Humans excel at adjusting movements and acquiring new skills through feedback corrections and predictive control, yet how these feedback-feedforward computations evolve in the motor system remains unclear. We investigated this process by examining how humans learned a novel, continuous visuomotor mirror reversal (MR) tracking task over multiple days. Using a frequency-dependent system-identification approach and responses to cursor perturbations, we dissociated feedback-driven corrections from predictive feedforward adjustments. Our findings reveal two distinct learning pathways: early learning relies on rapid, corrective feedback at lower frequencies, while feedforward control gradually emerges at higher frequencies, compensating for feedback limitations. These findings suggest that motor learning involves a dynamic interplay between feedback and feedforward control, providing mechanistic insights into sensorimotor learning, with implications for optimizing motor skill acquisition and neurorehabilitation strategies

The future has always captivated human imagination, with efforts to assess disease prognosis dating back to ancient Egyptian times: “If the heart trembles, has little power and sinks, the disease is advancing … and death is near … ” (Papyrus Ebers, circa 1550 BC). However, the risks of relying on predictions were also acknowledged in antiquity: “…The prophecy has been taken from the prophets and given to the fools and babies instead … ” (Babylonian Talmud: Baba Bathra 12b). Recent advancements in medicine have significantly enhanced prognostic accuracy. The availability of comfortable and reliable wearable devices capable of measuring cardiac, neuronal, and other physiological signals, combined with sophisticated machine learning algorithms designed to interpret these signals in real time (Davoodi et al., 2024; Elul et al., 2024; Fira et al., 2024; Kerr et al., 2024), marks the dawn of a new era in preventive medicine—the age of real-time prediction.

Injectable hydrogels are promising biomaterials for treating aneurysms, life-threatening blood-filled saccular lesions, enabling complete filling of the aneurysm and supporting tissue repair. Yet, the challenge is to enable clinical translation as hydrogels must not protrude into the parent vessel, nor migrate from the aneurysm cavity. Here, injectable, negatively-charged, biologically and mechanically compatible hydrogels with environment-sensitive swelling capabilities that cease swelling upon contact with blood are developed. Hydrogels are fabricated by copolymerizing sodium 2-acrylamido-2-methylpropanesulfonic acid (NaAMPS) and 3-sulfopropyl acrylate (KSPA) by using polyethylene glycol diacrylate (PEGDA). Three formulations (2%, 4%, and 6%) demonstrating a wide range of physiological-relevant stiffnesses are fabricated. The selected mechano-compatible 4% hydrogel exhibits a suitable swelling pressure (125 kPa) and supports high endothelial cell viability (> 75%). Importantly, the hydrogel demonstrates a significant differential swell with respect to blood (30 ± 4%), plasma (58 ± 3%), and PBS (82 ± 2%). This environment-selective swelling, upon exposure to blood, results in minimal directional swelling toward the parent artery, which can improve embolization outcomes. Hydrogel embolization in 3D-printed aneurysm models subjected to physiological blood flow shows no protrusion toward the main artery while completely blocking flow into the aneurysm. This approach provides promising opportunities for efficient embolization of a variety of aneurysms and vascular malformations.

Objective: Better understanding of ictogenesis may allow clinical interventions and potentially reduce the impact of epilepsy on patients' quality of life. This study aims to characterize the EEG changes during the preictal period.Methods: This work retrospectively analyzed long-term scalp EEG recordings collected at two neurology centers to characterize preictal activity (start point and duration) for each seizure using EEG features. A channel selection algorithm was implemented and localized preictal activity.Results: Out of 19 patients, 17 (89.5%) had a distinct preictal pattern, starting 83±60 minutes before seizure onset and lasting 56±47 minutes. Spectral Entropy and Hjorth mobility were consistently two out of the three features best distinguished preictal from interictal activity. The third distinguishing feature was either theta power, delta power, beta power, or gamma power. Preictal activity before two seizures in the same patient shared common electrodes and features but differed in duration and timing.Conclusions: Preictal activity, defined as prolonged intervals of uncommon EEG activity, varies in time, localization and signal patterns between individuals and varies in timing and duration between seizures of the same individual.

