Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging, among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
Accurate quantification of nanoparticle concentration is important in a host of fields, particularly in nanomedicine, electronics, and catalysis. Microfluidic systems present an opportunity to develop low-cost tests for nanoparticle quantification but often suffer technical challenges related to small sample volumes and optical interference from materials used to construct the device. Here we introduce a microfluidic device that integrates an ultrathin silicon nitride nanoporous membrane (nanomembrane) with an on-chip pressure transducer, designed to precisely quantify nanoparticle concentrations within a microfluidic device using an electrical readout for quantification. As nanoparticles are captured by the membrane under pressure-driven flow, the pressure differential across it changes and is measured by an on-chip transducer. The pressure transducer utilizes a thin PDMS membrane that deflects under pressure to change the cross-section and ionic flow resistance of an adjacent channel. This enables the determination of nanoparticle concentration by analysis of the kinetics of trans-membrane pressure changes relative to particle blockage of the nanomembrane. We also propose a statistical model of partial blockage and particle caking in nanoporous membranes, which accounts for distributions in pore and particle sizes. This model provides a more detailed understanding of nanoparticle filtration behavior and the kinetics of nanopore blocking, enabling accurate concentration determination. Experimental validation of the model on the data acquired by the microfluidic device demonstrates a lower limit of detection on the order of 108 particles per mL, offering a versatile, non-optical approach for the in situ quantification of nanoparticles in a microfluidic device.
Quantification using the Centiloid (CL) scale has become a valuable information to consider when interpreting amyloid-PET images and is now implemented in several software packages. This work aims to assess the comparability of CL from [18F]flutemetamol scans derived using several research and commercial quantification pipelines. This analysis relies on three datasets: a test-retest cohort, a group of clinically relevant patients with amnestic mild cognitive impairment (aMCI) and a subgroup from the BioFINDER-1 cohort enriched with scans with amyloid loads around potential clinical decision thresholds (0-50CL). Images from the Test-Retest and aMCI cohorts were processed across seven quantification pipelines: three commercial software platforms and four research tools, including the standard SPM8 workflow. The statistical analysis was based on three steps: 1) a repeatability analysis using the test-retest data; 2) a reproducibility analysis across all pipelines using the aMCI cohort; 3) an inter-software reliability analysis around three clinically relevant thresholds: 11, 25 and 37 CL using the aMCI and the BioFINDER-1 data. In the Test-Retest dataset composed of 10 Alzheimer's Disease (AD) patients, high test-retest repeatability and reliability were observed with an absolute bias of less than 5 CL. Within-individual coefficients of variation ranged from 2.6 to 4.4% and repeatability coefficients from ∼8 to ∼16 CL. CL quantification was generally reproducible across pipelines in a dataset of 80 aMCI individuals (R2 in [0.94-0.99], slope in [0.98-1.03], intercept in [-4, 4], but the 95% limits of agreement (LoAs) ranged between ∼±12 and ∼±21 CL. Agreement between software around the three clinically relevant thresholds was 92-100% (kappa 0.83-1) in the aMCI data (N = 80) and 75-99% (kappa 0.48-0.96) in the BioFINDER-1 subgroup (N = 110). In this study, CL quantification was shown to be robust across a range of currently available software platforms. Uncertainty estimates should always be considered when interpreting results. In clinical practice, the choice of quantification software should not impact patient management decisions.
Globally, 537 million persons live with diabetes, and a lifetime risk of up to 34% of developing diabetic foot ulcers (DFUs) necessitates strengthened preventive initiatives. The study aimed to develop and evaluate a clinical decision support system (CDSS) to be used by health care professionals in foot assessment and risk stratification as a base for prevention. Based on principles of human-computer interaction, the CDSS was developed for DFU risk assessment. Users, health care professionals from Region Västra Götaland in Sweden, evaluated the functions regarding effectiveness, efficiency, and satisfaction using a mixed methods usability testing approach. Expectations and experiences of using the CDSS were evaluated with the System Usability Scale (SUS). A total of 9 participants participated. User expectations of the CDSS, measured by SUS, averaged 77.2 (SD 14.6). Posttest SUS scores were 68.9 (SD 14.3), with a mean difference of 8.3 (P=.07), a nonsignificant reduction of usability after testing. The effectiveness of the CDSS in supporting users to complete 9 clinical tasks showed that for 7 (78%) tasks, at least 5 (56%) testers successfully achieved the intended goals. Tasks involving the identification of ingrown toenails and the confirmation of foot status, including risk stratification for the patient, were completed by fewer testers. Efficiency, measured as mean task completion time, ranged from 7 seconds to 9 minutes 20 seconds, and qualitative feedback informed recommendations for further system refinement. Users reported that a structured CDSS has the potential to support more equitable, consistent, and person-centered DFU prevention within a digital health service. A digital health service for DFU risk stratification was developed based on national and international guidelines. Although the users' expectations of the usability were higher compared to how they experienced the CDSS, the SUS test was near a threshold of 70, indicating that the system being tested was above average in usability. Further development and validation, both nationally and internationally, with continued attention to users' needs and contextual factors, are recommended.
