Bioengineering

In vitro fertilization (IVF) laboratories equipped with timelapse incubators generate vast quantities of sequential embryo images, yet the absence of standardized, annotated databases impedes the development of reproducible computational tools for embryo assessment. Here we describe the construction of a standardized time-lapse imaging database comprising 631 normally fertilized zygotes from 218 treatment cycles, integrating timelapse image sequences, patient clinical records, and embryo developmental outcomes. We further present a gradient boosting decision tree (GBDT) ensemble framework that integrates zygote morphokinetic parameters-continuous time-series features extracted via a validated CNN-based segmentation algorithm (US Patent US11210494B2)-with conventional embryo assessment grades (categorical features per the Istanbul consensus). The fusion framework employs equal-weight initialization followed by iterative residual-decreasing training to optimally combine heterogeneous feature types. Ablation analysis demonstrated that the integrated model achieved an AUC of 0.78, significantly outperforming morphokinetics-only (AUC 0.71) and conventional-only (AUC 0.65) models, confirming the complementary value of the two data modalities. The database and fusion framework provide a reproducible foundation for embryo development assessment and are generalizable to other multimodal data integration tasks in reproductive medicine.

Ovarian cancer is recognized as the deadliest gynecological malignancy. Diagnosis at advanced stages and the lack of effective screening program lead to poor survival rates, dropping to 17-39 % in stage III-IV diseases. Ultrasound (US) is the primary imaging modality for ovarian structures evaluation, but it is strongly affected by the operator expertise due to the complexity of adnexal masses and the physiological variability of ovarian morphology throughout a womans lifecycle. The International Ovarian Tumor Analysis (IOTA) group introduced definitions and predictive tools to standardize gynecological US interpretation. However, these tools still rely on subjective interpretation, thus highlighting the need for more objective solutions. Recent studies have explored artificial intelligence (AI) algorithms for gynecological US, mainly focusing on adnexal masses classification. Conversely, a robust solution supporting the identification and description of healthy and tumoral ovarian structures is still lacking. This paper proposes OvAi Focus, a framework including (i) a segmentation module for the identification of healthy ovaries, plus solid and cystic components of adnexal masses; (ii) a morphology module for the extraction of IOTA-based keywords to describe adnexal masses morphology. Segmentation module results were compared to ground truth masks, showing DICE scores from 0.62 for functional ovary to 0.87 for the whole adnexal mass. Morphology module was tested through interobserver agreement analysis, obtaining Fleiss Kappa from 0.16 to 0.57 and Percent Agreement from 47 to 90 %, in line with existing literature. OvAi Focus represents an innovative solution which could help overcoming subjectivity in gynecological US imaging interpretation.

Objective: To perform internal and external validation of a gradient-boosted decision tree (GBDT) fusion model that integrates zygote morphokinetic parameters with conventional embryo assessment features for blastocyst prediction, and to compare its discriminative performance against senior embryologists. Methods: This retrospective cohort study included 631 normally fertilized zygotes from 218 treatment cycles. A GBDT fusion model integrating 84 zygote morphokinetic parameters and 8 conventional assessment features was evaluated internally (5-fold cross-validation) and externally on a public dataset of 523 embryos with blastocyst outcomes. Model performance was assessed using area under the ROC curve (AUC), area under the precision-recall curve (AUPRC), F1 score, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Discrimination was compared with embryologist consensus using the DeLong test; agreement was assessed with Cohen's kappa. Results: The model achieved an internal AUC of 0.78 (95% CI 0.74-0.82), AUPRC 0.72, F1 0.73, sensitivity 0.74, specificity 0.77, PPV 0.72, and NPV 0.79. External validation on the public dataset demonstrated acceptable generalizability (AUC 0.76, 95% CI 0.71-0.81). The model significantly outperformed embryologist consensus (AUC 0.70, P<0.001) with moderate agreement (kappa=0.56). Decision curve analysis confirmed clinical net benefit at threshold probabilities of 0.15-0.55. Conclusions: The GBDT fusion model integrating zygote morphokinetics with conventional assessment demonstrates good discrimination and external generalizability for blastocyst prediction, providing an interpretable decision-support tool for embryo selection in IVF practice.

