Abstract: In recent years, the rapid development of artificial intelligence (AI) has spurred significant advances across a range of disciplines, not least in the domain of sustainable life sciences. This research paper investigates implemented AI solutions in sustainable life sciences, examining their global applicability and effectiveness over the period 2020 to 2024. Through a systematic review of recent literature and detailed case study analyses, we present quantitative data on AI performance indicators and sustainability metrics, underlining the benefits and challenges associated with these novel applications. In this study, interdisciplinary perspectives—spanning computer science, environmental science, biotechnology, and ethics—are synthesized to provide a comprehensive understanding of how AI can drive sustainability in life sciences, improve operational efficiency, and support decision making in various regulated industries. Real-world implementations from developed nations are examined to present comparative analyses, and data visualizations are employed to illustrate the financial, environmental, and performance metrics achieved by these AI systems. Ethical considerations are addressed throughout the study to ensure that the integration of AI in sustainable life sciences complies with current societal and environmental norms. The paper concludes with actionable recommendations and a five-year projection regarding the technology adoption curve for AI solutions in sustainable life sciences.

Abstract: This project aimed to develop and validate an efficient eye tracking algorithm suitable for the analysis of images captured in the visible light spectrum using a smartphone camera. In particular, the investigation primarily focused on comparing two algorithms, which were named CHT_TM and CHT_ACM, abbreviated from the core functions: Circular Hough Transform (CHT), Active Contour Models (ACM), and Template Matching (TM). In essence, CHT_TM significantly improved the running speed of the CHT_ACM algorithm, with not much difference in the resource consumption, and improved the accuracy on the x axis. CHT_TM achieved a reduction by 79% of the execution time. CHT_TM performed with an average mean percentage error of 0.34% and 0.95% in the x and y direction across the 19 manually validated videos, compared to 0.81% and 0.85% for CHT_ACM. Different conditions, like manually opening the eyelids with a finger versus without a finger, were also compared across four different tasks. This paper shows that applying TM improves the original eye tracking algorithm with CHT_ACM. The new algorithm has the potential to help the tracking of eye movement, which can facilitate the early screening and diagnosis of neurodegenerative diseases.

Abstract: Wearable inertial measurement units (IMUs) are increasingly used in human motion analysis due to their ability to measure movement in real-world environments. However, with rapid technological advancement and a wide variety of models available, it is essential to evaluate their performance and suitability for analyzing specific body regions. This study aimed to assess the accuracy and precision of an IMU-based sensor in measuring trunk range of motion (ROM). Twenty-seven healthy adults (11 males, 16 females; mean age: 31.1 ± 11.0 years) participated. Each performed trunk movements—flexion, extension, lateral bending, and rotation—while angular data were recorded simultaneously using a single IMU and a marker-based optoelectronic motion capture (MoCap) system. Analyses included accuracy indices, root mean square error (RMSE), Pearson’s correlation coefficient (r), concordance correlation coefficient (CCC), and Bland-Altman limits of agreement. The IMU showed high accuracy in rotation (92.4%), with strong correlation (r = 0.944, p < 0.001) and excellent agreement [CCC = 0.927; (0.977-0.957). Flexion (72.1%), extension (64.1%), and lateral bending (61.4%) showed moderate accuracy and correlations (r = 0.703, 0.564, and 0.430, p<0.05). RMSE ranged from 1.09° (rotation) to 3.01° (flexion). While the IMU consistently underestimated ROM, its accuracy in rotation highlights its potential as a cost-effective MoCap alternative, warranting further study for broader clinical use.

