.SignificanceCardiovascular disease remains one of the leading causes of death in the United States. Wearable optical systems are known to have errors and biases for individuals with different skin tones as well as different levels of obesity. By enabling the development and validation of wearable technologies across diverse populations, we advance equitable healthcare solutions and foster the creation of more reliable, personalized health monitoring systems.AimWe aim to develop a dynamic wrist phantom replicating the radial artery pulse, addressing physiological variations such as skin tone and obesity that impact wearable health technologies.ApproachA silicone-based phantom mimics human tissues’ mechanical and optical properties. A cam-driven pulsatile flow system simulated physiological blood flow, with key waveform features controlled by mechanical components. Optical properties were adjusted using titanium dioxide and carbon black to match Fitzpatrick skin tones I to VI, whereas radial artery depth variations simulated the effects of obesity. The phantom system incorporated a blood-mimicking fluid to replicate the optical absorption characteristics of whole blood.ResultsThe phantom successfully replicated photoplethysmography (PPG) waveforms at heart rates ranging from 59 to 118 beats per minute, demonstrating physiologically representative features such as systolic and diastolic peaks. Signal degradation was observed with increasing vessel depth and darker skin tones, consistent with real-world challenges in wearable device accuracy. The alternating signal/baseline signal ratio of the PPG signal decreased by up to 77.8% for darker skin tones and deeper vessels. The phantom also validated its performance against commercial wearables, supporting its utility in device testing.ConclusionsThis dynamic wrist phantom provides a robust platform for evaluating optical devices under controlled and representative conditions, addressing critical gaps in inclusivity and accuracy.

Electromagnetohydrodynamic pulsatile blood flow of Casson nanofluid with stenosis porous artery under periodic body acceleration and slip effects: A Reynolds and Vogel viscosity models.

Developing a reduced order model for pulsatile blood flow simulations using minimal three-dimensional simulation data.

IntroductionLiving animal models have been widely used for microsurgical training. Due to ethical concerns and animal care costs, non-living animal training models have gained significant popularity. The chicken thigh model has become a gold standard for preclinical microsurgical training. Efforts to optimize this model by using vessel perfusion have been previously reported. This study aims to further optimize the chicken thigh training model and to suggest a novel porcine rib model based on the implementation of the pulsatile perfusion technique.MethodsTwenty optimized chicken thigh (OCTM) and twenty novel porcine rib (NPRM) training models were prepared. Model morphological parameters, preparation time, and cost were analyzed. A peristaltic pump was used for arterial perfusion, and the infusion bag was used for venous system perfusion. Training models were used for vessel dissection, microvascular anastomosis performance, and patency evaluation. Sixteen microsurgeons were invited to evaluate the surgical fidelity of both training models.ResultsTraining model preparation time was 20.7 ± 1.9 min and 18.2 ± 1.5 min for the OCTM and the NPRM, respectively. The average artery and vein diameter (2.3 ± 0.2 mm and 2.9 ± 0.2 mm in OCTM; 1.7 ± 0.2 mm and 1.6 ± 0.2 mm in NPRM) were applicable for the broad spectrum of microvascular exercises. The arterial pulsatile flow was visually detectable and allowed side branch visualization and management. The average cost of the model was 10.7 ± 0.7 euro for OCTM and 14.1 ± 0.7 euro for NPRM. OCTM and NPRM demonstrated high surgical fidelity (OCTM: 0.86 ± 0.06; NPRM: 0.78 ± 0.1; p = 0.01) compared to the reference model (0.96 ± 0.02).ConclusionsThe optimized non-living animal models represent efficient, reliable, and cost-effective alternatives to living-animal models for microsurgical training at different levels of expertise. Suggested models present a highly realistic simulation of pulsatile blood flow and can be effectively used to train vessel dissection, side branch management, microvascular anastomosis, bleeding control, and anastomosis patency evaluation. Perfused pulsatile non-living animal training models can be incorporated into the microsurgery training curriculum and may significantly enhance complex microsurgical skills and reduce surgical anxiety in young specialists.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12909-025-07979-x.