Background/PurposeDiagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). MethodsPersonalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. ResultsAverage area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. ConclusionThis study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Optical coherence tomography (OCT) is widely used for retinal disease assessment, but automated quantitative analysis remains challenging because of anatomical variability and noisy imaging conditions. This study presents an interpretable OCT classification framework based on four anatomically guided retinal layers, combining preprocessing, adaptive segmentation, targeted feature engineering, and supervised classification to identify Normal, CNV, DME, and Drusen cases. Layer-specific descriptors included statistical, derivative, fluid-related, and GLCM texture markers. Feature correlation and ranking analyses showed that the proposed descriptors were highly complementary, that the most informative features were concentrated in layers 2 and 4, and that layers 1 and 3 contributed supportive structural information. Among the evaluated classifiers, the neural network performed best, achieving an accuracy of 98.17%, sensitivity of 97.88%, specificity of 99.38%, and AUC of 0.9985. Computational analysis showed efficient training and inference, with a total training time of 2216.3 s, prediction speed of approximately 160000 observations per second, and a compact model size of about 11 kB. These results demonstrated that anatomically guided feature extraction can provide accurate, efficient, and interpretable OCT disease classification, offering a practical alternative to less transparent end-to-end deep learning approaches.

Successful instrumented fusion of the lumbar spine is a complex surgical challenge, with positive patient outcomes dependent on careful surgical planning. Material selection is of critical importance to a mechanical construct supporting successful spinal fusion. Therefore, the aims of this study were to (a) evaluate the potential clinical use of finite element analysis (FEA) and (b) conduct a retrospective mechanical analysis of different implant materials in patients having undergone spinal fusion using FEA. Our methodology involved segmenting the spine from post-operative computed tomography (CT) image data from patients with previous spinal fusion. FEA models representing post-surgery cases were developed and different biomechanical loading conditions such as compression, flexion, bending and extension whilst testing pedicle screws of different materials were simulated. Patient specific finite element models were created, and biomechanical analysis were completed for all three patients. Polyetheretherketone (PEEK) constructs typically demonstrated lower peak implant stress when compared to titanium constructs for all spinal fusion levels. Furthermore, increasing the spinal fusion level resulted in significant differences in the maximum von Mises stress within both the bone and the instrumentation, whereas the 2-level fusion exhibited comparable stress levels in the bone irrespective of the instrumentation material. This pilot explores the potential of FEA as a clinical tool for assessing device and bone stresses. In our cohort, different materials can influence the stresses in both the instrumentation and the instrumented vertebrae, suggesting FEA can be useful pre- operative tool with regards to instrument selection and post-operatively to assess instrumentation and bone stresses. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/742715v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@55959aorg.highwire.dtl.DTLVardef@d0b9d6org.highwire.dtl.DTLVardef@158c348org.highwire.dtl.DTLVardef@7ce828_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Pain relief is the principal patient-centered goal of surgery for symptomatic lumbar synovial facet cysts, yet comparative reviews have often emphasized cyst recurrence. Whether adding fusion improves postoperative pain or reduces later surgery remains uncertain. Objective: To compare decompression alone with decompression plus fusion, with postoperative back- and leg-pain outcomes as the primary domain. Methods: PubMed, Embase, Scopus, Web of Science, and the Cochrane Library were searched from inception to 2 June 2026. Comparative cohorts and case series with at least five patients were eligible. Twenty-two studies were re-extracted for VAS/NRS scores, change scores, and persistent or recurrent pain. Random-effects restricted maximum likelihood models with Hartung-Knapp inference were used; clinically distinct pain outcomes were analyzed separately. Results: Twenty-two studies (16 cohorts, 6 case series; 51,899 participants) were included. Two studies provided compatible final VAS data. Fusion did not improve postoperative back pain (MD -0.04, 95% CI -0.17 to 0.10; I2=0%) or leg pain (MD -0.03, 95% CI -0.28 to 0.21; I2=0%). Postoperative back pain (RR 0.58, 95% CI 0.14-2.30) and leg/radicular symptoms (RR 0.75, 95% CI 0.42-1.32) were also not significantly reduced. Fusion decreased confirmed cyst recurrence (RR 0.29, 95% CI 0.15-0.57) but not reoperation or subsequent lumbar surgery (RR 0.80, 95% CI 0.42-1.50). Conclusion: Current comparative evidence does not demonstrate superior postoperative pain control with routine fusion. Fusion reduces cyst recurrence without clearly reducing reoperation, supporting selective use when instability is present or anticipated.