Abstract: Muscular strength is an essential factor in sports performance and general health, especially for optimizing mechanical power, as well as for injury prevention. The present study biomechanically characterized the half squat (HS) using a systemic-structural approach based on mechanical power, called Power-Based Training (PBT), through which four phases of the movement were determined (acceleration and deceleration in descent, acceleration and deceleration in ascent). Five weightlifters from the Mexican national team (categories U17, U20 and U23) participated, who performed 5 repetitions per serie of HS with progressive loads (20%, 35%, 50%, 65% and 80% of the 1RM). The behavior of the CoM of the subject-bar system was recorded by photogrammetry, calculating position, velocity, acceleration, mechanical power and mechanical work. The results showed a significant reduction in velocity, acceleration and mechanical power as the load increases, as well as variations in the duration and range of displacement per phase. These findings evidence the importance of a detailed analysis to understand the neuromuscular demands of MS and to optimize its training. The PBT approach and global CoM analysis provide a more accurate view of the mechanics of this exercise, facilitating its application in future research, as well as in performance planning and monitoring.

Abstract: A novel head-mounted assistive device was designed, developed, and validated to enhance spatial awareness for individuals with visual impairments by integrating Time-of-Flight (ToF) sensors and haptic feedback. The device leverages three VL53L1X ToF sensors arranged at constant angular offsets to provide a forward-facing with field of view of about 81°, enabling to detect obstacles approaching from any directions. Each sensor is mapped to a dedicated coin vibration motor, positioned in alignment with the person’s head to deliver directional tactile feedback. The Arduino Pro Mini microcontroller acquires the distance measurements through the I²C protocol and generates pulse-width modulated (PWM) signals to modulate vibration strength based on the obstacle proximity. This mapping lets the user perceive each area and relative distance of objects nearby without relying on vision or auditory comments. The device was assessed under indoor conditions using fixed-distance trials from 150 cm down to 15 cm in 15 cm increments. Results show a dependable detection, within this range, with dimension deviations maintained within ±1 cm. Power draw was measured at around 495 mA throughout non-stop operation, and a runtime with a 1000 mAh lithium-polymer battery validated operational intervals of 2.6 to five hours, relying on motor usage frequency. The overall tool design prioritises compactness, comfort, and real-time responsiveness, imparting a low-cost and non-intrusive solution for improving mobility and environmental consciousness among visually-impaired users in indoor environments.

Abstract: Microphysiological systems (MPS) incorporating microfluidic technologies offer improved physiological relevance and real-time analysis for cell-based assays, but often lack non-invasive monitoring capabilities. Addressing this gap, we developed a microfluidic cell-based assay platform integrating an electrochemical biosensor for real-time, non-invasive monitoring of kinetic cell status through glucose consumption. The platform addresses the critical limitations of traditional cell assays, which typically rely on invasive, discontinuous methods. By combining enzyme-modified platinum electrodes within a microfluidic device, our biosensor can quantify dynamic changes in glucose concentration resulting from cellular metabolism. We have integrated a calibration function that corrects sensor drift, ensuring accurate and long-term measurement stability. In validation experiments, the system successfully monitored glucose levels continuously for 20 h, demonstrating robust sensor performance and reliable glucose concentration predictions. Furthermore, in cell toxicity assays using HepG2 cells exposed to varying concentrations of paraquat, the platform detected changes in glucose consumption, effectively quantifying cellular toxicity responses. This capability highlights the device's potential for accurately assessing the dynamic physiological conditions of cells. Overall, our integrated platform significantly enhances cell-based assays by enabling continuous, quantitative, and non-destructive analysis, positioning it as a valuable tool for future drug development and biomedical research.

Abstract: Adipose tissue plays a complex role in cardiovascular health. Cardiac adipose tissue (CAT) has been shown to correlate with coronary artery disease (CAD). The amount of fat surrounding the heart can affect major heart vessels by contributing to plaque development. In conditions like cardiac steatosis or fatty heart disease, the infiltration or accumulation of fat within the heart’s muscle impairs its function. Both CAT and cardiac steatosis may play a role in heart failure (HF). This review explores the different types of fat deposits surrounding the heart, focusing on the potential contribution of CAT to cardiovascular disease (CVD). Three main imaging modalities for assessing cardiac fat are discussed, including magnetic resonance imaging (MRI), computed tomography (CT), and echocardiography. The segmentation and quantification of the fat for each imaging modality is also presented, correlating these measurements with CVD risk. Each imaging modality offers distinct advantages and limitations in segmenting and quantifying fat. While advancements have been made, challenges persist in accurately measuring and interpreting the fat distribution around the heart. Future research should focus on refining segmentation techniques, establishing standardized protocols, and elucidating the specific mechanisms linking adipose tissue to CVD risk. By overcoming these limitations, cardiac fat imaging can be a valuable tool for improved risk stratification, personalized treatment strategies, and ultimately, better cardiovascular health outcomes.