BackgroundPulsatile perfusion is a developing technique that attempts to mimic the natural pulsatile flow of blood during cardiopulmonary bypass (CBP).PurposeThis systematic review and meta-analysis was conducted to show the effects of pulsatile perfusion in CPB compared to non-pulsatile.MethodsRandomized control trials that evaluated the implementation of pulsatile perfusion during cardiopulmonary bypass surgery were identified by a literature search in the following electronic databases (PubMed, Web of Science, Scopus, CENTRAL, and Embase) published from inception up to February 2024.ResultsThe search yielded 33 trials of which three studies demonstrated a low risk of bias, 29 studies showed some concerns, and one study presented a high risk of bias overall. The total number of patients was 3174 patients. The analysis showed that pulsatile perfusion led to a significant decrease in creatinine level [MD = −0.14, 95% CI (−0.24, −.04), P < 0.004], lactate level [MD = −8.21, 95% CI (−13.16, −3.25), P < 0.001], hospital stay [MD = −1.38, 95% CI (−2.51, −0.25), P = 0.016], ICU stay [MD = −0.47, 95% CI (−0.82, −0.13), P = 0.007], intubation time [MD = −3.73, 95% CI (−5.42, −2.04), P < 0.001], and increase in creatinine clearance [MD = 10.08, 95% CI (3.36, 16.80), P < 0.003]. However, no significant difference between the two regimens was detected in estimated glomerular filtration rate (eGFR), alanine transferase (ALT) level, AST (aspartate transferase) level, Blood urea nitrogen (BUN) level, acute renal failure (ARF), and mortality rates.ConclusionPulsatile perfusion showed some positive effects on creatinine, creatinine clearance, lactate level, hospital stay, ICU stay, and intubation time. However, there was no difference between the two methods on BUN, ALT, AST, eGFR, ARF, and death. Most of the outcomes showed significant heterogeneity, which requires more robust RCTs to be conducted to increase the quality and the certainty of evidence.

Pulsatile blood flow generates complex wall shear stress (WSS) patterns at the carotid bifurcation, which critically regulate endothelial function and structure. While physiological pulsatile WSS (PWSS) is essential for maintaining vascular health, low oscillatory WSS (OWSS) near the carotid sinus is closely associated with endothelial dysfunction, atherosclerotic plaque formation, and stenosis. Reproducing these hemodynamic conditions in vitro is therefore crucial for investigating endothelial mechanobiology and elucidating the pathogenesis of atherosclerosis. Although microfluidic technologies have emerged as promising platforms for simulating either pulsatile or oscillatory WSS, a system capable of simultaneously replicating both characteristic waveforms—as found in vivo at the carotid bifurcation—remains undeveloped. In this study, we designed a variable cross-section microfluidic channel using Computational Fluid Dynamics (CFD) simulations. Numerical results demonstrate that the optimized channel accurately reproduces low OWSS at a stepped section emulating the carotid sinus, alongside high PWSS in a downstream uniform section. Vortex formation induced by the step structure is identified as key to generating low OWSS, influenced by step height, channel width ratio, and input flow rate. This work provides a novel and robust methodology for designing microfluidic systems that mimic complex hemodynamic microenvironments, facilitating future studies on the interplay between distinct WSS patterns and endothelial dysfunction.

Current hemodynamic studies on vascular stenosis conditions are mostly limited to cases without interventional devices or where the devices remain stationary. To address this limitation, a bidirectional fluid–structure interaction (FSI) method was employed to theoretically evaluate the blood flow characteristics in vessels with varying stenosis rates and spacing distances, accounting for the coupling between blood flow and vascular deformation. In parallel, particle image velocimetry (PIV) was utilized to experimentally assess the pulsatile flow field within a tube containing the developed vascular interventional robot. The results indicate that under pulsatile blood flow, significant differences arise in the hemodynamic parameters of vessels with different degrees of stenosis. As the stenosis rate increases, key parameters such as blood flow velocity, blood pressure, and vascular wall shear stress (WSS) also increase. In vessels with two stenotic regions, the influence of the spacing distance between them on hemodynamic parameters becomes more pronounced with higher stenosis rates. Moreover, the spatial distribution and magnitude of the numerical simulation results closely match those obtained from experimental measurements, validating the accuracy and reliability of the computational method.

Navigating the complex and high-flow environment of human vasculature remains a major challenge for conventional endovascular tools and externally actuated tethered systems. While catheter-based approaches are the clinical standard, their limited steerability and force transmission hinder access to tortuous or distal vessels, especially in the brain. Untethered robotic systems have emerged as a promising alternative for enhanced flexibility and reachability. However, most designs struggle against the high, pulsatile blood flow in human arteries. Here, the study presents a magnetically actuated milli-spinner robot that overcomes existing limitations in navigating complex and high-flow vasculature. Capable of swimming at 23cm·s-1 (73 body lengths per second), the milli-spinner enables rapid, stable navigation through complex vasculature. This performance is driven by its hollow cylindrical structure with integrated helical fins and slits, which together generate a spinning-induced flow field that enhances propulsion efficiency and allows the robot to maintain stability and control even in dynamic, pulsatile blood flow environments. In addition to its navigation capabilities, the milli-spinner enables multifunctional treatment, including localized suction and shear for efficient clot removal, targeted drug delivery, and in situ embolization for aneurysm treatment. These features establish the milli-spinner as a versatile and powerful platform for next-generation, untethered endovascular interventions.