This study aims to improve the detection performance of age-related macular degeneration (AMD) in low-quality retinal images. BackgroundAMD is a leading cause of vision loss among older adults globally, and accurate detection is crucial for clinical management. However, low-quality optical coherence tomography (OCT) images significantly compromise diagnostic accuracy. ObjectiveTo enhance AMD detection in low-quality images using noise-augmented data augmentation and an improved YOLO deep learning model. MethodsPublic datasets from UCSD and Duke University were utilized; the training dataset comprised 24,980 OCT images (high-quality and noise-augmented low-quality), while the testing dataset included 1,000 images (584 AMD, 416 normal). The model is based on the YOLOv8n framework, integrated with Squeeze-and-Excitation blocks (SEblock) and Adaptive Sparse Self-Attention (ASSA), with an additional 160x160 detection layer for detecting small lesions. Evaluation metrics included accuracy, sensitivity, specificity, and F2-score. ResultsThe proposed model achieved an accuracy of 99.02%, sensitivity of 98.17%, specificity of 100%, and an F2-score of 98.50% on the Duke dataset. Detection rates were significantly improved compared to traditional methods, particularly in low-quality images, with a detection rate of 89.60%, markedly superior to original YOLOv8n (55.10%) and classical models like ResNet50. ConclusionThe enhanced model, employing noise-augmented training data and improved attention mechanisms, demonstrates excellent AMD detection capabilities in low-quality OCT images, showing broad potential for clinical applications.

AO_SCPLOWBSTRACTC_SCPLOWEarly tumor detection is critical for improving patient survival and recovery. Clinically, tissue palpation is routinely used to identify regions of abnormal stiffness, a hallmark of many pathological conditions. However, palpation is restricted to anatomically accessible sites and remains highly operator dependent. Here, we introduce a method for real-time quantitative stiffness mapping using an unmodified commercial endoscope, with the goal of enhancing diagnostic capabilities and restoring mechanical feedback during endoscopic procedures. Our approach combines shear wave elastography with speckle imaging and an innovative synchronization strategy that enables the measurement of shear wave propagation using an unmodified commercial endoscope. The resulting wave fields are analyzed with the noise-correlation-inspired (NCI) method[1], providing pixel-wise estimates of shear wave velocity and, consequently, quantitative maps of local tissue stiffness. The method demonstrated robust performance in both benchtop and endoscopic configurations. Validation was achieved on polymer phantoms as well as on ex vivo and in vivo biological tissues, highlighting its potential for minimally invasive biomechanical imaging and real-time tissue characterization.

Background: Vertebroplasty and balloon kyphoplasty are used for symptomatic vertebral hemangiomas, although comparative evidence is limited. We summarized pain relief, cement leakage, and recurrence after vertebral augmentation and assessed whether direct comparison of the two techniques was feasible. Methods: Five databases were searched from inception to January 2, 2026, with an update on July 5, 2026. Because only one small cohort directly compared vertebroplasty with kyphoplasty, outcomes were pooled as single-arm proportions or, for early pain change, as a mean difference using random-effects models. Prespecified subgroup, sensitivity, small-study effect, and influence analyses were performed. Results: Forty-four studies were included: 33 case series, 10 cohort studies, and one randomized trial. Kyphoplasty-specific evidence comprised one dedicated series and one comparative cohort. Any cement leakage occurred in 10.5% of patients (14 studies; 95% CI 5.7-18.4%), while trim-and-fill gave an exploratory adjusted estimate of 20.4%. Early pain reduction averaged 5.13 points on a 0-10 scale (8 studies; 95% CI 4.48-5.77; I2=89.4%). Complete or near-complete pain relief occurred in 79.4% of patients (10 studies), and recurrence, progression, or retreatment occurred in 3.9% (13 studies). Symptomatic cement leakage was uncommon at 0.4%. Conclusion: The available literature, which is mainly retrospective and vertebroplasty-based, supports substantial pain relief with infrequent symptomatic complications. Kyphoplasty data remain insufficient for a reliable technique comparison. Prospective studies with standardized clinical and imaging outcomes are needed.

