-score, Cohen's kappa, and OvO AUROC. Results: Soft voting achieved improvements in OvR AUROC (p_balanced = 0.0002, p_real-world = 0.0003), accuracy (p_balanced = 0.0008, p_real-world < 0.0001), Cohen's Kappa (p_balanced = 0.0006, p_real-world = 0.0054) and OvO AUROC (p_balanced < 0.0001, p_real-world = 0.0011) across both datasets, and a superior F1-value (pbalanced = 0.0028) for the balanced dataset. Conclusion: Soft voting enhances MedGemma's diagnostic discriminatory performance for paediatric radiological pneumonia detection. Our system enables privacy-preserving, near real-time clinical decision support with explainable outputs, having potential for integration into emergency departments. Our system's high specificity supports triage by flagging high-risk radiological pneumonia cases.

Saccadic eye movements are established biomarkers in neuroscience and clinical neurology, where video-oculography (VOG) remains the gold standard. However, VOG's high cost, bulky equipment, and poor portability restrict its clinical utility. Electrooculography (EOG) offers a promising alternative by detecting cornea-retinal potential changes during eye movements. To enable quantitative saccadic analysis using EOG as a VOG alternative, this study develops and validates a mathematical transformation model converting EOG data into VOG-equivalent values. A prospective observational study was conducted on 4 healthy adults without neurological or sleep disorders. Horizontal saccades were recorded simultaneously using EOG and VOG during controlled gaze shifts. EOG peak saccadic velocity was derived from voltage change rate, whereas VOG was calculated from angular displacement over time. A derivation dataset of fixed horizontal saccades ({+/-}20{degrees}) formulated the transformation model, achieving a strong correlation coefficient (r = 0.95 rightward, r = 0.93 leftward, p < 0.0001). Multiple filter settings were evaluated, and 0.3 Hz high-pass and 35 Hz low-pass filtering were identified as optimal. The fixed horizontal saccades derived model was applied to a validation dataset of random horizontal saccades, confirming robustness across saccades without significant differences from VOG measurements. These findings establish EOG's feasibility for quantitative analysis of horizontal saccades and provide a validated transformation model. By systematically optimizing filtering parameters, this approach enables EOG as a cost-effective VOG alternative while maintaining high-precision measurement accuracy.