Photoacoustics

Microbubble contrast-enhanced ultrasound (CEUS) relies on discriminating nonlinear bubble signals from linear tissue backscattering. While Singular Value Decomposition (SVD) filtering improves this discrimination, existing techniques often fail to retain the slowly-moving microbubble signals from static clutter. Here, we present a novel multi-stage singular value decomposition (MS-SVD) framework for ultrafast CEUS imaging. Our method employs plane-wave transmissions at multiple angles and acoustic pressure levels (implemented via duty-cycle modulation) and alternating transmit polarity. The beamformed data are then processed by three sequential SVD filters: (1) spatial-angular SVD to extract coherent signals across all transmit angles, (2) spatial-pressure SVD to separate linear fundamental and nonlinear harmonic components, and (3) spatiotemporal SVD to isolate moving microbubble echoes from tissue clutter. In in vitro flow phantoms and in vivo rat brain through a cranial window, MS-SVD dramatically improves microbubble detection compared to conventional SVD filtering, MS-SVD yields much stronger vascular contrast and suppresses tissue clutter to a greater extent. The resulting power-Doppler and super-resolution maps are notably cleaner and more complete: MS-SVD detects substantially more microbubble events in ULM, revealing finer vessel details and more accurate flow speeds. By capturing the full acoustic signature of microbubbles (both fundamental and harmonic), MS-SVD achieves higher contrast-to-noise and sensitivity in CEUS. These gains make it a powerful front-end for super-resolution ultrasound localization microscopy and other high-sensitivity microvascular imaging applications.

Background: Quantitative lipid assessment is central to identifying rupture-prone coronary plaques and represents a therapeutic target for lipid-lowering therapy. Near-infrared spectroscopy (NIRS)-derived lipid core burden index (LCBI) is well validated and widely used for detecting lipid-rich lesions. Optical frequency domain imaging (OFDI) is increasingly adopted for guiding percutaneous coronary intervention (PCI) due to its high-resolution structural imaging capabilities. Depolarization-sensitive OFDI (depOFDI) provides intrinsic lipid contrast and may enable combined structural and compositional plaque characterization within a single OFDI-based platform. Objective: To define an OFDI-derived lipid metric and evaluate its agreement with NIRS-derived LCBI. Methods: Thirty-three patients underwent both polarization-sensitive OFDI and NIRS-intravascular ultrasound imaging during PCI. After exclusion of 4 datasets, 29 co-registered pullbacks were analyzed. A signal-to-noise-corrected depolarization metric was used to identify lipid-rich regions and generate depOFDI chemograms. maxLCBI4mm value and location, as well as total LCBI, were computed and compared with NIRS. Results: depOFDI demonstrated strong agreement with NIRS, showing high correlation for maxLCBI4mm (r^2 = 0.862) and total LCBI (r^2 = 0.867), along with strong spatial concordance for the location of the maxLCBI4mm (r^2 = 0.900). Bland-Altman analysis of LCBI4mm showed minimal bias (10.7) with 95% limits of agreement of [81.4 to 102.8]. Conclusions: depOFDI enables accurate quantification of lipid burden alongside the high-resolution structural information inherently provided by OFDI. Because depolarization metrics can be derived from polarization-diverse detection available in many commercial OFDI systems, this approach provides a practical pathway toward comprehensive plaque characterization within existing PCI workflows, without the need for additional imaging modalities.