Ultrasound is a widely accessible and cost-effective medical imaging tool commonly used for prenatal evaluation of the fetal brain. However, it has limitations, particularly in the third trimester, where the complexity of the fetal brain requires high image quality for extracting quantitative data. In contrast, magnetic resonance imaging (MRI) offers superior image quality and tissue differentiation but is less available, expensive, and requires time-consuming acquisition. Thus, transforming ultrasonic images into an MRI-mimicking display may be advantageous and allow better tissue anatomy presentation. To address this goal, we have examined the use of artificial intelligence, implementing a diffusion model renowned for generating high-quality images. The proposed method, termed "Dual Diffusion Imposed Correlation" (DDIC), leverages a diffusion-based translation methodology, assuming a shared latent space between ultrasound and MRI domains. Model training was obtained utilizing the "HC18" dataset for ultrasound and the "CRL fetal brain atlas" along with the "FeTA " datasets for MRI. The generated pseudo-MRI images provide notable improvements in visual discrimination of brain tissue, especially in the lateral ventricles and the Sylvian fissure, characterized by enhanced contrast clarity. Improvement was demonstrated in Mutual information, Peak signal-to-noise ratio, Fr\'echet Inception Distance, and Contrast-to-noise ratio. Findings from these evaluations indicate statistically significant superior performance of the DDIC compared to other translation methodologies. In addition, a Medical Opinion Test was obtained from 5 gynecologists. The results demonstrated display improvement in 81% of the tested images. In conclusion, the presented pseudo-MRI images hold the potential for streamlining diagnosis and enhancing clinical outcomes through improved representation.

Carbon aerogels with exceptional electrical properties are considered promising materials for bioelectronics in signal detection and electrical stimulation. To address the mechanical incompatibilities of carbon aerogels with bio-interfaces, particularly for dynamic tissues and organs, the incorporation of hydrogels is an effective strategy. However, achieving excellent electrical performance in carbon aerogel-hydrogel hybrids remains a significant challenge. Two key factors contribute to this difficulty: 1) unrestricted hydrogel infiltration during preparation can lead to complete encapsulation of the conductive aerogel, and 2) the high swelling behavior of hydrogels can cause disconnection of the aerogel. Herein, a stretchable, highly conductive bioelectronic interface is achieved by forming an interlocking network between hierarchical porous carbon aerogel (PA) with polyvinyl alcohol (PVA) hydrogel. Partial exposure of the PA due to confined infiltration of PVA into the porous structure maintains the electrical performance, while the non-swellable PVA ensures mechanical stretchability and stability. The hybrid demonstrates excellent conductivity (370 S·m⁻¹), high charge storage capacity (1.66 mC cm⁻²), remarkable stretchability (250%), and long-term stability over three months, enabling effective signal recording and electrical stimulation. For the first time, carbon aerogel-hydrogel hybrids enable cardiac pacing both ex vivo and in vivo in rat heart models. Compared to conventional platinum electrodes, the PA-PVA electrodes require lower pacing voltages, suggesting potential advantages in power efficiency and reduced tissue damage. The electrodes can be integrated with a wireless implantable device for in vivo synchronous electrocardiogram monitoring and cardiac pacing, underscoring their potential for arrhythmia management.

Objective

To develop and validate an automated end-to-end methodology for analyzing retinal vasculature in large datasets of digital fundus images (DFIs), aiming to assess the influence of demographic and clinical factors on retinal microvasculature.

Design

This study employs a retrospective cohort design to achieve its objectives.

Participants

The research utilized a substantial dataset consisting of 32,768 digital fundus images obtained from individuals undergoing routine eye examinations. There was no inclusion of a separate control group in this study.

Methods

The proposed methodology integrates multiple stages: initial image quality assessment, detection of the optic disc, definition of the region of interest surrounding the optic disc, automated segmentation of retinal arterioles and venules, and the engineering of digital biomarkers representing vasculature characteristics. To analyze the impact of demographic variables (age, sex) and clinical factors (disc size, primary open-angle glaucoma [POAG]), statistical analyses were performed using linear mixed-effects models.

Main Outcome Measures

The primary outcomes measured were changes in the retinal vascular geometry. Special attention was given to evaluating the independent effects of age, sex, disc size, and POAG on the newly engineered microvasculature biomarkers.

Results

The analysis revealed significant independent similarities in retinal vascular geometry alterations associated with both advanced age and POAG. These findings suggest a potential mechanism of accelerated vascular aging in patients with POAG.

