Citations

BACKGROUND: Although the prognostic utility of positron emission tomography (PET) myocardial flow reserve (MFR) is well established, emerging data suggest that reduced subendocardial flows also predict adverse outcomes. However, the incremental value of subendocardial MFR (MFR SE ) beyond transmural MFR (MFR TM ) remains unclear. METHODS: We studied patients in a multicenter PET registry with normal perfusion on stress/rest Rb-82 PET, excluding those with a previous history of coronary artery bypass surgery, heart transplantation, or left ventricular ejection fraction <40%. The optimal MFR SE cutoff for predicting major adverse cardiovascular events (MACEs; death, myocardial infarction [MI], revascularization, or heart failure [HF] hospitalization) was determined using Youden’s index. Patients were stratified into 3 groups: concordant-normal (MFR TM ≥2.0; MFR SE ≥2.1), discordant (low-MFR SE , normal MFR TM ), and abnormal MFR TM . Clinical outcomes were compared by MFR groups. RESULTS: Among 6603 patients (normal N=4103; discordant N=885; abnormal N=1615) the mean age was 66.3±12.4 years, and 54% were women. Compared with the concordant-normal group, patients with discordant low-MFR SE were older and more likely to have hypertension, diabetes, peripheral artery disease, and previous percutaneous coronary intervention. The median MFR TM for normal, discordant, and abnormal groups were 2.86, 2.15, and 1.72, respectively. Over a median follow-up of 4.9 years, 1661 MACE events occurred. Discordant low-MFR SE patients had a higher risk of MACE (hazard ratio [HR], 1.41; 95% CI, 1.22–1.64) and all-cause mortality (HR, 1.36; 95% CI, 1.14–1.61) compared with concordant-normal patients. The discordant group had an intermediate absolute risk of MACE, with an adjusted annualized event rate of 5.79% (95% CI, 5.10–6.49) compared with 3.99% (95% CI, 3.67–4.30; P <0.001) in the concordant-normal group and 8.35% (95% CI, 7.71–9.00; P <0.001) in the abnormal MFR TM group. CONCLUSIONS: Subendocardial MFR reveals clinically meaningful risk heterogeneity among patients with preserved transmural flow reserve, helping refine risk stratification beyond traditional PET metrics.

Background: Acetabular overcoverage (pincer morphology) has been suggested to be both a source of pain and dysfunction and potentially protective against osteoarthritic change. Purpose: To compare the minimum 5-year clinical outcomes of patients undergoing primary hip arthroscopic labral repair with a lateral center edge angle (LCEA) of ≥40° as compared with nondysplastic controls with an LCEA of 25° to 40°. Study Design: Cohort study; Level of evidence, 3. Methods: A retrospective review was performed to identify all patients who underwent primary hip arthroscopic labral repair between 2009 and 2019 at a single academic institution. Patients with an LCEA of ≥40° (pincer) were propensity-matched to nondysplastic controls with an LCEA of 25° to 40° on a 1-to-2 basis by sex, age, body mass index, and surgery year. Patient-reported outcome measures (PROMs)—including visual analog scale, Tegner activity score, modified Harris Hip Score, International Hip Outcome Tool (iHOT-12), and Hip Outcome Score were analyzed—as were reoperations and conversions to total hip arthroplasty (THA). Results: A total of 55 patients with pincer morphology (preoperative LCEA: 41.9°± 2.1°) were matched to 110 nondysplastic controls (LCEA: 31.2°± 4.1°), resulting in a total of 165 hips in 163 patients (67% women, mean age: 37.7 ± 9 years). Pincer patients were corrected to a mean postoperative LCEA of 38.7°± 4.1º ( P < .001), with 22 pincer patients having residual overcoverage; namely, a postoperative LCEA ≥40° (range, 40°-45.8°). There were no differences between cohorts regarding intraoperative characteristics—including femoral and acetabular Outerbridge grade—as well as capsulotomy type and the presence of capsular repair ( P ≥ .210). At a mean 8-year follow-up (range, 5.1-13.2), there was no difference in any postoperative PROMs between pincers and controls ( P ≥ .215) and no difference in PROMs between patients with residual overcoverage and pincer patients with a postoperatively normalized LCEA ( P ≥ .291). At final follow-up, 4 patients in the pincer cohort (7%) and 21 controls (19%) converted to THA ( P = .046). Conclusion: Patients with lateral acetabular overcoverage demonstrated similar and satisfactory postoperative PROMs at a minimum 5-year follow-up compared with propensity-matched nondysplastic controls, regardless of whether the LCEA was corrected to <40°. Of note, nondysplastic controls demonstrated a higher rate of conversion to THA, suggesting a potentially chondroprotective role for acetabular overcoverage.