Contactless assessment of cardiopulmonary function remains an unmet need, with current approaches relying either on subjective clinical examination or on resource-intensive imaging. We evaluated a novel multipoint airborne ultrasound surface motion camera (SMC) designed to map thoracic vibration patterns without contact and to extract clinically relevant information through data-driven analysis. In a prospective observational study, clinically characterised participants underwent short-duration acquisitions during natural breathing and externally induced oscillations. The resulting signals were transformed into spatially and frequency-resolved maps and analysed using machine learning models to discriminate healthy individuals from patients with respiratory or cardiac disease. The approach proved feasible in a clinical setting and achieved excellent discrimination between healthy individuals and respiratory patients (area under the receiver operating characteristic curve (AUC) 0.90 {+/-} 0.07), including in patients with subtle abnormalities not detected by pulmonary function testing. Discrimination between healthy individuals and cardiac patients ranged from acceptable to excellent (AUC 0.76-0.90 depending on subgroup), with the highest performance observed in aortic stenosis. Model interpretability analyses revealed spatial and spectral patterns consistent with the known physiological organisation of lung mechanics and cardiac auscultation areas, supporting a structure-function relationship between recorded signals and underlying processes. These findings indicate that thoracic vibration transmission encodes spatially and spectrally organised information that can be captured without contact and exploited through explainable data-driven modelling. While the results require confirmation in larger populations, this approach may represent an operator-independent, low-burden extension of bedside assessment, with potential applications in early detection, triage, and monitoring of cardiopulmonary disease.

BackgroundRealistic PET/CT phantoms are essential for system evaluation, protocol optimization, and validation of advanced reconstruction methods. However, existing phantoms are often limited by simplified geometries, spatially uniform activity patterns, and complex preparation procedures. PurposeTo develop and evaluate PixelPrintPET, a 3D printing-based method for fabricating anatomically realistic PET/CT phantoms with spatially heterogeneous radiotracer distributions and a single-solution filling workflow that avoids physical compartmentalization. MethodsPixelPrintPET generates voxel-based printing instructions that encode spatially varying infill, which is realized during printing through modulation of filament extrusion, enabling heterogeneous activity distributions without compartmentalization of radioactivity at different activity concentrations. Calibration phantoms and anatomically structured phantoms were designed and printed using high-flow polylactic acid (PLA), with anatomical inputs derived from either digital atlas-based models or patient imaging data. The printed phantoms were subsequently filled by immersion in a radioactive solution, allowing activity distribution to be controlled by the internal porous structure. A bottom-up filling procedure with reduced surface tension was developed to ensure uniform infiltration and minimize air entrapment. Phantoms were imaged on the PennPET Explorer PET/CT system, and quantitative performance was evaluated using contrast recovery coefficient (CRC), target-to-background ratio (TBR), and comparisons with simulated or patient-derived reference data. ResultsA strong linear relationship between infill ratio and normalized signal (R2 = 0.998) was demonstrated by the calibration phantom, enabling reliable mapping between structure and activity. Additionally, air entrapment was minimized to less than 1% of the total phantom volume. In the contrast recovery phantom, CRC values were consistent with measurements using traditional phantoms. The brain phantom reproduced atlas-derived contrast patterns, with gray-to-white matter differences within 5% after accounting for resolution and other system effects. The patient-based thorax phantom showed high reproducibility across repeated scans, with differences within 3%, and closely matched the input patient image with regional differences within 10% in all regions except the lung. ConclusionsPixelPrintPET enables the fabrication of realistic, reproducible, and versatile PET/CT phantoms with a voxel-level control of the activity distribution. This approach provides a practical solution for generating patient-specific and application-specific phantoms, with the potential to accelerate system validation, protocol development, and clinical translation of advanced PET/CT technologies.

Fibrogenesis is essential to wound healing, but aberrant fibrogenesis is a driver of many chronic diseases and cancers. Lysyl oxidases (LOX) play a pivotal role in fibrogenesis by catalyzing the oxidation of lysine residues to reactive aldehydes (allysine) in collagens and elastin, resulting in the crosslinking and excessive deposition of these extracellular matrix components. Currently, rapid and robust histological assays to visualize the spatial distribution of LOX activity are lacking, hindering the precise validation of anti-fibrotic therapies. Here, we present a histological fluorescent staining method to visualize fibrogenesis (active fibrosis) and LOX activity in tissue sections utilizing a bioorthogonal tag and a click reaction with a turn-on fluorophore. Notably, requiring only two commercial reagents, this protocol can be completed in under two hours and is compatible with other imaging modalities, including second-harmonic generation and immunofluorescence staining. We validated this method across various healthy and fibrotic mouse and human tissue specimens.