Background: Artificial intelligence (AI) systems for glaucoma diagnosis and prognostication from visual fields (VF) are under active development, yet do not audit for vertical-meridian-respecting field loss - known sequelae of stroke, hemorrhage, and neoplasm. We developed a self-supervised encoder of automated perimetry that learns anatomically interpretable VF structure without labels, and evaluated its capacity to identify suspected neurologic VF patterns in an independent public glaucoma dataset. Methods: We pretrained a 128-dimensional masked autoencoder on 23,223 unlabeled Humphrey VFs (patient-grouped training split of 28,943 fields from 3,871 patients; UWHVF, all-comers perimetry), using monocular pattern-deviation input. A supervised linear classifier over vertical-midline latent dimensions was trained on per-eye expert neurological/non-neurological labels and assessed under hard-negative cross-validation, with specificity evaluated on 100 held-out, structurally separated UWHVF controls. External evaluation used the Harvard-Glaucoma Fairness dataset (Harvard-GF; 3,300 patients with paired VF and optical coherence tomography [OCT] from a single academic center), which contributed no data at any training stage. Results: Masked reconstruction recovered structure concordant with retinal neuroanatomy: 50 of 128 latent dimensions emerged spatially specialized, versus 23 for the total-deviation encoder. The classifier achieved cross-validated balanced accuracy 0.78 (95% CI, 0.75-0.82) and AUC 0.85 (95% CI, 0.82-0.89), with no false positives among the 100 held-out controls. Applied to Harvard-GF without fine-tuning, it identified a top-20 of 1,748 glaucoma-labeled patients (1.1%) with morphology inconsistent with glaucoma; all 20 were positive on the rule-based Neurological Hemifield Test (mean score 62.4), and OCT showed preserved superior (Cohen d = +0.68; P < .001) and inferior (d = +0.63; P = .003) retinal nerve fiber layer versus severity-matched controls. Conclusions: A self-supervised VF encoder learned anatomically interpretable visual field structure from unlabeled data and identified suspected neurological cases in a curated glaucoma dataset, with expert, rule-based, and OCT corroboration. Visual field datasets used to train glaucoma AI may benefit from neurological screening before model training; the encoder reported here supports such audits and provides a foundation for neuro-ophthalmic AI beyond fundus photography and OCT.

BackgroundTemporomandibular joint (TMJ) biomechanics can be characterized by mandibular motion, masticatory muscle activity, and bite force generation. When acquired synchronously, these functional variables can serve as model-ready inputs for subject-specific computational analyses of internal joint mechanics. However, existing tools typically measure these signals using separate hardware and software platforms, limiting synchronized acquisition within a clinically practical chairside workflow. MethodsWe developed and technically evaluated a compact multimodal platform for chairside acquisition of TMJ functional data and demonstrated its analytical utility in an illustrative orthognathic surgery application. The platform integrates motion, bite force, muscle activity, acoustic, and event-timing measurements with software for real-time preview, protocol guidance, and synchronized export. We assessed technical performance and chairside feasibility and analyzed representative pre- and postoperative data from an orthognathic surgery patient using kinematic, force-control, and computational modeling workflows. FindingsMotion capture demonstrated submillimeter accuracy, with static and dynamic errors of approximately 0.04 mm and 0.12 mm. Bite force sensors showed excellent linearity (R{superscript 2} = 0.998). Chairside deployment required approximately 15 minutes each for setup and data collection. The illustrative case demonstrated that synchronized chairside data can support preoperative and postoperative kinematic analysis, bite force control capacity assessment, and estimation of TMJ disc stress. InterpretationThe proposed platform enables time-efficient chairside acquisition of synchronized, model-ready multimodal datasets for quantitative TMJ biomechanical assessment. This platform and workflow could support subject-specific biomechanical analysis and future clinical studies of temporomandibular joint function.