Changes in physiological pressures play a key role in the development and progression of human disease processes. Thus, the assessment of pressures within blood vessels and other bodily compartment is crucial in the diagnosis and management of multiple medical conditions. Presently, techniques for pressure measurement are invasive or have limited accuracy and scope of assessment. Utilizing the subharmonic signal of ultrasound contrast agents offers a promising solution that could address these limitations. After the initial development of this technology in the late nineties, further investigation has brought subharmonic pressure estimation from in vitro exploration to attempts at clinical implementation. However, lack of availability of subharmonic imaging on most clinical scanners, and variability of subharmonic response with different contrast agents have impeded clinical acceptance and widespread use of this modality. This review examines subharmonic imaging and the use of ultrasound contrast agents for estimating physiological pressures, particularly in the heart and portal venous system. A focus is placed on clinically relevant physiologic pressures and their existing measurement approaches, the physics of subharmonic signal generation, in vitro studies demonstrating key findings, and more recent clinical trials. The review also highlights present limitations and future research directions that may help advance clinical translation.
The therapeutic efficacy of systemic treatments in cancer therapy is invariably limited by the biophysical barriers, including vascular endothelial barrier, extracellular matrix, and elevated interstitial fluid pressure. Ultrasonic cavitation, as a non-invasive modality, can induce a series of biological effects that leverage mechanical forces to breach these biophysical barriers and remodel the tumor microenvironment. This review traces the paradigm shift from thermal coagulation to mechanochemical modulation, where acoustic forces are transduced into profound biological responses via mechanosensitive ion channels and immunogenic signaling pathways. We summarize recent advances in the intelligent engineering of ultrasound-active materials, from vascular-targeted microbubbles and phase-change nanodroplets to oxygen-independent piezocatalysts. Meanwhile, we clinically evaluate the utility of cavitation in enhancing drug delivery and remodeling immune environment, and highlight the milestone approval of histotripsy for non-thermal ablation. Finally, we discuss critical challenges regarding stochasticity and biosafety, proposing a roadmap toward artificial intelligence-guided, closed-loop dosimetry. We predict that by integrating physical mechanics with biological engineering, ultrasonic cavitation may alleviate multidrug resistance and immunosuppression in cancer therapy.
Volumetric muscle loss has a severe impact on patients' quality of life, and current treatments often result in poor functional and aesthetic outcomes. This work aims to improve upcoming alternative treatments with tissue-engineered products, which currently are limited in size due to their production process. The methods used for achieving a cell-instructive growth milieu are designed for small volumes, lacking in nutrient supply structures and overall highly manual. To address these issues, we utilize a streamlined printing approach involving fused filament fabrication (FFF)-based spinning to produce fibrous muscle tissue engineering scaffolds. Similar to melt electro writing thin strands are drawn (around 100 µm), while retaining the advantages of FFF printing like fast and reliable production as well as a higher geometric freedom regarding patterns, shape, porosity and height tunable in the relevant range for volumetric muscle loss (width 0.5-15 cm, height 0.1-15 mm). These scaffolds are then combined with two bioinks which are infiltrated deeply into the scaffold with drop-on-demand printing. The first bioink consists of C2C12 myoblasts embedded in a collagen-matrigel matrix, while a second, sacrificial bioink is used to create vascular structures. Numerical simulations allowed for the scaffold design to be tailored, resulting in an anisotropic scaffold capable of repeated elastic deformation and spatio-temporal control of cell orientation, with up to 79.6% of aligned cells. This facilitates local isostatic conditioning, which is expressed in enhanced myotube formation. The tissue precursors simultaneously exhibit high biomechanical congruence (0.9-4.7 MPa), a high suture retention force (3.2 N per stitch) and shape retention (up to 80%), further augmented by the streamlined manufacturing process. These properties are pivotal for its prospective clinical translation.
