Abstract: Background: Intervertebral motion is a fundamental aspect of spinal biomechanics, crucial for understanding lumbar spine function, pain mechanisms and surgical out-comes. Various methods exist for measuring and interpreting it, each with its own advantages, limitations and specific applications. However, a comprehensive and standard taxonomy of study types for the measurement and interpretation of in vivo intervertebral motion in the lumbar spine is lacking. Objectives: This review aimed to systematically identify, characterize and categorize the diverse study types deposited in the literature. Eligibility criteria: Only studies in English and of lumbar spine intervertebral motion in living subjects were considered and only those that employed objective measurement of motion sequences were included. Sources of evidence: A comprehensive literature search was performed in PubMed, CINAHL and SCOPUS for articles published between January 2000 and October 2025. Charting methods: After removal of duplicates, all studies were subjected to Title and abstract screening, followed by Full-text screening of potentially eligible studies. Data selected were charted into tables under the headings: Author, year, country, purpose, technology, participants, measurement, interpretation, radiation dosage and significance of findings. Results: Forty-nine studies were abstracted and are described under 11 study types. These formed a taxonomy constituting the following 6 categories: Normal biomechanical mechanisms, Pathological and injury mechanisms, Direct kinematic measurement, Spinal stabilization, Dynamic radiography and Clinical markers. The resulting taxonomy will serve as a resource for researchers, clinicians and policymakers by facilitating a more coherent understanding of the field and promoting standardization in research design and re-porting.

Abstract: Intraoperative patient motion degrades static registration. We propose an adaptive AR guidance system with feedback-driven registration updates. The system employs particle filter-based motion compensation and multi-scale ICP refinement. In 10 cadaver experiments with head motion up to ±20 mm, average TRE remained under 1.5 mm, compared with 2.7 mm in static ICP. Accuracy improved by 43%, and frame rate stayed at 26 fps. The system enables real-time adaptation in dynamic surgeries.

Abstract: Various recurring medical events encourage innovative patient well-being through con-nected health strategies based on an elegant digital environment that prioritizes safety, comfort, and beneficial outcomes for both patients and medical staff. This article aims to investigate and highlight the potential of advanced, reliable, high-precision, and secure medical observation and intervention missions. These involve an intelligent digital envi-ronment integrating smart materials combined with intelligent digital monitoring. These medical implications concern robotic surgery and drug delivery through image-assisted implantation, as well as wearable observation and assistive tools. The former requires high-precision motion and positioning strategies, while the latter enables sensing, diag-nosis, monitoring, and central task assistance. Both advocate minimally invasive or non-invasive procedures and precise supervision through autonomously controlled processes with staff participation. The article analyzes the requirements and evolution of medical interventions, robotic actuation technologies for positioning actuated and self-moving in-stances, monitoring of image-assisted robotic procedures using digital twins and aug-mented digital tools, and wearable medical detection and assistance devices. A discussion including future research perspectives and conclusions terminate the article. The different themes addressed in the proposed paper, although self-sufficient, are supported by exam-ples of the literature, allowing a deeper understanding.

Abstract: Electrocardiography (ECG) is a key biosensing technique for assessing cardiac function and autonomic activity. Accurate detection of R-wave peaks and precise calculation of R-R intervals (RRIs) are essential for heart rate variability (HRV) analysis, yet automated detection algorithms remain vulnerable to misdetections caused by noise or baseline fluctuations. Conventional correction methods based on filter or threshold adjustments may introduce new errors, highlighting the need for an intuitive manual correction function. To address this issue, we developed a prototype graphical user interface (GUI)-based ECG viewer implemented in Fortran for high computational efficiency. The system enables interactive insertion or deletion of R-wave peaks with immediate recalculation of RRIs and automatic updates of related analyses, including power spectral density, histograms, Lorenz plots, and polar plots. Validation using synthetic ECG signals at four sampling frequencies (125-1000 Hz) and three time scales (2, 5, and 10 s) compared GUI-derived RRIs with gold-standard RRIs, showing correction errors below 0.7% and stable update times within 20-30 ms. When applied to real ECG recordings from the MIT-BIH Arrhythmia Database using the MLII lead from records 115 and 122, the same RRI-based comparison achieved accuracies exceeding 0.985 at ±10 ms and reaching 1.000 at ±20 ms or higher. These results confirm that the proposed system provides reliable and immediate feedback and is applicable to physiological data. The algorithm may support future applications in research, clinical, and educational domains of biosignal processing.