In this paper, we introduce a speckle contrast optical spectroscopy/tomography (SCOS/SCOT) configuration based on an integrated imaging approach exploiting 113 micro-objectives mounted on a commercial CMOS camera that operates without fiber coupling, suitable for direct skin contact measurements and simultaneous multiple source-detector separation acquisitions. This compact system was validated ex vivo on phantoms and in vivo by monitoring the blood flow on the forearm muscle of a healthy human subject. The measurements, performed at multiple source-detector separations and camera exposure times, demonstrate excellent agreement with the theory based on the correlation-diffusion model. In vivo data demonstrate the capability of tracking pulsatile blood flow with a high signal-to-noise ratio (>4 harmonics of the cardiac pulse frequency detected) and sensitivity to small changes in muscle blood flow. This micro-objective array-based design overcomes a key challenge towards wearable SCOS/SCOT devices.

Understanding cerebral circulation is crucial for early diagnosis and patient-oriented therapies for brain conditions. However, blood flow simulations at the organ scale have been limited. This work introduces a framework for modeling extensive vascular networks in the human cerebral cortex and conducting pulsatile blood flow simulations. Using a patient-specific cerebral geometry, we applied a parallelized adaptive constrained constructive optimization algorithm to create a comprehensive pial vascular network in the left hemisphere, starting from the main cerebral arteries. The resulting network included over 75000, 103000, and 55000 vessels for the anterior, middle, and posterior territories, respectively. Pial vessel diameters featured a median [interquartile range, IQR] value of [Formula: see text]. We integrated the pial vascular network model with the Anatomically-Detailed Arterial Network (ADAN) model to conduct one-dimensional (1D) blood flow simulations under normotensive and hypertensive conditions. Viscoelastic dissipation proved to be a key ingredient in the characterization of the hemodynamic environments in the pial circulation. In the normotensive scenario, mean blood pressure in the pial vessels resulted in a median [IQR] value of [Formula: see text]. The flow pulsatility index and its corresponding damping factor were effective descriptors of the hypertensive state. The median [IQR] pulsatility index in the normotensive state was [Formula: see text], and in hypertension it increased up to [Formula: see text], while its corresponding damping factor in the normotensive state was [Formula: see text], and in the hypertensive state it was reduced to [Formula: see text]. We observed large regional pressure gradients in terminal vessels, with pressure levels ranging from [Formula: see text] in normotension to [Formula: see text] in hypertension. Additionally, the pulsatility index at terminal vessels increased with distance from the Circle of Willis in the hypertensive case, contrasting with the decreasing pattern seen in normotension. This approach provides a unique characterization of hemodynamics in the pial vascular network of the human cerebral cortex, paving the way for research into microcirculatory environments, the link between hemodynamics and neural function, and their roles in conditions like stroke and dementia.

Accurate assessment of intracranial aneurysm rupture risk, particularly in Middle Cerebral Artery (MCA) aneurysms, relies on a detailed understanding of patient-specific hemodynamic behavior. In this study, we present an integrated framework that combines Computational Fluid Dynamics (CFD) with Proper Orthogonal Decomposition (POD) and machine learning (ML) to efficiently model pulsatile blood flow using a Casson non-Newtonian fluid model, without incorporating fluid-structure interaction (FSI). Patient-specific vascular geometries were reconstructed from DICOM imaging data and simulated using ANSYS Fluent to capture key hemodynamic factors, including velocity components, pressure, wall shear stress (WSS), and oscillatory shear index (OSI). POD was applied to reduce the dimensionality of the CFD data while retaining the dominant energetic flow structures. Results showed that fewer than 10 POD modes were sufficient to capture over 99% of the energy for pressure and WSS, while OSI required significantly more modes due to its inherent complexity. Machine learning models were trained on the reduced-order features to predict hemodynamic fields across time snapshots. The hybrid POD-ML approach yielded reasonable predictions for pressure and WSS in both training and test sets, while OSI prediction accuracy decreased in the test region, indicating the need for more advanced modeling strategies. The proposed method significantly reduces computational cost while preserving critical hemodynamic information, making it well-suited for real-time or near-real-time clinical decision support. This work demonstrates the potential of combining data-driven techniques with CFD for efficient, non-invasive risk assessment and treatment planning in cerebral aneurysm management.

Thin-film NiTi intrasaccular implant with flaps for aneurysm treatments.