Conclusions

This novel methodology allows for the comprehensive and quantitative analysis of retinal vasculature, facilitating the investigation of its correlations with specific diseases. By enabling the reproducible analysis of extensive datasets, this approach provides valuable insights into the state of retinal vascular health and its broader implications for cardiovascular and ocular health. The software developed through this research will be made publicly available upon publication, offering a critical tool for ongoing and future studies in retinal vasculature.

In patients with ischaemic stroke, retrograde perfusion of the penumbra by the leptomeningeal collateral vessels (LMCs) is a strong predictor of clinical outcome, thus raising the possibility that enhancing LMC flow could offer a novel therapeutic approach. Here, using computational modelling we show that LMCs experience elevated fluid shear stress that is significantly higher than that in other blood vessels during ischaemic stroke in animals and humans. We take advantage of this to selectively enhance flow in LMCs using shear-activated nanoparticle aggregates carrying the vasodilator nitroglycerin (NG-NPAs) that specifically release drug in regions of vessels with high shear stress (≥100 dyne/cm2). The NG-NPAs significantly increased LMC-mediated penumbral perfusion, decreased infarct volume, and reduced neurological deficit without altering systemic blood pressure in a rat ischaemic stroke model. The NG-NPAs also did not cause known common side effects of systemic nitrate administration, such as systemic hypotension, cerebral vascular steal, cortical vein dilation, or intracranial pressure elevation. Systemic administration of free NG at the maximal tolerated dose, which was ten times higher than the dose of NG used in the NG-NPAs, did not enhance LMC perfusion and dropped blood pressure. Thus, packaging NG within shear-activated NPAs can potentially enable this widely available vasodilator to become a highly effective therapeutic for ischaemic stroke.

The invention, provides a method and a device for delivery of foam to a target site of a respiratory system of a subject.

Background

We investigated inflammation-induced changes in femoral hematopoietic bone marrow using advanced magnetic resonance imaging (MRI) techniques, including T2-weighted imaging, scalar T2 mapping, and machine learning-enhanced T2 distribution analysis to improve the detection of bone marrow microstructural alterations. Findings were correlated with histological markers and systemic inflammation.

Methods

Using a 9.4-T magnet, T2-weighted and multislice multiecho sequences were applied to evaluate bone marrow in female C57BL/6J mice divided into three groups: (1) controls; (2) lipopolysaccharide-induced acute inflammation (LPS); and (3) streptozotocin (STZ)- and LPS-induced diabetic inflammation (STZ + LPS). T2 relaxation times and their distributions with scalar mapping and model-informed machine learning (MIML) were analyzed. Correlations with histological iron levels and blood neutrophil counts were assessed.

Results

T2-weighted imaging showed a reduced signal-to-noise ratio in inflamed bone marrow (p = 0.034). Scalar T2 mapping identified decreased T2 relaxation times (p = 0.042), moderately correlating with neutrophil counts (ρ = 0.027) and iron levels (ρ = 0.016). MIML-enhanced T2 distribution analysis exhibited superior sensitivity than scalar T2 mapping, revealing significant reductions in the first T2 distribution peak (p = 0.0025), which strongly correlated with neutrophil counts (ρ = 0.0016) and iron sequestration (ρ = 0.0002). Histology confirmed elevated iron deposits in inflamed marrow, aligning with systemic inflammation.

Conclusion

Combining T2-weighted imaging, scalar T2 mapping, and MIML-enhanced T2 distribution analysis offers complementary insights into inflammation-induced bone marrow remodeling. T2 distribution analysis emerged as a more sensitive tool for detecting microstructural changes, such as iron sequestration, supporting its potential as a noninvasive biomarker for diagnosing and monitoring inflammatory diseases.

Relevance statement

This study highlights the potential of advanced MRI T2 analysis and machine learning methods for noninvasive detection of inflammation-induced microstructural changes in bone marrow, offering promising diagnostic tools for inflammatory diseases.

Key Points

This study investigated inflammation-induced changes in bone marrow with T2 MRI and MIML.

MIML outperformed quantitative scalar T2 analysis, increasingly detecting inflammation and iron sequestration in the hematopoietic bone marrow.

T2 MRI with MIML analysis could aid in the early diagnosis and management of inflammatory diseases.