Conventional transfemoral amputation disrupts native neuromuscular pathways, limiting prosthetic joint control, sensory feedback, and the perception of the prosthesis as part of the body. To ameliorate these pathologies, we restored the agonist-antagonist relationship of residual muscles in two individuals with above-knee amputation through an interventional surgical revision. Participants trained with a bionic knee prosthesis before and after the surgical revision while generating neuromuscular, cortical, functional, and affective data. Both individuals demonstrated improvements after the revision that could not readily be attributed to training effects, including: 1) increased proprioceptive afferents and stronger activation in cortical regions associated with sensorimotor integration of their missing joints, 2) improved control of the bionic knee during functional tasks including sit-to-stand and stair ascent, and 3) generally greater prosthesis embodiment, proprioception, and phantom limb definition as assessed through questionnaires and interviews. In contrast, training outcomes were more participant-specific and more variably correlated with amount of exposure, especially before the revision. These pilot findings suggest that revisional augmentation of residual neuromuscular tissues to restore agonist-antagonist dynamics may promote sensorimotor coherence and enhance both functional and perceptual integration with a bionic prosthesis, and remaining participant-specific heterogeneities may be attributable to inter-individual difference in residual limbs neuromuscular system, amputation history, and personal beliefs about prosthesis usage.

Introduction: Critical-sized bone defects cannot heal spontaneously, requiring additional, often burdensome, treatment. Thus, various synthetic substitute materials have been investigated regarding their treatment capacity. Poly-L-lactic acid (PLLA) and polyglycolic acid (PGA) have emerged as promising biodegradable scaffold materials. The addition of inorganic materials such as calcium carbonate (CC) has also been shown to be advantageous. This study investigates the effect on bone regeneration of PLLA-PGA-CC scaffolds in critical-sized bone defects over a two-year observation period using sheep as an animal model. Methods: Critical-sized mandible angle defects were created in twelve female merino sheep. Mandibular defects were reconstructed with PLLA-PGA-CC scaffolds in four sheep, while the remaining eight served as negative control (defects left empty). The scaffolds were manufactured using computer-aided design and manufacturing, incorporating an interconnected porous structure and fixated with polyether ether ketone cages. Bone regeneration was evaluated using computed tomography (CT) imaging at 3, 12, and 24 months postoperatively. Bone volume was assessed quantitatively. Additionally, a histological analysis was performed. Results: Surgical procedures were successful and without major complications. CT assessment showed more bone regeneration in the scaffold group (mean volume: 7,472 mm3) than in the control group (4,168 mm3, p = 0.1) at 24 months postoperatively. Resorption of the scaffolds and formation of compact lamellar bone tissue were confirmed by histological analysis. However, the osteoconductive properties of the scaffolds were limited, with only minimal ingrowth of bone tissue into the porous structure. In both groups, fibrous tissue infiltration and the formation of cyst-like cavities in the defect region were observed. Conclusion: PLLA-PGA-CC scaffolds were found to be biocompatible and enhanced bone regeneration compared to the control group. Due to fibrous tissue infiltration and the lack of osteoconductivity, the suitability of the material for critical-sized bone defect reconstruction is limited.

Background: Cerebrospinal fluid (CSF) circulation is important for maintaining homeostasis of the central nervous system. Previous studies have largely focused on the ventricular system, the craniocervical junction, or local spinal segments, leaving the overall and spatially heterogeneous characteristics of CSF flow across the craniospinal axis insufficiently characterized. The spinal subarachnoid space (SAS) is often treated as a homogeneous annular compartment surrounding the spinal cord, an approach that may obscure directional differences among its internal regions. Methods: This single-center, exploratory, prospective imaging study enrolled 15 healthy volunteers. All participants underwent 3.0-T electrocardiography-gated two-dimensional cine phase-contrast magnetic resonance imaging (Cine PC-MRI) and high-resolution T2-weighted imaging. CSF was evaluated at the level of the cerebral aqueduct outlet/fourth-ventricle inlet, C1-C2, C5-C6, T5-T6, L1-L2, and the lumbar cistern. Region-of-interest (ROI)-based quantitative analysis using Q-Flow software recorded mean velocity, absolute peak velocity, and directional peak velocity. Results: Multiplanar Cine PC-MRI showed that CSF phase signals within the spinal SAS were not uniformly distributed but formed two principal flow regions, ventral and dorsal. Mean velocity and absolute peak velocity were similar between the ventral and dorsal regions, whereas directional peak velocity differed (1.50 +/- 2.98 cm/s vs. -0.38 +/- 3.23 cm/s, P = 0.036). High-resolution T2-weighted imaging showed denticulate ligaments, nerve roots, and associated fibrous connective tissue in the lateral transition zones between the two regions. Conclusions: In healthy adults, CSF flow in the spinal SAS was not synchronous motion within a single homogeneous compartment; rather, it showed longitudinal oscillatory flow in ventral and dorsal regions coupled to the cardiac cycle. These findings provide preliminary in vivo evidence for studies of CSF hydrodynamics across the craniospinal axis and an imaging basis for investigating CSF circulation disturbances in conditions such as hydrocephalus, Chiari malformation, syringomyelia, and arachnoid adhesions.