An LBM-FEM robust and efficient fluid-structure coupling scheme for partitioned numerical simulation of blood flow-aortic valve interaction.

Our research was to investigate the determinants of perfusion index (PI), which can get into the increase and decrease of PI and also build the model on PI as well as the relationship between PI and different study variables. PI, calculated using a pulse oximeter, shows the ratio of the pulsatile blood flow to the non-pulsatile blood flow or static blood in peripheral tissue. The information of PI, together with their preditors, was collected/measured from the students of Statistics at Rajshahi University, adapting the stratified random sampling through a questionnaire containing 52 questions. Weights, heights, pulse rate, diastolic blood pressure, systolic blood pressure, and perfusion index are recorded by respective measurement tools. Univariate analysis was used to determine significant determinants, and bivariate association and correlation can also be implied. It was found that there was a positive correlation between PI and diastolic blood pressure, PI and weight, PI and exercise time, and PI and eating mangoes. A statistically significant difference was detected between the PI of males and females and smokers and non-smokers (p&lt;0.05). Also, a significant association (p&lt;0.05) was found between blood pressure levels and classes of different PI. Finally, a model was created involving the most significant determinants: sex, smoking status, exercise time, and diastolic blood pressure. As the value of R2 is 0.759, there is a scope for further extension of this research, including the increase of sample size, study variables, and involvement of different age groups of people. PI is the indication of sound health, strength of heart, and way of detecting heart disease, so we should take necessary steps to facilitate exercise time and maintain a healthy life. We also have to eat a healthy diet to raise the perfusion index.

Fibrin networks, the structural framework of thrombi, undergo fatigue failure under pulsatile blood flow, posing risks of embolic events like stroke, pulmonary embolism. While fibrin's fracture behavior under monotonic loading has been intensively explored, its fatigue mechanism that governs thrombus embolization is understudied. Multiscale experiments, modeling of condensed fibrin networks, reveal a paradoxically low fatigue threshold despite high fracture energy. Mechanistically, cyclic loading induces irreversible α‐to‐β transitions in fibrin's molecular chains, causing molecular creep that dissipates strain energy, propagates cracks via rupture of stress‐localized fibers. A multiscale continuum model quantitatively links this nanoscale mechanics to macroscale fatigue, providing an alternative mechanism of thrombus rupture as a molecular disorder driven by cumulative conformational damage. These findings reexamine widely adopted fibrous hydrogel design paradigms while bridging molecular structure characteristics to clinical thrombosis–a critical step toward predictive rupture risk assessment, targeted therapeutic strategies.

Background: Vascular dysfunction is increasingly recognized as a shared contributor to both cognitive impairment and late-life depression (LLD). However, the combined diagnostic value of cerebral hemodynamics, neuroimaging markers, and neuropsychological outcomes remains underexplored. This study aimed to investigate the associations be-tween transcranial Doppler (TCD) ultrasound parameters, cognitive performance, and depressive symptoms in older adults with mild cognitive impairment (MCI) and LLD. Importantly, we evaluated the integrative value of TCD-derived indices alongside MRI-confirmed white matter lesions (WMLs) and standardized neurocognitive and affective assessments. Methods: In this cross-sectional study, 96 older adults were enrolled including 78 cognitively unimpaired individuals and 18 with MCI. All participants underwent structured clinical, neuropsychological, and imaging evaluations including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS-15), MRI-based Fazekas scoring of WMLs, and TCD ultrasonography of the middle cerebral artery. Hemodynamic variables included mean blood flow velocity (MBFV), end-diastolic velocity (EDV), pulsatility index (PI), and resistive index (RI). Logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of MCI. Results: Participants with MCI showed significantly lower MBFV and EDV, and higher PI and RI (p < 0.05 for all) compared with cognitively unimpaired participants. In multivariate analysis, lower MBFV (OR = 0.64, p = 0.02) and EDV (OR = 0.70, p = 0.03), and higher PI (OR = 3.2, p < 0.01) and RI (OR = 1.9, p < 0.01) remained independently associated with MCI. ROC analysis revealed excellent discriminative performance for RI (AUC = 0.919) and MBFV (AUC = 0.879). Furthermore, PI correlated positively with depressive symptom severity, while RI was inversely related to the GDS-15 scores. Conclusions: Our findings underscore the diagnostic utility of TCD-derived hemodynamic parameters—particularly RI and MBFV—in identifying early vascular contributions to cognitive and affective dysfunction in older adults. The integration of TCD with MRI-confirmed WML assessment and standardized cognitive/mood measures represents a novel and clinically practical multi-modal approach for neurovascular profiling in aging populations.

