Abstract: Real-time monitoring of key parameters (e.g., substrate) is crucial for the precise control of biological fermentation processes. To address the technical bottlenecks of significant lag in offline analysis and the limitations of traditional online sensors, this study de-signed and implemented a universal AI-enabled electronic nose system. The system features a modular hardware architecture integrating a high-sensitivity MOS gas sensor array, a precision constant-temperature chamber, and low-noise signal acquisition circuits to ensure signal stability. On the software side, a software architecture was designed based on the RUP 4+1 view model, employing multi-threaded technology for parallel data processing. An innovative five-stage sampling period was designed to match the dynamic response of MOS sensors, facilitating reliable data acquisition. Combined with a truncated average filtering strategy and peak response feature ex-traction, a lightweight single-hidden-layer neural network model was constructed for real-time prediction. Taking the real-time prediction of methanol concentration during glucoamylase fermentation by Pichia pastoris as a case study, the system demonstrated outstanding performance: R² reached 0.9998, RMSE was 13.5326 ppm, and the prediction delay was less than 1 second. The proposed system provides a robust, efficient, and universally applicable hardware-software solution, demonstrating significant potential for intelligent biomanufacturing.

Abstract:

This study investigates the application of soybean peroxidase (SBP), an enzyme extracted from a soybean processing byproduct, for the decolourization and oxidative treatment of three industrial azo dyes: Acid Orange 7 (AO7), Acid Orange 20 (AO20), and Reactive Black 5 (RB5), each at a concentration of 50 µM. These dyes are widely used in textile, paper, and leather industries and persist in wastewater. Optimization experiments were conducted at room temperature (approximately 22°C) to examine the effects of pH, SBP activity, and hydrogen peroxide (H₂O₂) concentration. Optimal degradation conditions were identified as: for RB5, pH 3.5, 0.075 U/mL SBP, and 0.0375 mM H₂O₂; for AO7, pH 3.0, 0.5 U/mL SBP, and 0.0375 mM H₂O₂; and for AO20, pH 3.0, 0.0025 U/mL SBP (200-fold less than for the isomeric AO7) and 0.0625 mM H₂O₂. Under these conditions, dye removal kinetics followed pseudo-first-order behaviour during the initial stages, assuming constant SBP and H₂O₂ concentrations. However, accumulation of oligomeric products, depletion of H₂O₂ over time, and enzyme inactivation caused deviation from first-order kinetics. The initial rate constants and half-lives were 10.7 min^-1 and 0.065 min (AO7), 7.3 min^-1 and 0.095 min (AO20), and 8.5 min^-1 and 0.081 min (RB5). When normalized to enzyme activity, AO7 showed the highest catalytic efficiency. More than 95% decolourization was achieved in under 30 seconds. These findings support the use of SBP as a low-cost, eco-friendly, and effective biocatalyst for the rapid treatment of dye-containing industrial wastewater.

Abstract: Accurate prediction of Electrocardiogram (ECG) signals is crucial for early diagnosis and continuous cardiac monitoring. To address the challenges posed by the non-stationary nature of ECG signals, this study introduces a novel deep learning prediction method that synergistically combines Variational Mode Decomposition (VMD), the Cao method, and a Long Short-Term Memory (LSTM) neural network. The proposed method first applies VMD to adaptively decompose the raw ECG signal into several Intrinsic Mode Functions (IMFs), which effectively reduces nonlinearity and non-stationarity. Subsequently, the Cao method is utilized to compute the minimum embedding dimension for each IMF, thereby optimally configuring the input structure for the LSTM network. Each IMF component is then predicted independently by an LSTM model trained with the Adam optimizer. The final reconstructed ECG signal is derived from the aggregate of these individual IMF predictions. Evaluated on the benchmark MIT-BIH Arrhythmia Database, the proposed method achieves a root mean square error (RMSE) of 0.001326 and a mean absolute error (MAE) of 0.001044, demonstrating high predictive precision. The results from a comparative study indicate that the proposed method surpasses several established prediction methods, confirming its effectiveness and potential for practical application in enhancing ECG signal analysis.