Three-dimensional photoacoustic tomography (3D-PAT) enables noninvasive structural and functional imaging with optical absorption contrast and ultrasonic detection depth. However, its spatial resolution is limited by acoustic diffraction, and incomplete detection geometry can substantially degrade image fidelity and quantitative accuracy. Here, we present a ULM-guided model-based reconstruction framework, termed 3D-PAULMprior that incorporates sub-diffraction vascular priors from concurrent ultrasound localization microscopy (ULM) into 3D photoacoustic reconstruction. The method uses weighted regional Laplacian regularization to integrate high-resolution vascular information into the inverse problem, thereby enhancing vascular sharpness, suppressing limited-view artifacts, and improving blood oxygen saturation estimation. We validated 3D-PAULMprior using numerical simulations, tissue-mimicking phantoms, and in vivo mouse brain imaging. Compared with conventional reconstruction, 3D- PAULMprior improved spatial resolution by over 50%, increased contrast-to-noise ratio by 261.2%, and enhanced structural similarity index by 24.6%. In vivo, 3D-PAULMprior recovered vascular structures that were poorly resolved or missing in conventional reconstructions and produced more spatially confined sO2 maps. These results establish 3D-PAULMprior as a robust multimodal reconstruction strategy for high-resolution structural and functional photoacoustic imaging.

Tissue phantoms that mimic microvasculature and perfusion are essential for modelling vascular function, guiding interventions, and calibrating imaging systems, which require faithful replication of vascular geometry and flow. Conventional fabrication strategies, including wire-based molding, lithographic micromachining, and additive manufacturing, offer useful capabilities but remain constrained by predefined designs, rectangular channel cross-sections, limited scalability, and high production costs. Reliance on predefined digital vascular models restricts design flexibility and limits the ability to capture the natural variability and complexity of real vascular systems. Here, we present a lithography-free, fractal-generating approach based on a modified Lifted Hele-Shaw Cell (LHSC) technique, in which vascular networks emerge spontaneously via interfacial fluid instabilities. Unlike pre-designed methods, these structures are governed by fluid properties and flow conditions, enabling adaptive, physiologically relevant geometries with smooth Gaussian cross-sections and natural diameter tapering. We demonstrate four phantom designs: a planar vascular tree, an anatomically guided cerebral network, a retinal vascular model, and a conformable curved substrate phantom. Validation using Laser Speckle Contrast Imaging confirms structural fidelity and physiologically relevant flow consistent with Murrays law. This platform uniquely integrates realistic vascular architecture with emergent, fractal driven formation, highlighting its potential as a reproducible and biologically relevant alternative to conventional vascular phantom fabrication. Furthermore, the availability of such realistic in vitro vascular models can reduce reliance on animal experiments and contribute towards more ethical and sustainable preclinical research.

Mitochondria are essential for cellular energy production and are particularly critical for brain development and function. Neurons rely predominantly on oxidative phosphorylation for energy production, rendering the brain highly vulnerable to mitochondrial dysfunction. Consequently, impaired mitochondrial function contributes to a broad spectrum of neurological and systemic disorders, making mitochondria attractive therapeutic targets. Despite this importance, there is currently no non-invasive, spatially resolved method to assess mitochondrial function in the intact living brain. Here, we establish a non-invasive functional MRI approach--4D Oxy-wavelet MRI--to probe in vivo mitochondrial electron transport chain (ETC) function in a spatially specific manner across the lifespan, from fetal to adult brains. This method employs a low-rank k-t sub-Nyquist acquisition strategy to achieve simultaneous structural and functional imaging with high spatial (78 m) and temporal ([~]14 ms) resolution, enabling motion-robust imaging in multi-fetal mouse pregnancies. Mitochondrial ETC function is interrogated by measuring oxygen homeostasis responses to brief hypoxic challenges, analyzed using computational time-frequency wavelet profiling. We validate this approach in mouse models of mitochondrial respiratory chain disease and late-onset Alzheimers disease, from in utero fetuses to adults, and demonstrate reproducibility and specificity using pharmacological hyperemia and ETC complex I inhibition. We further show parallel wavelet responses in placenta and fetal brain, enabling multi-organ interrogation of the placenta-brain axis. Finally, we present first-in-human feasibility data, supporting translational potential for non-invasive assessment of mitochondrial function in living brains across the lifespan.