The development of porous scaffolds with tunable mechanical and structural properties is essential for advancing tissue engineering strategies. In this study, we present a noninvasive, adjustable method for generating porous collagen scaffolds by utilizing micron-sized phase-shift droplets in combination with dual-frequency ultrasound. These microdroplets, generated via a microfluidic chip and composed of a liquid perfluoropentane core stabilized by a phospholipid shell, were embedded within collagen hydrogels and served as ultrasound-responsive cavitation nuclei. A 3.5 MHz imaging transducer was employed to trigger acoustic droplet vaporization of the embedded microdroplets, transitioning them into microbubbles. Then, a 200 kHz therapeutic transducer induced bubble oscillation and collapse, leading to localized pore formation. This combined ultrasound strategy enabled both vaporization and bubble implosion at reduced pressure thresholds compared to conventional acoustic droplet vaporization methods. Theoretical modeling using the Marmottant model predicted microbubble dynamics and corresponding pore sizes, which were validated through scanning electron microscopy and histological analysis. Ultrasound-treated scaffolds containing droplets exhibited significantly increased porosity of 56.53 ± 3.91% compared to untreated controls, with a pore diameter of 39.42 ± 10.28 μm, observed via scanning electron microscopy. Rheological analysis revealed enhanced elasticity and structural resilience in ultrasound-treated scaffolds. Finally, in vitro studies confirmed that fibroblast viability remained high within the treated scaffolds, with cells observed in close proximity to ultrasound-generated pores. This work introduces a tunable and clinically relevant strategy for fabricating functional scaffolds that could support tissue regeneration and customizable healing environments.
Dynamic acoustofluidics enables precise, contact-free manipulation of particles, colloids, and cells and shows great potential for applications in physics, materials science, and life sciences. However, existing strategies struggle to realize contrast-based selective manipulation primarily because the pressure fields are time invariant. Here, we introduce a space-time acoustofluidic tweezer (STAT) that uses frequency detuning-induced pseudo-space-time modulation of standing surface acoustic waves to enable dynamic, contrast-dependent control of microparticles and cells. Experiments and simulations show that, under STAT manipulation, positive (PACP) and negative (NACP) acoustic contrast particles can undergo low-frequency, shear- and longitudinal-like harmonic motions, respectively. Under certain driving conditions, NACPs can be selectively guided along programmed paths, whereas PACPs remain stably patterned. Overall, STAT offers a gentle, biocompatible way to selectively drive oscillation, transport, and sorting among particles and cells of different acoustic contrasts, broadening the capabilities of acoustofluidic systems for biomedical applications.
The establishment of monoclonal, stably transduced cell lines is a critical step in functional genomics and drug discovery. However, conventional methods are often time-consuming, labor-intensive, and prone to compromising cell viability. Here, we present a microfluidic single-cell sorting system based on laser-induced jetting (LIJet) that significantly improves the efficiency and quality of stable cell line generation. This system integrates a light-responsive substrate with metal coating and a PDMS microfluidic chip featuring an array of microwells, enabling single-cell capture, identification, and non-contact precision release. A 532 nm nanosecond pulsed laser is used to generate localized microjets, which accurately eject target cells from the microwells. In addition to achieving a 100% sorting success rate and maintaining over 95.3% post-sorting cell viability, the system supports long-term on-chip culture and viral transduction with full real-time monitoring. We demonstrated the platform's functionality by performing on-chip ZsGreen lentiviral transduction of human lung adenocarcinoma PC9 cells, followed by fluorescence-based single-cell selection, ultimately establishing monoclonal cell lines with stable transgene expression. This platform offers notable advantages in low-damage manipulation, dynamic monitoring, and functional perturbation, providing a robust and efficient solution for the construction of stably transduced cell lines, gene function screening, and phenotypic analysis across a variety of biomedical applications.