Abstract:

This narrative review aims to highlight and analyze the supervision of precision robotic surgical interventions. These are autonomous, closed-loop procedures, assisted by image and managed by intelligent digital tools. These administered procedures are designed to be safe and reliable, adhering to the principles of minimal invasiveness, precise positioning, and non-toxicity. Thus, a precision intervention uses non-ionizing imaging-assisted robotics, controlled by a precise positioning device, forming an autonomous procedure augmented by artificial intelligence tools and supervised by digital twins. This intelligent digital management allows staff to plan, train, predict, and execute interventions under human supervision. Patient safety and staff efficiency are linked to non-ionizing imaging, minimal invasiveness through image guidance, and strict delimitation of the intervention zone through precise positioning. This contribution includes therapeutic and surgical interventions, imaging strategies integrating diagnostic and assistance functions, intelligent digital tools including digital twins and artificial intelligence, image-guided procedures including autonomous and precision robotic surgical interventions increased by machine learning, as well as augmented healthcare monitoring. All topics addressed in this analysis are supported by examples from the literature.

Abstract: Ultrasound patches represent a transformative advancement beyond conventional ultrasonography, evolving into intelligent theranostic systems for personalized healthcare. This evolution is propelled by synergistic innovations in flexible piezoelectric materials and integrated designs. The development of piezoelectric polymers, lead-free ceramics, and bio-composite materials has laid the foundation for long-term, conformal, and biosafe interfacing with the human body. Structurally, miniaturized transducer arrays, multimodal integration, and bioinspired interfaces have enabled high-precision deep-tissue sensing and spatiotemporally controlled energy delivery. These capabilities are converging to create closed-loop platforms, as demonstrated in continuous cardiovascular monitoring, image-guided neuromodulation for neurological disorders, on-demand drug delivery, and integrated tumor therapy with real-time feedback. Despite persistent challenges in material biocompatibility, energy efficiency, and clinical standardization, the future of ultrasound patches lies in their deep integration with multimodal sensing, machine learning, and adaptive control algorithms. This path will ultimately realize their potential for intelligent, closed-loop theranostics in chronic disease management, telemedicine, and personalized therapy.

Abstract:

From 1950 to the present, plastic production and use have increased mainly because plastics possess qualities like stability, light weight, versatility, and decreasing production costs. However, most plastics are not biodegradable, and only a small portion is recycled worldwide. Bioplastics serve as an alternative if they are biodegradable and derived from residual materials, promoting a circular economy. PHB is a polymer with characteristics similar to some commercial plastics. It was discovered in the 1920s and has been examined by researchers and engineers since then due to its potential as a biodegradable bioplastic. Some microorganisms can produce PHB under controlled conditions. In this work, PHB production was analyzed using two strains: Bacillus subtilis and Bacillus megaterium. Using two byproducts—whey and glycerol—as substrates and varying the culture media compositions. Both byproducts and both strains are suitable for PHB production; the absence of nitrogen and trace element sources enhances PHB yield. Additionally, bacterial growth, substrate uptake, and PHB production were modeled using logistic growth and the Luedeking-Piret models.

Abstract: For effectively assessing bloods, red blood cells (RBCs) aggregation and blood viscosity have been measured in microfluidic environments. However, the previous methods still face several challenges (dead-volume loss, RBCs sedimentation, hematocrit-sensitive blood velocity, and precise flow-rate control). In this study, a novel method is suggested to resolve several issues. Air cavity (Vair = 250 μL) is secured above blood column (at least 100 μL) loaded into a driving syringe. To probe RBCs aggregation and blood viscosity, a microfluidic chip consists of a main channel (γ > 1000 s-1) and an aggregation channel (γ < 50 s-1). Blood is supplied into a microfluidic chip with two-step blood delivery (i.e., air-compression for RBCs aggregation, and syringe pump for blood viscosity). RBCs aggregation index and blood viscosity are obtained from time-lapse image intensity and blood flow-rate in both channels. As performance demonstrations, first, measurement accuracy of fluid viscosity is validated with glycerin solution. Then, the present method is adopted to probe difference in hematocrit and dextran concentration. At last, the proposed method is employed to detect heat-shocked RBCs (45 ~ 50 °C for 40 min). In conclusion, the proposed method has the ability to accurately measure substantial changes in RBCs or blood medium.