Noninvasive monitoring of plaque inflammatory dynamics remains an unmet need. We previously developed a monocyte-mimetic nanoprobe, termed MoNP-SPION, for MRI detection of atherosclerotic lesions. Here we demonstrate MoNP-SPION enables longitudinal tracking of plaque inflammatory status in a clinically relevant mouse model. Following 16 weeks of plaque induction, mice were maintained on high-fat diet or switched to chow for 6 weeks to model persistent versus resolving plaque inflammation. MoNP-SPION-enhanced MRI was performed at 3- and 6-weeks post-adjustment, and arterial tissue was collected for histological assessment. Mice maintained on high-fat diet exhibited persistent hypointense T2* signal at the carotid bifurcation and aortic root, whereas chow-transitioned mice showed progressive signal attenuation, consistent with histological evidence of reduced plaque burden and inflammation. These findings establish MoNP-SPION as an effective molecular MRI probe for longitudinal assessment of plaque inflammatory dynamics, supporting its potential for monitoring atherosclerosis progression and therapeutic response.

Understanding processes such as blood-brain barrier (BBB) disruption and tumor progression can greatly benefit from simultaneous molecular, functional, and hemodynamic imaging in deep tissue, yet few existing imaging modalities can provide all three in a single system. Here, we present an integrated imaging platform that combines 3D photoacoustic tomography with ultrasound localization microscopy (3D-PAULM) to enable intrinsically co-registered, multiparametric imaging. 3D-PAULM unifies multispectral photoacoustic molecular imaging, ultrasound B-mode imaging, microbubble-enhanced power Doppler, and ultrasound localization microscopy, and concurrently measures blood oxygenation, blood perfusion, microvascular flow dynamics, and molecular probes from near-infrared dyes and photoswitchable phytochromes. We apply 3D-PAULM to quantify BBB leakage in focal ischemia and systemic inflammation, and to perform high-sensitivity molecular imaging of solid tumors alongside functional mapping of tumor hypoxia and super-resolved vascular remodeling. Together, these results establish 3D-PAULM as a versatile platform for integrated functional and molecular imaging in deep tissue.

PurposeTo develop a data-efficient deep learning framework for rapid reconstruction of highly accelerated 4D arterial spin labeling (ASL) magnetic resonance angiography (MRA) with robust generalization using extremely limited acquired data, addressing the challenges of prolonged acquisition and reconstruction time. MethodsA simulation-driven, few-shot transfer learning approach was adopted by leveraging publicly available 3D time-of-flight (TOF)-MRA data to generate realistic multi-coil complex-valued pseudo-ASL k-space datasets for large-scale pre-training. A 3D unrolled reconstruction network was trained on this simulated data using a histogram-weighted loss and subsequently extended to 4D using lightweight temporal fusion modules. Fine-tuning was performed using only two experimentally acquired 4D ASL-MRA datasets. The method was evaluated on retrospectively and prospectively undersampled Cartesian 4D ASL-MRA data acquired at 3T and compared with compressed sensing (CS) and locally low-rank (LLR) reconstructions. ResultsThe proposed method achieved superior reconstruction quality compared with CS and LLR, with improved vessel depiction, particularly in distal branches, and enhanced temporal fidelity. Quantitative evaluation demonstrated higher vessel-masked peak signal-to-noise ratio and structural similarity index measure, along with increased error entropy, indicating reduced noise and structured artifacts. The initial pre-trained model already outperformed conventional methods, while additional 4D fine-tuning further improved performance. Robust reconstruction was demonstrated in prospectively undersampled data and multi-slab acquisitions, enabling large-coverage, time-resolved angiography within clinically feasible scan times (4-6 min). ConclusionsSimulation-driven pre-training combined with few-shot fine-tuning enables accurate and rapid reconstruction of highly accelerated 4D ASL-MRA in data-limited settings. The proposed framework provides a practical pathway toward clinically feasible, non-contrast dynamic cerebrovascular imaging.