White blood cells (WBCs) and their subpopulations play critical roles in detecting blood cancers due to their distinct biological and biochemical characteristics. Infrared (IR) spectroscopy offers a rapid, label-free, and non-destructive approach to probe molecular composition, making it a promising tool for biomedical diagnostics. The objective of this proof-of-principle study is to investigate the possibility of IR spectroscopy combined with chemometrics to differentiate leukemia from lymphoma, and to assess the capability of whole WBCs and their subpopulations in distinguishing the two diseases. We based our study on 21 pediatric patients including 11 leukemia and 10 lymphoma cases, with in total 86,016 IR spectra measured from whole WBCs and the subpopulations. Data pipeline was established, including steps of spectral preprocessing, classification, and data fusion. Particularly, data fusion was implemented via low-, middle-, and high-level strategies, with the aim of combining spectra from different cell types and investigating their capability of differentiating the two blood cancers. The classification, both with and without data fusion, was benchmarked via the patient-wise cross-validation. A balanced accuracy of 80.0% was achieved based on IR spectra of whole WBCs. Further improvement was observed when combining whole WBCs and its subpopulations, with the best performance of 90.0% from combining whole WBCs and granulocytes with high-level data fusion strategy. The performance was observed consistent for both linear and nonlinear classifications based on linear discriminant analysis (LDA) and support vector machine (SVM), respectively. The results indicate the promising potential of IR spectroscopy of blood samples to distinguish leukemia and lymphoma with the help of chemometric approaches. Further, WBC subpopulations, particularly granulocytes, were proven to contain complementary information to whole WBCs for differentiating leukemia from lymphoma. This provides critical insights for biomedical practice in blood cancer diagnostics.
Radiation-induced pneumonitis (RP) is a critical complication of radiotherapy in lung cancer patients, and its early detection remains a challenge due to the limited availability of annotated CT imaging data and the subtle nature of disease evolution. The objective of this study is to enhance the detection and localization of RP in CT by integrating advanced data augmentation, self-supervised learning, and synthetic data generation techniques. A conditional Generative Adversarial Network (cGAN) was used to create synthetic RP images conditioned on lung segmentation masks to create anatomically plausible data for augmenting the training sets. The pipeline was created to possess double-stage self-supervised training with hierarchical pretext tasks to achieve robust features. The performance of the proposed framework, for a 5-fold cross-validation, has an average accuracy of 94.04%, precision of 92.06%, with a recall of 95.1%, an F1-score of 93.56%, and area under the curve of 95.94%. The model was demonstrated to possess superior performance and stability in RP detection and localization, which suggests potential clinical translation. The paper offers a novel fusion of cGAN-generated synthetic data, spatial attention, and contrastive learning to address RP detection in limited data. Interpretability is achieved by introducing Bayesian uncertainty estimation to provide translational value in clinical practice.
To investigate the best combination of parameters by changing the collimator and increment in the volumetric modulated arc therapy after modified radical mastectomy for breast cancer. Ten patients with left breast cancer and ten patients with right breast cancer who underwent modified radical mastectomy were selected in our research cohort. Treatment plans were established utilizing the 3-arc beam configuration spanning 240 degrees. The collimator angle was systematically adjusted in increments of 10 degrees, ranging from - 90 to 90 degrees. Additionally, increment values of 10, 20, 30, and 40 were employed for each corresponding collimator angle. All other planning parameters remained constant across the plans. Finally the impact of different values was analyzed in terms of plan quality and execution efficiency. Regarding the dose distribution within the target and the ipsilateral lung, the optimal collimator angles were observed to range from - 60 to 30 degrees for the left breast and from - 30 to 60 degrees for the right breast. As for the increment value, 10 yielded the optimal outcome, 40 resulted in the least desirable outcome, and 20 and 30 demonstrated a balanced effect between 10 and 40. In terms of execution efficiency and treatment complexity, the impact of the collimator angle on the results exhibits symmetric distribution, and the most favorable collimator angle approaches 0 degrees. Additionally, with a gradual increase in the collimator angle, the execution efficiency diminishes while the complexity correspondingly increases. In the context of treatment planning following modified radical mastectomy for left and right breast cancer, optimal collimator angles fall within the ± 30-degree range, while increment values of 20 and 30 degrees yielded the best overall outcomes, ensuring the attainment of the prescribed dose while accounting for execution efficiency and treatment complexity.
Early detection of right ventricular (RV) dysfunction is essential in pulmonary arterial hypertension (PAH) but remains challenging using conventional echocardiography. This study investigates the feasibility of a noninvasive, physics-based framework using three-dimensional (3D) echocardiography that integrates myocardial strain and volumetric flow analysis to characterize RV mechanical performance across stages of PAH. A prospective pilot study (N = 15) enrolled healthy controls, PAH patients with preserved RV size, and PAH patients with RV dysfunction. Deformation was evaluated by principal strain analysis and by conventional (longitudinal, circumferential) components. Hemodynamic metrics included hemodynamic forces and energetic properties that were derived using a physics-informed volumetric echocardiographic particle image velocimetry (V-Echo-PIV) method applied to contrast-enhanced acquisitions. Deformation analysis revealed that longitudinal strain was significantly reduced even in PAH patients with preserved RV dimensions, while second principal (secondary) strain showed a distinctive sign reversal, indicating a paradoxical systolic lengthening, early in the disease. The analysis of hemodynamic forces showed a marked reduction in systolic propulsion across all PAH stages. In contrast, energetic abnormalities were predominantly observed at later stage of the disease. The integration of 3D myocardial strain with fluid dynamics provides a comprehensive physiological assessment of RV remodeling. While strain and systolic propulsion appear as sensitive markers for early dysfunction, diastolic energetics may support disease staging. This noninvasive framework shows promise for early detection and longitudinal monitoring of PAH patients.