ABSTRACT Pulmonary vein thrombosis (PVT) is an uncommon but potentially fatal condition that is often underdiagnosed due to nonspecific clinical presentation. We report a case of a 47‐year‐old woman with a thrombus in the right inferior pulmonary vein incidentally found during a robotic‐assisted bronchoscopy biopsy (RAB) and endobronchial ultrasound (EBUS) performed for mediastinal evaluation and lymph node sampling. PVT was confirmed with computed tomography pulmonary angiography, and the patient was successfully treated with anticoagulation therapy. A review of the literature revealed that reports of PVT detected via EBUS are exceedingly rare and usually malignant in nature. To our knowledge, this is the first peer‐reviewed case report describing PVT identified incidentally during EBUS, underscoring the potential of this technique to reveal clinically significant vascular abnormalities.

Post-transplant lymphoproliferative disorder (PTLD) involving the central nervous system (CNS) is a rare but serious life-threatening complication seen in recipients of solid organ transplant. Primary CNS encompasses 5–15% of all types of PTLD diagnoses, and heart transplant recipients represent 3–5% of those reported cases. Diagnosis is often delayed due to the highly variable presentation, with some cases remaining undiagnosed for years. Multidisciplinary collaboration is crucial for early diagnosis and management. A 53-year-old woman patient presented with altered mental status. MRI revealed nodular ventriculitis and bilateral periventricular hyperdense infiltrates. CSF studies demonstrated lymphocytic pleocytosis, elevated protein, and EBV-PCR-positive results. A stereotactic brain needle biopsy confirmed the presence of EBV-positive diffuse large B-cell lymphoma, consistent with primary CNS PTLD, 14 months after her heart transplant. Despite appropriate management, the patient experienced progressive neurological decline and ultimately suffered a fatal intracerebral hemorrhage. We demonstrate the importance of the early diagnosis and variable presentation of post-heart-transplant PTLD. The importance of surveillance regardless of EBV status and close monitoring of disease progression due to potential life-threatening complications, such as fatal hemorrhages. Therefore, primary CNS-PTLD remains a challenging disease and is being increasingly recognized with improved transplant recipient survival and prolonged exposure to chronic immunosuppression.

Background Delirium is a frequent and serious complication in critically ill patients and associated with adverse outcomes. Automated pupillometry has emerged as a potential diagnostic tool for the detection of delirium. However, pupillometric parameters are also influenced by pain, potentially confounding their relationship to delirium. The aim of this study was to evaluate the association between automated pupillometry parameters and delirium in patients with high risk of pain (surgical patients) admitted to intensive care units (ICU). Methods This prospective multicenter observational study included adult surgical patients admitted to an ICU between April 2023 and August 2025. Delirium was assessed every 6 h after admission using CAM-ICU. Automated pupillometry together with pain intensity scores (VAS, CPOT, BPS) were performed concurrent with CAM-ICU. To account for repeated measurements within individual patients, a generalized linear mixed-effects model (GLMM) with patient as a random effect was used to evaluate the association between pupillometric parameters and diagnosed delirium. Results Forty-nine patients were included, yielding 417 measurements, of which 48 (12%) were done in patients who were simultaneously CAM-ICU positive. Among pupillometric variables, only average pupillary latency (LAT avg) was significantly shorter in patients with delirium ( p = 0.01). In the mixed-effects model adjusted for sedation depth, level of consciousness, and pain intensity, LAT avg. was independently associated with delirium (OR 1.55 per 0.01; 95% CI 1.13–2.13; p = 0.007). Conclusion In surgical ICU patients, shortened pupillary light reflex latency is associated with delirium. Automated pupillometry may serve as a useful tool for detection of delirium in this population. Clinical trial registration https://clinicaltrials.gov/study/ Identifier: NCT05811208.

Abstract For implantable closed loop neuromodulation systems, real-time neural sensing, biomarker detection, and targeted stimulation are essential components for delivering personalized therapy. Current limitations in sampling rate and the number of available recording channels in implantable systems may pose significant challenges, especially in epilepsy research where high-frequency biomarkers and multi-site monitoring are essential. The Brain Interchange (BIC) system of CorTec is an externally powered, wireless implantable system that enables sensing and stimulation up to 32 channels at 1 kHz addressing the current limitations in the field. In this study, we present a modular closed loop neuromodulation environment developed for the BIC, enabling real-time data streaming, event detection, and flexible stimulation control. The framework integrates a Simulink-based processing pipeline, real-time communication, and a graphical user interface supporting both open and closed loop paradigms. System performance was evaluated through impedance measurement, latency assessment across three stimulation modes, and real-time closed loop stimulation using electrocardiogram (ECG) for in-lab demonstration. This modular environment provides a flexible platform for developing and validating neuromodulation algorithms and can be extended to incorporate advanced biomarker detection and multi-channel neural interfaces for future clinical research.