Abstract: This report is a deep dive into the complex world of using neuromorphic chips to help people with severe brain damage regain control of their bodies. We’ll look at the fundamental science behind neuromorphic computing, explore the current landscape of brain-computer interfaces (BCIs), and confront the biological and ethical challenges of making such a technology a reality. The main idea is to create a kind of “digital nervous system” that could bypass damaged parts of the brain to restore basic functions like movement and breathing. This isn't just a technical paper; it’s a detailed exploration of the immense hurdles and profound questions that must be answered before we can truly build a bridge between mind and machine. This document is a starting point for anyone looking to understand this fascinating and difficult field.

Abstract: Objective: The aim of this preliminary study was to recognize motion patterns by classifying time series features obtained from electromyography (EMG) data of the upper limb muscles. Methods: In this study we tested six models Machine Learning (ML) classification models (Decision Trees, Support Vector Machines, Linear Discriminant, Quadratic Discriminant, K-Nearest Neighbors, and Efficient Logistic Regression) to classify time series features extracted from processed EMG data that were acquired from eight superficial muscles of two upper limbs over performing given physical activities in two main stages (supination and neutral forearm configuration) in initial and target (isometric) positions. Results: Findings indicate that aiming to classify stages of the upper limb with the highest performance following ML models should be used: 1) K-NN cityblock (F1 equals 0.973/0.992), K-NN seuclidean (0.971/0.996), K-NN minkowski (0.966 /0.992), K-NN co-sine (0.962/0.969) for the left limb; 2) K-NN euclidean (0.970 /0.989), K-NN cityblock (0.966 /0.986), K-NN seuclidean (0.959/0.985), K-NN minkowski (0.957/0.986) for the right limb. Conclusion: Motion patterns tested in this study can be recognized with the highest performance by applying following ML models to classify EMG data: K-NN city-block, K-NN seuclidean, and K-NN minkowski models.

Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder typically diagnosed through behavioral assessments and subjective reports. Electroencephalography (EEG) offers a cost-effective, non-invasive alternative for capturing neural activity patterns associated with the disorder. However, EEG-based ADHD classification remains challenged by overfitting, dependence on extensive preprocessing, and limited interpretability. Here, we propose T-GARNet, a novel neural architecture that integrates transformer-based temporal attention with Gaussian-mixture functional connectivity modeling and a cross-entropy loss regularized through α-Rényi mutual information. The multi-scale Gaussian kernel functional connectivity leverages parallel Gaussian kernels to identify complex spatial dependencies, which are further stabilized and regularized by the α-Rényi term. This design enables the direct modeling of long-range temporal dependencies from raw EEG while enhancing spatial interpretability and reducing feature redundancy. We evaluate T-GARNet on a publicly available ADHD EEG dataset using both leave-one-subject-out (LOSO) and stratified group k-fold cross-validation (SGKF-CV), where groups correspond to control and ADHD, and compare its performance against classical and modern state-of-the-art methods. Results show that T-GARNet achieves competitive or superior performance (88.3% accuracy), particularly under the more challenging SGKF-CV setting, while producing interpretable spatial attention patterns consistent with ADHD-related neurophysiological findings. These results underscore T-GARNet’s potential as a robust and explainable framework for objective EEG-based ADHD detection.

Abstract: Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, aka the Brain Computer Interface (BCI). However, achieving high accuracy in these decodings remains a challenge and an open research topic in BCI research. This study aims to enhance the accuracy of signal classification for identifying human emotional states. We utilized the publicly available EEG-Audio-Video (EAV) dataset that comprises EEG recordings from 42 subjects across five emotional categories. Our key contribution is to exploit the 2-dimensional contrast enhancement applied to the spectrogram for feature extraction, followed by classification using the EEGNet model. As a result, 12.5% improvement in classification accuracy over the baseline was achieved. This contribution demonstrates a potential advancement in BCI-based EEG signal processing in neuroscientific research.