Abstract: Mechano-sorptive phenomena (MSP) refer to the coupled mechanical response of polymers under simultaneous mechanical stress and fluid sorption. The most researched MSP are environmental stress cracking (ESC) and mechano-sorptive creep (MSC). ESC initiates at regions of localized stress and solvent sorption, presenting as brittle fracture, while MSC is characterized by large, time-dependent, and partially recoverable creep associated with transient bulk sorption. ESC experiments can however also result in significant plastic deformation, in which case the term environmental stress yielding (ESY) has been used. Similarly, MSC can evolve into tertiary creep followed by rupture, in which case the phenomenon is termed mechano-sorptive creep rupture (MSCR). Both behaviors originate from solvent diffusion into the amorphous phase leading to disruption of non-covalent interactions between polymer chains. This review bridges seemingly disconnected research to illustrate that ESC and MSC represent extremes on a continuum of MSP, rather than disparate phenomena. We identify the principles of polymer thermodynamics and experimental methods necessary to separate polymer deformation under MSC into reversible stress-induced swelling and irreversible non-equilibrium deformation. We propose that a better understanding of these phenomena is necessary for a variety of applications including biomimetic materials that mimic the mechanical adaptability of marine organisms.

Abstract: Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. Current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free Rapid Optical Imaging (ROI) biosensor with automated machine learning analysis for direct quantification of functional CAR T-cells from whole blood. This microfluidic platform integrates leukocyte separation, capture, and detection on a single chip, thereby eliminating centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing a CD19 CAR underwent red blood cell depletion by agglutination and microfiltration. The leukocyte-enriched fraction was then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by bright-field microscopy and automatically enumerated using a machine learning algorithm. A calibration curve was established across clinically relevant concentrations (1–1,000 cells/mL), with results validated against fluorescence microscopy and flow cytometry. This ROI biosensor enables rapid, quantitative, and label-free CAR T-cell detection from whole blood without specialized equipment or infrastructure. With further development, it could provide a cost-effective point-of-care tool for real-time immune monitoring and improved clinical management of patients receiving CAR T-cell therapy.

Abstract: Paradoxical insomnia is characterized by a discrepancy between subjective sleep com-plaints and objectively preserved sleep, yet its autonomic mechanisms remain poorly un-derstood. This study examined stage-specific autonomic characteristics of paradoxical insomnia using heart rate variability (HRV)–based statistical, multivariate, and machine learning analyses in a large population-based cohort. HRV features were extracted from non-overlapping five-minute windows across non–rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wake after sleep onset (WASO), and compared among paradoxical insomnia, objective insomnia, and normal sleep groups. Whole-night and consolidated sleep–stage HRV features showed substantial overlap among groups. In contrast, consistent stage-dependent differences emerged selectively during WASO, dur-ing which paradoxical insomnia exhibited distinct autonomic patterns relative to both comparison groups. Multivariate analysis showed the greatest group displacement dur-ing WASO, with UMAP centroid distances exceeding those observed during NREM and REM sleep. Supervised models trained on WASO-specific features achieved the highest classification performance, yielding an accuracy of 0.61 and an F1-score of 0.69 for para-doxical insomnia versus normal sleep, although overall discriminability remained mod-est. These findings indicate that autonomic alterations in paradoxical insomnia are pref-erentially expressed during post–sleep-onset wakefulness. Stage-resolved analysis identi-fies WASO as a physiologically informative window for objective phenotyping and for characterizing heterogeneity in insomnia subtypes.

Abstract: This study evaluated the influence of four thermal pretreatment techniques—autoclaving, hot air oven treatment, hot water immersion, and microwave irradiation—on Parthenium hysterophorus biomass to improve its biodegradability and biogas generation potential under batch anaerobic digestion. Among the investigated methods, hot air oven pretreatment at 110 °C for 90 minutes exhibited the most significant enhancement in biomass solubilization, as indicated by a 51.5% rise in soluble chemical oxygen demand (sCOD) and an increase in volatile fatty acids (VFAs) compared with the untreated control. These compositional improvements facilitated faster hydrolysis and led to a 25.73% higher cumulative methane yield in biochemical methane potential (BMP) assays. Structural analysis revealed pronounced alterations in the lignocellulosic matrix, with reductions in hemicellulose and partial delignification improving substrate accessibility. Complementary characterization using field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) confirmed the disruption of crystalline cellulose regions and modification of functional groups, supporting the observed biochemical improvements. Collectively, the results demonstrate that hot air oven pretreatment is a practical and energy-efficient approach for enhancing the digestibility of P. hysterophorus biomass, promoting its utilization as a sustainable feedstock for renewable biogas production and environmental management of this invasive weed.