Background: Anemia is an established marker of cardiovascular disease severity and risk which leads to elevations in resting myocardial blood flow (MBF) and impaired myocardial flow reserve (MFR) in patients without obstructive coronary artery disease (CAD). Anemia can potentially be detected opportunistically from blood pool density changes on computed tomography (CT) imaging. Objectives: We evaluated relationships between chamber density measurements with hemoglobin, positron emission tomography (PET) findings, and cardiovascular events. Methods: We included 33460 patients from 13 sites in the REFINE-PET who underwent PET and 24368 patients undergoing lung cancer screening chest CT. A deep learning model segmented cardiac chambers from CT images, then quantified chamber density. We evaluated the relationship between chamber density measures with resting MBF and MFR, as well as associations with death or myocardial infarction (MI). Results: We included a total of 57,828 patients. A higher density in myocardium compared to left ventricle blood pool was associated with reduced MFR (adjusted odds ratio 3.02 per SD increase, 95% confidence interval[CI] 2.72 - 3.38) and an increased risk of death or MI in (adjusted hazard ratio[HR] 1.38 per SD increase, 95% CI 1.26-1.51). Having myocardial density higher than blood pool density was also associated with cardiovascular death in patients undergoing low-dose chest CT (adjusted HR 1.73, 95% CI 1.20-2.52). Conclusions: In a large multimodality dataset, lower cardiac chamber density is associated with impaired MFR and independently associated with cardiovascular events. These biomarkers can be automatically extracted from CT to provide physiologic insights and potentially guide patient care.

Background: Three-dimensional visualization and quantitative analysis of cerebral arteries on 3DRA are central to endovascular treatment planning, device selection, and cerebrovascular research. Manual segmentation is time-consuming and operator-dependent, yet no open-source deep learning model has been prospectively validated for this task on 3DRA. Methods: A nnUNet v2 model was trained for binary cerebral artery segmentation on 400 consecutive 3DRA acquisitions from three angiographic systems, comparing four configurations across architectures and loss functions. The best-performing configurations were prospectively validated on 40 patients using a dual approach: quantitative metrics (DSC, clDice, HD95, ASD, Precision, Recall), and blinded expert qualitative evaluation by two interventional neuroradiologists assessing 12 arterial segments, a global quality score, and clinical usability across 40 test cases. Results: The ensemble model achieved median DSC 0.917, clDice 0.932, and HD95 1.494 mm. Global quality scores were significantly lower for nnUNet v2 than for expert segmentations (median 4 vs 5, p<0.001), but nnUNet v2 segmentations were rated clinically usable in 88-90% of cases versus 95-98% for expert segmentations, without significant difference on the binary usability criterion. A consistent proximal-to-distal quality gradient was identified, with comparable scores at proximal arteries and the largest differences at distal arterial segments. Conclusion: nnUNet v2 with topology-aware training provides clinically usable cerebral artery segmentations on 3DRA, prospectively validated through both quantitative metrics and structured expert qualitative assessment, and represents a reproducible open-source foundation for endovascular and research applications.