Abstract: High-Intensity Interval Training (HIIT) is widely used in sports and health due to its cardiovascular and metabolic benefits, requiring accurate monitoring of heart rate variations to assess performance. This study proposes an automated algorithm to identify key heart rate parameters in real time, eliminating the need for manual supervision. The algorithm detects local maxima and minima in heart rate signals recorded during HIIT sessions and calculates ascending and descending slopes, as well as intermediate averages, to evaluate cardiovascular response and recovery. The results demonstrate that the algorithm effectively identifies these parameters in all analyzed cases, providing objective insights into an athlete’s fitness level. Higher ascending slopes and lower descending slopes were associated with poorer physical condition, while a progressive increase in maxima and minima indicated proper HIIT execution and cardiovascular adaptation. This automated approach enhances performance monitoring, enabling personalized training adjustments and long-term fitness tracking. Future research should explore its applicability across different training populations and integrate additional physiological metrics.

Abstract: Tissue engineering represents a revolutionary approach to regenerating damaged bones and tissues. The most promising materials for this purpose are calcium phosphate-based bioactive ceramics (CaPs) and bioglasses, due to their excellent biocompatibility, osteoconductivity, and bioactivity. This review aims to provide a comprehensive and comparative analysis of different bioactive calcium phosphate derivatives and bioglasses, highlighting their roles and potential in both bone and soft tissue engineering as well as in drug delivery systems. We explore their applications as composites with natural and synthetic biopolymers, which can enhance their mechanical and bioactive properties. The review critically examines the advantages and limitations of each material, their preparation methods, biological efficacy, biodegradability, and practical applications. By summarizing recent research from scientific literature, this paper offers a detailed analysis of the current state of the art. The novelty of this work lies in its systematic comparison of bioactive ceramics and bioglasses, providing insights into their suitability for specific tissue engineering applications. The expected primary outcomes include a deeper understanding how each material interacts with biological systems, their suitability for specific applications, and the implications for future research directions.

Abstract: (1) Background: Blood pressure (BP) variability is an important risk factor for cardiovascular diseases. Still, existing BP monitoring methods often require periodic cuff-based measurements, raising concerns about accuracy and convenience. This study aims to develop a subject-independent, cuff-less BP estimation method using finger and toe photoplethysmography (PPG) signals combined with an electrocardiogram (ECG) without the need for an initial cuff-based measurement. (2) Methods: A customized measurement system was used to record 80 readings from human subjects. Fifteen features with the highest dependency on the reference BP, including time and morphological characteristics of PPG and subject information, were analyzed. A multivariate regression model was employed to estimate BP. (3) Results: The results showed that incorporating toe PPG signals improved the accuracy of BP estimation, reducing the mean absolute error (MAE). Using both finger and toe PPG signals resulted in an MAE of 9.63±12.54 mmHg for systolic BP and 6.76±8.38 mmHg for diastolic BP, providing the lowest MAE compared to previous methods. (4) Conclusions: This study is the first to integrate toe PPG for more accurate BP estimation and proposes a method that does not require an initial cuff-based BP measurement, offering a promising approach for non-invasive, continuous BP monitoring. conclusions.