Abstract Asthma is a chronic inflammatory condition marked by episodic airway narrowing, with exercise-induced bronchoconstriction (EIB) a common trigger. Up to 70% of athletes experience EIB-related symptoms, yet current management relies on reactive treatment rather than early physiological monitoring. This study presents a proof-of-concept multimodal wearable platform integrating impedance pneumography (IPG), electrocardiography (ECG), and inertial measurement unit (IMU) sensing to detect early physiological signatures of EIB. Candidate biomarkers include elevated heart rate, increased respiratory rate, and reduced thoracic impedance amplitude. Real-time embedded algorithms perform on-device feature extraction and threshold-based decision logic without cloud dependence, while IMU data provide posture context for signal interpretation. The system was evaluated through subsystem validation and a resistive-breathing protocol simulating bronchoconstriction. Results demonstrate reliable detection of characteristic cardiorespiratory changes, establishing the feasibility of real-time wearable detection of early EIB markers and supporting future large-cohort validation and form-factor refinement.

Abstract Pain assessment plays a critical role in clinical decision-making, yet conventional methods rely heavily on subjective self-reports such as the Visual Analog Scale (VAS) or the Numeric Rating Scale (NRS). These approaches fail in scenarios involving infants, sedated patients, unconscious individuals, or those with cognitive or communication impairments. To address this gap, this research proposes PainNet, an AI-driven, real-time facial pain recognition system that integrates deep learning-based object detection with Action Unit (AU)–based pain intensity scoring. The system uses YOLOv8 for rapid facial expression detection and incorporates Py-Feat to extract pain-relevant AUs, enabling computation of the Prkachin and Solomon Pain Intensity (PSPI) score. A balanced dataset comprising 500 images sourced from Delaware, BioVid, and RaFD was curated, annotated, and preprocessed for training. The YOLOv8-based binary pain vs. no-pain classifier achieved a mean Average Precision (mAP) of 0.89 and an F1-score of 0.80 on the test dataset, demonstrating strong performance in diverse environments and facial variations. The design emphasizes computational efficiency by activating AU analysis only when YOLOv8 detects probable pain, making PainNet suitable for continuous monitoring in ICUs, elder-care facilities, and telemedicine systems. This study demonstrates that the fusion of real-time object detection and clinically validated AU scoring can significantly advance automated, objective, and scalable pain assessment.

Abstract Interstitial lung disease (ILD) is a chronic, irreversible condition that is challenging to diagnose due to subtle symptoms and limitations of conventional tools. Noninvasive lung sound analysis using phonopulmography offers a promising alternative. This study evaluated the feasibility of a deep learning model for distinguishing normal and pathological lung sounds in ILD. Lung sound recordings were collected from 12 subjects (6 healthy, 6 ILD) and segmented beat-to-beat using simultaneously acquired ECG and phonopulmography, yielding 2,280 segments. Mel-frequency cepstral coefficients (MFCCs; 13 per segment) were extracted, and a one-dimensional CNN-based You Only Listen Once (YOLO) model was trained for segment classification. Evaluation used Leave-One-Out Cross-Validation, with patient-level classification by aggregating segment predictions via majority vote. On the test set (190 segments per class), the model achieved an average segment-level accuracy of 0.738 ± 0.087 and mean AUC of 0.850 ± 0.062. Precision, recall, and F1-score were 0.775 ± 0.148, 0.755 ± 0.183, and 0.738 ± 0.089, respectively. These preliminary results suggest that beat-level MFCCs with a 1D CNN-based YOLO model enable accurate segment- and patient-level classification. This feasibility study highlights the potential of AI-assisted auscultation for noninvasive monitoring and clinical decision support in ILD management, pending further validation in larger cohorts.

Abstract Brain arteriovenous malformations (AVMs) result from abnormal growth of vascular structures. AVMs pose a significant rupture risk, and its treatment is challenging due to the complex hemodynamics within the tangled nidus. Currently, investigating AVM hemodynamics remains a challenge due to the low resolution of clinical imaging modalities. In this study, a high-fidelity Computational Fluid Dynamics (CFD) simulation of a brain AVM from a KRAS-mutated mouse model is developed from micro-CT images. Brain AVMs were induced in mice through systemic delivery of AAV-BR1-CAG vector encoding KRASG12V-GFP transgene to cerebral endothelial cells. High-resolution microCT imaging at 6.551 𝜇𝑚 pixel size captured the complex vascular anatomy. The cerebral arteries were reconstructed using the 3D Slicer and Blender software. The curvilinear immersed boundary (CURVIB) method was employed with approximately 65 million grid points and spatial resolution of 0.7−2.3 𝜇𝑚. Our preliminary results reveal heterogeneous flow distribution through the nidus, with blood channeling through two primary pathways. The extremely low Reynolds number (0.038) indicates viscous-dominated flow characteristic of microcirculation. Our work presents the first high-resolution CFD simulation of brain AVM in the literature, providing a foundation for investigating flow-mediated vascular remodeling, rupture risk assessment, and therapeutic intervention planning in future.