Purpose: To develop and evaluate a flexible, software-defined radar platform for contactless, vendor-independent motion detection and correction in MRI. Methods: A continuous-wave (CW) Doppler radar was implemented using a software-defined radio and the open-source GNU Radio framework. The system was deployed inside a 1.5T MRI scanner and synchronized with MRI acquisitions. We evaluated the performance in a custom-developed internal motion phantom and in healthy volunteers to track respiration and bulk motion. The radar-derived signal was validated against cine MRI and used to demonstrate both retrospective and prospective motion management techniques in phantom and in healthy volunteers. Results: The radar provided robust motion signals that correlated strongly with image-based ground truth signals in both phantom and volunteer experiments. Signal characteristics were found to be frequency-dependent, enabling optimization for different motion regimes. Retrospective correction of free-breathing abdominal data using the radar signal effectively suppressed respiratory artifacts, achieving image quality comparable to a self-gating approach. Prospective triggering successfully reduced motion artifacts in the phantom study. The system also reliably detected sporadic events such as swallowing during neck imaging. Conclusion: Software-defined radar was demonstrated to be an effective platform for both prospective and retrospective motion correction. Its independence from the MRI system, ultra-wide band capabilities, and body-region versatility enable the adaptation of the technique for a wide range of imaging applications and protocols.

BackgroundPrevious studies evaluated large language model (LLM) performance on the American Society of Nuclear Cardiology (ASNC) Board Preparation Exam. Without domain-specific context, the best model (GPT-4o) achieved 63.1%, below the estimated 65% passing threshold and the 78% mean score of human fellows-in-training (FITs). Providing textbook context improved GPT-4o to 73.8% on text-only questions, but still fell short of human trainees. Whether next-generation LLMs with retrieval-augmented generation (RAG) can exceed this gap is unknown. MethodsClaude Opus 4.7 and GPT-5.5 were administered all 168 questions (141 text-only, 27 image-based) from the 2023 ASNC Board Preparation Exam across 5 iterations each, using RAG with a nuclear cardiology textbook, companion atlas, and ASNC clinical guidelines. Claude used local FAISS-based semantic retrieval; GPT-5.5 used Azures cloud-hosted vector store. Performance was compared to prior LLM results and 13 human FITs. ResultsAcross 5 iterations, Claude Opus 4.7 achieved a mean accuracy of 86.3% {+/-} 1.4% (text 88.8%, image 73.3%). GPT-5.5 achieved 86.7% {+/-} 2.2% (text 88.5%, image 77.0%) but refused a mean of 12.2 questions (7.3%) per iteration due to safety filters. Both models surpassed the human FIT mean (78.0%) and the estimated passing threshold. Compared to GPT-4o without context (63.1%), this represents a 23-percentage-point improvement in 18 months. ConclusionNext-generation LLMs with RAG now surpass average human trainee performance on nuclear cardiology board preparation questions, suggesting significant potential as educational tools and knowledge-reference aids in cardiovascular imaging. Condensed AbstractAcross 5 iterations each, Claude Opus 4.7 and GPT-5.5 with retrieval-augmented generation achieved mean accuracies of 86.3% and 86.7% on the 2023 ASNC Board Preparation Exam (168 questions), both surpassing the mean human fellow-in-training score of 78%. GPT-5.5 refused a mean of 12.2 questions (7.3%) per iteration due to safety filters. These results represent a 23-percentage-point improvement over the best prior LLM without context (63.1%), demonstrating that RAG-enhanced LLMs have reached human-level proficiency in nuclear cardiology knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/26352768v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@5f2465org.highwire.dtl.DTLVardef@4e80d3org.highwire.dtl.DTLVardef@1ebbb93org.highwire.dtl.DTLVardef@167d3c1_HPS_FORMAT_FIGEXP M_FIG C_FIG Overview of the three-study research arc evaluating LLM performance on the 2023 ASNC Board Preparation Exam. Study 1 (2024) tested four LLMs without context (best: GPT-4o, 63.1%). Study 2 (2025) added textbook context to GPT-4o (73.8%). Study 3 (2026, current) evaluated Claude Opus 4.7 and GPT-5.5 with retrieval-augmented generation across 5 iterations each (mean 86.3% and 86.7%, respectively), both surpassing the human fellow-in-training mean of 78%. Right panel shows the performance scale with key thresholds.