Abstract: With the rising global population and increasing energy demands, sustainable bioproducts such as bioethanol offer essential alternatives to fossil fuels. Unlike first-generation bioethanol derived from food crops like corn, second-generation bioethanol is produced from lignocellulosic (LC) biomass, a non-food resource that addresses sustainability concerns. Consolidated Bioprocessing (CBP) integrates enzyme production, hydrolysis, and fermentation in a single step, using either microbial consortia or engineered microorganisms, reducing costs and simplifying the process compared to separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). However, CBP systems are complex due to the dynamic interactions between microbial consortia, metabolic pathways, and process conditions. Addressing these complexities requires advanced modeling techniques that effectively capture non-linear relationships and optimize process parameters. Machine learning-based models have the potential to advance the field of CBP by enabling data-driven approaches to capture complex bioprocess dynamics, improve prediction accuracy, and optimize bioproduct production in CBP systems, thus paving the way toward commercial viability. This review gives an actual overview of relevant key processes CBP, the current state of modeling CBP, its limitations, and the emerging role of machine learning (ML) as a solution to CBP’s modeling challenges. It details recent modeling techniques for CBP, including polynomial models, response surface methodologies, with detailed discussions on regression models and neural network models. In this paper, a summarized review of first-order principle-based modeling approaches as well as data-driven modeling approaches is included, emphasizing advancements that contribute to the scalability and efficiency of CBP for bioproduct production. This review provides new perspectives and insights on the modeling of consolidated bioprocessing for utilizing low-cost lignocellulosic biomass in bioproduction.

Abstract: Achieving effective tissue integration depends mainly on the biological performance of polymeric implants. Poly-ether-ether-ketone (PEEK) is widely used as an implant material; however, its inert nature results in limited biological interactions. Various surface modification techniques have been investigated to enhance its bioactivity and overall biological performance. In this study, the PEEK surface was activated using low-pressure oxygen plasma and functionalized with phosphate and calcium ions. Comprehensive surface characterization by contact angle, scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Fourier Transform Infrared (FT-IR) confirmed the effect of plasma and the ionic surface incorporation. The biological response was evaluated through cell viability, adhesion, and proliferation in NIH/3T3 fibroblasts and HOS osteoblasts, and the results indicated the efficacy of the surface modifications. Therefore, the proposed treatments provide an efficient strategy to improve the biological performance of PEEK-based implants.

Abstract: Digital twin applications for water resource recovery facilities require frequent model recalibration to maintain predictive accuracy under dynamic operational conditions. Current calibration methodologies face critical limitations: manual protocols demand extensive expert intervention and iterative parameter adjustments spanning weeks to months, while automated optimization algorithms impose elevated computational burdens that struggle to converge within practical timeframes. This study introduces Expert Systems with Neuro-Evolution of Augmenting Topologies (ES-NEAT), integrating genetic algorithms, artificial neural networks, and transfer learning to preserve and transfer calibration knowledge across recalibration scenarios. Application to the full-scale Eindhoven WRRF over six months, calibrating 33 parameters across multiple temporal scenarios, demonstrated 72.1% and 49.0% Kling-Gupta Efficiency improvement over manual calibration for tank-in-series and compartmental model structures, respectively. Transfer learning reduced subsequent recalibration computational time by 50-70% while maintaining prediction accuracy, transforming initial 10-12 hour optimizations into 3-6 hour recalibrations through knowledge preservation. Performance degradation analysis established 2-month optimal recalibration intervals under observed operational variability. The methodology enables practical digital twin implementation by transforming recalibration from episodic expert-dependent burden into continuous, automated learning processes operating at timescales matching operational decision-making needs.

Abstract: Over the past two decades, robotic surgery has witnessed a rapid and widespread adoption across almost all surgical specialties, with the da Vinci Surgical System (Intuitive Surgical) emerging as the dominant platform worldwide. Its technological advantages—such as improved ergonomics, precision, and minimally invasive access—have contributed to its success. However, the substantial costs associated with acquisition, maintenance, and disposable instruments represent a significant limitation, especially in public healthcare systems. In recent years, new robotic platforms have entered the market with the explicit goal of improving cost-effectiveness while maintaining comparable clinical performance. This study aims to conduct a comprehensive economic evaluation of three robotic surgical platforms currently in use at ASST Grande Ospedale Metropolitano Niguarda Hospital, through a cost-minimization analysis and break-even point calculation. Cost data were collected from the hospital's internal management system and supplemented with published literature to ensure a realistic and robust estimation of direct and indirect costs. A deterministic sensitivity analysis was applied, varying key parameters—such as number of surgeons, number of instruments, and procedure duration—within predefined ranges to assess the variability of outcomes under different assumptions. This allowed the identification of threshold values and critical cost drivers that influence the economic sustainability of each system. The results of this analysis provide valuable insights into the comparative cost-efficiency of the platforms evaluated and may guide hospital administrators and policymakers in making informed decisions regarding the adoption and allocation of robotic surgical technologies. By integrating real-world data with economic modelling, this study contributes to the growing body of evidence aimed at optimizing the value of innovation in surgical practice.