Static force plates (FP) are the gold standard for measuring ground reaction forces (GRF) and computing joint moments through inverse dynamics in gait analysis. However, they are restricted to controlled environments, and the number of steps analyzed is limited by the plates embedded in the floor. To address these limitations, portable solutions such as sensorized insoles, socks, or shoes have emerged. Yet, creating wearable systems capable of measuring three-dimensional GRF in real-world conditions remains challenging. Current sensorized shoes often incorporate thick sensors (up to 2 cm), reducing usability and limiting their application in pathological populations or dynamic tasks like running. This study evaluates the usability of ShokacShoes, a novel sensorized shoe integrating three thin, three-dimensional force sensors, and explores its potential as a Wearable Force Plate (WFP). Eight healthy participants performed slow, natural, and fast walking using two insole configurations. Force and temporal metrics were derived from WFP and FP data. Results indicate that WFP enables accurate step segmentation and detects significant effects of speed and insole type on temporal and force metrics, confirming its reliability under different walking conditions. Comparisons with FP revealed differences in force metrics and signal morphology, though temporal parameters remained consistent. These results are likely due to sensor quantity and positioning. Thereby, ShokacShoes represent a valid solution capable of measuring three-dimensional forces within commercial footwear. Future work will focus on validating the applicability of a new version of ShokacShoes against gold-standard FP in a comprehensive validation study involving diverse real-world scenarios and pathological conditions.

ObjectiveRobotic-assisted needle insertions lack haptic feedback, a key sensory cue for detecting tissue transitions and regulating puncture force. Modeling this feedback requires an understanding of soft-tissue biomechanics during insertion. Vibroacoustic signals generated by needle-tissue interactions may provide an additional sensing modality, but their interpretation requires validation against anatomical ground truth. MethodsA multimodal framework was developed to correlate vibroacoustic signals with high-resolution post-puncture microCT ({micro}CT) imaging in Manduca sexta, an insect model containing interconnected soft-tissue layers. A custom clip-on prototype recorded vibroacoustic signals during manual needle insertions. Three trajectory-marking strategies were evaluated to determine 3D coordinates of soft-tissue layer crossings and to assess correlations between acoustic events and anatomical transitions. Distances between layer crossings and needle displacement were used for spatiotemporal alignment of vibroacoustic and {micro}CT data. ResultsA {micro}CT-compatible nylon string preserved puncture trajectories without artifacts and enabled high-resolution 3D reconstruction of anatomy and needle paths. Fusion of vibroacoustic and imaging data allowed identification of acoustic events associated with tissue entry, exit, and transitions. ConclusionBy integrating high-resolution {micro}CT imaging with vibroacoustic sensing, this study establishes a biologically grounded framework for validating the relationship between vibroacoustic signals and anatomical tissue transitions during needle insertion, providing a basis for future quantitative analyses. SignificanceThis work provides initial evidence for correlating vibroacoustic signals recorded during needle insertion with corresponding {micro}CT-identified tissue barriers. Because vibroacoustics offers substantially higher temporal and spatial resolution than most imaging modalities, it has the potential to improve tissue sensing and procedural accuracy in future needle-based interventions.