Red-emitting carbon quantum dots (HP-CQDs) were synthesised for the first time from aqueous leaf extracts of Hamelia patens through single-step, reagent-free microwave-assisted carbonisation (750 W). The resulting nanoparticles displayed a narrow hydrodynamic size distribution centred at 3.9 nm, consistent with atomic force microscopy measurements showing a maximum height of 2.81 nm. Under 400 nm excitation, the CQDs exhibited a characteristic red emission maximum at 675 nm, representing a rare example of long-wavelength-emitting green CQDs derived from plant biomass. UV-Vis absorption bands at 224 and 256 nm were assigned to {pi}-{pi}* transitions of aromatic carbon domains and n-{pi}* transitions associated with carbonyl-containing surface groups, respectively. X-ray photoelectron spectroscopy (XPS) indicated a carbon-rich composition (C: 67.24%, O: 31.25%, N: 1.52%) with prominent C-O (42.67%) and C-C/C=C (42.64%) contributions. ATR-FTIR further confirmed the retention of hydroxyl, ether, and aliphatic functionalities following carbonisation. The excitation-wavelength-independent emission peak position implicates discrete surface molecular states rather than a heterogeneous distribution of emitters. HP-CQDs exhibit potent DPPH radical scavenging activity (IC50 = 141.8 {micro}g mL-1), comparable to ascorbic acid (IC50 = 114.8 {micro}g mL-1), and maintain >95% cell viability in both HeLa and RPE-1 cells up to 250 {micro}g mL-1. Confocal microscopy demonstrates concentration-dependent cytoplasmic accumulation and selective perinuclear localization at 300 {micro}g mL-1. In vivo biodistribution in zebrafish larvae confirms systemic uptake with statistically significant fluorescence enhancement at 500 {micro}g mL-1 (p < 0.01), establishing HP-CQDs as biocompatible red-fluorescent probes with dual imaging-antioxidant functionality. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/724069v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1dbe864org.highwire.dtl.DTLVardef@763ed0org.highwire.dtl.DTLVardef@115e9b9org.highwire.dtl.DTLVardef@1a3941e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Indocyanine green (ICG) J-aggregates (JAs) are self-assembled particles characterized by a sharp and strong absorption peak in the near-infrared region ([~]890 nm), enhanced photostability, low fluorescence, and high photothermal conversion efficiency, compared to monomeric ICG. These attributes make ICG-JAs promising contrast agent candidates for photoacoustic imaging (PAI). However, traditional methods for synthesizing ICG-JAs often yield particles without targeting ability, which limit their applications. Thus, to synthesize targeted nanoscale JA, complex and multi-step encapsulation and filtration processes are generally required. To solve this issue, we introduce a robust and rapid strategy for direct synthesis of targeted nanoscale ICG-JA by co-assembling ICG and ICG-azide dyes under optimized formulation conditions that do not require encapsulation. The resulting nanoscale JAAZ particles (nJAAZ) exhibit diameters of [~]120-150 nm and are amenable to direct bio-orthogonal functionalization via copper-free click chemistry for the attachment of virtually any targeting ligands and/or biomolecules. We further demonstrate the strong photoacoustic signal generation of these nJAAZ in vitro and in vivo, highlighting their potential as a modular high-performance contrast agent platform for PAI. This work establishes a scalable and tunable platform for engineering functional JAs, opening new avenues for targeted molecular imaging and theranostic applications.