Abstract: Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction V_HS/V_tot, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30-0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5-8% and establishes reference yields of 300-430 NmL CH4 gVS⁻¹ at 35 °C. At pilot scale, operation at 3-4 bar absolute increases the CH4 fraction by 10-20 percentage points relative to ~1 bar, while maintaining yields of 0.28-0.35 L CH4 gCOD⁻¹ and production rates of 0.8-1.5 Nm³ CH4 m⁻³ d⁻¹ under OLRs of 4-30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2-7.6 and the free NH3 fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO2 solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH4 m-³ d-¹, NmL CH4 gVS-¹, L CH4 gCOD-¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling.

Abstract:

Bioengineered functional salivary tissues can advance regenerative therapies, preclinical drug testing, and fundamental understanding of salivary gland dysfunction. Current salivary tissue models are typically Matrigel-based, hydrogel-based or scaffold-free organoid systems, with limited physiological relevance or mimicry of cell-cell and cell-extracellular matrix (ECM) interactions. We previously developed elastin-alginate cryoelectrospun scaffolds (CES) that resemble the topography and viscoelastic properties of healthy salivary ECM, and validated their potential for stromal cell culture, delivery, and in vitro fibrosis modeling. Here, we evaluated the utility of CES to support 3D cocultures of salivary gland epithelial and mesenchymal cells in vitro. We compared CES with honeycomb-like topography (CES-H) to densely packed electrospun nanofibers (NF) and CES with fibrous topography (CES-F) for their ability to support SIMS epithelial cell attachment, morphology, 3D clustering, phenotype and organization into distinct clusters when cocultured with stromal cells. Both CES-F and CES-H supported epithelial cell attachment and clustering; in particular, CES-H most effectively supported the self-organization of epithelial and stromal cells into distinct 3D clusters resembling the structure of native salivary tissue. Stromal cells were essential for maintaining the phenotype of epithelial cells cultured on CES-H, laying the foundation for development of in vitro tissue models.

Abstract: The wearable healthcare is shifting from a passive tracking to an active, closed-loop care by integrating the polymeric three-dimensional (3D)-printed microneedle arrays (MNAs) with the soft electronics and wireless modules. This review is surveying about the design, materials, and the manufacturing routes that enable the skin-conformal MNA wearables for a minimally invasive access to the interstitial fluid and precise but localized drug delivery. The 3D printing processes, including the stereolithography (SLA), digital light processing (DLP), and two-photon polymerization (2PP), are providing a micron-scale control on the needle geometry, lumen formation, and also surface features. The biocompatible polymers and the stimuli-responsive composites are underpinning the dissolving, hydrogel-forming, conductive, and the drug-loaded architectures. We are highlighting the biomedical applications spanning the continuous biosensing, neuromuscular and cardiovascular biopotential recording, painless vaccination, and also the on-demand or closed-loop therapeutics through microfluidics, acoustics, or iontophoresis. The powering and the connectivity strategies, from the energy harvesting and inductive links to the smartphone-based analytics, are advancing the autonomous operation. Looking ahead, the converging advances in the multimaterial printing, nano/biofunctional coatings, and the artificial Intelligence (AI)-driven control are promising the “wearable clinics” that can personalize the monitoring and therapy in real time, thus accelerating the translation of MNA-integrated wearables from the laboratory prototypes to the clinically robust, patient-centric systems.

Abstract: In the following we integrate and place in cutting-edge, scientific and technological contexts, the approaches and discoveries of our collaboratory, a meta-laboratory comprising cross-disciplinary collaborations across laboratories at fourteen different universities and clinics in seven different countries with a shared lead investigator. By integrating insights from four decades of research and discovery, applied across cell types and different tissues, organ systems and organisms, we have aimed to elucidate the interplay between movement of organisms and physiology of their tissues, organs and organ systems’ resident cells. We highlight the promise of increasing imaging and computing power as well as machine learning/artificial intelligence approaches, to delineate the Laws of Biology. Codifying these laws will provide a foundation for the future, to promote not only discovery of underpinning mechanisms but also sustainability of our natural resources, from our brains to our bones, which serve as veritable “hard drives”, physically rendering a lifetime of cellular experiences and millennia of evolution.