Abstract: The increasing demand for recombinant proteins has driven innovation in bioprocessing strategies using Komagataella phaffii as a host organism. Conventional fed-batch cultivation with pure methanol induction remains widely used but presents challenges including high methanol consumption, extended downtime, and elevated operational costs. This study evaluates alternative strategies combining mixed induction (methanol/sorbitol) with continuous cultivation to enhance productivity, sustainability, and improved economic outcome. Using KEX2 protease as a model industrial recombinant protein, we compared four cultivation modes: fed-batch with methanol (benchmark), fed-batch with mixed induction, continuous with methanol, and continuous with mixed induction. Cell growth, volumetric yield, and specific productivity were evaluated at 5L scale and then modelled to simulate industrial scales (40 L and 400 L). Results demonstrate that continuous cultivation with mixed induction significantly improves yield up to 9-fold compared to conventional fed-batch and reduces methanol usage and oxygen demand. Techno-economic simulations reveal that a 40 L continuous process can match or exceed the output of two 400 L fed-batch runs, while lowering capital and operating costs and minimising environmental footprint. This integrated strategy offers a scalable, cost-effective, and safer alternative for recombinant protein production, supporting the development of compact and sustainable manufacturing platform.

Abstract: Axial postural abnormalities in Parkinson’s Disease (PD) are traditionally assessed us-ing clinical rating scales, although picture-based assessment is considered the gold standard. This study evaluates the reliability and clinical relevance of two markerless body-tracking frameworks, the RGB-D-based Microsoft Azure Kinect (MAK) and the RGB-only Google MediaPipe Pose (MP), using a synchronous dual-camera setup. Forty PD patients performed a 60-second static standing task. We compared MAK with three MP models (at different complexity levels) across horizontal, vertical, sagittal, and 3D joint angles. Results show that lower-complexity MP models achieved high congruence with MAK for trunk and shoulder alignment (ρ > 0.75), while the lateral view signifi-cantly improved sagittal tracking (ρ ≥ 0.72). Conversely, the high-complexity model introduced significant skeletal distortions. Clinically, several angular parameters emerged as robust metrics for postural assessment and global motor impairments, while sagittal angles correlated with motor complications. Unexpectedly, a more up-right frontal alignment was associated with greater freezing of gait severity, suggest-ing that static postural metrics may serve as proxies for dynamic gait performance. In addition, both RGB-only and RGB-D frameworks effectively discriminated between postural severity clusters. These findings demonstrate that MP models are a reliable alternative to RGB-D sensors for objective postural assessment in PD, facilitating the widespread application of objective posture measurements in clinical contexts.

Abstract: Facial expressions are crucial in conveying emotions and for engaging in social interactions. The facial musculature activations and their pattern of movements under emotions are similar in all humans; hence, facial expressions are considered a behavioral phenotype. Facial features related to the expression of various emotions change under different health impairments, including in cognitive decline and pain experience. Hence, evaluating these facial expression deviations in comparison to healthy baseline conditions can help in the early detection of health impairments. Recent advances in machine learning and computer vision have introduced a multitude of tools for extracting human facial features and researchers have explored the application of these tools in early screening and detection of different health conditions. Advances in these studies can specially help in telemedicine applications and in remote patient monitoring, and potentially reduce the current excessive demand on the healthcare system. In addition, once developed, these technologies can assist healthcare professionals in emergency room triage, early diagnosis, and treatments. The aim of the present review is to discuss the available tools that can objectively measure facial features and to record the studies that use these tools in various health assessments.

Abstract: The increasing demand for high-quality dragon fruit in the European market requires efficient quality assessment methods. This study explores a non-destructive image analysis approach for classifying ripe dragon fruits based on fruit ripeness and weight. A low-cost system equipped with visible and ultraviolet lighting was employed to capture images of 60 ripe dragon fruits over a storage period, extracting parameters such as visible and ultraviolet perimeter, maximum and minimum diameter and area, and RGB color coordinates. In a first step, the main characterization magnitudes were confirmed. A ripening index was calculated based on soluble solid content and acidity. Then, a cluster analysis was used to segregate the fruits into three quality characteristics based on the ripening index and weight. In a second step, a step-by-step discriminant analysis was conducted to classify the fruits into the three quality categories (based on the laboratory measured weight, soluble solid content and total acidity) using the non-destructive magnitudes extracted from the image analysis. The proposed classification system achieved an accuracy of nearly 85 \% of well classified dragon fruits, effectively segregating dragon fruits into the three established categories. urthermore, the established model could select the very high-quality dragon fruit (riper and larger fruits) with 93 \% of correctly dentified products.Compared to conventional destructive methods, this non-destructive approach offers a promising, cost-effective, and reliable solution for quality assessment. The findings highlight the potential for integrating smart technologies into fruit classification processes, during automatic harvest and postharvest operations, ultimately improving efficiency, reducing labor costs, and enhancing product consistency in the dragon fruit industry.