Abstract: Three-dimensional (3D) printing, also known as additive manufacturing, is rapidly transforming the healthcare landscape by enabling the creation of patient-specific solutions across a wide range of clinical applications. This review explores recent advancements in 3D printing technologies and their implementation in surgical planning, implant fabrication, drug delivery systems, and bioprinting. A major highlight is the role of 3D printing in custom prosthetics design, where the technology allows for highly personalized, anatomically accurate prosthetic limbs that improve functionality, comfort, and aesthetic integration. By leveraging digital scanning and computer-aided design (CAD), prosthetics can be fabricated rapidly and cost-effectively, making them accessible even in low-resource settings. In pharmaceutical science, 3D printing enables the development of complex drug delivery systems tailored to individual pharmacokinetic profiles, enhancing therapeutic efficacy and patient compliance. Bioprinting, another emerging domain, holds promise for tissue regeneration and organ fabrication using living cells and bioinks. Despite its transformative potential, the adoption of 3D printing faces challenges such as regulatory hurdles, material limitations, and the need for interdisciplinary training. Nonetheless, ongoing innovations and regulatory progress suggest a promising future for 3D printing in delivering personalized, efficient, and accessible healthcare solutions—including the growing field of custom prosthetics.

Abstract: As one of the main routes of vaccine production, cell suspension culture technology has become an inevitable trend in the development of biopharmaceutical industry. Its main advantage is that it can maximize the product quality while achieving high yield through accurate and effective process control means. As the basic material needed in the process of cell suspension culture, cell culture medium mainly provides energy through substances such as glucose. In order to obtain high-quality and high-performance cell products, it is necessary to continuously monitor the culture process. Traditional biochemical method can achieve the purpose of monitoring, but the detection cycle is long which making it hard to meet the needs of real-time monitoring. In contrast, Near-infrared (NIR) spectroscopy can detect glucose concentration in cell culture medium in real time without loss and it has the advantages of fast, accurate, and high sensitivity. In this study, a partial least squares regression (PLSR) model was established based on the near infrared spectrum of cell medium containing a certain amount of glucose to verify its validity and feasibility. Efforts have been made on the processing of sample outlier, spectrum preprocessing and wavelength optimization method. And good prediction results were obtained with the coefficient of determination (R2) of the model was 0.998, the cross-validation mean square error (RMSECV) was 0.0036 and the residual prediction deviation (RPD) value was 4.15. On this basis, in order to further improve the prediction accuracy of the model, this paper introduced glutamine variables to establish a multi-component fusion model and further optimized the combined wavelength selection method. The results showed that the model could achieve better prediction accuracy when using our proposed optimization method which indicated that the method of real-time detection of glucose concentration in cell medium based on near infrared spectroscopy was feasible.

Abstract: Research on abdominal aortic aneurysm (AAA) primarily focusses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. Computational fluid dynamics (CFD), finite element analysis (FEA) and fluid-structure interaction (FSI) are widely used to simulate AAA hemodynamics and biomechanics. However, the accuracy of these simulations depends on utilization of realistic and sophisticated boundary conditions (BCs), which are essential for properly integrating the AAA with the rest of the cardiovascular system. Recent advances in machine learning (ML) techniques have introduced faster, data-driven surrogates for AAA modeling. These approaches can accelerate segmentation, predict hemodynamics and biomechanics, and assess disease progression. However, their reliability depends on high-quality training data derived from CFD and FEA simulations, where BC modeling plays a crucial role. Accurate BCs can enhance ML predictions, increasing the clinical applicability. This paper reviews existing BC models, discussing their limitations and technical challenges. Additionally, recent advancements in ML and data-driven techniques are explored, discussing current state, future directions, common algorithms and limitations.