We hypothesize that intestinal microbiome dysbiosis may contribute to Parkinson's disease (PD) pathogenesis. Our prior proof-of-concept clinical trial demonstrated that a precision prebiotic intervention improved microbiota dysbiosis and alleviated gastrointestinal and motor symptoms in PD patients. Building on this, we analyzed plasma extracellular vesicles (EVs) from participants to explore EVs as a dynamic PD biomarker and to assess the systemic effects of a microbiota-directed intervention. Using mass spectrometry-based proteomics of EVs from PD and healthy control (HC) participants, we identified distinct human and bacterial proteins in plasma-derived EV. Crucially, this offers a holistic systemic readout of the microbiota-gut-brain axis by quantifying both host and microbial components. We found that EV proteomic profiles differed between PD and HC samples as well as between unmedicated/mild and medicated/moderate PD participants. Furthermore, the microbiota-directed prebiotic intervention induced an acutely modifiable PD signature, shifting host and microbial EV proteomic profiles toward the HC profile. Using a combined 16-feature host-microbe signature, we built a multiple linear regression model that accurately distinguishes PD status from HC (R2 = 0.88) and successfully stratified disease severity (R2 = 0.72). Based on these findings, we suggest that: (1) a precision prebiotic mixture can modulate PD-associated proteomic signatures and (2) plasma EV proteomics may be a platform to capture these biological responses and to explore potential diagnostic and staging biomarkers in the context of microbiome-targeted interventions.
Limited understanding of the biological processes that govern metastatic dissemination hinders its prevention and treatment1. Here, using 501 longitudinally collected primary and metastatic tumour samples from 24 patients with non-small cell lung cancer (NSCLC) enrolled in the TRACERx lung study and PEACE autopsy programme, we infer tumour evolution from diagnosis to death. With DNA-sequencing data encompassing 70% of the metastases that were radiologically detected before death and paired multi-region sampled primary tumours, we show that the genomes of metastases diverge markedly from those of their ancestral primary tumour, with additional driver alterations and genome doubling events occurring after metastatic dissemination. In 62.5% of patients, multiple primary tumour subclones disseminated, each founding a distinct metastasis. These metastases served as sources of onward spread: more than half of the metastases sampled were seeded by other metastases. The duration that metastases existed in situ influenced their likelihood of seeding further metastases. Most metastatic migrations started and ended in the same anatomical cavity. The few subclones that exited the thorax to seed metastases disseminated widely and were enriched for somatic copy-number alterations, suggesting that chromosomal instability may facilitate extrathoracic spread. This spatial and temporal evolutionary analysis sheds light on the extent of metastatic diversity and seeding in advanced NSCLC-which tends to be underestimated in single metastasis biopsies-and identifies genomic and clinical mediators of metastatic progression.
This article presents the design and the numerical analysis of a smart label-free Surface Plasmon Resonance (SPR) sensor to detect the concentration of haemoglobin in blood and the concentration of glucose in the urine samples. The suggested sensor uses a thin film of silver (Ag) on the prism's surface to excite the surface plasmons. The finite element method (FEM) was used to do numerical simulations to optimize the layer thickness and to analyse the sensor's performance in terms of the sensitivity and the Figure of merit (FOM). The results of the simulation showed that there is a linear correlation between resonance wavelength shift and change in analyte refractive index. The optimised design obtained a sensitivity of 288.29 °/RIU, QF of 780.80 [Formula: see text], SNR of 15.62, FoM of 492.51 [Formula: see text] and CSF of 539.20. The label-free methodology involves no chemical tagging and thus allows biosensing that is quick, real-time and economical. The suggested SPR sensor has great possibilities to be implemented in the non-invasive biomedical applications, diagnostics and point-of-care monitoring. In addition, machine learning models were employed to predict sensor sensitivity based on structural and optical parameters, demonstrating the strong capability of data-driven approaches for rapid performance estimation and design optimization.