Abstract: This research addresses to find a suitable design solution in order to model the behavior of human center of mass and implement this in an exoskeleton structure especially designed for children walking assistance and rehabilitation. One of the most general problems on exoskeleton designs is represented by the ground – foot contact on exoskeleton behavior under kinematic and dynamic conditions. Thus, for solving this, the main research objective is to develop a mechanical system which will substitute the human center of mass CoM behavior on an exoskeleton especially designed for children with Duchenne Muscular Dystrophy. The research core focuses on modelling the human CoM behavior under kinematic circumstances and transferring this through a mechanical system conceptual design. The obtained results will validate the proposed mechanical system through a comparative analysis between numerical processing, virtual prototyping and experimental specific methods and procedures.

Abstract: In medical diagnostics, there are strong reasons to distinguish between subcutaneous adipose tissue (SAT) and intermuscular adipose tissue (IMAT), as infiltration of IMAT into muscle causes health complications with aging (sarcopenia), diabetes, cardiovascular disease, and obesity. Since such assessments are performed using stationary and labor-intensive imaging modalities (MRI, CT), the development of portable devices based on ultrasound measurements, could aid proactive medicine and expand screening capabilities. The aim of this model study was to demonstrate the feasibility of differentially assessing SAT and IMAT by extracting evaluation criteria from propagating ultrasound signals. A set of 25 phantoms, using gelatin gel as the muscle matrix and oil for the SAT and IMAT compartments, formed a network with gradual changes in SAT and IMAT ranging from zero to 50%. Ultrasound signals were recorded at frequencies of 0.8 and 2.2 MHz, and assessment criteria were used, including ultrasound velocity and intensity derivatives. The intersection of decision rules based on evaluation criteria generated domains of possible recognition solutions. In parallel, using the same data partitions for training and test sets, artificial neural network (ANN/LSTM) analysis was applied. Both approaches demonstrated diagnostically acceptable SAT and IMAT resolution, opening up prospects for the ultrasound method.

Abstract: This study introduces a novel mixed reality (MR) TMJ dislocation teaching program developed using HoloLens 2, through collaboration among interdisciplinary teams. The program offers an immersive learning experience, enabling learners to visualize and interact with detailed 3D temporomandibular joint (TMJ) models and practice different reduction techniques repeatedly. Real-time feedback of the virtual model enhances the learning process. The 3D printed skull model provided haptic feedback and further strengthened the positive feedback by MR model, reinforcing muscle memory. Despite some challenges related to the learning curve and cost, the program shows promise in medical education for complex clinical procedures. Future research directions include comparing traditional teaching methods, evaluating long-term skill retention, and exploring MR applications in other clinical procedures. Overall, this project demonstrates the potential of MR technology in advancing medical education and skill acquisition.

Abstract: Accurate and efficient simulation of airflows in human airways is critical for advancing the understanding of respiratory physiology, disease diagnostics, and inhalation drug delivery. Traditional computational fluid dynamics (CFD) provides detailed predictions but is often mesh-sensitive and computationally expensive for complex geometries. In this study, we explored the usage of physics-informed neural networks (PINN) to simulate airflows in three geometries with increasing complexity: a duct, a simplified mouth-lung model, and a patient-specific upper airway. Key procedures to implement PINN training and testing were presented, including geometry preparation/scaling, boundary/constraint specification, training diagnostics, nondimensionalization, and inference mapping. Both laminar PINN and SDF-mixing-length PINN were tested. PINN predictions were validated against high-fidelity CFD simulations to assess accuracy, efficiency, and generalization. Results demonstrated that nondimensionalization of the governing equations was essential to ensure training accuracy for respiratory flows at 1 m/s and above. Hessian-matrix-based diagnosis revealed a quick increase in training challenges with flow speed and geometrical complexity. Both the laminar and SDF-mixing-length PINNs achieved comparable accuracy to corresponding CFD predictions in the duct and simplified mouth-lung geometry. However, only the SDF-mixing-length PINN adequately captured flow details unique to respiratory morphology, such as obstruction-induced flow diversion, recirculating flows, and laryngeal jet decay. The results from this study highlight the potential of PINN as a flexible alternative to conventional CFD for modeling respiratory airflows, with adaptability to patient-specific geometries and promising integration with static or real-time imaging (e.g., 4D CT/MRI).