Objective: Coronary artery calcification (CAC) assessment for cardiovascular risk stratification is traditionally achieved using ECG-gated computed tomography (CT). Automated deep-learning (DL) algorithms may streamline opportunistic CAC detection and scoring, particularly on non-gated CT scans. This study evaluated the performance of a fully automated DL-based CAC scoring algorithm ("DL-CAC") against expert human scoring. Methods: The algorithm was trained on 1,260 chest CT scans from multiple databases to automatically identify coronary calcium, calculate Agatston scores, and assign a cardiovascular disease (CVD) risk classification. Performance was assessed on a holdout dataset (n=500) comprising ECG-gated calcium scoring CT scans and lung cancer screening non-gated chest CTs as well as in an external, independent CT dataset (n=129) from liver transplant candidates. Agreement with expert scoring was assessed using intraclass correlation coefficient (ICC) for Agatston scores and Cohen's {kappa} for CVD risk classification. Results: The algorithm demonstrated high agreement with expert scoring in the pooled calcium scoring and lung cancer screening cohorts, with an ICC of 0.947 for Agatston scores and {kappa} of 0.936 for CVD risk classification. For liver transplant candidates, the algorithm exhibited substantial agreement with expert scoring of non-gated CT scans ({kappa}=0.79) and a sensitivity of 90.4% and specificity of 96.4% in high-risk cases. Conclusion: These findings suggest that DL-based CAC scoring on non-gated CT scans may be a feasible alternative to traditional methods and could support opportunistic cardiovascular risk assessment in routine imaging. Further validation is warranted to assess clinical integration in broader practice settings.

PurposeTo present the design and validation of a lowcost, microcontrollerbased gas delivery system that automates fixed inspired respiratory stimuli for MRI experiments. MethodsThe system uses three solenoid valves controlled by an Arduinobased circuit to switch between premixed medical gas cylinders according to predefined timing protocols. By using the MRI scanner external timing signal, gas delivery can be synchronised with image acquisition. Both a permanently installed configuration and a portable enclosure were constructed using commercially available components, with a total material cost of approximately {pound}650. The system was integrated with a singleuse breathing circuit and evaluated using hypercapnic and hyperoxic stimulus paradigms. Endtidal oxygen and carbon dioxide were measured using a respiratory gas analyser and physiological responses were assessed using BOLD MRI at 3 T. ResultsThe system delivered reliable, repeatable gas transitions during MRItriggered protocols. During hypercapnia (n{square}={square}15), the mean increase in endtidal carbon dioxide was 8.7{square}{+/-}{square}1.8{square}mmHg from a baseline of 32.2{square}{+/-}{square}3.1{square}mmHg, producing a mean grey matter BOLD signal increase of 3.2{+/-}1.7%. During hyperoxia (n{square}={square}15), the mean increase in endtidal oxygen was 292.3{square}{+/-}{square}59.0{square}mmHg from a baseline of 114.5{square}{+/-}{square}10.7{square}mmHg, with an associated BOLD signal change of 1.2{+/-}1.7%. Across both protocols respiratory and BOLD responses were consistent across participants. ConclusionThis microcontrollerbased system provides an inexpensive and reliable method for administering fixed inspired respiratory stimuli with automated MRI synchronisation. It offers an intermediate option between simple manual systems and higher cost commercial gas blenders, making it well suited for technical and methodological studies in cerebrovascular reactivity, hyperoxiaBOLD and related applications.

Understanding skeletal muscle metabolism involves real-time monitoring of key cellular parameters, such as calcium ions (Ca2+), adenosine triphosphate (ATP), cyclic adenosine monophosphate (cAMP), and intracellular temperature. Fluorescent protein (FP)-based biosensors are used for live-cell imaging of these signals with high spatiotemporal resolution. Differentiated myotubes are in vitro models used for physiological muscle metabolism research. However, efficient transfection of FP-based biosensors into these cells is challenging. Here, we developed an electroporation-based strategy for delivering recombinant protein biosensors into fully differentiated myotubes. Biosensors for Ca2+, ATP, cAMP, and temperature were recombinantly produced using Escherichia coli and introduced into myotubes using electroporation. Electroporation conditions were optimised to maximise delivery efficiency, preserve cell viability, and minimise cellular damage. We established a robust intracellular delivery system that effectively demonstrated Ca2+, ATP, and temperature dynamics. Furthermore, we achieved the successful co-delivery of two biosensors that enabled dual imaging of Ca2+ and cAMP in response to stimulation.