Abstract: Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems.

Abstract: Soil mulching materials play an important role in regulating the greenhouse crop microclimate, as they influence light distribution, plant physiological activity, and crop yield. The aim of this study was to evaluate the effects of two plastic mulches (black polypropylene and white polyethylene mulch) on the microclimate, photosynthetic activity, crop development, yield, and fruit quality of sweet pepper (Capsicum annuum L.) grown under greenhouse conditions. The trial was developed during a spring–summer growing cycle in a multispan greenhouse divided into two compartments(sectors) separated by a vertical polyethylene sheet. In the eastern sector of the greenhouse (control treatment), a black polypropylene agrotextile mulch with a thickness of 0.225 μm was installed, while in the western sector, a white polyethylene plastic mulch (black on the inner side) with a thickness of 30 μm was used. The use of white polyethylene mulch resulted in slightly higher mean and maximum PAR inside the greenhouse by up to 3.7 % compared with black polypropylene mulch, leading to slightly higher leaf-level PAR and net photosynthetic rate. Although no significant differences were observed in plant morphology or fruit quality parameters, marketable yield increased by 66% and total yield by 40 % under white polyethylene mulch. Slight increases in internal air temperature were recorded without exceeding critical thresholds, while relative humidity remained largely unaffected. The use of reflective mulches represents a low-cost and sustainable strategy to improve pepper yield and radiation-use efficiency in passively ventilated greenhouse systems under Mediterranean climatic conditions.

Abstract: Mediterranean greenhouses are characterized by the use of passive climate control techniques, thus avoiding energy inputs that would make crop production more expensive. This study was carried out in Almería (Spain), in a greenhouse divided in two sectors. (West sector: with double roof with a pink spectrum converter film combined with an increased ventilation surface, ratio of vent surface/greenhouse surface SV/SC = 26.0%; East sector: acted as a control with only standard ventilation surface, SV/SC = 16.6%). This study analysed the effect of a double roof and an increased ventilation surface on the main fungal diseases in different crops (Solanum lycopersicum L., Capsicum annuum L., and Cucumis sativus L.). Different diseases were found that develop naturally, powdery mildew (Leveillula taurica) in both the tomato and the pepper crop, and early blight (Alternaria linariae) only in the tomato crop. In the case of cucumber crop, three diseases that developed naturally were found, (i) downy mildew (Pseudoperonospora cubensis), (ii) powdery mildew (Podosphaera xanthii) and (iii) gummy stem blight (Stagonosporopsis spp). The sector that combined the double roof and the increased ventilation surface had lower disease levels compared to the control sector, with statistically significant differences.

Abstract: This study presents an experimental evaluation of different optical fibers for soft tissue laser ablation using the Echolaser system, developed by Elesta S.p.A., for minimally invasive therapies. Eight fibers with varying core diameters, numerical apertures, and tip geometries (flat, conical radial, and spherical) were compared to investigate the influence of optical properties on ablation dimensions and thermal profiles. Experiments were conducted at 1064 nm with powers of 3, 5, and 7 W and delivered energies ranging from 1200 to 3600 J. Results highlight how fiber characteristics affect tissue ablation, identifying configurations suitable for minimally invasive prostate applications. These findings provide an experimental reference for the development of laser-based biomedical approaches.

Abstract: Regenerative rehabilitation can enhance skeletal muscle mass, function, and size following traumatic injuries such as volumetric muscle loss (VML). We previously optimized fibrin-laminin hydrogels for muscle regeneration and an electrically stimulated eccentric contraction training (EST) for muscle rehabilitation. The goal of this study was to examine the combined effect of these two therapies in maximizing tissue recovery. A VML defect was created by removing ~20% of muscle mass from the tibialis anterior (TA) muscle in adult male Lewis rats. The injured TA muscles were treated with fibrin-laminin (FBN450) hydrogel. EST was implemented 2 weeks post-injury at both 100 Hz and 150 Hz frequencies and was continued for 4 weeks. The results showed no improvement in muscle mass or function with combined FBN450 and EST application. Histological analysis revealed reduced type 2B myofiber size and percentage in the combined hydrogel and EST treatment group. Gene expression studies showed increased inflammatory and fibrotic signaling with no concomitant increase in myogenic markers. Collectively, these results indicate that the FBN450 hydrogel therapy did not synergize with EST to improve outcomes following VML.