Purpose: To validate an automated pipeline to detect and quantify focal retinal and choroidal pulsation areas that are synchronous with the cardiac cycle in video indocyanine green angiography (ICGA). Design: Retrospective, observational, hypothesis-generating validation study Subjects, Participants: Consecutive patients with a diagnosis of central serous chorioretinopathy (CSCR) in one or both eyes. Methods: Videos were acquired on Heidelberg HRA+OCT. The pipeline consisted of three steps: signal extraction, foci detection, and quantification. After registration of the constituent frames, each pixel's intensity signal was analyzed at the presumed cardiac frequency (tested from a sample of three detectable frequencies). A synchrony score combining local phase coherence with oscillation amplitude was then derived and computed using a standard deviation ({sigma}) above each video's background oscillation value. Two masked graders marked the retinal and choroidal pulsation areas twice. We compared detection of the pulsation areas against grader consensus using a receiver operating characteristic curve (using multiple grid sizes to divide the scan area) and, separately, using a signal-based area-reduction method to obtain an optimum {sigma} value. Main Outcome Measures: Agreement between the automated algorithm and human graders in detection of pulsation foci, and the optimum threshold multiplier ({sigma}). Results: We studied 20 ICGA videos from 20 eyes. At the 16-pixel grid size, the pipeline achieved a mean area under the curve (AUC) of 0.914, sensitivity of 0.86, and specificity of 0.80. Grader agreement improved with larger grid size, reaching substantial-to-strong levels for choroidal annotations. The two independent validation methods demonstrated similar {sigma} values that differed by 0.62{sigma}, supporting {sigma}=4.0 as the optimum value. Conclusions: We report the first automated method to quantify retinal and choroidal vascular pulsation on video ICGA. It measures pixels that oscillate over time with the presumed cardiac cycle and works reliably at the spatial scale (grid level) where experts agree. Pulsatile hemodynamics may add a new vascular biomarker for glaucoma, diabetes, hypertension, and pachychoroid diseases.

Physiological mirror activity (pMA) is the increase in involuntary muscle activity observed on the contralateral side during unilateral voluntary movement in neurologically healthy participants. This cross-sectional study aimed to explore the relationship between pMA and corticomuscular coherence (CMC) during finger dexterity tasks in young and older adults. Thirty-one right-handed young adults and 24 older adults performed a left-hand finger dexterity task. Electroencephalogram (EEG) signals were recorded from C3 and C4, and electromyogram (EMG) signals were collected from bilateral finger flexors and extensors. pMA was quantified as the change in right-hand EMG from rest to task. Gamma-band CMC was calculated from task-related EEG-EMG pairs, and its association with pMA was analyzed. In young adults, greater pMA was associated with lower CMC (C3- and C4-right flexors), whereas in older adults, greater pMA was associated with higher CMC (C3-left flexor). Young adults may suppress pMA emergence by appropriately monitoring and inhibiting activity, in the hand not performing the task. Conversely, in older adults, the mobilization of the ipsilateral motor cortex may have contributed to pMA emergence. This study suggests that the neuromuscular mechanisms involved in pMA during finger dexterity tasks differ between young and older adults.

Accurate evaluation of extracellular matrix (ECM) mineralization at the nano-scale is essential for establishing relevant in vitro bone models. This is particularly important with the development and increased application of three-dimensional (3D) cell models for biological research. Transmission electron microscopy (TEM) allows to perform ultra-structural analysis of cells and ECM organization, but its application in in vitro bone models remains limited, due to the potential alteration or loss of the mineral phase during sample preparation. In this study, we compared two TEM sample preparation methods - the conventional chemical fixation and the anhydrous methods - to evaluate their ability to preserve the mineralized ECM in MC3T3-E1 cells cultured as monolayers and as alginate-encapsulated bone spheroids. Chemical fixation preserved cellular ultra-structure and collagen organization, allowing for detailed assessment of cells and ECM organization. Although mineral deposits were detected and their needle-like morphology assessed, characterization of more immature deposits was partially limited by the effects of uranyl acetate and the overall sample preparation process, which could lead to alteration or loss of less stable mineral phases. The anhydrous preparation method resulted in limited preservation of cellular and ECM morphology and did not allow reliable identification of mineral deposits. When applied to spheroids, the chemical fixation method preserved the 3D architecture, collagen-rich ECM and inner mineral deposits, confirming spheroids as a relevant model for bone studies. Overall, these results highlight the need for optimized sample preparation strategies that preserve both ultra-structure and mineral components for accurate nano-scale characterization of bone mineralization.