Abstract: In darts, the dominant limb typically has an advantage due to superior performance characteristics. However, with training, the non-dominant limb can achieve nearly similar accuracy. Research suggests that left-handed individuals tend to have more balanced dexterity between their hands compared to right-handed individuals, who show a stronger preference for their dominant hand. This may provide a slight advantage for left-handed players. This study analysed 12 participants (male and female, aged 20-25 years), including one left-handed male and one left-handed female, with the rest being right-handed. Each participant completed 18 throws with both their dominant and non-dominant limbs. Data collection was conducted using the XSENS MVN Awinda motion capture system, which employs inertial sensors placed on the hand, forearm, upper arm, and shoulder of both limbs. The MT Manager software extracted values such as angular variation, acceleration, and angular velocity, ensuring precise and synchronized data for analysis. Results showed higher scores and shorter throw durations when using the dominant hand. Male participants scored higher with both dominant and non-dominant limb. The left-handed female showed greater dexterity balance between both limbs and the left-handed male showed better coordination supporting the idea that left-handed individuals may have a natural advantage in dexterity symmetry.

Abstract: The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series (2 Hz) and heart rate variability (HRV) indices, including mean RR, SDRR, VLF, LF, HF, LF/HF, and HF peak frequency, were extracted from ECG signals over 5-minute and 10-minute segments. HRV indices were calculated using fast Fourier transform (FFT). The classification was performed using Logistic Regression (LGR), Random Forest (RF), XGBoost (XGB), One-Class SVM (OCS), Isolation Forest (ILF), and Local Outlier Factor (LOF). A balanced dataset of 5-minute and 10-minute segments was evaluated using k-fold cross-validation (k = 3, 4, 5). Performance metrics, including recall, F-score, and PR-AUC, were computed for each classifier. Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. Among all models, OCS with k = 3 achieved the highest classification accuracy for both 5-minute and 10-minute data. These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest.

Abstract: Background/Objectives: The sense of agency (SoA)—the feeling of control over one’s actions and their consequences—is a fundamental aspect of volition, learning, and self-awareness. Disruptions in SoA are implicated in various neuropsychiatric conditions, including functional neurological disorders (FND). In emerging human-machine interfaces (HMIs), preserving SoA is critical for usability and therapeutic impact. This scoping review synthesizes computational models that explain the neural mechanisms underlying SoA and explores their application in the design and optimization of HMIs for both rehabilitation and skill learning. Methods: A narrative synthesis of peer-reviewed literature was conducted, focusing on models rooted in predictive coding, Bayesian brain theory, active inference, and linear-quadratic-Gaussian (LQG) optimal control. Simulation studies were also included to illustrate theoretical mechanisms in practical XR-based rehabilitation contexts. Results: The review highlights the role of internal forward models, efference copies, and sensory feedback in the generation and regulation of SoA. It shows how Kalman filter and LQG control frameworks model belief updating and motor planning, explaining disrupted SoA in FND and its restoration via hypnotic suggestion and virtual sensory perturbations (exafference). EEG microstate dynamics and directed brain connectivity studies reveal distinct SoA-related patterns differentiating novice and expert performance in skill learning. Key regions implicated include the supplementary motor area, parietal cortex, cerebellum, and prefrontal cortex, connected via structural pathways. Conclusions: Integrating computational frameworks such as active inference and Kalman filtering with causal reasoning (e.g., Ladder of Causation) offers a powerful lens to understand and modulate SoA with exafference. These insights support the co-design of adaptive XR-based HMI systems for neurorehabilitation and cognitive-motor skill acquisition.

Abstract: Solid-state fermentation (SSF) involves the growth of microorganisms on solid substrates, mimicking natural environments of many species. Due to sustainability concerns, transforming agro-industrial by-products into value-added products through SSF has been increasingly studied. Brewer’s spent grain (BSG), the main by-product of beer production, mostly consists of barley grain husks, making BSG a great support for microorganism cultivation. Although autoclaving remains the standard sterilization and pretreatment method of substrates, electric field technologies and its attendant ohmic heating (OH) have great potential as an alternative technology. In the present work, pretreatment of BSG by OH was explored in SSF with Aspergillus niger to produce commercially valuable enzymes. OH favored the solubilization of phenolic compounds, total protein and reducing sugars, significantly higher than autoclaving. SSF of treated BSG led to the production of lignocellulosic enzymes, with xylanases being the most active, reaching 540 U/g, a 1.5-fold increase in activity compared to autoclaved BSG. Protease activity was also improved 1.6-fold by OH, resulting in 49 U/g. Our findings suggest that OH treatment is an effective alternative to autoclaving and that its integration with SSF is a sustainable strategy to enhance by-products valorization through enzymes production with many industrial applications, according to circular economy guidelines.