This study proposes a novel data-driven framework for rapid and accurate prediction of unsteady hemodynamics in a patient-specific cardiovascular model, based on a Deep Learning-based Reduced-Order Model (DL-ROM). High-fidelity simulations of pulsatile blood flow were used to generate a wall shear stress (WSS) dataset. To reduce dimensionality while preserving essential dynamics, Dynamic Mode Decomposition (DMD) was employed, capturing dominant spatiotemporal modes linked to flow instabilities and coherent structures. The extracted DMD time coefficients were then used to train an attention-augmented Convolutional Neural Network with a Long Short-Term Memory layer, enabling the prediction of temporal WSS evolution. The DL-ROM accurately reconstructs high-resolution flow fields at unseen time points, achieving pointwise relative errors below 3% across most of the aneurysm surface. Compared to traditional full-order models, the proposed approach offers over two orders of magnitude reduction in computational cost, supporting near real-time inference. Analysis of DMD mode components further revealed region-specific flow dynamics, with high-frequency oscillations concentrated near the aneurysm dome. This work introduces an efficient and interpretable surrogate model for transient hemodynamic analysis, with promising implications for clinical applications such as rupture risk stratification, fast patient screening, and future integration across multiple aneurysm geometries.

This study applies Computational Fluid Dynamics (CFD) and mathematical modeling to examine uterine and umbilical arterial blood flow during pregnancy, providing a more detailed understanding of hemodynamic changes across gestation. Statistical analysis of Doppler ultrasound data from a large cohort of more than 200 pregnant women (in the second and third trimesters) reveals significant increases in the umbilical arterial peak systolic velocity (PSV) between the 22nd and 30th weeks, while uterine artery velocities remain relatively stable, suggesting adaptations in vascular resistance during pregnancy. By combining the Navier–Stokes equations with Doppler ultrasound-derived inlet velocity profiles, we quantify several key fluid dynamics parameters, including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), Reynolds number (Re), and Dean number (De), evaluating laminar flow stability in the uterine artery and secondary flow patterns in the umbilical artery. Since blood exhibits shear-dependent viscosity and complex rheological behavior, modeling it as a non-Newtonian fluid is essential to accurately capture pulsatile flow dynamics and wall shear stresses in these vessels. Unlike conventional imaging techniques, CFD offers enhanced visualization of blood flow characteristics such as streamlines, velocity distributions, and instantaneous particle motion, providing insights that are not easily captured by Doppler ultrasound alone. Specifically, CFD reveals secondary flow patterns in the umbilical artery, which interact with the primary flow, a phenomenon that is challenging to observe with ultrasound. These findings refine existing hemodynamic models, provide population-specific reference values for clinical assessments, and improve our understanding of the relationship between umbilical arterial flow dynamics and fetal growth restriction, with important implications for maternal and fetal health monitoring.

Computational approach offers insights into hemodynamics and pulsatile blood flow.

PurposeTo develop a flow pulsatile‐resolved pseudo‐continuous arterial spin labeling (FPR‐pCASL) technique for noninvasively assessing cerebral microvascular pulsatility.MethodsIn FPR‐pCASL, a pCASL sequence with single‐shot three‐dimensional gradient and spin echo acquisition was performed while simultaneously recording cardiac pulse signals using a pulse oximeter, and the pCASL images were retrospectively binned into multiple cardiac phases to resolve pulsatile microvascular blood flow. Both numerical simulations and in vivo experiments were performed to investigate the confounding factors of cardiac‐induced variations of labeling efficiency and arterial transit time on pCASL signal fluctuations. An in vivo experiment was conducted to examine and optimize the postlabeling delay in FPR‐pCASL. A test–retest study and a cross‐validation study were performed to evaluate the reproducibility and reliability of FPR‐pCASL for microvascular pulsatility index (PI) measurements. An aging study was conducted to investigate the effect of aging on cerebral microvascular PI.ResultsNumerical simulation showed that cardiac‐induced variations in labeling efficiency and arterial transit time contributed to, at most, 3% and 2% of the pCASL signal fluctuations, respectively, which is nearly an order of magnitude smaller than the measured PI. A good test–retest reproducibility in microvascular PI (intraclass correlation coefficient = 0.86, p = 0.002) and strong correlations with macrovascular PIs by phase‐contrast MRI (r = 0.72, 0.63, 0.71, and 0.74 for internal carotid artery, vertebral artery, middle cerebral artery M2/3, and M4, respectively; p < 0.05) were obtained. The elderly adults showed higher microvascular PI values than younger adults (p < 0.001).ConclusionThis work has demonstrated the feasibility and reliability of the FPR‐pCASL technique for directly assessing cerebral microvascular pulsatility.