Accurate classification of endometrial pathology is clinically challenging due to the heterogeneous and focal nature of precancerous and malignant lesions. Vascular remodeling is closely linked to tumor progression and may serve as a biomarker for malignancy. We aim to characterize a label-free optical-resolution photoacoustic microscopy (OR-PAM) approach for high-resolution imaging and quantitative characterization and separability assessment of endometrial vasculature. A custom-built OR-PAM system was used to image 34 fresh uterus samples with histologically confirmed diagnoses: normal, benign, endometrial intraepithelial neoplasia (EIN), and endometrial cancer (EC). Thirty-one quantitative vascular features were extracted from structural and spectral analyses of the photoacoustic data, and five statistically significant and minimally correlated features were selected for the separability assessment framework. A pairwise cosine similarity matrix based on these features was computed to construct a weighted similarity network, which was embedded into a two-dimensional (2D) space with a force-directed layout. A logistic regression boundary was applied to the 2D embedding to evaluate separability between normal/benign and EC/EIN clusters. A logistic regression classifier was developed from a cosine similarity matrix and cross-validated using a leave-one-out strategy. The cosine-similarity network graph placed 39 of 40 images on the expected side of the separation boundary. The logistic regression classifier yielded an area under the ROC curve (AUC) of 0.943, demonstrating strong discrimination between normal/benign and EC/EIN groups. OR-PAM combined with imaging feature analysis enables robust differentiation of endometrial pathologies and demonstrates potential as a noninvasive optical biopsy tool for endometrial assessment.
Current intravital imaging techniques for the mouse central nervous system (CNS) do not simultaneously provide micrometer-scale spatial resolution, whole-brain coverage, and sub-minute temporal resolution, limiting organ-wide interrogation of CNS fluid dynamics in vivo. Here, we introduce intravital synchrotron radiation-based hard X-ray micro computed tomography (SRµCT), a modality that enables dynamic whole-brain imaging at micrometer-scale spatial resolution in living mice. We performed intravital SRµCT of mouse CNS fluid spaces at three synchrotron radiation facilities, imaging both anesthetized free-breathing and mechanically ventilated animals, with and without retrospective cardiac gating. This approach achieves complete brain coverage with temporal resolution of up to 23 s and voxel sizes down to 6.3 µm, at an effective spatial resolution better than 20 µm, enabling time-resolved visualization of cerebrospinal fluid (CSF) contrast distribution and quantitative analysis of tissue motion across the entire brain. By combining micrometer-scale resolution, whole-organ field of view, and dynamic intravital imaging, SRµCT closes a long-standing methodological gap between optical microscopy and magnetic resonance imaging. Intravital SRµCT provides access to spatiotemporal information that cannot be obtained with existing techniques and establishes a framework for testing and integrating mechanistic models of CSF dynamics and solute transport at the scale of the whole brain.
Infections, limited biocompatibility, and material degradation remain major challenges for metallic implants in biomedical applications. To address these issues, this study presents a multifunctional coating strategy for Grade 2 titanium using a base layer of segmented polyurethane (Tecoflex SG-80A), followed by the co-deposition of titanium dioxide nanoparticles and gentamicin sulfate. Corrosion polarization tests revealed enhanced passivation of polyurethane-coated surfaces with no signs of pitting corrosion. Coatings showed porous microstructures with both nanoparticles and antibiotics distributed within and along pore edges. Energy-dispersive X-ray spectroscopy (EDX) confirmed the surface presence of both components. Thermogravimetric analysis indicated loadings of 0.17 ± 0.02 mg of gentamicin and 0.30 ± 0.04 mg of TiO2 per specimen. SEM and AFM analyses showed that over 86% of the surface was covered with gentamicin and nanoparticles. Contact angle measurements revealed a hydrophilic character (35°) for coatings containing both gentamicin and TiO2 nanoparticles, favorable for biological interactions. Cytotoxicity assays using dental pulp mesenchymal cells and fibroblasts demonstrated no cytotoxic effects after 72 h, whereas antibacterial tests against Staphylococcus aureus and Escherichia coli indicated inhibitory effects. Gentamicin release from the coatings followed the Korsmeyer-Peppas model, suggesting a diffusion-driven profile. These results support the development of durable, biocompatible, and antibacterial coatings for titanium implants that can reduce infection risk, enhance corrosion resistance, and support tissue integration.