Abstract: Background/Objectives: Three-dimensional cell culture systems are relevant in vitro models for studying cellular behavior. In this regard, the present study investigates the interaction between human osteoblast-like cells and 3D-printed scaffolds mimicking physiological and osteoporotic bone structures under simulated microgravity conditions. The objective is to assess the effects of scaffold architecture and dynamic culture conditions on cell adhesion, proliferation, and meta-bolic activity, with implications for both osteoporosis research and space medicine. Methods: Poly (lactic acid) (PLA) scaffolds with physiological (P) and osteoporotic-like (O) tra-becular architectures were 3D printed by means of the used deposition modeling (FDM) tech-nology. Morphometric characterization was performed using micro-computed tomography (µCT). Human osteoblast-like SAOS-2 and U2OS cells were cultured on the scaffolds under static and dynamic simulated microgravity conditions using a rotary cell culture system (RCCS). Cell viability, adhesion, and metabolic activity were evaluated through BrdU, WST-1, and ELISA assays, with TNF-α secretion assessed to determine biocompatibility. Results: Both scaffold models supported osteoblast-like cell adhesion and growth, with enhanced colonization observed on the high-porosity O scaffolds under dynamic conditions. The dynamic environment facilitated increased surface interaction, amplifying the effects of scaffold archi-tecture on cell behavior. No inflammatory response was detected, confirming scaffold biocom-patibility.

Abstract: Chronic pain leads to not only physical discomfort but also psychological challenges, such as depression and anxiety, which contribute to a substantial healthcare burden. Still, pain detection and assessment remains a challenge due to its subjective nature. Indeed, current clinical methods may be inaccurate or unfeasible for non-verbal patients. Then, Electroencephalography (EEG) has emerged as a promising non-invasive tool for pain detection. However, EEG-based pain detection faces challenges such as noise, volume conduction effects, and high inter-subject variability. Deep Learning (DL) models have shown potential in overcoming these challenges by extracting nonlinear and discriminative patterns. Despite advancements, these models often require a subject-dependent approach and lack of interpretability. To address these limitations, we propose a threefold DL-based framework for coding EEG-based pain detection patterns. i) We employ the Kernel Cross-Spectral Gaussian Functional Connectivity Network (KCS-FCnet) to code pairwise channel dependencies for pain detection. ii) Furthermore, we introduce a frequency-based strategy for class activation mapping to visualize pertinent pain EEG features, thereby enhancing visual interpretability through spatio-frequency patterns. iii) Further, to account for subject variability, we conduct cross-subject analysis and grouping, clustering individuals based on similar pain detection performance, functional connectivity patterns, sex, and age. We evaluate our model using the Brain Mediators of Pain dataset and demonstrate its robustness through subject-dependent and cross-subject generalization tasks for pain detection on non-verbal patients.

Abstract: The advancement of inertial measurement unit (IMU) technology has opened new opportunities for motion analysis, yet its widespread adoption in clinical practice remains constrained by the high costs of proprietary systems, lengthy setup procedures, and the need for specialized expertise. To address these challenges, we present a multi-IMU system designed with streamlined calibration, efficient data processing, and a focus on accessibility for patient-facing applications. Although initially developed for human gait analysis, the modular design of this system enables adaptability across diverse motion tracking scenarios. This work outlines the system’s technical framework, including protocols for data acquisition, derivation of gait variables, and considerations for user-friendly software deployment. We further illustrate its utility by measuring lower-limb gait kinematics in near-real time and providing stride-to-stride biofeedback using a single sensor. These initial results underscores the potential of this system for both laboratory-based gait assessment and rehabilitation interventions in clinical environments and future work will assess validation against traditional optical motion capture methods.